Science, Faculty of
Botany, Department of
DSpace
UBCV
Huang, Kaichi
Doctor of Philosophy - PhD
University of British Columbia
Uncovering the genetic and evolutionary basis of adaptation and speciation is a major focus of evolutionary biology. The recent accumulation of population-scale genomic data makes it possible to identify loci underlying adaptive divergence and speciation, as well as the genomic architecture that facilitates these processes. Chromosomal inversions, a genomic rearrangement in which a segment of a chromosome is reversed end to end, have received much attention as one of the most important genomic architectures contributing to adaption and speciation. In this thesis, I used high-quality genomic data from wild sunflowers to explore the role of inversions in adaptation and speciation. I first conducted a comprehensive literature review to investigate what we have learned about the establishment and likely evolutionary role of chromosomal inversions in plants. In my second study, I made use of reduced representation sequencing data and newly developed population genomic approaches to identify seven putative chromosomal inversions that contribute to adaptive divergence of a sand dune ecotype of the prairie sunflower (Helianthus petiolaris). I further employed comparative genetic mapping to validate the inversions and identified key environmental variables significantly associated with these inversions using genome-environment association analyses. I then broadened my study to investigate the role of inversions in molecular evolution across different wild sunflowers. I showed that inversions are ideal recombination modifiers from an evolutionary standpoint because by allowing recombination among chromosomes of the same orientation but not between orientations they can facilitate adaptive divergence with gene flow, while largely averting the accumulation of deleterious mutations due to recombination suppression. Lastly, I used multiple approaches to identify loci underlying parallel ecological divergence in two dune ecotypes in H. petiolaris and found that inversions contribute disproportionately to parallel genetic evolution. Future directions include identifying genes and mutations underlying key traits associated with the inversions, examining the association of inversions with assortative mating, and exploring the roles of other structural variants in facilitating adaptation and speciation.
THE GENOMICS OF ADAPTATION AND SPECIATION IN WILD SUNFLOWERS by Kaichi Huang B.Sc., Sun Yat-sen University, 2015 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Botany) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) April 2021 © Kaichi Huang, 2021 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: The genomics of adaptation and speciation in wild sunflowers submitted by Kaichi Huang in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Botany Examining Committee: Loren Rieseberg, Botany, UBC Supervisor Darren Irwin, Zoology, UBC Supervisory Committee Member Amy Angert, Botany, UBC Supervisory Committee Member Keith Adams, Botany, UBC University Examiner Sally Aitken, Forestry, UBC University Examiner Marc Johnson, University of Toronto Mississauga External Examiner iii Abstract Uncovering the genetic and evolutionary basis of adaptation and speciation is a major focus of evolutionary biology. The recent accumulation of population-scale genomic data makes it possible to identify loci underlying adaptive divergence and speciation, as well as the genomic architecture that facilitates these processes. Chromosomal inversions, a genomic rearrangement in which a segment of a chromosome is reversed end to end, have received much attention as one of the most important genomic architectures contributing to adaption and speciation. In this thesis, I used high-quality genomic data from wild sunflowers to explore the role of inversions in adaptation and speciation. I first conducted a comprehensive literature review to investigate what we have learned about the establishment and likely evolutionary role of chromosomal inversions in plants. In my second study, I made use of reduced representation sequencing data and newly developed population genomic approaches to identify seven putative chromosomal inversions that contribute to adaptive divergence of a sand dune ecotype of the prairie sunflower (Helianthus petiolaris). I further employed comparative genetic mapping to validate the inversions and identified key environmental variables significantly associated with these inversions using genome-environment association analyses. I then broadened my study to investigate the role of inversions in molecular evolution across different wild sunflowers. I showed that inversions are ideal recombination modifiers from an evolutionary standpoint because by allowing recombination among chromosomes of the same orientation but not between orientations they can facilitate adaptive divergence with gene flow, while largely averting the accumulation of deleterious mutations due to recombination suppression. Lastly, I used multiple approaches to identify loci underlying parallel ecological divergence in two dune iv ecotypes in H. petiolaris and found that inversions contribute disproportionately to parallel genetic evolution. Future directions include identifying genes and mutations underlying key traits associated with the inversions, examining the association of inversions with assortative mating, and exploring the roles of other structural variants in facilitating adaptation and speciation. v Lay Summary Uncovering the genetic and evolutionary basis of adaptation and speciation is a major focus of evolutionary biology. In this thesis, I explored the genomic mechanisms of adaptation. I identified chromosomal structural changes in dune-adapted sunflowers and revealed the importance of these structural changes in facilitating ecological divergence. These discoveries advance our understanding of species’ adaptation to different environments and the formation of biodiversity. vi Preface I was the primary force behind the design, execution, analysis and presentation of this research with guidance and consultation from my advisor, Loren Rieseberg. Many others made meaningful contributions to the final product: Rose Andrew collected the samples and generated the RAD sequence data as well as the soil and vegetation cover data used in chapter 3. Greg Owens and Kate Ostevik helped with the analyses. Sequence data used in chapters 4 and 5 were taken from previously published resources within the Rieseberg lab: Dylan Burge collection seeds and soil samples from wild sunflower populations; Marco Todesco and Natalia Bercovich led common garden experiments and the generation of resequencing data; Jean-Sébastien Légaré, Greg Owens and I conducted variant calling. Sam Yeaman provided conceptual advice for chapter 5 and Mojtaba Jahani helped with the analyses. I am preparing manuscripts that describe the work in chapters 4 and 5. A version of chapter 2 has been published in: • Huang, K and Rieseberg, LH. 2020. Frequency, origins, and evolutionary role of chromosomal inversions in plants. Frontiers in Plant Science. 11, 296. A version of the work in chapter 3 has been published in Molecular Ecology: • Huang, K, Andrew, RL, Owens, GL, Ostevik, KL and Rieseberg, LH. 2020. Multiple chromosomal inversions contribute to adaptive divergence of a dune sunflower ecotype. Molecular Ecology. 29, 2535-2549. vii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ........................................................................................................................ vii List of Tables ............................................................................................................................... xii List of Figures ............................................................................................................................. xiii List of Abbreviations ...................................................................................................................xv Acknowledgements ................................................................................................................... xvii Dedication ................................................................................................................................. xviii Chapter 1: Introduction ................................................................................................................1 1.1 Background ..................................................................................................................... 1 1.2 This thesis ....................................................................................................................... 5 1.2.1 Study system ........................................................................................................... 6 1.2.2 Breakdown of chapters ........................................................................................... 9 Chapter 2: Frequency, origins, and evolutionary role of chromosomal inversions in plants .............................................................................................................................................14 2.1 Detection of inversions and their pervasiveness in plants ............................................ 15 2.1.1 Cytogenetic studies ............................................................................................... 15 2.1.2 Comparative genetic mapping .............................................................................. 17 2.1.3 Comparative genomic studies ............................................................................... 19 2.1.4 Population genomic approaches ........................................................................... 23 viii 2.1.5 Different likelihood of the establishment of inversions and translocations .......... 25 2.2 Origin and establishment of inversions ......................................................................... 27 2.3 Role in speciation .......................................................................................................... 34 2.4 Sex chromosomes and sequential inversions ................................................................ 37 2.5 Concluding remarks ...................................................................................................... 39 Chapter 3: Multiple chromosomal inversions contribute to adaptive divergence of a dune sunflower ecotype .........................................................................................................................41 3.1 Introduction ................................................................................................................... 41 3.2 Methods......................................................................................................................... 45 3.2.1 Plant materials and RAD sequencing ................................................................... 45 3.2.2 SNP calling ........................................................................................................... 45 3.2.3 Local population structure analysis ...................................................................... 46 3.2.4 Genetic map construction ..................................................................................... 49 3.2.5 Genome-environment association analysis ........................................................... 51 3.3 Results ........................................................................................................................... 53 3.3.1 SNP Calling .......................................................................................................... 53 3.3.2 Detection of putative chromosomal inversions ..................................................... 54 3.3.3 Genetic maps ......................................................................................................... 59 3.3.4 Genome-environment association analysis ........................................................... 62 3.4 Discussion ..................................................................................................................... 65 3.4.1 Identification of inversions ................................................................................... 65 3.4.2 Inversions contribute to adaptive divergence ....................................................... 68 ix Chapter 4: Inversions largely avert the accumulation of deleterious mutations despite suppressing recombination between large blocks of genes ......................................................72 4.1 Introduction ................................................................................................................... 72 4.2 Methods......................................................................................................................... 75 4.2.1 Samples and variant calling .................................................................................. 75 4.2.2 Estimation of recombination rate .......................................................................... 78 4.2.3 Transposable element abundance ......................................................................... 79 4.2.4 Protein evolution ................................................................................................... 80 4.3 Results ........................................................................................................................... 83 4.3.1 SNP dataset ........................................................................................................... 83 4.3.2 Recombination rate ............................................................................................... 83 4.3.3 Transposable element abundance ......................................................................... 87 4.3.4 Protein evolution ................................................................................................... 89 4.4 Discussion ..................................................................................................................... 96 Chapter 5: Inversions contribute disproportionately to parallel adaptation to sand dunes ...........................................................................................................................................101 5.1 Introduction ................................................................................................................. 101 5.2 Methods....................................................................................................................... 104 5.2.1 Samples and SNP calling .................................................................................... 104 5.2.2 Identification of selective sweeps ....................................................................... 106 5.2.3 Regions of parallel evolution and enrichment analyses ...................................... 108 5.2.4 Phylogeny of parallel regions ............................................................................. 109 5.2.5 Functional analyses of parallel regions ............................................................... 110 x 5.3 Results ......................................................................................................................... 111 5.3.1 SNP dataset and regions of selective sweeps ...................................................... 111 5.3.2 Regions of parallel evolution and enrichment analyses ...................................... 114 5.3.3 Phylogeny of parallel regions ............................................................................. 117 5.3.4 Functional analyses of parallel regions ............................................................... 119 5.4 Discussion ................................................................................................................... 121 Chapter 6: Conclusion ...............................................................................................................127 6.1 Discussion ................................................................................................................... 127 6.2 Caveats ........................................................................................................................ 129 6.3 Future directions ......................................................................................................... 130 Bibliography ...............................................................................................................................133 Appendices ..................................................................................................................................156 Appendix A - Chapter 3 supplementary materials .................................................................. 156 A.1 Location and ecotype assignment of each population, as well as the population genotype frequencies of putative inversions. ...................................................................... 157 A.2 Characterization of MDS outlier regions. ............................................................... 158 A.3 LD plots for all chromosomes. ................................................................................ 162 A.4 Genetic map of non-dune ecotype compared to the HA412HOv2 genome. .......... 166 A.5 Genetic map of dune ecotype compared to the HA412HOv2 genome. .................. 173 A.6 Genome-environment association for coverage variables. ..................................... 180 A.7 Genome-environment association for soil variables. .............................................. 182 Appendix B - Chapter 4 supplementary figures ..................................................................... 187 B.1 LD plots for all inversions. ..................................................................................... 187 xi B.2 Genomic distributions of LTR-RTs in the three genome assemblies. .................... 189 B.3 Distribution of window-based π0/π4 in each species. .............................................. 190 B.4 Comparison of π0/π4 within inversions to genome-wide average for regions with a similar recombination rate. ................................................................................................. 191 B.5 Comparison of Pnonsense/P0 within inversions to genome-wide average for regions with a similar recombination rate. ...................................................................................... 192 B.6 Comparison of π0/π4 and Pnonsense/P0 between populations inversion pet09.01. ..... 193 B.7 Comparison of π0/π4 and Pnonsense/P0 between populations inversion pet11.01. ..... 194 Appendix C - Chapter 5 supplementary figures ..................................................................... 195 C.1 Sample information by individual for chapter 5. .................................................... 195 C.2 Maximum likelihood trees of parallel regions. ....................................................... 200 xii List of Tables Table 2.1 - Summary of inversions from comparative genomics studies. .................................... 22 Table 3.1- Clusters of MDS outliers obtained with lostruct. ........................................................ 55 Table 3.2 - Summary of the putative inversions. .......................................................................... 58 Table 3.3 - Bayes factors of genome-environment association analyses with coverage and soil data for putative inversions treated as single loci. ................................................................ 63 Table 5.1 - GO enrichment of genes in parallel regions within inversion pet11.01. .................. 120 xiii List of Figures Figure 2.1 - Effective reduction in recombination of inversions by selection against recombinant gametes in meiosis. ............................................................................................................... 17 Figure 2.2 - Models for the establishment of inversions. ............................................................. 30 Figure 2.3 - Results of local population structure analyses. ......................................................... 34 Figure 2.4 - The model for the role of inversions in speciation. ................................................... 36 Figure 3.1 - Weir and Cockerham’s FST between dune and non-dune ecotypes. ......................... 54 Figure 3.2 - Characterization of the MDS outlier region on chromosome 5 (pet05.01). .............. 57 Figure 3.3 - Map of Great Sand Dune National Park showing genotype distributions of all putative inversions. ............................................................................................................... 59 Figure 3.4 - Genetic map comparisons for all putative inversions. .............................................. 61 Figure 3.5 - Genome-environment association for % grasses, coverage PC1, soil NO3 nitrogen and soil PC2. ......................................................................................................................... 64 Figure 4.1 - Recombination rate across the genome of Ha412HOv2.0. ....................................... 84 Figure 4.2 - Plot of linkage disequilibrium for Helianthus petiolaris inversion pet15.01 on chromosome 15. .................................................................................................................... 85 Figure 4.3 - Divergence (FST) between inversion orientations. .................................................... 86 Figure 4.4 - Recombination rate and long terminal repeat retrotransposon (LRT-RTs) density. . 88 Figure 4.5 - Comparison of long terminal repeat retrotransposon density between orientations. 89 Figure 4.6 - Genetic load (π0/π4) versus genetic diversity (mean of π0 and π4) across three sunflower species. ................................................................................................................. 90 xiv Figure 4.7 - Genome-wide correlation of π0/π4 and recombination rate in Helianthus annuus, H. argophyllus and H. petiolaris. .............................................................................................. 91 Figure 4.8 - Comparison of π0/π4 within inversions ann13.01, arg10.01 and pet09.01 to the genome-wide average for regions with a similar recombination rate. .................................. 92 Figure 4.9 - Recombination rate and proportion of nonsense mutations. ..................................... 93 Figure 4.10 - Comparison of π0/π4 and Pnonsense/P0 between populations for inversion pet05.01. 95 Figure 5.1 - Selective sweep regions in GSD system and MON system. ................................... 113 Figure 5.2 - Parallel regions between the GSD and MON dune systems. .................................. 116 Figure 5.3 - Maximum likelihood trees of parallel regions showing different sources of adapted alleles found in the dune ecotypes. ..................................................................................... 118 xv List of Abbreviations ANN Helianthus annuus ARG Helianthus argophyllus BF Bayes factor CLR composite likelihood ratio cM centiMorgan DNA deoxyribonucleic acid GATK Genome Analysis Tool Kit GBS genotyping-by-sequencing GWA genome-wide association GEA genome-environment association GSD Great Sand Dune National Park and Preserve LD linkage disequilibrium LG linkage group LTR-RT long terminal repeat retrotransposons MDS multidimensional scaling MON Monahans Sandhills State Park PCA principle component analysis PCR polymerase chain reaction PET Helianthus petiolaris POD pseudo-observed data xvi QTL quantitative trait locus/loci RAD restriction site associated DNA SNP single nucleotide polymorphism WGS whole genome sequencing xvii Acknowledgements I am deeply indebted to many people and organizations for their help and encouragement throughout this research. Many thanks to: Loren Rieseberg, who always has endless innovative ideas and always replies emails promptly and fulfills his responsibility for students in his busy time, for all the intellectual inspiration as well as the freedom and resources to explore any question that intrigued me; My supervisory committee, Darren Irwin and Amy Angert for their thoughtful comments and insight; Kate Ostevik and Greg Owens for leading the way with their suggestions and generosity; All my kind lab mates for the help with my experiments and analyses, as well as the moral support and joy, especially, Mojtaba and Mariana for all the fun talks we had over the years; All the people from the evolution community that I ever talked to for their constructive and inspiring conversations; My friends in Vancouver for the companionship and pleasure in all kinds of extracurricular activities; Chinese Scholarship Council for the financial support for the first four years of my study. Special thanks are owed to my family for their steadfast support throughout all these years of my study abroad. xviii Dedication To research and education in the field of evolutionary biology. 1 Chapter 1: Introduction 1.1 Background Species are often found living in heterogeneous environments. Local adaptation arises when organisms have higher average fitness in their local environment compared to individuals from elsewhere (Blanquart et al. 2013; Savolainen et al. 2013). In this process, natural selection increases the frequencies of alleles and genomic structural changes underlying phenotypic traits that shift the population toward a local optimum. Understanding the genetic features of adaptation is a critical step in solving fundamental questions in evolutionary biology, such as the origin of adaptive variation, repeatability of evolution, and the nature and distribution of effect sizes of adaptive loci (Rausher and Delph 2015; Hoban et al. 2016). In addition, because ecological divergence among populations can be a potent force driving the evolution of reproductive isolation (Schluter 2001; Coyne and Orr 2004), identifying the genetic basis of adaptation to contrasting environments will help elucidate the process of speciation. Lastly, comprehensive knowledge of local adaptation of wild species may provide guidance on the introduction of beneficial alleles for crop improvement and sustain global food security under climate change (Dwivedi et al. 2008; Nair 2019). The recent revolution in sequencing technology has promoted the accumulation of population-scale genomic data, and numerous methods have been developed to identify the genes and alleles that may be responsible for local adaptation at the whole genome level (Hoban et al. 2016). However, much remains to be understood about the genetic architecture and evolutionary mechanisms of adaptation (Via 2012; Yeaman 2013), especially when adaptation occurs in the presence of gene flow, which is the focus of my dissertation. 2 Gene flow can both facilitate and impede local adaptation. It can fuel adaptation through the introduction of beneficial alleles; advantageous mutations that arise in a local population may spread to other populations through the movement of gametes or individuals, therefore promoting the collective evolution of populations (Rieseberg and Burke 2001). Furthermore, crosses between small fragmented populations with reduced genetic diversity often generate heterosis, indicating that gene flow can increase genetic diversity and rescue populations from inbreeding depression (Keller and Waller 2002). On the other hand, gene flow may be a constraining force that acts to prevent differentiation due to drift or adaptation to local conditions. When populations are exchanging genes, maladapted alleles from other populations may be brought to the locally-adapted population and lower the fitness of those individuals, thus impeding ecological divergence (Lenormand 2002; Paul et al. 2011; Farkas et al 2016). When there is ongoing gene flow between populations adapting to different environmental conditions, and fitness trade-offs between alleles contributing to adaptation, natural selection will favor recombination modifiers that suppress the recombination between these alleles (Ortiz-Barrientos et al. 2016). Recombination rates between a pair of loci can be changed by two main mechanisms: the rate at which crossovers occur between linked loci (genic modifiers), or through genomic rearrangements such as chromosomal inversions. A number of models have explored the role of these modifiers in reducing recombination during ecological speciation (Charlesworth and Charlesworth 1979; Pylkov et al. 1998; Lenormand and Otto 2000; Kirkpatrick and Barton 2006). Compared to genic modifiers, inversions should spread more readily because inversions are completely linked to the loci under selection (Ortiz-Barrientos et al. 2016). Moreover, inversions typically suppress recombination over larger regions of the genome than local allelic modifiers (Ortiz-Barrientos et al. 2016), thus increasing the chance of 3 ecological speciation by facilitating the accumulation of additional adaptive mutations, as well as genetic incompatibilities (Rieseberg 2001; Noor et al. 2001). Lastly, although inversions suppress recombination near and between breakpoints, they are found to increase recombination rates elsewhere in the genome, which might be beneficial for adaptation (Schultz and Redfield 1951; Farre et al. 2013). Chromosomal inversions have received much attention as one of the most important genomic architectures contributing to adaption and speciation. As mentioned above, inversions have the potential to permit adaptation in the presence of gene flow by reducing recombination between favorable combinations of alleles. The last few decades have witnessed the development of a number of models that examine the role of inversions in adaptation and speciation based on this effect (Noor et al. 2001; Rieseberg 2001; Ortiz-Barrientos et al. 2002; Hoffman and Rieseberg 2008; Feder and Nosil 2009). In these models, inversions reduce gene flow for large regions of the genome and maintain favorable genotypic combinations at loci affecting adaptation to different environments, resulting in greater selective advantage and driving the spread of inversions (Hoffman and Rieseberg 2008; Kirkpatrick and Barton 2006). Additionally, inversions might cause tighter effective linkage between genes that contribute to local adaptation and those causing assortative mating, thereby facilitating speciation (Trickett and Butlin 1994). Finally, by reducing gene flow within the inverted regions, inversions may enable the accumulation of intrinsic genetic incompatibilities (Noor et al. 2001; Navarro and Barton, 2003). These recombination suppression models offer a means for resolving the widely-recognized antagonism between divergent natural selection and recombination (Felsenstein 1981). Although recombination suppression has the potential to facilitate adaptation, there are potential downsides as well, such as increased transposable element (TE) activity (Montgomery 4 et al. 1987; Bartolome et al. 2002) and the accumulation of deleterious mutations (Kaiser and Charlesworth 2009). As recombination modifiers, chromosomal inversions should be subject to these potential downsides. However, on the other hand, recombination is nearly unimpeded between chromosomes sharing the same inversion orientation (Ortiz-Barrientos et al. 2016), which might allow selection to reduce TE and deleterious mutation accumulation, thereby partially mitigating this potentially negative impact of inversions. While such deleterious impacts of inversions have been reported for Drosophila, we are unaware of such reports in plants. This information gap can also be addressed with the aid of population genomic data. Another potentially important consequence of recombination suppression is a change in the distribution of effect sizes at adaptive loci. By combining the effects of multiple linked alleles, inversions and other recombination modifiers are likely to increase the effect sizes “seen” by selection, creating a more concentrated genetic architecture (Yeaman and Whitlock 2011). This can have important impacts on evolutionary responses (Kim and Rieseberg 1999; Via and Hawthorne 2002; Yeaman and Whitlock 2011), as large effect alleles are more likely to be established by natural selection (Orr 1998), persist under gene flow-selection balance (Yeaman and Otto 2011), and contribute to parallel genetic evolution (Bailey et al. 2017; MacPherson and Nuismer 2017). In addition, a more concentrated genetic architecture is expected to limit the range of adaptive responses when populations encounter similar selective pressure, further increasing the likelihood of parallel genetic evolution. Despite their potential importance, the role of inversions in parallel evolution has not yet been specifically explored by theoretical or empirical studies. Many population genomics approaches have been developed to identify selective sweeps based on the distribution of sequence polymorphisms, the allele frequency spectrum, and linkage disequilibrium (LD) (Hahn 2019). These approaches, coupled with the 5 accumulating sequencing data from population genomic studies, not only permit identification of loci under parallel selection in different systems (Hohenlohe et al. 2010), but they also provide insights into the genetic architectures that facilitate or constrain genetic responses to selection (Conte et al. 2015; Yeaman et al. 2018). 1.2 This thesis I am interested in the genomic structural changes and evolutionary forces underlying the ecological divergence of species. Plants show great capacity to adapt to heterogeneous ecological environments and species often include multiple ecotypes (i.e. ecological races with distinct morphological and/or physiological characteristics generated by environment-specific selection), which appear in many cases to have diverged in the presence of gene flow. In addition, chromosomal inversions have been widely reported within and between plant species (Sybenga 1975; Burke et al. 2004; Dvorak et al. 2018) and a number of case studies have associated them with locally favored traits in heterogeneous environments (Lowry and Willis 2010; Lee et al. 2017), consistent with their proposed role in adaptive divergence and speciation in the face of gene flow. Moreover, genomic resources for plants have been accumulating rapidly thanks to the substantial investment in crop genomics. Therefore, plants provide extraordinary opportunities for studying this topic. In this thesis, specifically, I examine two dune sunflower ecotypes, as well as three well-studied annual species in the genus Helianthus, to explore the contribution of chromosomal inversions in adaptation, as well as their putative susceptibility to the accumulation of deleterious load. 6 1.2.1 Study system Helianthus is a genus within the family Asteraceae that contains approximately 50 species of annual and perennial sunflower species native to North America (Stephens et al. 2015). It is a model system in evolutionary biology, well known for hybridization, speciation, polyploidy, and domestication, as well as remarkable ecological divergence (Rieseberg et al. 2003; Bock et al. 2013; Ostevik et al. 2016; Hübner et al, 2019). Most studies of ecological adaptation in the genus have focused on the common sunflower, H. annuus, the progenitor of the domestic sunflower, and its close annual relatives because of the distinct ecotypes that have arisen during the colonization of different habits by these species. Helianthus annuus is the most widely distributed species in the genus. It is generally found on mesic soils, but can grow in a variety of disturbed or extreme habitats, such as arid deserts and salt marshes. A morphologically distinctive ecotype of H. annuus from southern Texas, previously known as H. annuus subsp. texanus, is adapted to high temperatures and drought (Whitney et al. 2010), as well as elevated herbivore pressure (Whitney et al. 2006). The silverleaf sunflower, H. argophyllus, is the closest relative of H. annuus and, unlike the wide range of its relative, H. argophyllus is endemic to southern Texas. Despite its limited range, H. argophyllus includes two locally adapted ecotypes: a tall and late-flowering ecotype that is mainly found inland and a short, early-flowering ecotype found on barrier islands of Texas (Moyers 2015). The third well-studied species Helianthus petiolaris, the prairie sunflower, also has a broad distribution across North America. It consists of two subspecies Helianthus petiolaris ssp. petiolaris, commonly found in the southern Great Plains, and H. petiolaris ssp. fallax, found in more arid environments on the Colorado Plateau (Heiser 1961). Across most of its range, H. petiolaris is found on sandy soils. However, two ecotypes of H. petiolaris ssp. fallax 7 at Great Sand Dunes National Park and Preserve (GSD) in Colorado and Monahans Sandhills State Park (MON) in Texas are adapted to active sand dunes (Ostevik et al. 2016). At GSD, the dune ecotype differs from non-dune populations of H. petiolaris ssp. fallax, which are abundant on the sand sheet below the dunes, for a number of ecologically relevant phenotypic traits, such as seed size, branching pattern, and root architecture (Andrew et al. 2012). Comprehensive field and common garden experiments showed that multiple reproductive barriers isolate the two ecotypes, including strong extrinsic selection against immigrants and hybrids, conspecific pollen precedence (post-mating preferential use of pollen from plants of the same species), as well as a weak crossability barrier (Ostevik et al. 2016). Previous genetic analysis show that this ecotype diverged from typical H. petiolaris within the last 10,000 years (Andrew et al. 2013) and substantial and asymmetric gene flow have been reported between dune and non-dune populations (Andrew et al. 2012). Moreover, genetic differentiation between the ecotypes is largely restricted to several large genomic regions while background divergence is extremely low (Andrew and Rieseberg 2013), forming “genomic islands”, making it a good system to study the genomic architecture of local adaptation with gene flow. Like the ecotype at GSD, the dune ecotype at MON inhabits active sand dunes while the typical non-dune ecotype can be found in sandy soils nearby. Several traits also differentiate this ecotype from typical H. petiolaris, including larger seeds, more ovate leaves and thinner peduncles (Rogers et al. 1982; Ostevik 2016). In addition, crosses between the dune ecotype and non-dune plants are less fertile due to segregation for a chromosomal translocation (75-80% viable pollen) than typical intraspecific crosses, which average close to 95% viable pollen (Heiser 1958; Chandler et al. 1986). These differences in habitat, morphology, and chromosome structure led early taxonomists to consider the dune ecotype as a distinct species H. neglectus 8 (Heiser 1958; Rogers et al. 1982). However, studies using molecular data found that dune and non-dune plants show very little genetic divergence, and gene flow between the dune plants and nearby H. petiolaris ssp. fallax is no different from that between different regions of H. petiolaris (Raduski et al. 2010). Therefore, this taxon is better treated as an ecotype of H. petiolaris rather than as a separate species, and it appears to represent another case of local adaptation with gene flow, similar to the one at GSD. Helianthus is well known to have extremely diverse karyotypes. Comparative genetic mapping has identified a large number of inversions and translocations between and within species (Rieseberg et al. 1995a; Burke et al. 2004; Barb et al. 2014; Ostevik et al. 2019). Given the high rates of chromosomal evolution reported for Helianthus, as well as the aforementioned importance of chromosomal inversions in local adaptation, the labile genome structure offers a possible mechanism for the large genomic regions of high divergence in the dune sunflower and may play a key role in the evolution of ecotypes in Helianthus. Another interesting feature of the genus, especially the annual clade, has been repeated adaptation to active sand dune habitats. This is especially striking in H. petiolaris, in which dune ecotypes, typified by those at GSD and MON, have evolved similar traits, including larger flowering heads, larger seeds, and reduced branching (Heiser 1958; Ostevik 2016), suggesting that the two systems have undergone parallel ecological divergence in the face of gene flow. Genetic studies also showed that there is some repeatability of the QTLs underlying the repeated increase in seed size (Ostevik 2016). This system thus provides an excellent opportunity for studying the impact of genome architecture on parallel genetic evolution. The genetic and genomic resources for sunflower have been rapidly growing (Kane et al. 2011), both due to the importance of H. annuus in agriculture and its growing popularity in 9 ecological and evolutionary studies. With the recent completion of a chromosome-level reference genome of common sunflower (Badouin et al. 2017), our understanding of the genomics of adaptation and speciation have the potential to be greatly advanced from the studies in this system. 1.2.2 Breakdown of chapters In this thesis, I study the genomic basis of adaptation and speciation, focusing on the role of inversions in these processes, using Helianthus species as a study system. I start by reviewing what we know about inversions in plants. I then examine the genomic architecture underlying the divergence between dune and non-dune ecotypes of H. petiolaris at GSD. Following the finding that chromosomal inversions play a critical role in local adaptation in sunflower, I then examine whether inversions exhibit predicted downsides as well, including the accumulation of TEs and deleterious mutations owing to suppressed recombination. Finally, I study two cases of dune adaptation in sunflower to evaluate the impact of genome architecture on the patterns of parallel genetic evolution across the genome. Taken together, these studies demonstrate the important role of chromosomal inversions in local adaptation. Chapter 2: Frequency, origins, and evolutionary role of chromosomal inversions in plants Chromosomal inversions have the potential to play an important role in evolution by reducing recombination between favorable combinations of alleles. Until recently, however, most evidence for their likely importance derived from dipteran flies, whose giant larval salivary chromosomes aided early cytogenetic studies. The widespread application of new genomic 10 technologies has revealed that inversions are ubiquitous across much of the plant and animal kingdoms. In this chapter, I review the rapidly accumulating literature on inversions in the plant kingdom and discuss what we have learned about their establishment and likely evolutionary role. I show that inversions are prevalent across a wide range of plant groups. I find that inversions are often associated with locally favored traits, as well as with traits that contribute to assortative mating, suggesting that they may be key to adaptation and speciation in the face of gene flow. I also discuss the role of inversions in sex chromosome formation, and explore possible parallels with inversion establishment on autosomes. The identification of inversion origins, as well as the causal variants within them, will advance our understanding of chromosomal evolution in plants. Chapter 3: Multiple chromosomal inversions contribute to adaptive divergence of a dune sunflower ecotypes Both models and case studies suggest that chromosomal inversions can facilitate adaptation and speciation in the presence of gene flow by suppressing recombination between locally adapted alleles. Until recently, however, it has been laborious and time-consuming to identify and genotype inversions in natural populations. In this chapter, I apply reduced representation sequencing data and newly developed population genomic approaches to identify putative inversions that differentiate a dune ecotype of Helianthus petiolaris from populations found on the adjacent sand sheet. I detect seven large genomic regions that exhibit a different population structure than the rest of the genome and that vary in frequency between dune and non-dune populations. These regions also show high linkage disequilibrium and high 11 heterozygosity between, but not within arrangements, consistent with the behavior of large inversions, an inference subsequently validated in part by comparative genetic mapping. Genome-environment association analyses show that key environmental variables, including vegetation cover and soil nitrogen, are significantly associated with inversions. The inversions co-locate with previously described “islands of differentiation,” and appear to play an important role in adaptive divergence and incipient speciation within H. petiolaris. Chapter 4: Inversions largely avert the accumulation of deleterious mutations despite suppressing recombination between large blocks of genes Chromosomal inversions can act as recombination modifiers that suppress recombination between alleles contributing to local adaptation. However, recombination is also critical for the purging of deleterious variants, such as transposable elements (TEs) and maladaptive changes in proteins. Thus, inversions are predicted to accumulate such mutations, as has been reported for other genomic regions of low recombination. Exploring patterns of molecular evolution for potentially negative consequences of inversions will deepen our understanding of this important genomic architecture. In this chapter, I investigate the distribution of TEs and coding sequence evolution within a number of inversions across different sunflower species. I find that inversions do not show an enrichment of TEs or elevated deleterious loads relative to background levels. However, there is a minor increase in load associated with inversions in populations that are polymorphic for a given inversion compared to populations that are monomorphic for one of the inversion orientations. These results suggest that inversions are ideal recombination modifiers from an evolutionary standpoint. By suppressing recombination between, but not within 12 orientations, inversions permit independent evolution of large blocks of genes, while largely avoiding the penalties typically associated low recombination rates. Chapter 5: Inversions contribute disproportionately to parallel adaptation to sand dunes The probability of parallel genetic evolution is a function of the direction and strength of phenotypic selection and constraints imposed by genetic architecture. Architectures that limit genetic variability, or the independence of this variability will reduce the number of possible adaptive solutions, thereby increasing the repeatability of evolution. Inversions, for example, capture locally adapted alleles and suppress recombination between them, which limits the range of adaptive responses. Also, the combined phenotypic effect of alleles within inversions is likely to be greater than that of individual alleles, which should further increase the contributions of inversions to parallel evolution. In this chapter, I tested the hypothesis that inversions contribute disproportionately to parallel genetic evolution, by comparing the genetic architecture of adaptation to sand dunes in Helianthus petiolaris, which has independently adapted to dune habitats in Colorado and Texas, USA. Previous studies have identified seven polymorphic inversions in both dune ecotypes, four of which are in common to dune populations at both sites. I identified selective sweeps in the two dune systems and looked for shared regions. As predicted, the majority of regions were found within inversions. However, some shared sweeps were found outside inversions, indicating that inversions are not the only genetic features constraining dune adaptation. Also, some inversions are not involved in parallel evolution, possibly because of differences in selective pressures between the two sites. Phylogenetic analyses of shared regions indicate that in inversions the same allele was targeted by selection, 13 while for other regions very divergent alleles were enriched in the two dunes. Functional analyses showed that inversions contain major QTLs and important genes responsible for ecological divergence of the dune ecotypes. These results imply that inversions represent an important component of genome architecture that facilitates parallel genetic evolution by clustering locally adapted alleles and limiting the diversity of viable genetic responses to parallel ecological selection. Chapter 6: Conclusion In the final chapter, I integrate the results of the preceding chapters and make some general conclusions about adaptation and speciation in wild sunflowers. I discuss the strengths and limitations of this research and raise possible future directions. 14 Chapter 2: Frequency, origins, and evolutionary role of chromosomal inversions in plants Species and ecotypes are often differentiated by chromosomal rearrangements, such as translocations and inversions. The latter were first discovered by Sturtevant (1921) when comparing genetic linkage maps of closely related Drosophila species. Sturtevant further deduced that inversions reduce the rate of recombination in heterozygotes (which is key to their main evolutionary role), and validated this claim through observations of the giant larval salivary chromosomes found in Drosophila. Inverted regions were subsequently identified from banding patterns of chromosomes in many other species and became the first genetic markers used to reconstruct phylogenies (Krimbas and Powell 1992). The abundance of inversion polymorphisms detected in these studies also inspired population geneticists to investigate patterns of variation within and between species of Drosophila (Dobzhansky 1970). Until recently, most evidence regarding the frequency and evolutionary role of inversions came from studies of Dipteran systems, such as Drosophila (Noor et al, 2001; Ortiz-Barrientos et al. 2002), Anopheles (Ayala and Coluzzi 2005; Ayala et al. 2010) and Rhagoletis (Feder et al. 2003a,b). This was partly due to the ease of identifying inversions in Dipteran salivary gland chromosomes, but also because of the widespread recognition of the importance of inversions in this group. Over the past two decades, however, comparative genetic mapping and genomic approaches have revealed that inversions are ubiquitous across the plant and animal kingdoms, either fixed between or polymorphic within species (Wellenreuther and Bernatchez 2018). Detailed studies of the genetic contents and establishment of inversions have not only confirmed 15 a longstanding hypothesis that inversions play an important role in adaptation by reducing recombination between favorable combinations of alleles (Kirkpatrick and Barton 2006; Lowry and Willis 2010), but also that they contribute to speciation in a similar way - by suppressing recombination between local adapted alleles and those causing assortative mating (Trickett and Butlin 1994). In this paper, we first review various approaches that have been employed to detect inversions within and between plant species, as well as studies that report on inversion abundance across a wide range of plant groups. We then discuss possible scenarios for the origin and spread of inversions inspired by theoretical and empirical studies. We further illustrate their important role in speciation with case studies that have associated inversions with traits known to underlie ecological adaptation and reproductive isolation. We also discuss the role of inversions in sex chromosome formation and whether the stepwise establishment of inversions seen on sex chromosomes might also occur on autosomes. Finally, we suggest avenues for future studies to bridge gaps in our understanding of the evolutionary role of inversions in plants. 2.1 Detection of inversions and their pervasiveness in plants 2.1.1 Cytogenetic studies Most early evidence of inversions in plants came from two sources. First, as alluded to above, inversions could sometimes be inferred from the chromosome banding patterns seen in karyotypes (Greilhuber and Speta 1976; Konishi and Linde-Laursen 1988; Rodriguez et al. 2000). This approach worked well in plants with small numbers of large and distinctive chromosomes, but was impractical in most species of plants. Also, even when feasible, only very 16 large inversions were typically detectable. Nonetheless, these karyotypic analyses indicated that inversions were not uncommon in plants. A second source of information about inversions came from studies of chromosome pairing in meiosis (Lewis and Roberts 1956; Sybenga 1975; Ahmad et al. 1979; Gopinathan and Babu 1986; Anderson et al. 2010). This approach relied on that fact that crossing over in inversion heterozygotes creates distinctive meiotic configurations (Sybenga 1975; Figure 2.1). While such an approach is feasible in taxa with small and/or morphologically similar chromosomes, it under-estimates inversion abundance because recombination within inversions is required for their detection. Thus, small inversions, or those in low-recombining regions of the genome, will be missed. In recent decades, in situ hybridization approaches have been widely employed to study karyotype evolution within and between species. Such approaches are most powerful in groups such as the Brassicaceae and Solanaceae, in which virtually repeat-free BAC contigs covering much of the genome are available for use as probes, permitting “comparative chromosome mapping” (Lysak and Lexer 2006). Successful application of this method has led to the discovery of numerous inversions across various clades of the Brassicaceae, but especially Arabidopsis and Brassica (Lysak et al. 2006, 2007; Mandakova and Lysak 2008; Mandakova et al. 2015; Lee et al. 2017), well as among and within species in Solanum (Szinay et al. 2012). However, suitable sets of chromosome-specific painting probes are needed for the broader application of this approach in other plant groups. 17 Figure 2.1 - Effective reduction in recombination of inversions by selection against recombinant gametes in meiosis. (a) In individuals that are heterozygous for a pericentric inversion, a single crossover within the inversion generates unbalanced gametes that contain a duplication and a deletion. (b) In individuals that are heterozygous for a paracentric inversion, a single crossover within the inversion produces a dicentric bridge and an acentric fragment. The acentric fragment is lost because it cannot be drawn to either end and the chromosomal bridge breaks at random point during segregation, resulting in two deletion products. Lines of blue and orange colors represent homologous chromosomes and small circles indicate centromeres. 2.1.2 Comparative genetic mapping With the development of DNA markers in the latter part of the 20th century, it became feasible to develop genetic linkage maps that were sufficiently dense to permit detection of chromosomal rearrangements between plant genomes. More recently, advances in high throughput sequencing and computational methods permit high-resolution genetic mapping and inference of large structural variants from low coverage sequence data (e.g., Flagel et al. 2019). Comparative DCBADBCADCBADBCAPairingDCBAACBADCBDACBDDCBAACBADCBDACBDSegregationCBCBDADACrossoverPairingCBCBDADACrossoverSegregationDCBACBAADBCADCBAACBAACBDDCDBNormal productDuplication A and deletion DNormal productDuplication D and deletion ANormal productDeletionNormal productDeletionPericentric inversion Paracentric inversion(a) (b)(lost) 18 genetic mapping has been broadly applied, especially in crop rich families such as Solanaceae, Poaceae, and Brassicaceae. While successful, the number of rearrangements detected depends in part on marker density and recombination rates. If either is low, then rearrangements will be missed. We summarize a few well known examples below, both to illustrate that inversions are common in essentially all plant taxa analyzed, but also for comparison to genomic studies (below), which show that comparative mapping, like chromosome banding and meiotic analyses, greatly under-estimated inversion numbers. In Solanaceae, for example, the genomes of potato and tomato were found to differ by only five paracentric inversions (Bonierbale et al. 1988; Tanksley et al. 1992), while at least 19 inversions and 6 chromosome translocations differentiate potato and pepper (Wu et al. 2009; summarized in Szinay et al. 2012). In Poaceae, genetic maps based on restriction fragment length polymorphism markers identified an inversion on the short arm of chromosome 9 between sorghum and maize, which also differentiates maize from its close relative Zea mexicana (Berhan et al. 1993). Likewise, Ahn and Tanksley (1993) showed that while the rice and maize genomes are largely conserved in gene order, multiple inversions and translocations occurred after the polyploidization of maize. Analyses of different intraspecific maps of Brassica oleracea revealed that small inversions among morphotypes were the most frequent form of rearrangements followed by translocations (Kianian and Quiros 1992). A comparison between Arabidopsis thaliana and Brassica nigra also identified numerous translocations and even more inversions between the two genera (Lagercrantz 1998). The comparative mapping approach has also been applied to ecological and evolutionary model systems, including Mimulus, Populus, and Helianthus. This work has identified numerous chromosomal rearrangements from the ecotypic to interspecific level. For example, Lowry and 19 Willis (2010) discovered an inversion between annual and perennial ecotypes of Mimulus guttatus by comparing maps from multiple F2 mapping populations. Using a similar approach, Fishman et al. (2013) identified two reciprocal translocations and three inversions between Mimulus cardinalis and M. lewisii. Using evidence from both linkage and physical maps, possible inversions were also inferred between Populus species (Drost et al. 2009; Tong et al. 2016). In Helianthus, low density linkage maps of Helianthus annuus and H. petiolaris suggested that three inversions and as many as eight translocations differentiated the species (Rieseberg et al. 1995a; Burke et al. 2004). However, a recent follow-up study (Ostevik et al. 2019), which employed higher density genetic maps and a novel algorithm for synteny block detection, found 50-60 inversions between the species and 6-8 translocations. Thus, low-density maps appear better able to detect translocations than inversions, presumably because detection of the former requires fewer markers and is less sensitive to marker ordering errors. Ostevik et al. (2019) also applied their algorithm to the comparisons of new maps for the two subspecies of H. petiolaris, as well as high-density genetic maps previously published for three other species (Barb et al. 2014). Up to 74 inversions and 15 translocations were found across the five taxa. Lastly, Huang et al. (2019) developed genetic maps for dune and non-dune ecotypes of H. petiolaris and successfully identified multiple inversions, but no translocations. 2.1.3 Comparative genomic studies In recent years, the ever-increasing number of high quality genome assemblies and other genomic datasets have greatly facilitated the detection of chromosomal rearrangements and uncovered very large numbers of inversions between closely-related species (Table 2.1). For example, a de novo assembly of Arabidopsis thaliana Ler-0 strain revealed 47 inversions 20 between its genome and that of the widely used Col-0 accession (Zapata et al. 2016), although some unknown fraction of these inversions might have been introduced by mutagenesis. A reference genome of Arabidopsis lyrata was compared to that of A. thaliana, and 154 inversions were identified, as well as two reciprocal translocations and three chromosomal fusions previously revealed by genetic mapping (Yogeeswaran 2005; Hu et al. 2011). Several inversions were also found between cucumber and melon (Huang et al. 2009; Garcia-Mas et al. 2012), and five paracentric and one pericentric inversions were revealed between cultivated and wild cucumber with the aid of comparative fluorescence in situ hybridization (Yang et al. 2012). Whole-genome sequencing of pepper confirmed previously reported large inter-chromosomal translocations and identified 367 inversions between pepper and potato (Qin et al. 2014), about 20x more than were identified via comparative mapping. In addition, a total of 214 inversions were identified between rice (Oryza sativa) and its close relative O. brachyantha (Chen et al. 2013). And a comprehensive study using homologous gene sequences showed that short paracentric inversions and short intra-chromosomal translocations were the most common rearrangements in the grass family Poaceae (Dvorak et al. 2018). Other well characterized examples come from comparisons of different cultivars of cotton (Yang et al. 2019) and grapevine (Zhou et al. 2019); details in Table 2.1. Using the number of inversions reported in these studies and species’ divergence times obtained from the literature, we estimated the rate of inversion evolution to be about 15-30 inversions per million years (Table 2.1). However, this estimate should be treated with caution since the quality of genome assemblies and the methods employed to identify chromosome rearrangements varied among studies. Also inversion sizes typically were not reported in these studies, except for Zapata et el. (2016) and Dvorak et al. (2018), who showed that most 21 inversions are small in size. Some of the variation in rates of inversion evolution reported for different groups likely derives from different size cut-offs used to report inversions. For example, the range of inversion sizes reported by Zapata et al. (2016) and Dvorak et al. (2019) are almost completely non-overlapping (Table 2.1). In the future, it would be useful if studies would report the size distributions of inversions, as well as the extent of sequence divergence between inversion haplotypes. 22 Table 2.1 - Summary of inversions from comparative genomics studies. Species Common name Number of inversions Size Number of translocations or fusions Divergence time (MYA) Evolutionary rate (inversion/MYA) Reference Arabidopsis thaliana strains Thale cress 47 115bp-1.17Mbp 0 - - Zapata et al. 2016 Arabidopsis lyrata / A. thaliana Thale cress / lyrate rockcress 154 - 5 10 15.4 Hu et al. 2011 Cucumis sativus Wild and cultivated cucumbers 5 paracentric, 1 pericentric - 0 - - Yang et al. 2012 Cucumis sativus / Cucumis melo Cucumber / melon Several - - 10 - Huang et al. 2009; Garcia-Mas et al. 2012 Capsicum annuum / Solanum lycopersicum Pepper / tomato 468 - 612 20 23.4 Qin et al. 2014 Capsicum annuum / Solanum tuberosum Pepper / potato 367 - 430 20 18.35 Oryza sativa / O. brachyantha Rice 214 - 0 15 14.27 Chen et al. 2013 Gossypium hirsutum Upland cotton 60 - 1314 - - Yang et al. 2019 Gossypium hirsutum / G. arboreum Cottons 39 - 35 1-2 18.5-39 Gossypium hirsutum / G. raimondii Cottons 15 - 29 1-2 7.5-15 Vitis vinifera cultivars Grapevine 1513 - 3786 - - Zhou et al. 2019 Aegilops tauschii Tausch's goatgrass 44 1.6-8.0Mbp - 3 14.67 Dvorak et al., 2018 Triticum turgidum subgenome A Wild emmer wheat 91 3 30.33 Triticum turgidum subgenome B 65 3 21.67 Brachypodium distachyon Purple false brome 82 35 2.34 Oryza sativa Rice 20 47 0.43 Sorghum bicolor Sorghum 33 53 0.62 23 2.1.4 Population genomic approaches While these comparative approaches offer a means for determining the number of inversions between species, they typically tell us little about the distribution of inversion polymorphisms within species or the traits that are associated with the inversions. However, this information is needed to understand how inversions are established, as well as their role in adaptive evolution and speciation. Fortunately, two population genomic approaches have recently been developed that permit inference of inversions from resequencing data when paired with high quality reference sequences. One approach detects potential inversions by scanning the genome for regions of high linkage disequilibrium (LD) among linked markers (Kemppainen et al. 2015). The rationale for this approach is that recombination suppression should produce very high LD among SNPs within inversions. Although gene conversion and double recombination can break down LD in the middle of old and large inversions, such as that observed for some inversions in Drosophila (Korunes and Noor 2019), whether this is common in plants is unclear. Other mechanisms that reduce recombination, such as pericentromeric heterochromatin, will also lead to high LD regions. Thus, the LD scan should be complemented by an analysis of genotypic relationships within the predicted inversion using principle component analysis (PCA) or a similar method. Because inversions only suppress recombination in heterozygotes, three distinct genotypic clusters should be detected by PCA within an inversion representing each inversion orientation (0/0, 1/1), plus heterozygotes between inversion haplotypes (0/1). While LD scans have been employed to search for inversions in animals (Faria et al. 2019), we are unaware of their application to plants. 24 A second approach takes advantage of the effects of inversions on population structure. This approach assumes that the lack of gene flow between inversion haplotypes will result in differences in patterns of genetic relatedness between inverted and collinear regions. These outlier regions can be detected by conducting windowed analyses of population structure across the genome, such as that implemented by the Local PCA/population structure (lostruct) program developed by Li and Ralph (2019). As with the LD method, analyses of genotypic relationships within the predicted inversion can offer further support for the putative inversion. The local population structure approach has been used to detect polymorphic inversions from RAD sequencing data or whole-genome shotgun data within the wild sunflower species, Helianthus annuus, H. argophyllus and H. petiolaris (Huang et al. 2019; Todesco et al. 2019). While many of the inversions predicted by this method in sunflower have been subsequently confirmed via comparisons of reference sequences, comparative mapping, or Hi-C sequencing, two were not, indicating that these population genomic approaches can offer suggestive evidence of inversions, but are less diagnostic. Other mechanisms, such as recent introgression, would generate patterns similar to those of an inversion. Conversely, small inversions, or inversions lacking elevated population structure or high LD outside of inversion breakpoints, might not be detected by these methods. Using these population genomic approaches, the genotypes of multiple individuals can be simultaneously determined for all detected inversions, which provide useful information on their frequency and geographic distribution. Mapping the breakpoints of inversions will also be helpful for developing PCR markers to determine patterns of inversion polymorphism across large numbers of individuals. This sets the stage for associating traits and environmental factors with inversion haplotypes, thereby revealing the evolutionary forces that shape the pattern of 25 inversion polymorphisms. Although additional independent lines of evidence are encouraged to confirm putative inversions suggested by such methods, population genomic approaches, coupled with ever-expanding population sequencing data, have great potential to further our understanding of the prevalence and evolutionary role of inversions in plants, especially in non-model species. 2.1.5 Different likelihood of the establishment of inversions and translocations As discussed above, comparative mapping of plant species typically identified more inversions than inter-chromosomal translocations or “fusions” (terminal reciprocal translocations). In general, as mapping or sequencing resolution increases, so does the number of inversions detected (Wu and Tanksley 2010; Hu et al. 2011; Chen et al. 2013; Qin et al. 2014; Ostevik et al. 2019). In contrast, little or no increase in the numbers of inter-chromosomal translocations is reported with increasing resolution. Some studies (not discussed here) have focused exclusively on chromosomal-scale translocations and fusions/fissions or did not clearly differentiate rearrangement types and thus are not relevant to this question. We suspect that variation in the abundance of major inter-chromosomal translocations versus inversions relates more to differences in the likelihood of their establishment than to variation in mutation rates. Translocation heterozygotes involving different chromosomes will show mis-segregation during meiosis and produce unbalanced and inviable gametes (King 1993). This strong heterozygous disadvantage (underdominance) of inter-chromosomal translocations makes them difficult to establish. On the other hand, plants seem to be more tolerant of intra-chromosomal rearrangements such as inversions. While recombination between inversion orientations is predicted to result in inviable gametes, the evidence for this is surprisingly sparse 26 and comes mainly from interspecific crosses. Meiotic abnormalities diagnostic of inversions, along with reduced pollen viability, have been reported, for example, in hybrids of Gibasis venustula and G. speciose (Kenton 1981), Vigna umbellate and V. minima (Gopinathan and Babu 1986), as well as between races of Paspalum notatum (Stein et al. 2004), but the fertility loss is typically much smaller than for most translocations. Surprisingly, inversions segregating within species often have no visible effect on fertility, such as reported for Brassica oleracea (Kianian and Quiros 1992) and maize (Fang et al. 2012). In Mimulus and Helianthus, crosses between ecotypes that are separated only by inversions do not show reduced pollen viability (Lowry and Willis 2010; Ostevik et al. 2016; Huang et al. 2019), although meiotic abnormalities diagnostic for inversions have been reported for interspecific crosses (Chandler et al. 1986). This suggests that the reduction in recombination associated with inversions within plant species is typically achieved by disrupting pairing and crossing over between inverted regions (Searle 1993) rather than selection against inviable recombinant gametes. Regardless of the cause, the minimal underdominance of many inversions should ease their establishment. In a number of comparative genomic studies, more translocations were reported than inversions (Table 2.1). Variation in the abundance of inversions and translocations seen in Table 1 stems partly from differences in methods, criteria (e.g., size cut-offs), and power for detecting structural variants, as opposed to real differences in their frequency. Some studies (Yang et al. 2019; Zhou et al. 2019) applied whole-genome alignment, long-read alignment and short-read alignment to detect both inter- and intra-chromosomal translocations of various sizes (transposed genomic segments), while some (Hu et al. 2011) have focused exclusively on large inter-chromosomal reciprocal translocations. More robust conclusions about the prevalence of 27 inversions and translocations will not only require that studies be more parallel in terms of data and methodology employed, but also that they take rearrangement size into account. 2.2 Origin and establishment of inversions There are a number of different molecular mechanisms by which inversions can arise, including ectopic recombination between copies of repeated sequences such as transposable elements, tRNA genes or segmental duplications, or by chromosomal breakage and repair by non-homologous end-joining (Gray 2000; Feschotte and Pritham 2007; Delprat et al. 2009). Both mechanisms have been shown to occur in plants, especially in maize (Lister et al. 1993; Ziolkowski et al. 2003; Zhang and Peterson 2004; Yu et al. 2011; Knoll et al. 2014). Epigenetic modification, given its role in transposable element de-activation and heterochromatin formation, may also play an important role in chromosome evolution in plants (Li et al. 2017). Given the high fraction of plant genomes occupied by transposable elements and other duplicated sequences, inversion mutation rates are likely to be high. However, the relative importance of these different mechanisms and the overall incidence of inversions in natural populations remain to be explored. Like other genetic mutations, inversions can change in frequency as a consequence of genetic drift or selection. Early models of chromosomal evolution assumed that most underdominant rearrangements became established through drift (White 1973; Lande 1979). However, the fixation of a strongly underdominant mutation through drift is unlikely, except under extreme conditions, such as can be found in small founder populations and/or through inbreeding. The conditions for fixation are relaxed for neutral or weakly underdominant mutations such as inversions. Nonetheless, the fact that inversions are frequent in outcrossing 28 species with large effective population sizes, co-vary with ecological variation, and underlie important adaptive traits (Hoffman and Rieseberg 2008; Lowry et al. 2010; Todesco et al. 2019), suggests that the establishment and spread of large inversions is most likely driven by natural selection. The jury is still out for the many small inversions that differentiate plant genomes. Meiotic drive has been proposed as another possible mechanism for the fixation of chromosomal rearrangements. While meiotic drive may very well explain the establishment of large inter-chromosomal translocations, it appears to be too infrequent to account for the abundance of inversions seen both within and between species (Coyne 1989). It also has been hypothesized that inversions could be favored if breakpoints disrupt an open reading frame or alter gene expression (Hoffman and Rieseberg 2008). While we are unaware of a case in plants where such changes have been shown to be adaptive, it is important to keep in mind that few breakpoints have been characterized for inversions with clear phenotypic effects. In the best-studied examples in plants (or animals), selection favoring the establishment of inversions appears to arise indirectly from their impact on reducing recombination within the inverted region. Thus, most recent evolutionary models for the establishment of inversions have focused on this property (Kirkpatrick and Barton 2006; Burger and Akerman 2011; Feder et al. 2011; Charlesworth and Barton 2018). The importance of recombination to the establishment and spread of inversions was initially put forward by Dobzhansky (1970) based on studies of Drosophila, in which inversions typically have little impact on fertility. Dobzhansky argued that genes within inversions were co-adapted, meaning that the fitness of the alleles held together by the inversion would be greater than the sum of their independent effects. A newly arisen inversion carrying a co-adapted set of alleles would spread to fixation in a population unless it was under balancing selection or there 29 was ongoing migration from other populations. Unfortunately, we do not know whether such co-adaptation (i.e., favorable epistatic interactions) is common within inversions. Newer models suggest that such epistatic interactions are not required if the inversions bring together two or more alleles that are adapted to the same local environment and there is ongoing migration between environments (Kirkpatrick and Barton 2006; Figure 2.2a,b). In this situation, the newly derived inversion will have a selective advantage over the ancestral collinear arrangement that carries mixtures of adapted and maladapted alleles (Kirkpatrick and Barton 2006; Burger and Akerman 2011). However, a recent re-examination of the model showed that the selective advantage of an inversion will be small if the loci contained within the inversion are already tightly linked (Charlesworth and Barton 2018). Thus, the conditions under which an inversion is favored in this model are less permissive that previously thought. 30 Figure 2.2 - Models for the establishment of inversions. (a) Kirkpatrick and Barton (2006) model. At the starting point, population 1 and 2 occur in different environments, but are connected by gene flow (maroon arrows). Different alleles (red and green colors) at multiple genes underlying the same locally adapted trait (deep color and light color triangles) are favored in local environments (green and grey backgrounds). The ancestral chromosome carries mixtures of adapted and maladapted alleles in the face of gene flow, while a new inversion carries only the locally adapted alleles (yellow bars). The inversion is therefore favored and rises to high frequency in population 2. (b) Inversions become established through a process similar to (a) but by carrying a combination of alleles at two loci that are adapted to different aspects of the local environment (triangles and squares in different colors). (c) Mixed geographic model proposed by Feder et al. (2011). At the starting point, population 1 and 2 are allopatric. Multiple locally adapted alleles (triangles and squares in different colors) are fixed due to lack of gene flow, and an inversion carrying a full complement of these alleles is present at low frequency in population 2 through mutation-purifying selection balance or genetic drift. At secondary contact, the reduction in recombination caused by the inversion results in a selective advantage over collinear regions, leading to rise of inversion frequency. Red crosses indicate that chromosomes carrying maladaptive combinations of alleles are eliminated in each environment. Population 1 Population 2 Population 1 Population 2Population 1 Population 2 Population 1 Population 2 Population 1 Population 2Population 1 Population 2 Population 1 Population 2 Population 1 Population 2(a)(b)(c)XXXXXXXXX 31 Empirical studies that associate multiple locally adapted traits or genes with inversions offer indirect support for this model. For example, Lowry and Willis (2010) showed that the chromosomal inversion differentiating annual and perennial ecotypes in Mimulus guttatus was associated with flowering time and morphological traits, as well as fitness in inland and coastal environments. Follow-up studies indicated that the inversion was associated with life history divergence and environmental variation, as well as adaptive trade-offs among growth, reproduction, and herbivore resistance (Oneal et al. 2014; Lowry et al. 2019). Similarly, in wild Zea mays an inversion on chromosome 1 showed a strong altitudinal cline in population frequency and statistical association with phenotypic traits such as culm diameter (Fang et al. 2012). However, it is often unclear whether the inversions have captured pre-existing combinations of locally adapted alleles or whether such allelic combinations accumulated after inversion establishment. A number of studies in plants have successfully addressed this question, thereby offering more direct support for the Kirkpatrick and Barton (2006) model. Lee et al. (2017) made use of available collinear local adapted genotypes in Boechera stricta for genetic mapping and showed that pre-existing locally adaptive alleles may be captured by young inversions and contribute to local adaptation and incipient speciation. Likewise, Coughlan and Willis (2019) showed that key life history QTLs mapping to an inversion differentiating annual and perennial Mimulus guttatus mapped to the same region in a population involving annual M. guttatus and a collinear perennial species, M. tilingii, thereby showing that loci contributing to local adaptation predate the inversion in this system as well. Inversions on chromosome NC6 of Noccaea caerulescens are found to group pre-existing metal homeostasis genes, which may explain their fixation and role in speciation (Mandakova et al. 2015). 32 Inversion establishment in the Kirkpatrick and Barton (2006) model is also constrained by migration rates. Gene flow between different environments must be sufficiently high to generate a selective advantage for the new inversion. But high gene flow will lead to recombination between adapted and non-adapted alleles, reducing the likelihood that a new inversion would bring together a complete set of locally adapted alleles. A possible solution to this issue was suggested by Feder et al. (2011), who developed a mixed geographic model, in which adaptation to different environments occurs in allopatry, so that it is straightforward for a new inversion to capture a full cassette of adaptive alleles. Subsequent range expansion and secondary contact would give the new inversion a selective advantage over collinear regions (as in the Kirkpatrick and Barton model), leading to its establishment (Figure 2.2c). Given that range fluctuations are common in plants, and that this model permits inversion establishment from standing variation, we suspect that it might be a common mechanism. Evidence that secondary contact promotes the spread of inversions has been found in birds (Hooper and Price 2017), but to date there has been little relevant data in plants. However, new data from Helianthus sunflowers implies that secondary contact and hybridization may contribute importantly to the establishment of large inversions. We used a combination of population genomic, comparative mapping, and HiC sequencing to detect numerous polymorphic inversions within Helianthus annuus, H. argophyllus and H. petiolaris (Huang et al. 2019; Todesco et al. 2019), which are sympatric and known to hybridize with multiple other species. However, when we applied a similar population genomic approach to the analyses of two Helianthus species that are largely (H. bolanderi; data from Owens at el. 2016) or completely (H. niveus; data from Zhang et al. 2019) allopatric, we failed to find clear signals of inversions 33 (Figure 2.3). A mixed geographic model might explain why inversions are only found in Helianthus species that have extensive range overlap with others taxa. Secondary contact can also shape current pattern of inversions within species. Phylogenomic analyses of inversions segregating within Helianthus species revealed that these inversion haplotypes typically are highly divergent, pre-dating the split between species (Todesco et al. 2019). While such a pattern could be due to balancing selection, the lack of trans-specific inversion polymorphisms (i.e., none of the inversions are polymorphic in more than one species), suggests that they might have been acquired from other, possibly extinct, species instead. This could have occurred via introgression or species fusion. Note that the latter would also account for the “extinction” of donor species. Evidence for the origin of inversions through introgression is known from animals (Feder et al. 2003b; Tuttle et al. 2016), but evidence in plants is slim. Clearly, phylogenomic analyses of inversion origins and ages in other plant groups should be a priority for future studies. 34 Figure 2.3 - Results of local population structure analyses in (a) Helianthus petiolaris (data from Huang et al., 2019), (b) H. bolanderi (data from Owens at el., 2016) and (c) H. niveus (data from Zhang et al., 2019). Variant calling and multidimensional scaling (MDS) follow the same methods described in Huang et al. (2019). Only the first MDS coordinate is plotted. Clusters of MDS outliers, which indicate putative inversions (and have been confirmed with other methods), are identified in H. petiolaris (indicated with dotted circles) but not in the other two species. 2.3 Role in speciation Early models of chromosomal speciation were based on the assumption that inversions and other chromosomal rearrangements reduced gene flow between taxa through their effects on hybrid fitness. However, due to the paucity of evidence of reduced fitness in hybrids heterozygous for inversions, as well as the theoretical difficulties associated with fixing strongly underdominant mutations, another class of models was developed based on the effects of inversions on ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●0.000.250.5001 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17ChromosomeMDS01(a)●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●0.000.250.5001 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17ChromosomeMDS01(b)●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●−0.6−0.4−0.20.00.201 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17ChromosomeMDS01(c) 35 recombination rates within the inverted region (Trickett and Butlin 1994; Rieseberg 2001; Noor et al. 2001). These recombination suppression models offer a means for resolving the widely-recognized antagonism between divergent natural selection and recombination, permitting adaptive divergence and speciation in the presence of gene flow (Ortiz-Barrientos et al. 2016). In this simplest model (Trickett and Butlin 1994), an inversion reduces recombination between loci contributing to local adaptation and those causing assortative mating, permitting adaptive divergence and potentially speciation in the presence of gene flow (Figure 2.4). Such a genetic architecture appears to be common in plants. For example, the chromosomal inversion that contributes to local adaptation in inland and coastal environments in Mimulus guttatus is also associated with flowering time and other life history differences (Lowry and Willis 2010; Oneal et al. 2014), thereby contributing to assortative mating between the annual and perennial ecotypes. In M. lewisii and M. cardinalis, inversions were also found to co-localize with a series of floral trait QTLs, such as corolla length and flower color, which are important in both prezygotic and postzygotic isolation in this species pair (Fishman et al. 2013). Similarly, in wild Arabidopsis thaliana, an inversion on chromosome 4 was reported to be strongly associated with fecundity under drought and an early flowering allele (Fransz et al. 2016). In Boechera stricta, Lee et al. (2017) found multiple linked phenology QTLs, including flowering differences, within an inversion that differentiates subspecies. In sunflower, inversions were associated with multiple ecological relevant traits, such as seed size and various soil and climate characteristics, as well as flowering time, revealing their role in ecotype formation and ecological speciation (Huang et al. 2019; Todesco et al. 2019). 36 Figure 2.4 - The model for the role of inversions in speciation proposed by Trickett and Butlin (1994). An inversion facilitates speciation by suppressing recombination between genes involved in local adaptation (red and green triangles) and those underlying assortative mating traits, as such flowering time (black and white asterisks). The ancestral chromosome carries mixtures of adapted and maladapted alleles due to recombination. Individuals that are locally adapted to the environment of population 2, but carries the white assortative mating allele, will tend to mate with individuals adapted to the other environment and produce maladaptive offspring in population 2. Individuals with a new inversion that captures only the locally adapted alleles and black assortative mating allele do not suffer the reproductive cost from recombination. The inversion is therefore favored and contributes to further divergence between populations. Red crosses indicate that chromosomes carrying maladaptive combinations of alleles are eliminated in each environment. While all of these examples demonstrate recombination suppression between locally adapted alleles and an assortative mating trait (most frequently flowering time), it is important to keep in mind that plant ecotypes and species often exhibit eco-geographic isolation. For example, in dune versus non-dune ecotypes of sunflower, the strongest reproductive barriers are immigrant inviability and extrinsic selection against hybrids (Ostevik et al. 2016). Strong selection against small seeds on the dunes, combined with a negative trade-off between seed size and seed number, underlies both barriers. Thus, isolation in this system is mainly due to a classic locally adapted trait (seed size), which maps to three inversions (Todesco et al. 2019), rather than assortative mating traits such as flowering time variation or conspecific pollen precedence. This situation is not unique to sunflowers and implies that the Trickett and Butlin (1994) model XX     Population 1 Population 2 Population 1 Population 2 Population 1 Population 2 37 should be applicable to any trait that causes reproductive isolation, not just those contributing to assortative mating. In addition to the Trickett and Butlin model, inversions have also been proposed as a means for maintaining hybrid incompatibilities in the face of ongoing gene flow (Noor et al. 2001), facilitating the accumulation of additional hybrid incompatibilities (Navarro and Barton 2003), and extending the time window for reinforcement to evolve (Servedio 2000; Servedio 2009). Also, by suppressing recombination, inversions can extend the effects of genes that contribute in some way to isolation over larger genomic regions (Rieseberg 2001), thereby generating “genomic islands of divergence” (Oneal et al. 2014; Twyford and Friedman 2015; Huang et al. 2019). Lastly, by combining the effects of multiple locally adapted alleles, the selective advantage of an inversion is expected to be greater than that of individual alleles, permitting divergence under higher migration rates (Rieseberg 2001; Kirkpatrick and Barton 2006). However, of these potential roles, only the association of inversions with genomic islands of divergence has been documented in plants. 2.4 Sex chromosomes and sequential inversions Although most plants are hermaphroditic (co-sexual), some plant species have evolved separate male and female sex morphs (i.e., dioecy). The transition from a co-sexual breeding system to dioecy typically involves the formation of sex chromosomes, in which recombination suppression evolves between male and female sterility loci (Charlesworth 2012). Inversions offer a straightforward means for suppressing recombination between newly formed X and Y (or Z and W) chromosomes and have been reported in numerous animal systems, as well as in a handful of plant species. For example, two large inversions were found to define the non- 38 recombining region between Y and X chromosomes in papaya, suggestive of a role in sex chromosome formation (Wang et al. 2012). The evolution of sexually antagonistic genes, which are favored in one sex but not in the other, provides additional selection pressure to reduce recombination between sex chromosome pairs; otherwise antagonistic alleles will be transmitted to the opposite sex. As a consequence, over time recombination suppression typically expands to cover most of the sex chromosome pair. Interestingly, in many animals and some plant species, such expansions appear to be episodic, producing “evolutionary strata” across sex chromosomes, i.e. spatial clusters of X-Y or Z-W orthologs with similar divergence estimates (Wright et al. 2016). Such strata are often closely associated with inversions, leading to suggestions that the stepwise establishment of inversions might be responsible for this pattern of divergence. Currently, the best evidence for this hypothesis in plants comes from papaya, in which two distinct evolutionary strata were discovered that correspond perfectly with the boundaries of the two inversions (Wang et al. 2012). In Silene, both strata (Nicolas et al. 2005; Bergero et al. 2007) and inversions (Zluvova et al. 2005; Hobza et al. 2007) have been reported, but they are not explicitly linked. Other authors have noted that recombination suppression associated with sex chromosome divergence sometimes occurs through other mechanisms such as transposable element insertion (e.g., Xu et al. 2019). Therefore, it is possible that suppressed recombination comes first, followed by the accumulation of inversions. An example in which recombination suppression precedes chromosomal rearrangements has been reported in fungi (Sun et al. 2017), but as far as we are aware, evidence for such a scenario is lacking in plants. The stepwise accumulation of inversions need not be restricted to sex chromosomes. An inversion on an autosome could initially become established by capturing multiple locally 39 adapted alleles, as proposed in the Kirkpatrick and Barton (2006) model. Subsequent inversions that added new locally adapted alleles into the non-recombining block would be favored by selection. This stepwise extension of recombination suppression presumably would create evolutionary strata similar to that seen on sex chromosomes. The apparent clustering of inversions seen in comparisons between cucumber and melon (Garcia-Mas et al. 2012), as well as between domesticated rice and Oryza brachyantha (Chen et al. 2013), are consistent with this hypothesis. Future dissection of the structure and divergence patterns of complex inversions should be a priority. 2.5 Concluding remarks Since the discovery of inversions in Drosophila close to a century ago, numerous verbal and quantitative models have explored their potential role in evolution and possible mechanisms for their establishment. Comparative genetic mapping and genomic studies have revealed that chromosomal inversions are far more prevalent than previously imagined. However, these studies often focus on large inversions only and/or fail to report on inversion sizes or the extent of sequence divergence between inversion haplotypes. These information gaps should be addressed in future comparisons of reference assemblies. Likewise, an ever-increasing number of studies in plants suggest that inversions play a key role in adaptive divergence and speciation in the presence of gene flow. However, the genes and mutations underlying key traits associated with the inversions are difficult to identify because of strong linkage disequilibrium within the inverted region. Analyses of collinear genomes that are expected to differ for many of the same genes will aid in this process, as shown by two examples here. Population genetic and molecular tools would also help pinpoint the 40 genetic changes within inversions that are responsible for adaptive differences or speciation. Of particular interest are regions near inversion breakpoints, since the inversions have the potential not only to disrupt open-reading frames or associations with regulatory elements, but also to change the local chromosome landscape of genes (e.g. potentially moving genes closer or further away from heterochromatic regions). Lastly, little is known in plants about potential downsides of inversions such as increased transposable element activity and the accumulation of deleterious mutations, both of which are a predicted consequence of suppressed recombination. This information gap that could also be addressed with population genomic analyses. Despite the rapid accumulation of examples of the importance of inversions in a variety of ecological and evolutionary processes, information of their origin is scarce. Inversions can become established in several ways, but models based on the advantages of reducing recombination between locally adapted alleles when there is migration between different environments seem most plausible. This process is likely aided in some instances by a period of allopatry between hybridizing populations so that the full set of locally adapted alleles can be captured by the new inversion. Phylogenomic analyses of closely related species are needed to determine the origins and extent of divergence between inversion haplotypes, since evidence suggests that hybridization and introgression may contribute to inversion establishment and subsequent evolutionary dynamics. Lastly, we urge students of chromosomal evolution to assess whether inversions are clustered in the genome and if evolutionary strata can be discovered on autosomes, similar to what has been reported for sex chromosomes. 41 Chapter 3: Multiple chromosomal inversions contribute to adaptive divergence of a dune sunflower ecotype 3.1 Introduction Genetic differentiation between differently adapted populations can be highly variable across the genome. During the process of adaptive divergence, genomic regions under selection will display strong differentiation, while ongoing gene flow between populations will homogenize other regions, generating heterogeneous patterns of genomic divergence (Wu 2001; Nosil et al. 2009). Large islands of differentiation, namely “genomic islands of divergence”, are commonly seen in recently diverging populations, ecotypes, and species, including well-known examples in Rhagoletis (Feder et al. 1988), Anopheles (Turner et al. 2005), Heliconius (Nadeau et al. 2012) and Helianthus (Andrew and Rieseberg 2013). The causes of these large islands are not fully understood (although see McGaugh and Noor 2012; Berg et al. 2017). It has been proposed that divergence hitchhiking, in which gene exchange is reduced adjacent to a locus under strong divergent selection, could generate large regions of differentiation, but the conditions under which it occurs are limited (Via 2012; Feder and Nosil, 2010). Chromosomal inversions represent another possible explanation for such islands because they can suppress recombination and impede gene flow across large genomic regions (Butlin 2005; Hoffman and Rieseberg 2008). Inversions have long been viewed as important in local adaptation and speciation (Wellenreuther and Bernatchez 2018; Dobzhansky and Sturtevant 1938). One primary reason is that, by suppressing recombination, inversions can establish and maintain favorable combinations of locally adapted alleles, despite gene flow with non-adapted populations 42 (Rieseberg 2001; Kirkpatrick and Barton 2006). The critical importance of inversions in local adaptation has been revealed by emerging studies that document the association of inversions with adaptive traits within species (Feder et al. 2003b; Lowry and Willis2010; Kirubakaran et al. 2016; Wellenreuther and Bernatchez 2018 for review). Beyond their role in adaptation, inversions can preserve alleles that cause intrinsic genetic incompatibilities in hybrids, and facilitate the accumulation of new incompatibilities, thereby aiding species’ persistence in the face of gene flow (Noor et al. 2001; Navarro and Barton 2003). Finally, inversions can establish linkage between locally adapted alleles and those causing assortative mating, which is typically required in models of speciation with gene flow (Felsenstein 1981; Trickett and Butlin 1994; Servedio 2009). Much of what we know about inversions (at least until very recently), comes from studies of Dipteran flies, whose very large larval salivary gland chromosomes permit detection of inversions from chromosome banding patterns (Krimbas and Powell 1992). However, in most other organisms, more time-consuming and/or expensive methods have been required, such as analyses of meiotic configurations (Heslop-Harrison 2013), comparative genetic mapping (Kirubakaran et al. 2016), Hi-C sequencing (Dixon et al. 2018), optical mapping (Tang et al. 2015), paired-end mapping (Lamichhaney et al. 2016), or long-read sequencing. The laboriousness and/or expense of these methods have hindered our understanding of the frequency and importance of inversions in natural populations. Recently, population genomic approaches have been applied to detect potential inverted regions, including methods based on linkage disequilibrium (LD) (Faria et al. 2019; Arostegui et al. 2019) and local population structure (Li and Ralph 2019). The linkage disequilibrium (LD) approach takes advantage of the expectation that inversions will create high LD between (but not within) inversion haplotypes. The local 43 population structure approach assumes that the lack of gene flow between inversion haplotypes will lead to systematic differences in patterns of genetic relatedness between inverted and collinear regions. Such differences can be detected by conducting windowed analyses of population structure across the genome (Li and Ralph 2019). Both methods offer an efficient means for identifying putative inversions and estimating their frequency in natural populations. In this study, we focus on the genetic architecture of adaptation in a dune-adapted ecotype of the prairie sunflower Helianthus petiolaris Nutt. This widespread annual sunflower inhabits sandy soils in the Central and Southwestern USA. However, in the Great Sand Dunes National Park and Preserve (GSD), Colorado, an ecotype of this species occurs in active sand dunes. This dune ecotype differs from conspecific populations, which are abundant on the sand sheet below the dunes, for a number of ecologically relevant phenotypic traits, including seed size, branching, and root architecture (Andrew et al. 2012). Despite its origin less than 10,000 years ago (Andrew et al. 2013), multiple reproductive barriers isolate the two ecotypes, including strong extrinsic selection against immigrants and hybrids, conspecific pollen precedence, as well as a weak crossability barrier (Ostevik et al. 2016). Nonetheless, substantial and asymmetric gene flow have been reported between dune and non-dune populations (Andrew et al. 2012), as predicted by models of isolation with gene flow. Moreover, genetic differentiation between the ecotypes is largely restricted to several large genomic regions while background divergence is extremely low (Andrew and Rieseberg 2013), making it a good system to study the evolution of genomic islands of divergence. The underlying mechanism for these large regions of high divergence was not previously determined, but chromosomal inversions represent a leading hypothesis given their ability to impede introgression, as well as the high rates of chromosomal evolution reported for Helianthus (Burke et al. 2004; Ostevik et al. 2019). 44 Our analyses complement a recently submitted study from our group on the genetic architecture of local adaptation across three sunflower species (Todesco et al. 2019). In that study, we used whole genome shotgun sequence (WGS) data to sample genetic variation across the ranges of three sunflower species, including H. petiolaris. The study detected numerous large haplotypes in all three species that co-varied with ecologically relevant phenotypic, climate, and soil variation. Further analyses show that many of the haplotypes (but not all), were associated with structural variation, including inversions. One population from GSD (ten individuals) was included in this study, and it appeared to be enriched for structural variants. Thus, we also wished to validate this observation with more extensive sampling from GSD and surrounding regions, as well as to exploit a comprehensive data set on local variation in soil fertility and plant cover on the dune and surrounding sand sheet to better assess the role of inversions in divergent adaptation with gene flow. Specifically, we employ RAD sequence data previously generated for this system (Andrew et al. 2013) and apply a local population structure approach to detect and genotype putative inversions in this system. We also conduct additional population genomic analyses (including LD analyses) and develop two genetic maps (one for each ecotype) to further validate these inferences. Lastly, we search for associations between the genotypic data and key environmental factors, including soil nutrient availability and vegetation coverage. We address four main questions: 1) Can structural variants such as inversions be detected with RAD sequencing data? 2) If so, are they enriched in the dune habitat at GSD as previously suggested? 3) Likewise, do they correspond closely to the genomic islands of differentiation (i.e., high FST regions) previously reported between dune and non-dune sunflowers? and 4) Lastly, is there evidence that inversions contribute importantly to adaptive divergence in this system? 45 3.2 Methods 3.2.1 Plant materials and RAD sequencing Our study employs the plant materials and RAD sequencing (Baird et al. 2008) data set previously reported by Andrew and Rieseberg (2013) and Andrew et al. (2013). Twenty populations from dune, non-dune and intermediate habitats in the GSD were sampled (Andrew et al. 2013, Appendix A.1), and five unrelated individuals from each of the 20 populations were subjected to RAD sequencing by Floragenex (Portland, OR) using the restriction enzyme PstI. All samples were barcoded and sequenced with at least 60 bp reads, with a subset sequenced with 80 bp reads. The first 5 bp covering the restriction site and relatively low-quality 20 bp at 3' end of the 80 bp reads were trimmed with PRINSEQ v0.20.4 (Schmieder and Edwards 2011), yielding reads with equal length of 55 bp, to avoid biases in alignment due to sequences of different lengths. 3.2.2 SNP calling We re-called SNPs from the RAD sequencing data since much better reference genomes are now available for cultivated sunflower (Helianthus annuus), a close relative of H. petiolaris. Briefly, RAD sequences were aligned to reference genome Ha412HOv2.0 with bwa mem v0.7.17 (Li 2013) using the default settings. Variant calling was performed with the Genome Analysis Tool Kit v4.0.8.1 (GATK; DePristo et al. 2011). Sample alignments were processed with the GATK ‘HaplotypeCaller’ and samples were jointly genotyped using GATK’s ‘GenotypeGVCFs’ chromosome by chromosome. Variants of all chromosomes were later merged with ‘MergeVcfs’ in Picard tools (http://broadinstitute.github.io/picard/). Only bi-allelic SNPs were selected for 46 downstream analyses. SNPs were filtered with GATK ‘VariantFiltration’ with filter expression “QD < 4.0 || FS > 20.0 || MQ < 40.0 || MQRankSum < -5.0” and individual genotypes with depth less than 30 were set as missing. Loci that were non-variant or varied only due to singletons after filtering, as well as those with > 40% missing data, were excluded from the data set. Finally, SNPs with excess heterozygosity were filtered with GATK’s ‘VariantFiltration’ filter expression "ExcessHet < 20.0" to avoid misalignment on paralogous regions. We did not prune SNPs based on LD because SNPs within an inversion are likely to be in LD. Because the new reference genome provides physical locations of the SNPs and has much more complete chromosome coverage compared to the one used by Andrew and Rieseberg (2013), we re-calculated Weir and Cockerham’s FST (Weir 1996) between dune and non-dune ecotypes with VCFtools (Danecek et al. 2011) to examine genetic divergence across the new reference genome and re-localize regions of divergence. 3.2.3 Local population structure analysis We analyzed patterns of population structure across the genome using the R package lostruct (Li and Ralph 2019), in order to detect regions of abnormal population structure that might be generated by chromosomal inversions. The genome was divided into non-overlapping windows with size of 50 SNPs and principle component analysis (PCA) was calculated for each window to reflect local population structure. To measure the similarity of patterns of relatedness between windows, Euclidean distances between matrices were calculated for the first two principle components (PCs) and then mapped using multidimensional scaling (MDS) into 40-dimensional space. Because each inversion may deviate genetic structure in different directions, projection on a higher dimensional space increases the chance of capturing the effects of all possible 47 inversions. Different window sizes were tested and examined by plotting MDS values along the chromosomes to choose the optimal value that had sufficient coverage across the genome and offered enough smoothing to reduce noise. The SNP data set was converted to BCF format with BCFtools v1.9 (Li 2011) before input to lostruct. To identify localized genomic regions with extreme MDS values relative to the genome-wide background average, we first defined outlier windows as those with absolute values greater than 4 standard deviations from the mean across all windows for each of the 40 MDS coordinates. We then tested whether outlier windows were chromosomally clustered with 1,000 permutations of windows over chromosomes to evaluate differences from random expectation where outliers are randomly distributed among chromosomes. For each MDS coordinate with more than 4 outlier windows, we selected the first chromosome with a significant excess of outliers (p < 0.01) for further examination. For each coordinate, outlier windows that deviated in different directions were examined separately. Adjacent outliers with less than 4 windows between them were kept as a cluster. In cases where the same chromosome had outlier clusters across multiple MDS coordinates, we calculated Pearson's product moment correlation coefficient between the MDS coordinates using sample genotype matrices and collapsed the ones with correlation > 0.8 by selecting the coordinate with the larger number of outliers. The coordinates of the putative inversions were defined by the start position of the first outlier window to the end position of the last outlier window. While inversions are a major driver of MDS outliers detected by lostruct (Li and Ralph 2019), MDS outliers can be generated by other processes as well, such as linked selection. Therefore, we performed a series of additional analyses to look for additional population genomic signatures of inversions. Due to suppressed recombination, haplotype blocks with 48 different orientations should evolve largely independently, resulting in distinct nucleotide differences between them. Therefore, for an inversion segregating in a population, a PCA of population structure should divide the samples into three distinct groups representing the two orientations, with heterozygotes between the arrangements forming an intermediate cluster. To test this, we calculated PCAs with SNPrelate (Zheng et al. 2012) using all SNPs from each putative inversion. To identify the composition of groups of genotypes, we used the R function “kmeans” with the method developed by Hartigan and Wong (1979) to perform clustering on the first PC, using the maximum, minimum and middle of the range of PC scores as the initial cluster centers. The discreteness of the clustering was evaluated by the proportion of the between-cluster sum of squares over the total. The K-means cluster assignment was used as the genotype of the sample. If the groups detected in the PCA represent homozygotes and heterozygote for the orientations, we expect the central group to have high heterozygosity relative to the other two groups. For each region identified, we extracted all variable sites across the outlier windows and calculated the proportion of heterozygous sites over the total as heterozygosity for each individual in each group identified by k-means clustering. To examine the effect of recombination suppression of the putative inversions, intrachromosomal LD was calculated among all SNPs with minor allele frequencies > 5%. Pairwise LD (R2) values were calculated using PLINK v1.9 (Chang et al. 2015; Purcell et al. 2007) for each chromosome with all samples. Values of SNPs were grouped into 1 Mbp windows and the second largest R2 value was plotted using ggplot2 (Wickham 2016). For chromosomes with MDS outlier regions, R2 was also calculated with individuals homozygous for the more common orientation only. 49 Only the regions displaying clustering of three distinct groups in the PCA with higher heterozygosity in the middle group and high LD were kept as putative inversions in downstream analyses. For each region, allele frequency differences between ecotypes were estimated using “prop.test” in R and the genotype frequency for each population was plotted onto a map of land cover classification downloaded from Multi-Resolution Land Characteristics Consortium (https://www.mrlc.gov/) at 30-m resolution. Deviations from Hardy-Weinberg equilibrium were tested for each region using VCFtools (Danecek et al. 2011). 3.2.4 Genetic map construction Genetic maps of dune and non-dune ecotypes were generated using F1 testcross mapping to validate our inversion detection approach. Pollen from a single dune plant (seed collected from population 1300) and a single non-dune plant (seed collected from a new population at Latitude 37.724, Longitude -105.718) from GSD, was used to fertilize individuals of the male sterile H. annuus HA89cms cultivar, which is highly homozygous. For each cross, the HA89cms individual that bore the most seeds (100-150 seeds) was selected to produce the F1 mapping population. Loci that are heterozygous in a wild parent are expected to segregate 1:1 in the corresponding F1 population, permitting the generation of a genetic map. DNA was extracted from germinated F1 seeds or, when germination failed, directly from seeds. Barcoded genotyping-by-sequencing (Poland et al. 2012) libraries were prepared using the restriction enzymes PstI and MspI. A depletion step with Duplex-Specific Nuclease (Evrogen, Moscow, Russia) was conducted on the libraries to reduce the proportion of repetitive sequences, including plastid DNA. The libraries were sequenced on an Illumina Hiseq 4000 instrument to produce paired end, 100 bp reads (Illumina, San Diego, CA, USA). Samples were demultiplexed using a 50 custom Perl script that also removed barcode sequences. FASTQ files were examined for quality but not trimmed. Raw reads were aligned to the Ha412HOv2.0 reference genome using NextGenMap v0.5.2 (Sedlazeck et al. 2013) and variants were called using GATK v4.0.8.1 as described above for the RAD sequences. Only SNPs were kept and filtered with the expression “QD < 15.0 || FS > 20.0 || MQ < 40.0 || MQRankSum < -5.0”, and individual genotypes with depth less than 30 were set as missing. Loci that were invariant after filtering and had a genotype missing rate > 50% were excluded. Genetic maps were built using R/qtl (Broman et al. 2003) and R/ASMap (Taylor and Butler 2017). Individuals with fewer than 50% markers genotyped were excluded, as were duplicate markers, markers with less than 50% of individuals scored, and markers with extreme segregation patterns (genotype frequency <0.3 or >0.7). The “mstmap.cross” function was used to construct linkage groups (LGs) with the remaining markers using a p-value of 10-15, which was chosen to minimize false linkages. Because marker phase was unknown prior to mapping, mirror image LGs were generated initially, and the function “switchAlleles” was used to reverse genotype scores for such LGs. Markers with segregation distortion p-value < 0.05 and missing rate < 0.1 were pulled aside from the map, and those with more than three double crossovers and markers with extreme (> 2 standard deviation) segregation distortion within a 21-marker window were removed using custom functions. LGs with less than two markers were discarded. Some less extreme markers that were originally placed aside were then pushed back into the map and the markers were filtered again with the same criteria. This step was done twice to reintroduce markers with segregation distortion p-value < 0.01, missing rate < 0.3 and the ones with segregation distortion p-value < 0.001 and missing rate < 0.5. The function “calc.errorlod” was 51 also used to filter genotyping errors. Finally, very small (1-5 markers) LGs were discarded, leaving 17 LGs for each ecotype. Due to sparse marker density on the LG that corresponded to chromosome 5 after filtering, markers that mapped to chromosome 5 on the H. annuus reference genome were extracted and genetic mapping was repeated using less stringent parameters. Markers that were located at the far end of LG 5, and those that disturbed synteny, were removed because they might represent misaligned markers from other chromosomes. This remapping was conducted for both dune and non-dune mapping populations and the new LGs were included in downstream genetic map comparisons. To compare marker orders, we took advantage of the fact that SNP markers were called against the Ha412HOv2.0 reference genome. Homologous reference chromosomes for each linkage groups were identified based on physical positions of markers and prior knowledge of the location of translocations between H. petiolaris and H. annuus (Ostevik et al. 2019). For each putative inversion, we asked whether markers from that region differed in order or genetic distance with respect to the reference genome and/or between ecotypes. 3.2.5 Genome-environment association analysis To further assess the role of putative inversions in dune adaptation in the ecotype, as well as to identify the environmental variables that might be driving divergent selection pressures, we used data on soil nutrient availability and vegetation coverage for each population to conduct genome-environment association (GEA) analysis. The collection and estimation of these measurements have been described in detail in a previous study (Andrew et al. 2012). Additional composite 52 variables of soil or cover data were generated by PCA previously (Andrew et al. 2012) and the first three PCs (soil PC1-3 and cover PC1-3) were re-used in the present analyses. The GEA analysis was performed using BayPass v2.1, which explicitly accounts for the covariance structure among the population allele frequencies resulting from population demography (Gautier 2015). We further filtered the SNPs by missing rate < 10% and minimum allele frequency > 10% and generated a data set of SNP frequencies for all populations. Population structure was estimated by running BayPass under the core model mode with all filtered SNPs. The covariance matrix from this analysis was then used as a control for population structure to evaluate associations of SNPs with each environmental variable. For each SNP, a Bayes factor (BF) was computed under the standard covariate model using the default importance sampling estimator approach. Scaling was performed for each environmental variable using the “-scalecov” option. Due to missing soil data in population 970, the analysis was run separately for soil variables and coverage variables. To further examine the associations between the putative inversions and environmental variables, we also performed a GEA analysis in which putative inversions were treated as single bi-allelic loci. A SNP data set excluding SNPs from within the putative inversions was used to estimate the covariance matrix to control for the effects of the MDS outlier regions on population structure. Bayes factors were calculated using the same core model mode in BayPass as described above. To calculate a significance threshold, we simulated pseudo-observed data (POD) with 1,000 SNPs using the “simulate.baypass” function implemented in BayPass with the covariance matrix generated under the core model, and analyzed the newly created POD for each 53 environmental variable as described above. The top 1% quantile of the POD BFs was computed as the threshold for significance. 3.3 Results 3.3.1 SNP Calling Using a high quality reference genome for cultivated H. annuus, 87.0% of RAD sequences were aligned on average, and 1,984,008 nucleotide sites were scored for at least 85% of the individuals, which correspond to approximately 36,073 restriction enzyme sites. After variant calling with GATK, a total of 260,478 variable sites were scored, among which 234,580 bi-allelic SNPs were selected, corresponding to 6.5 SNPs per restriction fragment. Filtering produced a data set of 37,930 high-quality bi-allelic SNPs across 17 chromosomes of the reference, which corresponds to approximately 12 sites per Mbp. This compares favorably to the 11,727 SNPs that could be positioned on chromosomes in our previous analyses (Andrew and Rieseberg 2013). Analysis of patterns of genetic divergence between the dune and non-dune ecotypes yielded similar results to the previous study (Andrew and Rieseberg 2013): low overall FST and high heterogeneity among sites with the largest clusters of outliers found on chromosomes 5,9 and 11 (Figure 3.1). However, highly divergent regions are more distinct and contiguous in the present study due to the larger number of SNPs and better genome assembly. In addition, a distinctive island can now be seen on the end of chromosome 7, which was not detected in the previous analysis. 54 Figure 3.1 - Weir and Cockerham’s FST between dune and non-dune ecotypes. Locations of putative inversions are indicated by red bars on top. Blue bars represent seed size QTLs identified in Todesco et al. (2019). 3.3.2 Detection of putative chromosomal inversions Using a window-based local population structure analysis implemented in lostruct, and our outlier discovery approach, we identified a total of 9 clusters of MDS outliers with our RAD SNPs (Table 3.1, Figure 3.2). ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●0.000.250.500.751.0001 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17ChromosomeF ST 55 Table 3.1- Clusters of MDS outliers obtained with lostruct. MDS coordinates for which the outlier regions were identified, reference chromosomes with start and end positions of MDS outlier clusters, numbers of MDS outlier windows, variance explained by PC1 and PC2 in PCA of outlier regions, proportions of between-cluster sum of squares in k-means clustering, as well as codes used in main text for putative inversions are shown. MDS Chromosome Start (bp) End (bp) Number of outlier windows PC1 variance (%) PC2 variance (%) Proportion of between-cluster sum of squares Region code MDS01 Ha412HOChr11 3587653 60627948 13 10.1 1.04 0.9851 pet11.01‡ MDS02 Ha412HOChr09 102388477 140632318 10 9.81 0.63 0.9914 pet09.01‡ MDS03 Ha412HOChr05 156436125 186198645 9 10.13 0.71 0.9898 pet05.01‡ MDS04 Ha412HOChr07 109423942 129416998 5 12.02 1.8 0.9746 pet07.01‡ MDS05 Ha412HOChr14 126811094 166275087 11 17.99 4.84 0.9521 pet14.01‡ MDS06 Ha412HOChr17 12368066 23002709 8 19.48 3.81 0.9709 pet17.01‡ MDS07 Ha412HOChr09 171481816 182472659 7 17.1 5.02 0.9282 pet09.02 MDS12 Ha412HOChr13 116213599 135318474 5 15.96 6.91 0.9286 -† MDS21 Ha412HOChr09 73794134 144545686 4 11.1 8.96 0.8486 -† †Not included in downstream analyses because do not appear to represent inversions. ‡Previously described by Todesco et al. (2019). In PCAs of seven outlier regions, individuals were aggregated into three discrete groups on the first PC, which explained much more variation than the second PC (Table 3.1, Figure 3.2b, Appendix A.2). The discreteness was supported by the high (>0.9) proportion of the between-cluster sum of squares over the total in k-means clustering (Table 3.1). Moreover, in all seven regions, heterozygosity of the middle group was significantly higher than within the other two groups (Figure 3.2c, Appendix A.2). These patterns are consistent with the presence of two clusters of individuals that are homozygous for alternative arrangements and an intermediate cluster of individuals that are heterozygous for the arrangements with no or very little recombination between them. Two exceptions were found, including one on chromosome 13 for MDS12, where samples formed only two groups in the PCA and the expected pattern of heterozygosity was not observed. Likewise, samples did not form distinct clusters for outlier region MDS21 on chromosome 9 (Table 3.1, Appendix A.2). Note that outlier region for MDS21 56 encompasses that of MDS02, which does act like a legitimate inversion, as well as an upstream region of the chromosome that generally does not. All outlier clusters were also characterized by high LD. For the two outlier clusters that did not form three distinct groups in the PCA, the MDS12 outlier region on chromosome 13 was characterized by high LD. Thus, we cannot rule out the possibility that this is an inversion, but that heterozygotes are rare and genotypes mis-classified. MDS21 includes a large high LD region, which represents the MDS02 outlier region, as well as a smaller high LD region at the start. Possibly the latter represents a small inversion that is in partial LD with the MDS02 outlier region. There also were a handful of very small high LD regions (e.g., on Chromosome 15 from 119-123 Mbp) that might represent inversions, but they did not pass our stringent criteria for MDS outliers. Lastly, while high LD was detected for the outliers when compared across all samples, recombination was not restricted within the homozygous group (Figure 3.2d, Appendix A.2, A.3), except for MDS21. These results are consistent with the role of inversions in altering recombination in heterozygotes while recombination in homozygotes remains unaffected. Overall, seven of the outlier clusters showed clustering of three distinct groups in PCA, higher heterozygosity in the middle group and high LD across the outlier region, and were kept as putative inversions for downstream analyses (Table 3.1). All the putative inversions, except one on chromosome 9 (pet09.02), overlapped substantially with large haploblocks identified in H. petiolaris using WGS data over its entire geographic distribution (Todesco et al. 2019; Table 3.1). These 7 putative inversions occurred on 6 chromosomes. A majority of them were located near the end of chromosomes, while the putative inversion on chromosome 7 (pet07.01) and the larger one on chromosome 9 (pet09.01) resided in the middle sections of the chromosomes 57 (Figure 3.1). Each of the putative inversions contained at least 5 MDS outlier windows (i.e. 250 SNPs) and their sizes varied between 11 and 57 Mbp (Table 3.1). Figure 3.2 - Characterization of the MDS outlier region on chromosome 5 (pet05.01). (a) Genome plot of corresponding MDS values across 17 reference chromosomes. Each dot represents a window of 50 SNPs, and outlier windows are highlighted in red. (b) PCA based on SNPs from outlier region. Three clusters identified using k-means clustering correspond to two homozygote groups (blue and red) and a heterozygote group (purple). (c) Heterozygosity for each of the groups identified in PCA. (d) LD plot for chromosome 5. Upper triangle with all individuals and lower triangle with only individuals homozygous for the more common orientation. SNPs were summarized and the second highest R2 values were presented in 1 Mbp windows. Purple bars represent the location of the inversion. Results of the other outlier regions are presented in Appendix A.2. All of the putative inversions displayed significant allele frequency differences between dune and non-dune ecotypes (p-value ranges from 0.024 for pet09.02 to 2.92x10-22 for pet05.01, Table 3.2), but the distributions of the genotypes for each inversion were variable. For several putative inversions, the sand dunes are enriched with samples homozygous for one of the ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 170 100 0 100 0 100 0 100 200 0 100 0 100 0 100 0 100 0 100 2000 100 0 100 2000 100 0 100 0 100 0 100 0 100 200 0 100 200−0.20.00.20.40.6MbpMDS03 ●●Non−outlierOutlier(a)●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●−0.3−0.2−0.10.00.10.2−0.1 0.0 0.1PC1 (10.13% PVE)PC2 (0.71% PVE)Cluster●●●012(b)●●0.050.100.150.200.250 1 2ClusterHeterozygosity Cluster012(c)0501001500 50 100 150MbpMbp0.000.250.500.751.00LD(d) 58 orientations (cluster 0 or cluster 2 identified by k-means clustering) (e.g., pet11.01 and pet05.01), while others showed more heterozygotes in the dunes (e.g., pet09.01) (Figure 3.3). For pet14.01, the “dune” orientation was not found in the non-dune habitat, although this orientation has a low frequency among samples, with only one individual identified as homozygous (Figure 3.3). Four of the seven putative inversions (pet05.01, pet07.01, pet09.01 and pet11.01) deviated significantly from Hardy-Weinberg equilibrium, with an excess of homozygous genotypes (Table 3.2). Table 3.2 - Estimated size, dune and non-dune frequencies of the “0” arrangement, p-values of “prop.test” for arrangement frequency differences between ecotypes, as well as p-values for Hardy-Weinberg Equilibrium test (PHWE) and heterozygosity deficit (PHET_DEFICIT) of the putative inversions. Region code Estimated size (Mbp) Dune frequency Non-dune frequency Pprop.test PHWE PHET_DEFICIT pet05.01 30 0.94 0.2 2.924x10−22 6.03x10−9 4.46x10−9 pet07.01 20 0.95 0.486 1.185x10-11 1.79x10-6 1.79x10-6 pet09.01 38 0.22 0.8 2.62x10-13 1.39x10-3 1.05x10-3 pet09.02 11 0.19 0.057 0.02353 0.424 0.332 pet11.01 57 0.03 0.586 1.669x10-15 7.43x10-7 7.43x10-7 pet14.01 39 0.11 0 NA† 0.296 0.296 Pet17.01 11 0.02 0.286 1.249x10-06 0.666 0.512 †Only one genotype found in non-dune ecotype for this region Most of the putative inversions were associated with regions of high FST between dune and non-dune ecotypes (Figure 3.1), especially in pet05.01, pet07.01, pet09.01 and pet11.01, where the largest divergence between ecotypes was found. Two exceptions were pet14.01 and pet09.02, for which the frequency of the “dune” orientation was relatively low. 59 Figure 3.3 - Map of Great Sand Dune National Park showing genotype distributions of all putative inversions. Genotypes are based on k-means cluster assignment in PCA. One of the arrangements (either 0 or 1) is more commonly found in dunes, which are represented by barren land surrounded by shrubby habitat in the map. Land cover classification downloaded from Multi-Resolution Land Characteristics Consortium (https://www.mrlc.gov/) at 30-m resolution and only two land cover types are shown on the map. 3.3.3 Genetic maps After SNP filtering, a total of 117 individuals and 9,926 markers from non-dune mapping population, and 128 individuals and 11,748 markers from dune mapping population, entered the map construction process. The final map for non-dune ecotype is made up of 2,559 markers at 801 unique positions with 98.5% of the map having a marker at least every 10 cM and 89.7% having a marker every 5 cM. Similarly, the map for the dune ecotype is made up of 3,077 markers at 571 unique positions with 96.8% of the map having a marker every 10 cM and 87.4% pet05.01 pet07.01 pet09.01 pet09.02pet11.01 pet14.01 pet17.01Barren Land (Rock/Sand/Clay)Shrub/Scrub0/00/11/1Land coverClusterLongitudeLatitude37.737.837.737.8-105.6 -105.5 -105.6 -105.5 -105.6 -105.5-105.6 -105.5 60 of it having a marker every 5 cM. Both of the final genetic maps correspond well with the expected 17 chromosomes and translocations found previously between H. petiolaris and H. annuus (Burke et al. 2004; Ostevik et al. 2019). The LGs are longer than the map reported by Burke et al. (2004), which is probably due to greater coverage of the genome. However, we cannot rule out the possible that a low level of genotyping error from our GBS mapping approach may have contributed as well, although note that our maps are comparable in length with maps for the two subspecies of H. petiolaris recently reported by Ostevik et al. (2019). Two LGs in the dune map were unexpectedly short (D_LG2 and D_LG5; Appendix A.4, A.5) due to few markers from the middle of the corresponding reference chromosomes, which caused the LGs to split after stringent filtering. After reconstruction with less stringent parameters, LG5s in both maps were of similar size and had enough coverage for map comparisons. In map comparisons of the putative inversions, pet05.01 exhibited the expected pattern of reverse marker orders between the two maps. In the map for non-dune ecotype, markers were largely syntenic with the reference genome, while in the map for dune ecotype, there was a continuous block of markers with inverted order relative to the reference (Figure 3.4). However, for pet07.01, pet09.01, pet09.02 and pet14.01, marker orders did not differ between the maps. But, for pet09.01, the many markers that mapped to this region formed tight clusters in both maps, indicating very low recombination in the wild non-dune and dune plants used to make these maps (Figure 3.4). This implies that both plants are heterozygous for the pet09.01 inversion, which would account for the recombination suppression observed. A similar pattern of reduced recombination was seen for pet11.01 and pet17.01 in the non-dune maps, but not in the map made from dune plant, in which markers from the region were in reverse order compared to the reference. Interestingly, markers with reverse order only covered part of the region for 61 pet11.01, which implies the presence of an adjacent low recombination region or sequential inversions (Figure 3.4). Figure 3.4 - Genetic map comparisons for all putative inversions. Maps for non-dune (top panels) and dune (bottom panels) are plotted relative to the HA412HOv2 reference genome. Regions identified by lostruct and the markers that fall within them are highlighted in orange. Parental genotype is indicated by circle in the lower right corner of each panel: red - cluster 0/0, blue - cluster 1/1, half blue half red - cluster 0/1. Different patterns of marker orders are shown: reverse ordering between ecotypes for pet05.01; recombination suppression in both maps for pet09.01; similar forward ordering for pet07.01, pet09.02 and pet14.01; as well as recombination suppression in one map and reverse ordering in another for pet11.01 and pet17.01. 150 160 170 18050751001251505075100125150pet05.01100 120 14020304050602030405060pet07.0180 100 120 140 160255075100255075100pet09.01150 160 170 180 190 200406080100406080100pet09.020 20 40 60 80051015051015pet11.01100 125 150 175306090306090pet14.0110 20 3002040600204060pet17.01Ha412HO (Mbp)H. petiolaris(cM)H. petiolaris(cM)Ha412HO (Mbp) Ha412HO (Mbp)Ha412HO (Mbp) 62 Genotyping of the inversions in the parental plants using GBS confirms our interpretations. The dune and non-dune parental plants were homozygous for different arrangements of pet05.01 and heterozygous for both arrangements at pet09.01. For pet11.01 and pet17.01, the dune plant was homozygous while the non-dune was heterozygous for the inversion, which explains the clustering of markers in the non-dune maps. 3.3.4 Genome-environment association analysis After stringent filtration, 8,383 SNPs were retained for GEA analysis. In GEA, we found several large genomic regions with consistently high BF values, most of which overlapped nearly perfectly with the putative inversions. When treated as single loci, the putative inversions typically exhibited associations that were similar in strength to the peaks seen for the genome-wide SNPs (Figure 3.5, Appendix A.6, A.7). The BF thresholds computed with POD ranged from 1.42 to 5.46 decibans (dB) depending on environmental variables. Several putative inversions displayed significant associations with environmental variables. The strongest signal of association was found for variables describing vegetation cover (e.g., % forbs, % grasses, and % debris), with the most striking one being pet05.01 with PC1 of coverage variables (Table 3.3). pet17.01 was also found to be associated with coverage variables, especially total cover. For soil characteristics, the strongest association was found for pet11.01 with NO3 nitrogen. pet11.01 also displayed a significant association with PC2 of the soil variables but it was not as strong. Pet07.01 displayed significant associations with a number of soil variables but not with any of the three soil PCs. In contrast, pet05.01 was marginally associated with soil PC2, but not with any of the individual soil variables. Interestingly, % grasses is strongly associated with both pet05.01 and pet11.01, 63 whereas % forbs is only associated with the former. This pattern might be related to nitrogen availability, since nitrogen (also associated with pet11.01) is often limiting for grasses, but not for legumes, which are the most frequent forbs on the dunes. Table 3.3 - Bayes factors of genome-environment association analyses with coverage and soil data for putative inversions treated as single loci. Asterisks indicate Bayes factors above significance thresholds computed with simulated POD samples Variable pet05.01 pet07.01 pet09.01 pet09.02 pet11.01 pet14.01 pet17.01 Grass 6.423* -1.917 -4.443 -8.937 7.064* -10.441 8.333* Forb 13.942* -5.516 -3.309 -8.141 -7.581 -7.601 -4.286 Debris 14.286* -4.332 -4.853 -9.645 -4.219 -10.423 0.572 Cover 17.758* -0.64 -2.146 -8.735 1.473 -9.978 11.316* Cover PC1 20.125* -0.471 -1.188 -7.905 0.016 -9.672 7.337* Total N 4.216 6.822* 5.223* 2.62 9.635* -6.687 -7.267 NO3-N 4.85 7.413* 4.64 2.319 10.517* -6.482 -7.171 Ca 0.13 8.1* -5.029 -6.64 -4.792 -6.115 -7.023 P -1.725 4.957* -3.374 -2.521 0.819 -8.546 -8.808 S -3.023 4.078* -4.504 -5.88 4.324* -9.177 -8.886 Soil PC2 6.048* 0.5 1.364 -2.347 8.469* -8.699 -5.121 Only the environmental variables with a significant association with at least one putative inversion are shown. 64 Figure 3.5 - Genome-environment association for (a) % grasses, (b) coverage PC1, (c) soil NO3 nitrogen and (d) soil PC2. Bayes factors (BFis, in deciban unit) was estimated using the importance sampling estimator approach in BayPass. SNPs on different reference chromosomes are represented in alternate colors. Red solid bars indicate the locations of 7 putative inversions and their BFis values when treated as single bi-allelic loci. Red horizontal dashed lines represent 1% significance thresholds computed from simulated samples. ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●−1001020301 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17ChromosomeBFis (dB)(a)●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●−1001020301 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17ChromosomeBFis (dB)(b)●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●−1001020301 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17ChromosomeBFis (dB)(c)●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●−1001020301 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17ChromosomeBFis (dB)(d) 65 3.4 Discussion Genomic islands of differentiation often arise between diverging populations connected by gene flow (Feder and Nosil 2009). While regions with higher than average differentiation can be created by divergence hitchhiking (Via 2012), such regions are unlikely to be large or to have the sharp boundaries often reported for islands of divergence. Inversions represent a more likely explanation for large and discrete islands since recombination is reduced across the entire inverted region. Also, unlike other recombination modifiers, inversions reduce recombination between arrangements, but not within them, which facilitates adaptive divergence. Theory indicates that inversions will be favored if they prevent recombination between locally adapted alleles when challenged by migration of non-adapted alleles (Kirkpatrick and Barton 2006). Inversions can also facilitate speciation by preventing recombination between locally adapted alleles and those contributing to assortative mating (Ortiz-Barrientos et al. 2016). Despite the clear importance of inversions in adaptation and speciation, it remains difficult to identify and genotype them, especially in non-model systems. Using a population genomic approach with RAD sequencing data, we detected seven putative chromosomal inversions that separate dune and non-dune H. petiolaris in GSD, which we validated by a combination of population genetic and comparative genetic mapping approaches. Also, we demonstrated that inversions account for the genomic islands of high divergence between the ecotypes and contribute to ecological divergence in this system. 3.4.1 Identification of inversions Employing the methods implemented in lostruct, which makes use of the effect that inversions have on population structure, we found clusters of windows with outlier MDS values, i.e. 66 genomic regions with extreme population structure compared to the rest of the genome, and we provided multiple lines of evidence showing that the majority of these signals are left by inversions. There are other processes that can generate a pattern of contiguous outlier MDS, such as selection coupled with gene flow, low recombination, or introgression. Linked selection can generate heterogeneous population structure across the genome (Li and Ralph 2019), especially when selection is strong and acts in the face of gene flow, and may also generate long LD blocks. However, the regions that we identified are typically > 10Mb. It is unlikely that the effect of selection would span a region of several to tens of Mbp on the genome in the absence of structural variation. Moreover, such regions under selection are expected to generate a continuous pattern of population structure in a PCA as opposed to the three discrete clusters with higher heterozygosity in the middle cluster reported here. Lastly, the finding of high LD across putative inversions when tested across all samples, but not within putative homozygous groups, distinguishes inverted regions from other regions of reduced recombination (e.g. centromeres), because other mechanisms of recombination suppression are expected to restrict recombination in all groups of individuals. Other small, blurred-edged regions of low recombination were also found in our LD analysis (e.g., on chromosome 8 from 85-100 Mbp and chromosome 17 from 185-205 Mbp; Appendix A.3), but they displayed symmetric patterns of LD in different sample sets and were often associated with low sequence coverage, suggestive of centromeres or other heterochromatic regions. Introgression from another species can also form two distinct haplotype blocks and generate patterns similar to those of an inversion (Li and Ralph 2019). However, gene flow and recombination will erode such patterns unless the introgression is recent. 67 Using genetic maps, we were able to validate one of the inversions (pet05.01) identified with population genetic data and provide additional support for three more based on suppressed recombination in putative inversion heterozygotes (pet09.01, pet11.01 and pet17.01). However, because the wild parents might have the same orientation for pet07.01, pet09.02 and pet14.01, we were unable to corroborate them. This demonstrates one of the weaknesses of the genetic mapping approach – mapping will only detect a subset of segregating inversions. In contrast, approaches based on population genetic data provide a fine-grained and comprehensive way to search for potential inversions, and our methods appear to be robust. Using RAD sequence data, we detected six structural variants identified from WGS data (Todesco et al. 2019) and one additional new putative inversion (pet09.02). We demonstrated that reduced representation sequencing data have the same power to detect inversions with SNP densities as low as 12 per Mbp. Moreover, with more extensive sampling across the habitat transition than that used by Todesco et al., we were able to better estimate population allele frequencies, as well as genetic divergence between ecotypes. We further demonstrated that these inversions are enriched in dune environment and that they correspond closely to genomic islands of differentiation at GSD (see below). However, there are limitations to our approach for detecting inversions. First, while a population genomic approach such as that employed here can provide initial clues regarding the existence of chromosomal inversions, additional independent evidence, such as comparative genetic mapping in this study or Hi-C sequencing analysis by Todesco et al. (2019), is needed to confirm the inversions for further investigation. Second, pinpointing the positions of breakpoints is not feasible given the low density of RAD markers. This can be challenging even with high-depth whole genome sequencing because of the abundance (typically) of repetitive sequences 68 near breakpoints (Tang et al. 2015). Third, the limited genomic coverage of RAD sequence data, together with the dependence on deviations in population structure, biases detection towards large inversions with high sequence divergence. Therefore, it is not suitable for estimating the rate of origin and size distribution of chromosomal variants. However, it offers a convenient way to explore the evolutionary role of inversions because large and highly divergent inversions are also the ones that are most likely to play an important role in local adaptation and speciation. Lastly, we expect that the approach we described here could be further improved by better tuning of window size and outlier thresholds to match population sizes and SNP densities. Despite these limitations, our workflow provides a feasible and economical way of examining inversion frequencies and their evolutionary role in natural populations. 3.4.2 Inversions contribute to adaptive divergence Previous work identified several large regions of differentiation that displayed signatures of divergent adaptation between dune and non-dune ecotypes in this system (Andrew and Rieseberg 2013). Our analyses showed that recombination is suppressed in these highly divergent genomic regions due to chromosomal inversions. Increasing evidence suggests that such islands of differentiation may be prevalent in early stages of speciation (Turner et al. 2005; Michel et al. 2010), and inversions have been shown to play an important role in maintaining ecological and genetic divergence in the face of gene flow (Rieseberg 2001; Noor et al. 2001; Feder et al. 2003b; Lowry and Willis 2010). Our findings add to the growing body of case studies on how structural chromosomal changes interact with local adaptation and gene flow to shape the genomic landscape of divergence in early stages of speciation. 69 Analyses of arrangement frequencies based on genotypes inferred from k-means showed that all of the inversions are significantly enriched on the dunes (Table 3.2), suggesting that they may be under selection, although for some inversions “non-dune” alleles are often found as heterozygotes on the dunes. This could be due to differences in the kinds and strength of selection on the inversions, but could also result from our sampling scheme. The individuals used in the study were collected as seeds from mature plants, and thus reflected post-mating population frequencies rather than that of living plants. If the inversions contribute to seedling survival in dunes, then we likely are under-estimating frequency differences between ecotypes. This is not implausible given that selection against immigrants is known to contribute strongly to reproductive isolation in this system (Ostevik et al. 2016). Nevertheless, genotype frequencies at pet05.01, pet07.01, pet09.01 and pet11.01 deviated significantly from Hardy-Weinberg expectations due to excessive homozygosity. The deficit of heterozygotes suggests that the inversions are underdominant. However, no reduction in pollen viability has been observed in crosses among dune and non-dune plants (Ostevik et al. 2016), so heterozygous disadvantage does not appear to be caused by meiotic abnormalities in inversion heterozygotes. Previous studies found that hybrids between dune and non-dune plants were selected against in both habitats, suggesting an extrinsic cause of maladaptive heterozygotes (Ostevik et al. 2016). Therefore, those regions may be important in local adaptation of both ecotypes. Deviations from Hardy-Weinberg equilibrium also suggest that inversions might contribute to assortative mating between ecotypes (see below). Additional evidence that the inversions contribute to local adaptation comes from the observation that four of the inversions (pet05.01, pet09.01, pet11.01 and pet14.01) co-localize with seed size QTLs identified in other work (Ostevik 2016; Todesco et al. 2019; Figure 3.1). 70 Large seeds help plants survive burial in actively moving sand dunes (Donovan et al. 2010; Ostevik et al. 2016), and seed size is the most divergent phenotypic trait between the ecotypes. These observations are further reinforced by the strong association of pet05.01 with vegetation cover, which is negatively correlated with dune stability. Among the inversion arrangements associated with increased seed size, pet14.01 was in relatively low frequency. However, this inversion underlies ecotype differentiation in another dune ecotype of H. petiolaris (Todesco et al. 2019). Possibly, pet14.01 was only recently introduced to GSD, so it will be interesting to monitor its frequency over the next 1-2 decades. Several inversions were also found to be associated with soil variables in our GEA analyses. Sand dunes are characterized by low nutrient availability, and a QTL for leaf N content maps to inversion pet11.01 (Todesco et al. 2019), which we have shown to be associated with soil N in this study, suggestive of a role in tolerance to low nutrients. Future mapping studies of related physiological traits would help reveal the mechanistic basis by which inversions, especially pet11.01, aid adaptation to low nutrient soils. In the study by Todesco et al. (2019), multiple traits and soil characteristics were constantly found associated with the same inversions in H. petiolaris. These signals could be caused by the low number of samples in the dunes and the resulting selection-driven linkage of the inversions among those samples. With denser sampling across the landscape, we were able to break the linkage of dune inversions and disentangle the effects in GEA. We show that various sets of inversions are responsible for different aspects of dune adaptation in this system. This suggests that the inversions possess different genetic contents that are adapted to different aspects of the environment, which may account for variation in their frequency across the landscape. 71 The observation that inversions are associated with different traits and environmental factors in the dune habitat implies that the inversions are likely favored because they maintain combinations of locally advantageous alleles despite ongoing gene flow with non-adapted populations (Kirkpatrick and Barton 2006). Models of parapatric and sympatric speciation have emphasized the importance of linkage between genes underlying local adaptation and those involved in reproductive isolation (Ortiz-Barrientos et al. 2016; Servedio 2009; Noor et al. 2001). A key assortative mating barrier between the ecotypes is conspecific pollen precedence (Ostevik et al. 2016). Thus, a hypothesis going forward is that loci causing conspecific pollen precedence will also be located within one or more of these inversions. 72 Chapter 4: Inversions largely avert the accumulation of deleterious mutations despite suppressing recombination between large blocks of genes 4.1 Introduction Inversions are thought to play an important evolutionary role by suppressing recombination between locally adaptive alleles. However, genetic recombination between homologous chromosomes is essential for removing deleterious mutations from the genome (Carvalho 2003; Keightley and Otto 2006). In genomic regions of low recombination, the efficiency of selection in eliminating slightly deleterious mutations is expected to weaken because deleterious mutations might hitchhike with genes that are under positive selection (the Hill-Robertson effects; Hill and Robertson 1966; Morrell et al. 2011). Population genomic analyses of Drosophila and other animal species have provided evidence for this evolutionary advantage of genetic recombination (Bachtrog 2003; Kaiser and Charlesworth 2009; Wang et al. 2013). Likewise, studies of a number of crops have reported a negative correlation between mutational load and recombination rate (Lu et al. 2006; Renaut and Rieseberg 2015), consistent with a critical role for recombination in plant species as well. In population genomic studies, deleterious load is typically detected by the study of protein coding genes. For example, within a species one can compare the average number of nonsynonymous differences per nonsynonymous site (referred to as πN) to the average number of synonymous differences per synonymous site (πS) to estimate the effect on selection on protein sequences (Hollister et al. 2015; Hahn 2019). An increase of this ratio usually indicates an increase in load of deleterious mutations within the population. In practice, people also use the 73 ratio of the diversity at zero-fold degenerate sites and the diversity at four-fold degenerate sites (π0/π4) as an approximation (Marsden et al. 2016; Yang et al. 2018; Chen et al. 2020). Zero-fold degenerate sites are sites where any nucleotide substitution will result in amino acid substitution, which is an approximation of nonsynonymous sites, while four-fold degenerate sites are those where four possible nucleotides will result in the same amino acid. In addition, nonsense mutations that cause premature termination of protein synthesis are normally highly deleterious and subjective to purifying selection (Chu and Wei 2019). Therefore, an increase in the proportion of nonsense mutations relative to total number of nonsynonymous mutations would also indicate increasing deleterious load (Renaut and Rieseberg 2015). Transposable elements (TEs) represent an another potentially abundant source of deleterious mutations. Not only are they the largest component of plant and animal genomes (typically > 50%), but they also contribute disproportionately to genomic variation within and between species. TE insertions, for example, often produce detrimental effects on fitness because they may disrupt gene sequences and/or alter gene expression and function (Lisch 2013). In addition, ectopic exchange between non-homologous element copies can produce deleterious chromosome rearrangements (Montgomery et al. 1987; Langley et al. 1988). Therefore, it is predicted that TEs will be more abundant in chromosome regions that have reduced levels of recombination, both because selection acting against deleterious mutations is weaker in regions of low recombination and because TEs are less likely to be involved in unequal recombination between elements located on homologous chromosomes in those regions (Langley et al. 1988; Ma and Bennetzen 2006). This effect of reduced recombination in TE accumulation is supported by a number of studies in Drosophila showing that TE abundance is strongly associated with the local recombination rate (Langley et al. 1988; Bartolome et al. 2002). In plants, examination of 74 the genome of Arabidopsis thaliana found that TE abundance did not correlate with recombination rate but instead shows a negative correlation with gene density, although inbreeding may reduce the importance of recombination rate variation (Wright et al. 2003). Because chromosomal inversions can reduce recombination, it has also been proposed that TEs should accumulate in inverted regions (Montgomery et al. 1987; Sniegowski and Charlesworth 1994). Such an increase in TE copy number within inversions has been reported for Drosophila (Eanes et al. 1992; Sniegowski and Charlesworth 1994). However, we are unaware of such reports in plants. Sunflowers are an excellent system to study the effects of inversions on the accumulation of deleterious mutations. First, large non-recombining haplotype blocks (haploblocks) have been found to play a critical role in ecotype formation in wild sunflowers (Todesco et al. 2020): seven of them underlie the formation of the texanus ecotype of common sunflower, Helianthus annuus (ANN); four gave rise to an early-flowering ecotype of the silverleaf sunflower, H. argophyllus (ARG); and seven were found to be associated with adaptive traits (e.g. seed size) in well-characterized dune ecotypes of prairie sunflower, H. petiolaris (PET). Most of these haploblocks were found to be linked to polymorphic chromosomal inversions (Todesco et al. 2020). Second, an increase in deleterious mutations, such as nonsynonymous substitutions and alternative stop codons, has previously been reported in regions of reduced recombination in cultivated sunflower (Renaut and Rieseberg 2015), suggesting that it should be possible to detect an effect of inversions on protein evolution in sunflower species, if it exists. Third, more than three quarters of the sunflower genome is comprised of TEs, of which 77% are long terminal repeat retrotransposons (LTR-RTs) (Staton et al. 2012; Badouin et al. 2017). Sunflower LTR-RTs are predominantly originated recently and exhibit transcriptional activity among wild species 75 (Cavallini et al. 2010; Kawakami et al. 2011; Renaut et al. 2014). However, whether and how the accumulation pattern of TEs is shaped by inversions has not previously been explored in sunflowers. In this study, we investigate the distribution of TEs, specifically LTR-RTs, and coding sequence evolution across the sunflower genome in multiple wild sunflower species, focusing on the impact of inversions on TE density and the accumulation of deleterious mutations in protein coding genes, quantified as elevated nonsynonymous diversity and increased incidence of nonsense mutations. We hypothesize that variation in recombination rate along the sunflower genome plays a crucial role in the accumulation of TEs and in modulating the efficacy of selection in removing deleterious mutations. We also predict an accumulation of TEs and an increase in genetic load in the regions associated with inversions due to the effect of recombination suppression. 4.2 Methods 4.2.1 Samples and variant calling In this study, we used the whole-genome sequencing data of three annual sunflower species (ANN, ARG, PET) from a previous study. The details of sample collection and DNA library construction were described in Todesco et al. (2020). In brief, samples were grown from seeds collected from 151 populations across the native range of each species (71 populations for ANN; 30 populations for ARG; 50 populations for PET). Nine to fifteen samples were selected from each population and grown for DNA extraction. Tissue from young leaves was collected from all individual plants, and genomic DNA was extracted from leaf tissue to prepare paired-end whole- 76 genome shotgun (WGS) Illumina libraries for 719 ANN, 299 ARG, and 488 PET individuals (Todesco et al. 2020). After treatment with duplex-specific nuclease to reduce the representation of repetitive sequences, the libraries were sequenced on HiSeq2500, HiSeq4000 and HiSeqX instruments to produce paired-end, 150-bp reads. Illumina adapters and poor quality reads were clipped using Trimmomatic v0.36 (Bolger et al. 2014) and reads shorter than 36 bp were discarded. These processes resulted in an average 6.34-fold coverage of gene space (Todesco et al. 2020). In this study, we re-generated the whole variant dataset from Todesco et al. (2020) by aligning all the samples to a new reference genome for H. annuus (Ha412HOv2.0), which used Hi-C (Marie-Nelly et al. 2014) for contig and scaffold ordering and was shown to have improved quality (Todesco et al. 2020). The filtered reads were aligned to the new reference genome using NextGenMap v0.5.3 (Sedlazeck et al. 2013) and resulting BAM files were concatenated, sorted, and duplicate-marked using samtools v0.1.19 (Li et al. 2009). Libraries sequenced in multiple lanes were merged with sambamba v0.6.6 (Tarasov et al. 2015), and PCR duplicates were remarked. Variant calling was performed with the Genome Analysis Tool Kit v 4.1.4.1 (GATK; McKenna et al. 2010). To reduce computational time in the variant calling process, we excluded genomic regions that contain transposable elements, which represent more than 3/4 of the sunflower genome (Badouin et al. 2017), as well as small unplaced chromosome contigs, chloroplast, and mitochondria. For each sample, a GVCF file was produced with the GATK ‘HaplotypeCaller’ with the parameter “--heterozygosity 0.01”. After individual variant calling, all samples from each species (a cohort) were jointly genotyped using GATK’s ‘GenomicsDBImport’ and ‘GenotypeGVCFs’. Samples sequenced in other projects (Drummond 77 2018; Hubner et al. 2019; K. Ostevik unpublished) were also included in joint variant calling to improve variant quality. The step was run over 1 Mbp regions of the genome for parallel computation, and the raw VCF chunks were then gathered by chromosome using ‘GatherVcfs’. In order to remove low-quality variants, we followed GATK best practices and conducted Variant Quality Score Recalibration (VQSR) to filter the raw VCFs. The 20 samples with the highest sequencing depth in each species were selected to produce a "gold set" using the following parameters: mapping quality > 50.0, missing rate < 10%, -1.0 < strand odds ratio < 1.0, minor allele frequency > 0.25, excess heterozygosity < 10.0, -1.0 < BaseQRankSum < 1.0, depth within one standard deviation from the mean and ExcessHet z-score > -4.5. The raw set of all variants were first filtered to remove sites with extremely heterozygosity (ExcessHet z-score < -4.5) and the gold set was then applied against this filtered set of variants to produce recalibration models for SNPs and indels using ‘VariantRecalibrator’. The 90% tranche for each cohort was selected based on these recalibration models using ‘ApplyVQSR’. After variant quality filtration in each species, we selected a subset of samples from the cohort for downstream analyses. For ANN and ARG, samples from all wild populations were kept (71 populations and 719 samples for ANN; 30 populations and 299 samples for ARG). For PET, we excluded samples that were defined as H. niveus (Todesco et al. 2020) and added two populations previously generated for sand dune ecotypes (K. Ostevik unpublished) for a total of 43 populations and 427 samples. An additional filter was applied for each subset to retain only bi-allelic SNPs with minor allele frequency > 0.01 and genotyping rate > 50%. Information on all samples and populations in the subsets, including the “GSD” and “MON” populations from sand dune ecotypes of PET, is described in Todesco et al. (2020). 78 4.2.2 Estimation of recombination rate To explore the effect of recombination on molecular evolution across the genome, we made use an integrated genetic map for cultivated sunflower (Todesco et al. 2020) and remapped the markers to our current reference genome. Recombination rate was calculated for each 500-kbp window based on the physical and genetic distance of the remapped markers. Because recombination rate is relatively constant among annual sunflower species (Barb et al. 2014), we used this result to approximate the pattern of recombination in all three species in our study. To estimate the extent of recombination suppression caused by the inversions, we also calculated linkage disequilibrium (LD) between SNPs across the chromosomes where the inversions reside. We removed SNPs with minor allele frequencies < 10%, thinned the variants to one per 5 kbp and calculated pairwise LD (R2) values between SNPs using PLINK v1.9 (Chang et al. 2015; Purcell et al. 2007). For each inversion, we calculated LD for all samples from populations that are polymorphic for the inversion orientations with a minor allele frequency > 0.1 and for samples from populations that contain only the homozygous genotype for the major orientation (Todesco et al. 2020). Values of SNPs were grouped into 500-kbp windows and the average R2 value was calculated for each window pair. To provide an additional assessment of the effect of inversions in suppressing recombination between orientations, we calculated Weir and Cockerham’s FST (Weir 1996) in 1-Mbp sliding windows with a step size of 200 kbp and 100-kbp sliding windows with a step size of 20 kbp, respectively, between samples homozygous for each orientation for each inversion using VCFtools (Danecek et al. 2011) with the SNPs generated on the new reference genome. 79 4.2.3 Transposable element abundance To test whether inversions harbor an excess of TEs, as would be predicted by theory (Montgomery et al. 1987), we made use of the three reference genomes of cultivated sunflowers (Ha412HOv2.0, XRQv2, PSC8) that have been found to contain different orientations of two inversions found in the wild populations (Todesco et al. 2020): Ha412HOv2.0 and XRQv2 have the same orientation for the inversion on chromosome 1 (ann01.01), while PSC8 has the alternate; XRQv2 and PSC8 possess the same orientation for the inversion on chromosome 5 (ann05.01), while Ha412HOv2.0 has the alternate. To generate comparable datasets, we conducted de-novo TE annotation for the three reference genomes using the program EDTA, which is a sophisticated pipeline that creates raw TE libraries using various structure-based programs and filters out false discoveries in raw TE candidates to generate a high-quality non-redundant TE library (Ou et al. 2016). The program was run with the default settings “--sensitive 0 --evaluate 0” and parameter “--anno 1” to perform whole-genome TE annotation on the 17 chromosomes of each genome assembly, and known coding sequences of sunflower from the gene annotation of the Ha412HOv2.0 genome (https://sunflowergenome.org/) were provided for TE filtration. We extracted LTR-RTs from the EDTA-generated annotations for downstream analyses because these elements are known to be transcriptionally active in sunflower species (Cavallini et al. 2010; Kawakami et al. 2011; Renaut et al. 2014). We summarized the density of LTR-RTs by calculating the proportion of structurally intact and fragmented elements (including solo-LTRs and truncated LTR-RTs) in sliding windows of 500 kbp across the genome. The TE densities were then compared with the recombination rates calculated on the Ha412HOv2.0 genome. Because recombination rates are highly heterogeneous across the genome, the accumulation of TEs within an inversion likely 80 depends in part on the local recombination environment. To control for the effect of genomic context on TE density, we divided the windows into three different categories based on the recombination rate: (1) high recombination rate for windows with recombination rate > 2 cM/Mbp, (2) reduced recombination rate regions with recombination rate between 0.01 and 2 cM/Mbp, and null recombination regions with recombination rate < 0.01 cM/Mbp (3) (Bartolome et al. 2002). For each inversion, windows of each category were compared to those of the same category from across the genome. TE proportions were logit-transformed and compared using Welch two sample t-tests. In order to test if different orientations of the inversions accumulate TEs differently, we extracted the regions of ann01.01 and ann05.01 based on the locations on each reference genome previously identified (Todesco et al. 2020) and calculated average TE density across the windows. Due to the difficulty of precisely identifying TE insertions from short read sequencing data, and the lack of an available reference genome for other species, we did not examine the pattern of TE density for other inversions. 4.2.4 Protein evolution In addition to TEs, we examined if inversions show a greater accumulation of deleterious mutations in protein coding genes relative to background levels. To detect signatures of relaxed negative selection, we explored the pattern of protein evolution using the coding sequences of all protein-coding genes from a recently generated gene annotation for the Ha412r2 reference genome. In brief, the annotation was produced using the program EuGene (Foissac et al. 2008) and the predicted gene sequences were filtered by removing genes that were aligned to a known TE with > 90% identity and over 80% of its length (https://sunflowergenome.org/). The remaining gene set contains 58,234 genes, 53,800 of which are protein-coding genes. We further 81 removed the coding sequences that do not begin with the regular "ATG" start codons or end with "TAG/TGA/TAA" stop codons or those whose lengths are not a multiple of three, leaving a total of 53,302 genes which were used for all downstream analyses. We first calculated the ratio of zero-fold to four-fold diversity π0/π4 (Marsden et al. 2016), which estimates the proportion of amino acid substitutions that are not removed by selection. The zero and four-fold degenerate sites were identified by iterating across all four possible bases at each site along the coding sequences and comparing the resulting amino acids. Sites where the four different bases resulted in four different amino acids were classified as zero-fold degenerate and those with no changes in amino acids were defined as four-fold degenerate. The diversity at zero-fold or four-fold degenerate sites was calculated as: ! = ℎ$%$&' where S is the number of segregating sites and hj is the heterozygosity for the jth segregating site defined as: ℎ = (( − 1 (1 − ,-.) where n is the number of haplotype sequences in the sample and pi is the sample frequency at the ith allele (Tajima 1989; Hahn 2019). The ratio of π0 and π4 was then calculated in sliding windows of 500 kbp using samples from all populations in each of the three species. The ratios were then compared to recombination rates and the values for the inversions were compared to genome-wide measures in a similar way to that in the above analysis of TE density, except that the ratios were log-transformed and recombination rate categories with less than 10 windows in an inversion were removed due to low statistical power. We also calculated π0/π4 for individuals 82 from populations that were polymorphic for an inversion, as well as from those that were monomorphic, to more precisely estimate inversion effects on the accumulation of deleterious load. Specifically, we chose populations that were homozygous for one orientation and those that were polymorphic with a minor allele frequency > 0.2, and compared π0/π4 between these samples. A region of the same size of the inversions on chromosome 4, where no inversions have been identified in sunflowers, was randomly chosen and the statistics were calculated with the same sets of samples to control for differences in demography. In addition, we randomly selected 20 samples from each species and calculated π0/π4 across the whole genome to estimate the deleterious load among species. Mutations that cause the generation of a premature stop codon or loss of an existing stop codon are likely to significantly alter protein structure and function and are considered highly deleterious (Renaut and Rieseberg 2015). Therefore, we further annotated the VCFs using the program snpEff (Cingolani et al. 2012) and extracted “nonsense” mutations (alternate stop codon) for each species. The deleterious load was represented as the ratio of the number of nonsense mutations Pnonsense and the number of mutations at zero-fold sites P0, which was used as an approximation of nonsynonymous mutations. We calculated this statistic in sliding windows of 500 kbp and compared the inversions to genome-wide measures in the same way as for π0/π4. For this analysis, we selected a subset of haploblocks from Todesco et al. (2020) for which there was solid evidence of an underlying inversion and found to be important in ecological adaptation. In ANN, we selected inversions ann01.01 and ann05.01, the structure of which are well-known from comparative genomic analysis, plus inversions ann13.01 and ann15.01, which are the largest inversions found in sunflowers (Todesco et al. 2020). In ARG, inversion arg10.01 was chosen because it was shown to underlie the formation of the early 83 flowering ecotype on the barrier islands of Texas (Moyers 2015; Todesco et al. 2020). In PET, we selected 4 inversions (pet05.01, pet09.01, pet11.01, pet14.01) which were found to contribute to dune adaptation in the species (Huang et al. 2020; Todesco et al. 2020). For pet14.01, we restricted our analyses to samples of H. petiolaris fallax because the inversion is not found in the other subspecies. Among these inversions, pet05.01, pet09.01 and pet11.01 were used for the comparison of genetic load between homozygous and polymorphic populations, because these inversions show very different distribution patterns between subspecies. 4.3 Results 4.3.1 SNP dataset By calling variants on a new high-quality reference genome, we obtained 17,185,351, 8,771,762 and 10,691,117 SNPs in the final datasets of all samples of ANN, ARG and PET, respectively, which are about twice the numbers in our previous datasets, despite application of similar filtering standards (Todesco et al. 2020). We subset the VCFs for the aforementioned wild populations for downstream analyses, resulting in 15,452,562, 8,706,003, and 8,797,015 bi-allelic SNPs for ANN, ARG and PET, respectively. 4.3.2 Recombination rate Analysis of patterns of recombination rate variation across the genome of Ha412HOv2.0 using the integrated genetic map showed that most of the chromosomes have large regions of low recombination around putative centromeric regions in the middle and high recombination toward the distal ends. Some chromosomes, such as Ha412HOChr06, have regions of low 84 recombination rate near one terminal end and are likely acrocentric (Figure 4.1). This is consistent with previous interpretations of recombination rate variation within ANN (Renaut et al. 2013). In the comparison between polymorphic and homozygous populations, all inversions were characterized by a region of high LD among samples from polymorphic populations, while this pattern was not found among samples from populations with homozygous genotypes (Figure 4.2; Appendix B.1). These results are consistent with the role of inversions in altering recombination in heterozygotes, while recombination in homozygotes remains unaffected. Figure 4.1 - Recombination rate across the genome of Ha412HOv2.0. The recombination rate was calculated for 500-kbp windows based on an integrated genetic map of cultivated sunflower (Helianthus annuus). Ha412HOChr13 Ha412HOChr14 Ha412HOChr15 Ha412HOChr16 Ha412HOChr17Ha412HOChr07 Ha412HOChr08 Ha412HOChr09 Ha412HOChr10 Ha412HOChr11 Ha412HOChr12Ha412HOChr01 Ha412HOChr02 Ha412HOChr03 Ha412HOChr04 Ha412HOChr05 Ha412HOChr060 50 100 150 0 50 100 150 0 50 100 150 0 50 100 150 200 0 50 100 150 2000 50 100 150 0 50 100 150 0 50 100 150 200 0 50 100 150 2000 50 100 150 200 0 50 100 1500 50 100 150 0 50 100 150 0 50 100 150 0 50 100 150 200 0 50 100 150 0 50 100 150024024024MbpRecombination rate (cM/Mbp) 85 Figure 4.2 - Plot of linkage disequilibrium for Helianthus petiolaris inversion pet15.01 on chromosome 15. Upper triangle with all individuals from populations polymorphic for the inversion and lower triangle with individuals from populations homozygous for the more common orientation. Pairwise R2 between SNPs were summarized and the average R2 values were presented in 500-kbp windows. Purple bars represent the location of the inversion. Results of the other inversions are presented in Appendix B.1. In most of the inversions, divergence between orientations is consistently high across the whole region relative to the low FST outside the rearranged regions (Figure 4.3), except for the abrupt gaps in the middle of pet05.10 and pet09.01, which results from the structural variation between H. petiolaris and the reference genome, which is for cultivated sunflower (Todesco et al. 2020). In the two largest inversions, ann13.01 and ann15.01, FST is more heterogeneous with 86 a gradual decline in the middle of the inverted region, implying considerable recombination between orientations away from the breakpoints. Figure 4.3 - Divergence (FST) between inversion orientations. Mean divergence is calculated over 1-Mbp sliding windows with a step size of 200-kbp (brown lines) and 100-kbp sliding windows with a step size of 20 kbp (cyan lines). Vertical dashed lines indicate the putative boundaries of each inversion. 87 4.3.3 Transposable element abundance Using the EDTA pipeline to annotate the three genome assemblies, we confirmed that the genomes of cultivated sunflower are composed of a high proportion of TEs (83.02% in Ha412HOv2.0, 83.33% in XRQv2, 83.21% in PSC8), with 71-72% of these genomes being LTR-RTs (71.50% in Ha412HOv2.0, 71.20% in XRQv2, 71.29% in PSC8). In agreement with previous studies of the cultivated sunflower genome (Staton et al. 2012), there is a major bias in TE composition towards Gypsy (Ha412HOv2.0 42.04%, XRQv2 41.93%, PSC8 41.63%) and Copia elements (Ha412HOv2.0 11.12%, XRQv2 12.04%, PSC8 12.52%). Class II TEs (DNA transposons) were much lower in abundance relative to LTR-RTs comprising about 12% of each genome (Ha412HOv2.0 11.50%, XRQv2 12.14%, PSC8 11.92%). The genomic distributions of LTR-RTs in the three assemblies are similar to those previously reported for the first reference genome for cultivated sunflower (Badouin et al. 2017; Appendix B.2). Using the window-based calculations based on the Ha412HOv2.0 genome, we found a strong negative correlation between density of LTR-RTs and recombination rate (p < 2.2´10-16; Figure 4.4a). The results were similar with only Gypsy or Copia elements (not shown). Our integrated genetic map has not been aligned to the genomes of XRQv2 and PSC8, but the similar distributions of LTR-RTs across the three genomes imply a comparably strong negative correlation between recombination rate and TE abundance. For the two inversions that are polymorphic between the reference genomes, the recombination rate of most windows in ann01.01 fell within the “reduced” category, whereas for ann05.01, both “reduced” and “null” windows were found. Recall that these recombination rates are derived from genetic mapping populations that are homozygous for the inversions. Thus, this is the background recombination rate for these windows rather than the recombination rate in the 88 inversion heterozygotes. When compared to windows of a similar recombination rate across the genome, ann01.01 exhibited significantly lower density of LTR-RTs (p = 9.17´10-4; Figure 4.4b). Likewise, for ann05.01, windows with a “null” recombination rate contained fewer LTR-RTs than windows with a similar recombination rate elsewhere in the genome (p = 6.62´10-22), but no significant difference was found between windows of the “reduced” recombination rate category across the genome and those within the inversion (p = 0.557; Figure 4.4c). In the comparison between orientations using multiple genome assemblies, neither of the inversions show a visible difference in TE density between orientations (Figure 4.5). Figure 4.4 - Recombination rate and long terminal repeat retrotransposon (LRT-RTs) density. (a) Genome-wide correlation of LTR-RT density and recombination rate in Ha412HOv2.0. The red line denotes the best-fit linear regression line with 95% confidence intervals shaded in grey. (b) Comparison of LTR-RT density within inversion ann01.01 to genome-wide. (c) Comparison of the LTR-RT density within inversion ann05.01 to genome-wide. Windows in each recombination rate category were compared separately. P-values are Welch two sample t-tests on logit-transformed values. −20240 2 4Recombination rate (cM/Mbp)logit(LTR retrotransposon density)(a)●●●●●●●●●●●●●●●●●●●●●●●p=0.000917−2024reducedRecombination ratelogit(LTR retrotransposon density)(b)●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●p=6.62e−20 p=0.557−2024null reducedRecombination rategenomewideinversion(c) 89 Figure 4.5 - Comparison of long terminal repeat retrotransposon density between orientations of (a) ann01.01 and (b) ann05.01. 4.3.4 Protein evolution We selected 20 samples from ANN, ARG and PET, respectively, and summarized zero-fold and four-fold diversity across the whole genome to estimate the genetic load of these samples. Across the three species, π0/π4 shows a negative correlation with levels of neutral heterozygosity (mean of π0 and π4; Figure 4.6). ARG samples have the lowest levels of neutral diversity among the species. However, they show disproportionately higher levels of diversity at zero-fold sites (Figure 4.6). ANN has similar but slightly higher levels of neutral diversity and lower levels of π0/π4 compared to PET. To test the significance of this inverse relationship while controlling for spurious signal caused by mathematical correlation between π0/π4 and genetic diversity, both π0 and π4 were scaled by dividing by the standard deviation of each, and the association between the average of the scaled values and the ratio between them were then tested (Irwin, 2018). This resulted in a statistically significant inverse relationship (Pearson’s r = -0.7116, p = 1.848×10-10). 0.000.250.500.751.000 1Inversion orientationLTR retrotransposon density(a)0.000.250.500.751.000 1Inversion orientationGenomeHa412HOv2.0XRQv2PSC8(b) 90 Figure 4.6 - Genetic load (π0/π4) versus genetic diversity (mean of π0 and π4) across three sunflower species. The statistics were estimated with 20 individuals randomly chosen from each species of Helianthus annuus (ANN), H. argophyllus (ARG) and H. petiolaris (PET), respectively. The black line denotes the best-fit linear regression line. Both π0 and π4 were scaled by dividing by the standard deviation of each before the significance of the negative correlation between the variables represented by both axes was tested. In all three species, the ratio of nonsynonymous and synonymous mutations as estimated by window-based π0/π4 is similar to that seen in other taxa (Yang and Gaut 2011) with a small proportion of the values greater than 2 (Appendix B.3). Mean π0/π4 ranges from 0.3-0.5, with the highest value found in ARG and the lowest in PET (ANN: 0.3787; ARG: 0.4248; PET: 0.3124). Across the genome, π0/π4 is negatively correlated with recombination rate in all three species, but the correlation is weak in PET (ANN: Pearson’s r = -0.1162, p < 2.2×10-16; ARG: Pearson’s r = -0.1752, p < 2.2×10-16; PET: Pearson’s r = -0.03836, p = 0.00448; Figure 4.7). ●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●0.210.220.230.240.250.260.002 0.003 0.004 0.005Genetic diversity (mean of π0 and π4)π 0π 4Species●●●ANNARGPET (estimated with 20 individuals per species)Zerofold to fourfold heterozygosity ~ neutral genetic diversity 91 Figure 4.7 - Genome-wide correlation of π0/π4 and recombination rate in (a) Helianthus annuus, (b) H. argophyllus and (c) H. petiolaris. The red lines denote the best-fit linear regression line with the 95% confidence intervals shaded in grey. Contrary to what was expected, most of the inversions that we examined showed no difference in deleterious load (p > 0.1) when compared to the genomic background in each recombination rate category. In the few comparisons where significant (p < 0.01) results were obtained, such as for windows with “reduced” recombination rate in inversion arg10.01, the inversions have lower π0/π4 values, indicating fewer nonsynonymous substitutions in those windows than the genome-wide average for regions with similar recombination rates (Figure 4.8, Appendix B.4). 92 Figure 4.8 - Comparison of π0/π4 within inversions (a) ann13.01, (b) arg10.01 and (c) pet09.01 to the genome-wide average for regions with a similar recombination rate. Windows of each recombination rate category were compared separately. P-values are Welch two sample t-test on log-transformed π0/π4 values. Results for the other inversions are presented in Appendix B.4. When looking at the ratio of nonsense mutations versus nonsynonymous mutations (Pnonsense/P0), we found a stronger negative correlation with recombination rate in all species (ANN: Pearson’s r = -0.2770, p < 2.2×10-16; ARG: Pearson’s r = -0.3104, p < 2.2×10-16; PET: Pearson’s r = -0.2183, p < 2.2×10-16; Figure 4.9a-c) than for π0/π4. Similar to the results with π0/π4, however, there was no excess in their proportion when compared with the genome-wide average for windows with a similar recombination rate (Figure 4.9d-f, Appendix B.5). One exception was in the “reduced” category for ann15.01 (p=0.0104; Appendix B.5), but the difference was small and the increase in the “null” category in the same inversion was not significant. ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●p=0.856 p=0.135 p=0.238−6−4−2024null reduced highlog(π0π 4)(a)●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●p=0.000183 p=0.644−6−4−2024reduced highRecombination rate(b)●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●p=0.393−6−4−2024reducedgenomewideinversion(c) 93 Figure 4.9 - Recombination rate and proportion of nonsense mutations. Genome-wide ratio of nonsense mutations and zero-fold mutations (Pnonsense/P0) was compared to recombination rate in (a) Helianthus annuus, (b) H. argophyllus and (c) H. petiolaris. The red lines denote the best-fit linear regression line with the 95% confidence intervals shaded in grey. The values within inversions (d) ann13.01, (e) arg10.01 and (f) pet09.01 were also compared to the genome-wide averages for regions in the same recombination rate category. Windows of each recombination rate category were compared separately. P-values are Welch two sample t-test on log-transformed Pnonsense/P0 values. Results of the other inversions are presented in Appendix B.5. −6−4−200 2 4log(PnonsenseP 0)(a)−6−4−200 2 4Recombination rate (cM/Mbp)(b)−6−4−200 2 4(c)●●●●●●●●●●●●●●●●●p=0.365 p=0.875 p=0.472−6−4−20null reduced highlog(PnonsenseP 0)(d)●●●●●●●●●●●●●p=0.147 p=0.398−6−4−20reduced highRecombination rate(e)●●●●●●●●●●p=0.00889−6−4−20reducedgenomewideinversion(f) 94 We also compared π0/π4 and Pnonsense/P0 between samples from populations that contain only one inversion orientation and samples from populations that are polymorphic. In all three inversions examined, samples from populations with both orientations showed higher π0/π4 and Pnonsense/P0 compared to samples in populations with only one orientation, while those values were not different or lower in the control region (Figure 4.10, Appendix B.6-7). The excess of π0/π4 and Pnonsense/P0 in polymorphic populations is more striking when samples of the same genotype from these populations were compared. There was one exception in the comparison of Pnonsense/P0 for pet11.01, where no significant difference was found between monomorphic and polymorphic populations. However, samples in monomorphic populations showed significantly elevated Pnonsense/P0 values in the control region, consistent with a demographic cause for the increase in nonsense mutations in these samples. 95 Figure 4.10 - Comparison of π0/π4 and Pnonsense/P0 between populations for inversion pet05.01. (a) Distribution map of inversion genotypes in populations used in the analyses. The statistics were calculated for samples from monomorphic populations (all 1/1 genotype) and polymorphic populations (with multiple genotypes). (b) Comparison of π0/π4 between monomorphic populations (green) and polymorphic populations (orange), as well as samples in the polymorphic populations that have the same inversion orientation (1/1) as the homozygous group (red). (c) Comparison of Pnonsense/P0 for the same groups of samples. The control is a region of the same size of the inversion randomly chosen from chromosome 4. P-values are Welch two sample t-tests on log-transformed statistics between samples from monomorphic populations and samples from polymorphic populations. Results of inversions pet09.01 and pet11.01 are presented in Appendix B.6-7. 28323640−115 −110 −105 −100 −95LongitudeLatitudeGenotype0/00/11/1(a)●●●●●p=0.535 p=2.53e−06−1.7−1.5−1.3control inversionlog(π0π 4)(b)●●●●●●●●●●●●●●p=0.000492 p=3.63e−08−5−4−3control inversionlog(PnonsenseP 0)MonomorphicPolymorphicPolymorphic (same orientation)(c) 96 4.4 Discussion Using three genome assemblies of cultivated sunflower and newly-generated SNP datasets in three wild sunflower species, we analyzed patterns of LD, sequence divergence, TE abundance and protein evolution for a number of inversions. The increased LD in populations containing different inversion orientations and the elevated sequence divergence between inversion haplotypes confirmed that inversions suppress recombination between chromosomes harboring different orientations. Nonetheless, little evidence of TE accumulation or elevated deleterious amino acid substitutions were found within these inversions. We recognize that our SNP dataset likely contains a fraction of genotyping errors and the statistics that we used for protein evolution might not provide precise measure for each gene sequence due to uncertainties in gene annotation and/or errors in SNP calling. However, in a previous study using the same sequences and SNP calling parameters, SNP calling accuracy was shown to exceed 99% for homozygous genotypes and averaged 97% across all genotypes (Todesco et al. 2020). Also, keep in mind that the purpose of this study is to investigate general trends in protein evolution across the genome. This, while errors in multiple steps could generate noise, there are unlikely to be biases towards either synonymous or nonsynonymous mutations. This is reflected in the distribution of π0/π4 in the three species, which is consistent with expectations from effective population size. For example, ARG has a much smaller effective population size compared to the other two species (Strasburg et al. 2011), and ARG samples show the lowest levels of genetic diversity but highest levels of the ratio between π0 and π4. This result is concordant with other studies that have reported evidence for the accumulation of deleterious mutations in taxa characterized by small effective population size and/or a history of population bottlenecks (Marsden et al. 2016; Yang et al. 2018; Chen et al. 2020), further 97 supporting the use of π0/π4 for measuring the load of deleterious mutations. Finally, all of the statistics that we used, especially Pnonsense/P0, displayed the predicted negative correlation with recombination rate across the genome in all three species, which indicates the efficacy of these statistics in capturing the effects of recombination rate variation. Despite the negative correlation with recombination rate seen for all stats, including the density of LTR-RTs, π0/π4 and Pnonsense/P0, none of the inversions that we examined displayed increased genetic load when compared to other regions of the genome with a similar background recombination rate. This result contrasts with evidence for the accumulation of deleterious mutations and increased TE density reported for Drosophila (Eanes et al. 1992; Sniegowski and Charlesworth 1994), perhaps suggesting that inversions in sunflowers are less prone to accumulate deleterious mutations. However, variation in meiotic exchange rates is not the only difference between chromosome regions; though we controlled for recombination rate, other properties, such as DNA conformation or gene density, could also affect the accumulation of genetic load (Sniegowski and Charlesworth 1994; Gendrel et al. 2002; Tsukahara et al. 2012; Kent et al. 2017). One explanation for the seemingly successful purging of deleterious mutations from inversions in sunflowers is that they suppress recombination only in heterozygotes (Ortiz-Barrientos et al. 2016). Our analyses of LD within different genotypes illustrate this - while inversions generate a large region of high LD among individuals from polymorphic populations, recombination is back to normal levels in samples having the same orientation. Unlike most inversions in Drosophila, where one of the orientations is rare and often found in the heterozygous state, resulting in reduced recombination rate among copies of that orientation (Eanes et al. 1992; Sniegowski and Charlesworth 1994), the inversions that have been identified 98 in sunflower are of relatively high frequency and homozygous genotypes are frequent, especially in places where selection for one of the orientations is strong (Todescto et al. 2020). This provides ample opportunity for recombination to happen, breaking the linkage of deleterious mutations to positively selected alleles, and increasing deleterious ectopic meiotic interchromosomal exchange between TEs, which permits selection for genomes with low TE copy numbers (Eanes et al. 1992). In addition, the pattern of FST in these inversions suggest that while inversions allow independent evolution of chromosomes with different orientations, there is a low level of gene exchange between orientations due to double recombination or gene conversion (Korunes and Noor 2019), which might also contribute to the purging of deleterious mutations. Inversions in sunflower are not completely free from the detrimental consequences of recombination suppression in heterozygotes. In populations where inversions are polymorphic, inversions exhibited moderately higher deleterious load compared to samples from populations where only one orientation is found even though the load in other genomic regions is similar or lower. This pattern persisted when samples with the same inversion orientation were compared, so this difference did not result from the inclusion of another highly divergent orientation, but rather from the impact of suppressed recombination in inversion heterozygotes. In pet11.01, samples in polymorphic populations were not significantly different from those from monomorphic populations in the proportion of nonsense mutations. However, there was higher background load in the control region in monomorphic populations, which appears to have masked the inversion-specific increase in deleterious load in polymorphic populations (Appendix B.7). 99 We also expect increased TE abundance in populations with a higher frequency of inversion heterozygotes since recombination is important in the removal of these elements, but because of the difficulty in precisely identifying TEs within inversions using short reads data, we could not test this hypothesis. Recombination plays a critical role in organismal evolution. By bringing beneficial mutations together, and by decoupling them from deleterious mutations, recombination increases both the rate of adaptive evolution and the efficiency with which deleterious mutations are purged from populations (Hill and Robertson 1966; Barton and Charlesworth 1998; Carvalho 2003; Keightley and Otto 2006). On the other hand, the suppression of recombination can facilitate adaptive divergence between populations by holding advantageous alleles together in the face of gene flow from non-adapted populations (Kirkpatrick and Barton 2006; Ortiz-Barrientos et al. 2016). Thus, recombination modifiers that can simultaneously permit high levels of recombination within populations, while reducing such recombination between ecologically divergent populations should be favored. Our analyses in sunflowers suggest that inversions can offer such versatility; recombination can occur among chromosomes of the same orientation, but not between orientations, thereby permitting divergence with gene flow, whilst largely averting the accumulation of deleterious mutations. This contrasts with other genomic features known to suppress meiotic recombination, such as telomeric and heterochromatic regions, which suppress recombination universally. Conversely, several other kinds of structural variants are likely to affect recombination in the same way as inversions, thus providing a similar evolutionary advantage. For example, hemizygosity resulting from insertion and deletion polymorphisms also precludes recombination between chromosomes with different haplotypes, but not within those possessing the same haplotype (He and Dooner 2009; Schwander et al. 2014; Lawrence et al. 100 2017). However, compared to inversions, hemizygosity may have some disadvantages because deleterious mutations would not be masked in hemizygous genotypes. Overall, our work supports theoretical claims regarding the evolutionary advantages of structural variants as recombination modifiers (Ortiz-Barrientos et al. 2016). 101 Chapter 5: Inversions contribute disproportionately to parallel adaptation to sand dunes 5.1 Introduction Parallel evolution is the repeated evolution of the same phenotype or genotype in different populations experiencing similar selection pressure (Schluter et al. 2004; Arendt and Reznick 2008). Although it is clear that organisms experiencing similar ecological conditions often display parallel phenotypic change (Jones et al. 1992; Rundle et al. 2000; Rajakaruna et al. 2003), evidence of parallel evolution at the genomic level has just started to accumulate with the increased availability of genomic sequences in multiple natural and experimental systems (Hohenlohe et al. 2010; Stern 2013; Bailey et al. 2015; Storz 2016). Understanding the extent to which populations experiencing shared selective pressures adapt through a shared genetic response offers a means for addressing a number of key issues in evolutionary biology. For example, the number and distribution of the genes or genomic regions evolving in parallel provides insights into the factors that facilitate or constrain adaptive genetic responses (Conte et al. 2015; Yeaman et al. 2018). The probability of parallel genetic evolution can be affected by the direction and strength of phenotypic selection, population size, and constraints imposed by genetic architecture (Bailey et al. 2017; MacPherson and Nuismer 2017). Theoretical studies have shown that parallel evolution becomes more likely as the strength of selection increases and the number of possible alleles decreases (Orr 2005) and that the probability of parallel evolution is greatest when pleiotropy is weak (Chevin et al. 2010). In the scenario of parallel evolution from standing genetic variation, the probability of parallel genetic 102 evolution also increases with the strength of natural selection and is particularly likely to occur for genes with large phenotypic effects (MacPherson and Nuismer 2017). Therefore, architectures that limit genetic variability, or the independence of this variability, will reduce the number of possible adaptive solutions, thereby increasing the repeatability of evolution. Chromosomal inversions have received much attention as one of the most important genomic architectures facilitating adaption and speciation. By capturing favorable alleles and suppressing recombination between them, inversions are thought to permit local adaptation in the presence of gene flow with non-adapted populations (Kirkpatrick and Barton 2006; Hoffman and Rieseberg 2008; Feder and Nosil 2009). However, because locally adapted alleles are tightly linked with the inversions, the range of adaptive responses is limited when populations encounter a new environment. Moreover, the combined phenotypic effect of alleles within inversions is likely to be greater than that of individual alleles (Kirkpatrick and Barton 2006). Because higher selection coefficients increase the probability that a locus will contribute to adaptation, especially when constrained by migration (Griswold 2006; Yeaman and Otto 2011), this should increase the likelihood of parallel responses to selection for inversions in different populations (MacPherson and Nuismer 2017). Currently, we are unaware of any theoretical work that specifically focuses on the role of inversions in parallel evolution. However, numerous recent studies have associated inversions with traits and loci involved in parallel evolution, including (1) an inversion Omy5 was found to be strongly associated with life-history differentiation of fluvial and anadromous Oncorhynchus mykiss in multiple populations, as well as between fluvial and adfluvial ecotypes (Iijima et al. 2018; Arostegui et al. 2019); (2) signatures of parallel evolution were restricted to a limited number of shared genomic regions, including three chromosomal inversions, between freshwater 103 and marine ecotypes of three-spined stickleback (Gasterosteus aculeatus) in the Eastern Pacific (Fang et al. 2020); and (3) following the colonization of Drosophila subobscura in North and South America, most inversions evolved latitudinal clines that paralleled those in Europe (Prevosti et al. 1988; Balanya et al. 2003). These empirical studies suggest that inversions may contribute disproportionately to parallel evolution in natural populations. Across the sunflower genus (Helianthus), multiple taxa have independently colonized sand dune habitats (Heiser et al. 1969). For example, the homoploid hybrid species, H. anomalus, is found exclusively in deep sand dunes while its two parent species are not. The Algodones Dunes sunflower, H. niveus ssp. tephrodes, inhabits sand dunes in southern California and northern Mexico, while its closet relative, H. niveus ssp. canescens, occurs in sandy soils. In prairie sunflower, H. petiolaris, two ecotypes have independently adapted to active sand dunes in the Great Sand Dunes National Park and Preserve (GSD), Colorado, and around Monahans Sandhills State Park (MON), Texas (Andrew et al. 2012; Ostevik 2016). Genetic study of the GSD and MON systems has revealed that quantitative trait loci (QTLs) underlying seed size, a trait that is critical in dune adaptation and that has evolved in parallel across multiple dune sunflowers, are largely shared between the two systems (Ostevik 2016). Furthermore, seven large non-recombining haplotype blocks are segregating in one or both of the dune ecotypes and appear to result from large inversions (Huang et al. 2020; Todesco et al. 2020). Several of these inversions co-localize with the previously identified seed size QTLs, suggestive of a role in parallel phenotypic evolution in these dune systems. Despite these advances in our understanding of the genetic basis of local adaptation in these dune populations, whether these instances of parallel evolution at inversions are representative of genome-wide patterns of parallel evolution in these independently derived dune ecotypes remains unknown. 104 To address this question, we made use of the whole-genome sequencing data available for wild sunflowers, and applied population genomic approaches to identify selective sweeps (i.e., loci under positive selection) in the two dune systems in H. petiolaris. We then identified swept regions that were shared by the dune ecotypes and tested the hypothesis that inversions contribute disproportionately to parallel genetic evolution. Lastly, we examined the genetic relationships and putative functions of the genes of the shared regions. The goals of these latter two analyses were to better understand the source(s) of genetic variation that contribute to parallel genetic evolution in this system, as well as the likely role of the swept regions in dune adaptation. 5.2 Methods 5.2.1 Samples and SNP calling In this study, we used the samples of Helianthus petiolaris described in Chapter 4. For samples of H. petiolaris sp. fallax, the dune and non-dune ecotypes were assigned based on collecting location (Todescto et al. 2020; K. Ostevik unpublished). In brief, 11 samples from active sand dune habitats in the GSD were classified as the “dune” ecotype in the GSD system, while a total of 184 samples from the sand sheet below the dune and from other populations of H. petiolaris sp. fallax were treated as non-dune. Likewise, 27 samples from active sand dunes at MON were classified as the “dune” ecotype in the MON system and 175 samples from nearby sand sheet populations and from other populations of H. petiolaris sp. fallax were also treated as non-dune. In the analyses of each dune system, we excluded all samples from within 100 km of the other 105 dune system, to remove its impact on sweep detection. Detailed information about the samples and populations is provided in Appendix C.1. For most of the analyses, we used the SNP dataset from whole-genome sequencing data of H. petiolaris generated in Chapter 4. For analyses that required polarization of the SNPs or outgroups, we generated another variant dataset of the species using all available samples of H. petiolaris, as well as samples from four perennial sunflowers (664647_GIG from H. giganteus, DEC_1895 from H. decapetalus, DIV_1956 from H. divaricatus, and GRO_2043 from H. grosseserratus; Todesco et al. 2020). These samples were sequenced and aligned to the new reference genome of H. annuus (Ha412HOv2.0) in the same way as described in Chapter 4. For each sample, a GVCF file was produced with ‘HaplotypeCaller’ in the Genome Analysis Tool Kit v 4.1.4.1 (GATK; McKenna et al. 2010) with the parameter “--heterozygosity 0.01” which better reflects the expected diversity in sunflowers compared to the default for humans. After individual variant calling, all samples of H. petiolaris and the four perennial samples were jointly genotyped using GATK’s ‘GenomicsDBImport’ and ‘GenotypeGVCFs’ on 1Mbp genomic regions without transposable elements, and the raw VCF chunks were then gathered by chromosome using ‘GatherVcfs’ (Chapter 4). Variant Quality Score Recalibration (VQSR) was performed using the top 20 samples with the highest sequencing depth in H. petiolaris as a "gold set" (Chapter 4) to keep as many consistent SNPs with the intraspecific dataset as possible. The raw variants were filtered to remove sites with extremely high heterozygosity (ExcessHet z-score < -4.5), and the gold set was then applied against this filtered set of variants to produce recalibration models for SNPs and indels using ‘VariantRecalibrator’. The 90% tranche for each cohort was selected based on these recalibration models using ‘ApplyVQSR’. After variant 106 quality filtration, the variants were further filtered to retain only bi-allelic SNPs with minor allele frequency > 0.01 and genotyping rate > 50% across all samples. 5.2.2 Identification of selective sweeps Selective sweeps in each of the dune systems were identified using three approaches that take advantage of different characteristics of genetic polymorphisms following selective sweeps. First, we employed the composite likelihood ratio (CLR) statistic, which is based on the site frequency spectrum of a single group of samples. Specifically, CLR contrasts the likelihood of the null hypothesis based on the genome-wide site frequency spectrum with the likelihood of a model where the site frequency has been altered by a recent selective sweep. The CLR statistic was computed using SweepFinder2 (DeGiorgio et al. 2016), which includes invariant (fixed) sites and accounts for the effects of negative selection on diversity, thereby increasing power and reducing false positives (Huber et al. 2016). For each dune system, SNPs of the dune samples (target group) were extracted from the dataset containing all samples of H. petiolaris as well as four perennial species and were polarized by comparing to the perennial samples. For each SNP, the outgroup allele was inferred as ancestral state if all of the four perennials were called and homozygous, and the allele matched one of the alleles of the target group. If this standard was not met, the site was defined as unpolarized. Unpolarized sites that were monomorphic within the target group were discarded. All unpolarized sites that were polymorphic within the target group were kept and assigned folded=1 in the input files for SweepFinder2. We generated the allele frequency input files for SweepFinder2 using a custom script and the recombination rate of each position was calculated based on the integrated genetic map of cultivated sunflower described in Chapter 4. The 107 empirical derived allele frequency spectra across all chromosomes were calculated using the -f option of SweepFinder2. CLRs were then calculated using the recombination map and pre-computed empirical spectrum with a grid size of 2 kbp. No correction for background selection was conducted because the genome-wide estimation of background selection has not yet been conducted in sunflowers. Because SweepFinder2 only examines sequence polymorphism within populations, some of the outlier regions that it identifies might come from broader selective sweeps (i.e., regional or species-wide sweeps). Therefore, the other two approaches we have employed to detect sweeps permit comparisons between dune and non-dune populations to filter for those restricted to the dune populations. A well- known characteristic of hard sweeps is a reduction of sequence diversity (π) in the selected region and at linked neutral sites (Smith and Haigh 1974; Hahn 2019). Thus, for each system, sequence diversity (π) was calculated for the dune and non-dune group separately using VCFtools (v0.1.14; Danecek et al. 2011) with 20-kbp sliding windows and a step size of 2 kbp. The reduction of diversity (ROD; Cheng et al. 2016; Lu et al. 2019) in the dunes was then estimated by summarizing the ratio of π for the dune group and non-dune group (πdune/πnon-dune). Another common feature of selective sweeps is an increase in genetic differentiation in the selected region and closely linked SNPs (Vitti et al. 2013; Weigand and Leese 2018). Therefore, we used VCFtools to calculate Weir and Cockerham’s FST (Weir 1996) using 20-kbp sliding windows and a step size of 2 kbp to estimate genetic divergence between the dune and non-dune groups. Calculations of both ROD and FST were based on SNPs from the intraspecific dataset of H. petiolaris (Chapter 4). 108 For each of the three statistics mentioned above, the top 5% of genomic windows were classified as outliers, and groups of adjacent outlier windows were merged into regions. Finally, outlier regions that were identified by SweepFinder2 and overlaps with regions identified by both ROD and FST methods were considered selected regions. 5.2.3 Regions of parallel evolution and enrichment analyses Genomic regions with outlier values in population genomic calculations may result from the same selective sweep due to genetic linkage. To account for the clustering of regions with signatures of selective seep, for the selected regions identified in each system, we extracted the SNPs in each region and calculated pairwise linkage disequilibrium (LD; R2) between each pair of SNPs between windows using PLINK v1.9 (Chang et al. 2015; Purcell et al. 2007). The LD between regions were summarized with the average value of the R2 between SNPs from each window. We also generated genome-wide null distributions of LD for the same physical distance. Specifically, we randomly selected 10,000 pairs of 5-kbp windows with the same physical distance and the LD between these windows were summarized using the same approach. Adjacent outlier regions were merged if the LD between these windows exceeded 95 percent of the null distribution (p < 0.05) of the same physical distance. After merging based on LD, the overlap of the selective sweep regions in GSD and MON systems were then identified using BEDTools (Quinlan 2014) and these overlapping regions were treated as regions involved in parallel evolution (hereafter “parallel regions”). After the identification of the parallel regions, we merged the regions again based on the LD between them using the same method mentioned above and the genetic distance between them. Clusters of regions were merged into a single larger region if the LD between closest 109 regions was greater than 95 percent of the null distribution, and the genetic distance was less than 5 cM. Regions that did not contain any SNPs were discarded. To test whether inversions show a stronger pattern of parallel evolution than non-inverted regions, we used a randomization approach in order to find the numbers and sizes of regions overlapping between parallel regions and chromosomal inversions and to estimate the significance of this overlap. Specifically, we held the position of the parallel regions constant and randomized the position of the inversions across the genome 10,000 times. For each permutation, we counted the numbers and lengths of the regions that overlapped with inversions, and built null distributions. Significance of enrichment was estimated using a one-tailed test by comparing the observed values with these null distributions. Using this statistical test, we asked whether the observed overlap between parallel sweeps and inversions is greater than what would be expected by chance. The test was conducted on the parallel regions before and after merging based on LD and genetic distance. 5.2.4 Phylogeny of parallel regions To estimate the source(s) of genetic variation giving rise to parallel sweeps, we conducted phylogenetic analyses to explore the sequence similarity among samples with respect to parallel regions. To obtain reliable phylogenetic results, we focused our analyses on 12 merged parallel regions that contained more than 100 SNPs. For each parallel region, we chose 5 samples from each of the dune populations, 5 from the non-dune populations in the proximity of each dune system, respectively, 5 samples from H. petiolaris sp. petiolaris and two perennial samples as outgroup. The SNPs within each region were then concatenated and analyzed using IQ-TREE (Nguyen et al. 2015). The maximum likelihood tree was constructed with the best-fit model 110 automatically selected by ModelFinder (Kalyaanamoorthy et al. 2017). Branch support was estimated using ultrafast bootstrap implemented in IQ-TREE (Hoang et al. 2018) with 1,000 bootstrap replicates. 5.2.5 Functional analyses of parallel regions Because inversions are expected to combine the effects of many genes, to explore their potential function we conducted GO enrichment analyses of the genes in parallel regions found within inversions to see if they were enriched in any functional categories. For this analysis, we employed the gene annotation of Ha412HOv2.0 that was described in Chapter 4. GO annotation of the Ha412HOv2.0 genes was conducted using information from the blast2GO dataset of another reference genome of cultivated sunflower XRQr2.0 (https://www.heliagene.org/), which has 47,689 out of 71,289 XRQr2.0 protein-coding genes annotated. Protein sequences of Ha412HOv2.0 genes were queried against those of XRQr2.0 (https://www.heliagene.org/) using BLASTP (https://blast.ncbi.nlm.nih.gov/). Matches with E-value > 10-5 and sequence identity < 40% were excluded and the best hit with lowest E-value and highest sequence identity was kept for each gene. All GO terms of the corresponding XRQr2.0 locus were attributed to each HA412v2.0 gene. After the parallel regions were merged, genes within regions that resided in inversions were extracted as candidates, and GO enrichment analysis was conducted on the candidate genes in each inversion using R package ClusterProfiler (Yu et al. 2012). In each analysis, we examined the GO terms with no less than 10 genes and no more than 5,000 genes in the “biological process” category and conducted the enrichment analyses. The Benjamini-Hochberg method was applied for multiple testing adjustment. 111 In addition to GO enrichment analyses, we compared the candidate regions to results from previous genome-wide association studies and genome-environment association studies in sunflowers (Todesco et al. 2020) to look for overlapping regions that are associated with certain traits or environmental variables. 5.3 Results 5.3.1 SNP dataset and regions of selective sweeps Using all samples from H. petiolaris and the perennial species for variant calling, we obtained a total of 21,836,165 bi-allelic SNPs in the final dataset, about twice of the number of SNPs in the intraspecific dataset (Chapter 4). By comparing with the outgroup species, we obtained information on ancestral states for about 58% of all SNPs. Unpolarized sites that were monomorphic within the target group were discarded, leaving a total of 5,996,827 sites in the GSD system and 9,405,793 in the MON system for input into SweepFinder2. For the CLR statistic computed by SweepFinder2, we obtained a total of 3,909 and 8,960 outlier regions in the GSD system and MON system, with an average size of 59,373 bp and 36,465 bp, respectively. We also found 7,159 and 7,742 outlier regions of ROD, as well as 5,907 and 4,569 outlier regions of FST, in GSD system and MON system, respectively. The outlier windows were not distributed evenly throughout the genome. For FST, outlier regions were mostly found in several large genomic regions across the chromosomes (Figure 5.1), which is in agreement with previous studies of genome-wide differentiation within the two dune systems (Andrew and Rieseberg 2013; Huang et al. 2020; Todesco et al. 2020). We found a number of large clusters of outlier windows that were supported by all three statistics in the GSD system, a 112 portion of which correspond to previously identified inversions in the species (Figure 5.1). The pattern of clustering is weaker in the MON system, especially with CLR and ROD. After identifying overlaps between the CLR, ROD and FST outlier windows, we obtained a total of 652 and 851 selective sweep regions in GSD system and MON system, with an average size of 145,339 bp and 46,515 bp, respectively (Figure 5.1). 113 Figure 5.1 - Selective sweep regions in (a) GSD system and (b) MON system. Outlier regions for each statistic and overlapping selective sweep regions are indicated by bars of different colors in different rings. From outside to inside: selective sweep regions (black), outlier regions of CLR (blue), outlier regions of ROD (orange) and outlier regions of FST (coral). Also shown are the locations of large non-recombining haplotype blocks (mainly due to inversions) identified in Todesco et al. (2020), several of which were shown to contribute to dune adaptation. [binary].01pet11.01pet14.01pet17.01pet17.03pet05.01pet07.01pet09.[binary].01pet11.01pet14.01pet17.01pet17.03pet05.01pet07.01pet09.01(a) (b) 114 5.3.2 Regions of parallel evolution and enrichment analyses After merging of selective sweep regions with high LD and searching for the regions that are shared between two dune systems, we identified 134 regions showing parallel sweeps across these independently derived ecotypes. The size of these parallel regions varied from 525 bp to 566 kbp with an average about 36 kbp. After merging, we reduced the number of parallel regions to 56 and the average size of regions was increased to 1.03 Mbp. Among the merged parallel regions, 44 regions covering 38.7 Mbp were found to reside within known inversions (Figure 5.2), with several large regions covering substantial proportions of inversions pet11.01, pet17.01 and pet17.03, and a number of scattered regions found in inversion pet09.01. This is consistent with previous findings of high sequence divergence associated with inversions in the dune ecotypes (Todesco et al. 2020), except that inversion pet17.01 was not previously reported to contribute to parallel divergence in these populations. The remaining 12 merged parallel regions were found to be outside of inversions on eight chromosomes (Figure 5.2). The sizes of these regions varied greatly from 28 kb for a region on chromosome 13 to 10 Mbp for the region near the center of chromosome 12. In total, these regions covered about 19.2 Mbp across the genome. While 4 out of the 8 polymorphic inversions in these dune systems contain parallel regions in our analyses, the parallel regions found outside inversions cover about 1% of the genomic space not covered by inversions (19.2 Mbp out of 2.9 Gbp), suggesting overrepresentation of parallel regions in inversions. By comparing the number and length of parallel regions within inversions to the null distribution from permutations, we also found a significant enrichment of parallel regions in the known inversions of the species (p = 4×10-4 for number of regions, and p = 6×10-4 for total 115 length). The result was also significant before the parallel regions were merged (p < 10-16 for number of regions, and p = 0.0173 for total length). 116 Figure 5.2 - Parallel regions between the GSD and MON dune systems. Selective sweep regions in each system after merging, and overlapping regions (parallel regions) are indicated by bars of different colors in different rings. From outside to inside: parallel regions (black), selective sweep regions in GSD system (orange), and selective sweep regions MON system (purple). Note that the locations of parallel regions that are less than 1 Mbp are represented by thicker bars for better visualization. Also shown are the locations of large non-recombining haplotype blocks (mainly due to inversions) identified in Todesco et al. (2020) that are polymorphic within one or both dune systems. Colored rectangles inside the rings indicate the locations of GWA peaks for the phenotypic traits seed area (blue) and days to budding (red); summarized from Todesco et al. (2020). [binary].01pet11.01pet14.01pet17.01pet17.03pet05.01pet07.01pet09.01 117 5.3.3 Phylogeny of parallel regions We conducted phylogenetic analyses for 12 parallel regions that contain sufficient numbers of SNPs for reliable phylogenetic reconstruction. Across most of the regions, dune samples from the same site were closely related, consistent with the pattern of a hard sweep of a single ancestral haplotype in each system (Hermisson and Pennings 2005). For most regions that do not reside in inversions, the dune samples from GSD and MON formed independent clusters, with the dune samples from each site clustering with one or more samples from local non-dune populations. However, not all non-dune samples from a site cluster together, suggesting gene flow or incomplete lineage sorting among the populations across the non-dune habitat (Figure 5.3a). This pattern is consistent with the scenario that the alleles found in the dunes were independently drawn from the non-dune populations in each of the dune systems. In some regions outside of inversion, such as a region found on chromosome 7, the dune samples at MON did not group with any samples from the non-dune populations, but instead formed its own cluster that showed deep divergence with other samples from the same species (Figure 5.3b). This pattern suggests that the allele targeted by selection in the dunes at MON may come from introgression from another species, while the one at GSD arose from within H. petiolaris. For those regions that were found within inversions, including two regions in inversion pet11.01 and two regions from the two inversions on chromosome 17, the dune samples from different sites are monophyletic, forming a distinctive cluster that is divergent from samples from the non-dune populations (Figure 5.3c; Appendix C.2). This is consistent with previous discoveries that the dune haplotypes of these inversions likely originated via introgression with an unknown, and possibly extinct, donor species (Todesco et al. 2020). Samples of H. petiolaris 118 sp. petiolaris were often found to group with the dune haplotype of the inversions, suggesting the existence of this adapted dune haplotype in the other subspecies, which is also consistent with previous discovery in Todesco et al. (2020). Figure 5.3 - Maximum likelihood trees of regions (a) Ha412HOChr02: 82397894-82927898, (b) Ha412HOChr07: 68115435-68247437, and (c) Ha412HOChr11: 59695130-62373169 showing different sources of adapted alleles found in the dune ecotypes. Samples were chosen from each of the dune populations (GSD_dune and MON_dune), the non-dune populations in the proximity of each dune system (GSD_nondune, MON_nondune), H. petiolaris sp. petiolaris (petpet) and perennial outgroups (GRO_2043 and DIV_1956). The trees were constructed using the SNPs within each region and analyzed using IQ-TREE. Nodes with bootstrap values > 70 are marked by turquoise circles. Note that branch length is not to the same scale among different trees. Results from the other regions are presented in Appendix C.2. GSD1009GSD1276GSD1707GSD1039GSD1732PET0393PET0394PET0395PET0396PET0397MON014MON106MON520MON862MON069PET0718PET0722PET0725PET0726PET0720DIV_1956GRO_2043PET0505PET0512PET0523PET0554PET0539GroupaaaaaaGSD_duneGSD_nonduneMON_duneMON_nonduneoutgrouppetpetHa412HOChr02_82397894_82927898GSD1009GSD1276GSD1707GSD1039GSD1732PET0393PET0394PET0395PET0396PET0397MON014MON069MON520MON106MON862PET0718PET0725PET0726PET0720PET0722DIV_1956GRO_2043PET0505PET0512PET0523PET0539PET0554GroupaaaaaaGSD_duneGSD_nonduneMON_duneMON_nonduneoutgrouppetpetHa412HOChr07_68115435_68247437GSD1009GSD1039GSD1276GSD1707GSD1732PET0393PET0397PET0394PET0395PET0396MON014MON069MON106MON862MON520PET0726PET0718PET0725PET0720PET0722DIV_1956GRO_2043PET0505PET0512PET0554PET0539PET0523GroupaaaaaaGSD_duneGSD_nonduneMON_duneMON_nonduneoutgrouppetpetHa412HOChr11_59695130_62373169(a) (b) (c)GSD1009GSD1039GSD1732GSD1276GSD1707PET0393PET0394PET0397PET0395PET0396MON014MON862MON069MON106MON520PET0718PET0720PET0725PET0722PET0726DIV_1956GRO_2043PET0505PET0512PET0554PET0539PET0523100100100100100100100100100100100975458100100100100100100100100100100GroupaaaaaaGSD_duneGSD_nonduneMON_duneMON_nonduneoutgrouppetpetHa412HOChr11_16016058_62368169 119 5.3.4 Functional analyses of parallel regions In the blastp search with the gene sequences of two reference genomes, 53,218 out of 53,302 protein-coding genes of Ha412HOv2.0 had best hits that passed the filtering thresholds among XRQr2.0 genes, 39,172 of which were annotated with GO terms from the blast2GO dataset of XRQr2.0. Using this GO annotation of the genes, we found numerous GO terms that were significantly enriched (p < 0.05 after multiple testing adjustment) in the regions that overlap with the inversion pet11.01 (Table 5.1). These GO terms are mostly associated with the biological process of pollination, including pollen recognition (e.g. GO:0048544 and GO:0008037) and the molecular reactions involved in the interaction between pistil and pollen (e.g. GO:0009875, GO:0006468 and GO:0007154; Table 5.2). This is of interest because of post-pollination assortative mating previously documented in the GSD system (Ostevik et al. 2016) In other regions that we examined, we found the GO term “ribonucleoprotein complex assembly” (GO:0022618) enriched in inversion pet17.03, but the connection between these genes and phenotype is unclear. In the comparison of the GWA studies and our population genomic results, the shared selective sweep regions co-localize with previously identified peaks in inversions pet09.01 and pet11.01 that showed strong association with seed size and flowering time (Figure 5.2). Two regions outside inversions were coincident with GWA peaks on chromosomes 6 and 13 that were also significantly associated with flowering time (Figure 5.2). 120 Table 5.1 - GO enrichment of genes in parallel regions within inversion pet11.01. P-values were adjusted using Benjamini-Hochberg method (p.adjust). ID Description p.adjust GO:0048544 recognition of pollen 7.64×10-30 GO:0008037 cell recognition 7.64×10-30 GO:0009875 pollen-pistil interaction 8.99×10-30 GO:0009856 pollination 3.28×10-26 GO:0044706 multi-multicellular organism process 3.28×10-26 GO:0022414 reproductive process 1.00×10-15 GO:0000003 reproduction 1.00×10-15 GO:0051704 multi-organism process 4.05×10-15 GO:0032501 multicellular organismal process 6.63×10-10 GO:0006468 protein phosphorylation 1.24×10-5 GO:0007154 cell communication 4.67×10-4 GO:0010411 xyloglucan metabolic process 8.62×10-4 GO:0016310 phosphorylation 9.29×10-4 GO:0046777 protein autophosphorylation 0.001405917 GO:0051026 chiasma assembly 0.012028868 GO:0006308 DNA catabolic process 0.024312147 GO:0010410 hemicellulose metabolic process 0.026251616 GO:0042138 meiotic DNA double-strand break formation 0.026807362 GO:0006766 vitamin metabolic process 0.04433873 GO:0007131 reciprocal meiotic recombination 0.045302912 GO:0035825 homologous recombination 0.045302912 GO:0000466 maturation of 5.8S rRNA from tricistronic rRNA transcript (SSU-rRNA, 5.8S rRNA, LSU-rRNA) 0.045302912 121 5.4 Discussion In this study, we evaluated the impact of genome architecture on the patterns of parallel genetic evolution between two cases of dune adaptation in prairie sunflower. We detected numerous genomic regions exhibiting the signature of selective sweeps in each system and identified many that were shared between the two dune ecotypes. The distribution and length of these regions varied across the genome, but a majority of the parallel regions were found to reside within chromosomal inversions, as predicted based on theoretical considerations. Our results also provide further support for an important role of inversions in adaption to heterogeneous environments. Although four inversions are common to dune populations at both sites, patterns of parallel evolution are not consistent among them. The inversions on chromosome 11 (pet11.01) and chromosome 9 (pet09.01) were both found to be frequent in dune populations at both sites (Todesco et al. 2020). However, while extensive parallellism was found within pet11.01, our approach only detected weak signals within inversion pet09.01, especially with CLR and ROD (Figure 5.1). This is likely because there is no strong difference in diversity between orientations for pet09.01. Possibly, adaptive mutation(s) within this inversion were associated with substantial genetic polymorphism before they were selected in the dune environment. This reflects the limitation of our approaches in detecting soft sweeps. In a soft sweep, linked neutral variation may be retained even if close to the locus of selection (Hermisson and Pennings 2005), thus leaving little signature of ROD in the target population. Tests based on the site-frequency spectrum have also been demonstrated to have low power for detecting soft sweeps (Pennings and Hermisson 2006). Nevertheless, our approach was still able to detect a substantial number of swept regions across the genome, especially for inversions pet11.01, pet17.03, and for inversion 122 pet17.01. It is noteworthy that the latter inversion was not found to exhibit parallel differentiation across these independently derived populations in previous studies, probably due to insufficient dune samples included in those studies. Frequent reuse of inversions during the repeated colonization of dune habitats suggests strong biases and constraints in adaptive evolution. This is consistent with models that examine the role of inversions in local adaptation based on their effects on recombination. In these models, inversions facilitate local adaptation by capturing multiple locally adapted alleles and preventing recombination with alleles from non-adapted populations. By maintaining such genotypic combinations at loci affecting adaptation, inversions limit the genetic options upon which selection can act as organisms adapt to a given environment, thereby increasing the potential for parallel genetic evolution. Previous studies that have examined the genetic architecture of ecologically relevant traits in H. petiolaris support this argument. Mapping of QTLs in biparental populations from the GSD and MON systems found a limited number of major QTLs for seed size and the QTL positions broadly overlap with inversions pet09.01 and pet14.01 in both systems (Ostevik 2016). A subsequent GWA study also found that the vast majority of SNPs exhibiting strong associations with seed size map to inversions pet09.01, pet11.01 and pet14.01 (Todesco et al. 2020; Figure 5.2). Likewise, SNPs displaying strong associations with flowering time are mainly confined to a limited number of genomic regions, most of which co-localize with the same trio of inversions (pet09.01, pet11.01 and pet14.01; Todesco et al. 2020; Figure 5.2). Moreover, we found that the parallel regions within inversion pet11.01 are enriched for genes related to pollen recognition and pollen-pistil interaction (Table 5.1). The processes of pollen recognition and pollen-pistil interaction play an important part in conspecific pollen precedence (Howard 1999), 123 and conspecific pollen precedence is a common form of reproductive isolation between species of Helianthus, as well as between the dune and non-dune ecotypes in GSD (Heiser et al. 1969; Rieseberg et al. 1995b; Ostevik et al. 2016). Whether conspecific pollen precedence exists in the MON system is unknown, but we suspect it might given that pollen recognition related genes in pet11.01 appear to be under parallel selection in both dune ecotypes. Another factor that may increase the contribution of inversions to parallel genetic evolution is that the combined phenotypic effect of alleles in an inversion is likely to be large and to exceed those of individual alleles (Kirkpatrick and Baron 2006). Such an architecture is expected to favor parallel genetic evolution (MacPherson and Nuismer 2017) because large effect alleles are more likely to be established by natural selection (Orr 1998) and to be maintained under gene flow selection balance (Yeaman and Otto 2011). A general caveat for interpreting our results is that because of the block inheritance of inversions, parallel adaptation on any gene within an inversion could leave an inversion-wide signal. This could increase the likelihood of detecting the signature of parallelism, as well as the number and length of parallel regions. While we doubt that such a detection bias could account for the many levels of genetic parallelism seen in dune sunflowers, we cannot rule the possibility that it contributes to the large inversion signal reported here. There are also several large clusters of swept regions outside of the inversions seen in each system, such as those found near the middle of chromosomes 2 and 10 in the GSD system and those in the middle of chromosome 12 and at distal end of chromosome 16 in the dune system at MON (Figure 5.1). This clustering could be viewed as evidence of “divergence hitchhiking”, in which new locally adapted mutations establish preferentially in physical linkage to already diverged genes, thereby generating genomic islands of differentiation, similar to that 124 in chromosomal inversions (Feder et al. 2012; Via 2012; Yeaman 2012). However, there may be more prosaic reasons for the clusters of swept regions in these dune systems. The mid-chromosome swept regions in GSD are in areas of low recombination, which likely explains their large size. The clusters of swept regions on chromosomes 12 and 16 are coincident with a translocation between the two chromosomes, which appears to be polymorphic at MON (Ostevik et al. 2020). Significantly, unlike inversions, the low recombination regions on chromosomes 2 and 12 (and elsewhere in the genome), did not show a strong signature of parallelism. Likewise, despite the large number of chromosome translocations differentiating sunflower taxa (Ostevik et al. 2020), translocations do not appear to have contributed to parallel genetic evolution in the dune systems analyzed here. Thus, inversions clearly stand out as one of the most impactful genetic architectures facilitating parallel genetic evolution. Several inversions had few or no parallel regions identified because their frequency increases significantly in the dunes in one of the systems but only marginally in the other. For example, inversion pet05.01 is enriched in the GSD system but it does not contribute to dune adaptation in the MON system, and inversion pet14.01 facilitates ecological divergence in MON system but is at very low frequency in GSD (Huang et al. 2020; Todesco et al. 2020). What is even more puzzling is that seed size and flowering time, which are key traits in dune adaptation, map to these inversions (Ostevik 2016; Todesco et al. 2020). Thus, the lack of parallelism for these inversions cannot be attributed a lack of key traits or small phenotypic effects. On the other hand, the dunes at GSD and MON differ greatly in size, temperature and biotic communities, which may generate differences in selective pressures on these inversions between the two sites. Also, it might be that the inversion haplotypes found in GSD and MON are no longer identical, leading to different responses to selection. 125 In addition to the contribution of inversions to parallel genetic evolution, there are some shared selective sweep regions found outside inversions (Figure 5.2). For example, a parallel region was found between 100Mbp and 110 Mbp on chromosome 13, which is adjacent to a small cluster of SNPs that show significant associations with flowering time in previous GWA studies (Figure 5.2). This indicates that factors other than genome architecture likely affect to the probability of parallel genetic evolution. For example, traits such as flowering time that play a dual role in local adaptation and assortative mating may be particularly likely to contribute to parallel divergence with gene flow. In our phylogenetic analyses of the parallel regions, we found different modes by which the selected alleles arise. In the case of inversions, the same haplotype was targeted by selection at the two sites, indicating parallel evolution of the inversions from standing genetic variation. Selected alleles at the same loci in different populations can have multiple independent mutational origins (Tishkoff et al. 2007; Pearce et al. 2009; Chan et al. 2010) or could arise by selection on the standing variation present in the ancestral populations (Colosimo et al. 2005; Roesti et al. 2014). In contrast to independent de novo mutation, adaptation from standing genetic variation is likely to be rapid (Barrett and Schluter 2008). The retention of inversion polymorphisms within H. petiolaris might have provided a directly available repertoire of adapted alleles for the dune environment and therefore promoted the reuse of these inversions in each system. Because these inversions may represent ancient introgressions (Todesco et al. 2020), these alleles might have been pre-tested by selection in past environments, further increasing the probability of parallel evolution (Schluter et al. 2004). However, for regions outside inversions, very divergent alleles were frequently enriched in the two systems, some of which might have origins from different species. This surprising result implies that parallel 126 adaptation at the genetic level between these dunes may have arisen via multiple, distinct modes, but use of model-based statistical approaches (e.g. Lee and Coop 2017) may be needed to confidently distinguish between them. Also, inclusion of other Helianthus species in the phylogeny could offer further details on the likely origin(s) of the introgressed alleles. Our study describes the distribution of shared selective sweeps between the GSD and MON systems, which represents a first step towards understanding the evolutionary and ecological factors that contribute to parallel genetic adaptation. Future studies should estimate strength of selection on those regions in the two systems using a combination of modeling (Messer and Neher 2012; MacPherson and Nuismer 2017) and empirical approaches (e.g. Goebl et al. 2020). Also helpful would be studies that dissect the genetic architectures of other ecologically relevant traits (Colosimo et al. 2004; Conte et al. 2015), including identification of the causal genes and mutations within the selected regions. Such studies will illuminate both the cause(s) of parallel evolution of adaptive traits and the particular evolutionary and demographic histories of the genetic variation underlying parallel adaptation (Colosimo et al. 2005). Lastly, dune adaptation has occurred many times in Helianthus, involving half a dozen different species or subspecies. Extending the population genomic approach taken in the present study to these other systems will provide greater statistical power for assessing the contribution of inversions to parallel local adaptation, likely leading to further insights into the factors that facilitate or constrain parallel genetic evolution. 127 Chapter 6: Conclusion 6.1 Discussion Each of the preceding data chapters has explored the genomic basis of adaptation and speciation from different angles. Together these chapters tell us something more broadly about the role of chromosomal inversions in evolution and how to study them. Inversions are prevalent across the plant kingdom and have the potential to facilitate evolutionary adaptation by suppressing recombination between favorable combinations of alleles, but until recently it has been laborious and time-consuming to identify and genotype inversions in natural populations. In my first data chapter (Chapter 3), I applied a newly developed population genomic approach on RAD sequence data, and identified several inversions that differentiate a dune ecotype of Helianthus petiolaris from its non-dune congeners. I further showed that these inversions account for the genomic islands of high divergence between the ecotypes, that they are enriched in dunes, and that they are associated with vegetation cover and several soil characteristics, and, therefore, likely contribute to local adaptation in the dune ecotype. This study not only provides additional evidence that inversions play a key role in adaptive divergence and incipient speciation in plants, but also demonstrates a novel and economical approach of identifying inversions in wild populations using reduced representation population genomic data. With growing appreciation of the importance of inversions in adaptation and speciation, as well as ever-increasing population sequencing data and high-quality reference genomes available for non-model systems, this approach has great potential for wide applications and will further our understanding of the prevalence and evolutionary role of inversions in natural populations. In addition, although many models and empirical studies have focused on the advantages of inversions in suppressing 128 recombination, few studies have explored the potential cost of it. Following these discoveries about inversions in sunflowers, in my second chapter (Chapter 4), I examined the potential negative consequences of inversions resulting from their impact on recombination rates. In particular, I asked whether sunflower inversions have accumulated genetic load as expected given the suppression of recombination by inversions, as well as previous reports of such an effect in Drosophila. Surprisingly, I found no significant increase in TE abundance or in deleterious mutations in coding sequences within a number of inversions across different sunflower species, suggesting that inversions largely avert the accumulation of deleterious mutations. I argue that this is because recombination is only suppressed in inversion heterozygotes, while occurring freely in inversion homozygotes. Lastly, in my third data chapter (Chapter 5), I tested the prediction that inversions disproportionately contribute to parallel genetic responses to similar environments due to a combination of their large effect sizes and the constraints inversions place on adaptation. This prediction was confirmed by my examination of shared selective sweeps in two dune ecotypes in H. petiolaris, which showed that shared swept regions are significantly over-represented within inversions. This suggests that the clustering of locally adapted alleles of inversions limits the diversity of viable adaptive genetic responses in populations experiencing similar selection pressures. These results, along with a number of other recent findings, illustrate the important role of chromosomal inversions in ecological divergence of wild sunflowers. When sunflower species inhabit heterogeneous ecological environments, inversions facilitate adaptive divergence between populations in the face of gene flow by capturing multiple favorable alleles that contribute to different aspects of adaptation in the local environment and suppressing the recombination between them. While reducing such recombination between ecologically 129 divergent populations, these inversions can simultaneously permit high levels of recombination within populations to avert the accumulation of deleterious mutations in the inverted regions. Furthermore, because inversions constrain adaptation, they often are repeatedly used when sunflower species encounter similar selective pressure. 6.2 Caveats There are several limitations to these studies. First, in Chapter 3, while the population genomic approach employed in this chapter can provide initial clues regarding the existence of chromosomal inversions, additional independent evidence using other methods is needed for further confirmation of these inversions. Also, this approach has limitations for identifying precise locations of inversion breakpoints and is biased towards detecting towards large inversions with high sequence divergence. The inferences in Chapter 4 about TE densities are limited to two inversions that differentiate cultivated sunflower reference genomes, which limits the strength of my inferences. Reference sequences are currently under development for several wild sunflower species, which should permit analyses of TE densities for numerous additional inversions, thereby increasing the robustness of my conclusions. Lastly, for Chapter 5, the approaches I used to study parallel genetic adaptation are designed to identify hard selective sweeps. More sophisticated methods that can also detect soft sweeps would permit a more comprehensive characterization of the loci contributing to local adaptation in both systems. Finally, the statistical power for testing for parallel genetic evolution is relatively low because only two dune systems were analyzed. Including additional dune systems in the analyses would strengthen my claim that inversions preferentially contribute to parallel local adaptation. 130 6.3 Future directions Although inversions have been suggested to play a key role in adaptive divergence and speciation in the presence of gene flow, and a number of studies have associated inversions with multiple ecological relevant traits as well as key environmental variables (e.g., Chapter 3), the genes and mutations underlying key traits associated with the inversions are difficult to identify because of strong linkage disequilibrium within the inverted region among samples from different populations. Genetic mapping using collinear genomes that might harbor different alleles for the adaptive traits would help pinpoint the loci within inversions that are responsible for adaptive divergence or speciation (Lee et al. 2017; Coughlan and Willis 2019). Model-based population genetic approaches also exist for identifying the genetic targets of selection within inversions (Kirkpatrick and Kern 2012; Guerrero et al. 2012; Rousset et al. 2014) and molecular genetic studies of candidate genes within the inversion could also be employed. Future studies using these approaches could provide further evidence that inversions carry multiple alleles that are adapted to the same local environment and deepen our understanding of the establishment of inversions. Likewise, identifying the causal genes and mutations within the shared selected regions between the dune ecotypes in Colorado and Texas will allow us to integrate functions, phenotypes, genetic architectures and ecological contexts to better illustrate the process of adaptation in natural populations. In simple models that explore the role of inversions in speciation, inversions permit speciation with gene flow by reducing recombination between loci contributing to local adaptation and those causing assortative mating (Trickett and Butlin 1994). Conspecific pollen precedence has been identified as a key assortative mating barrier between the dune and non-dune ecotypes in Colorado (Ostevik et al. 2016), and the study in Chapter 5 has shown that an 131 inversion that contributes to dune adaptation is enriched in genes involved in pollen recognition and pistil-pollen interactions. Thus, another possible extension of this work is to map the loci that underlie various aspects of conspecific pollen precedence to see if those inversions contributing to ecological divergence also contain genes for postpollination assortative mating. Testing whether this isolation barrier also isolates dune and non-dune ecotypes in Texas, and mapping the underlying loci (if the barrier exists), would provide a more comprehensive understanding of parallel ecological divergence and (possibly) parallel speciation between the two systems. Coupled with information on the genes and mutations underlying key ecological and assortative mating traits, one could also determine whether this assortative mating barrier arose due to pleiotropic effects of the locally adaptive alleles, or due to reinforcement facilitated by inversions (Servedio 2000; Servedio 2009). Dune adaptation is common among annual Helianthus species. Exploring the genomic architecture of adaptation in other dune sunflowers such as H. niveus ssp. tephrodes, Helianthus niveus ssp. niveus, H. debilis ssp. debilis, and H. anomalus would make it possible to determine whether the inversions identified in the dune ecotypes of H. petiolaris contribute to dune adaptation in other taxa in the same genus. Such studies would also increase our ability to detect general patterns in the process of ecological divergence and offer greater statistical power to explore factors that determine the repeated recruitment of loci in parallel genetic evolution. Finally, high rates of chromosomal evolution in Helianthus provide an excellent opportunity for studying the role of genome structure in adaptation and speciation. Beyond the inversions studied in this thesis, other chromosomal structural variants, such as translocations, insertions and deletions may also play an important role in the evolution of sunflower species. Translocation heterozygotes involving different chromosomes usually produce unbalanced and 132 inviable gametes (King 1993), making them more difficult to establish than inversions. However, inter-chromosomal translocations are commonly found to differentiate species and subspecies in sunflower (Rieseberg et al. 1995a; Burke et al. 2004; Barb et al. 2014; Ostevik et al. 2020). Therefore, it would be interesting to compare and contrast the roles of translocations and inversions in ecotypic divergence and reproductive isolation. Moreover, there is evidence suggesting that hemizygosity affects recombination in the same way as inversions and contributes to ecotype formation in sunflower (Todesco et al. 2020). Further exploration of the prevalence of hemizygosity in sunflowers, as well as of the distribution of TEs and deleterious mutations in such regions, would broaden our understanding of the evolutionary role(s) of these structural variants. Taken together, these proposed future studies will lead to a more comprehensive understanding of how genome architecture impacts plant adaptation and speciation. 133 Bibliography Ahmad, Q. N., E. J. Britten, and D. E. Byth. 1979. Inversion heterozygosity in the hybrid soybean × Glycine soja: evidence from a pachytene loop configuration and other meiotic irregularities. Journal of Heredity, 70(6), 358-364. Ahn, S., and S. D. Tanksley. 1993. Comparative linkage maps of the rice and maize genomes. Proceedings of the National Academy of Sciences, 90(17), 7980-7984. Anderson, L. K., P. A. Covey, L. R. Larsen, P. Bedinger, and S. M. Stack. 2010. Structural differences in chromosomes distinguish species in the tomato clade. Cytogenetic and Genome Research, 129(1-3), 24-34. Andrew, R. L., and L. H. Rieseberg. 2013. Divergence is focused on few genomic regions early in speciation: incipient speciation of sunflower ecotypes. Evolution, 67(9), 2468-2482. Andrew, R. L., K. L. Ostevik, D. P. Ebert, and L. H. Rieseberg. 2012. Adaptation with gene flow across the landscape in a dune sunflower. Molecular Ecology, 21(9), 2078-2091. Andrew, R. L., N. C. Kane, G. J. Baute, C. J. Grassa, and L. H. Rieseberg, 2013. Recent nonhybrid origin of sunflower ecotypes in a novel habitat. Molecular Ecology, 22(3), 799-813. Arendt, J., and D. Reznick. 2008. Convergence and parallelism reconsidered: what have we learned about the genetics of adaptation?. Trends in Ecology and Evolution, 23(1), 26-32. Arostegui, M. C., T. P. Quinn, L. W. Seeb, J. E. Seeb, and G. J. McKinney. 2019. Retention of a chromosomal inversion from an anadromous ancestor provides the genetic basis for alternative freshwater ecotypes in rainbow trout. Molecular Ecology, 28(6), 1412-1427. Ayala, F. J., and M. Coluzzi. 2005. Chromosome speciation: humans, Drosophila, and mosquitoes. Proceedings of the National Academy of Sciences, 102(suppl 1), 6535-6542. Ayala, D., M. C. Fontaine, A. Cohuet, D. Fontenille, R. Vitalis, and F. Simard. 2010. Chromosomal inversions, natural selection and adaptation in the malaria vector Anopheles funestus. Molecular Biology and Evolution, 28(1), 745-758. Bachtrog, D. 2003. Adaptation shapes patterns of genome evolution on sexual and asexual chromosomes in Drosophila. Nature Genetics, 34(2), 215-219. Badouin, H., J. Gouzy, C. J. Grassa, F. Murat, S. E. Staton, L. Cottret, C. Lelandais-Briere, G. L. Owens, S. Carrere, B. Mayjonade, L. Legrand, N. Gill, N. C. Kane, J. E. Bowers, S. Hubner, A. Bellec, A. Berard, H. Bergès, N. Blanchet, M. Boniface, D. Brunel, O. Catrice, N. Chaidir, C. Claudel, C. Donnadieu, T. Faraut, G. Fievet, N. Helmstetter, M. King, S. J. Knapp, Z. Lai, M L. Paslier, Y. Lippi, L. Lorenzon, J. R. Mandel, G. Marage, G. Marchand, E. Marquand, E. Bret-Mestries, E. Morien, S. Nambeesan, T. Nguyen, P. Pegot-Espagnet, N. Pouilly, F. Raftis, E. 134 Sallet, T. Schiex, J. Thomas, C. Vandecasteele, D. Vares, F. Vear, S. Vautrin, M. Crespi, B. Mangin, J. M. Burke, J. Salse, S. Munos, P. Vincourt, L. H. Rieseberg, and N. B. Langlade. 2017. The sunflower genome provides insights into oil metabolism, flowering and Asterid evolution. Nature, 546(7656), 148-152. Bailey, S. F., N. Rodrigue, and R. Kassen. 2015. The effect of selection environment on the probability of parallel evolution. Molecular Biology and Evolution, 32(6), 1436-1448. Bailey, S. F., F. Blanquart, T. Bataillon, and R. Kassen. 2017. What drives parallel evolution? How population size and mutational variation contribute to repeated evolution. BioEssays, 39(1), 1-9. Baird, N. A., P. D. Etter, T. S. Atwood, M. C. Currey, A. L. Shiver, Z. A. Lewis, E. U. Selker, W. A. Cresko, and E. A. Johnson, 2008. Rapid SNP discovery and genetic mapping using sequenced RAD markers. PloS One, 3(10), e3376. Balanya, J., L. Serra, G. W. Gilchrist, R. B. Huey, M. Pascual, F. Mestres, and E. Sole. 2003. Evolutionary pace of chromosomal polymorphism in colonizing populations of Drosophila subobscura: an evolutionary time series. Evolution, 57(8), 1837-1845. Barb, J. G., J. E. Bowers, S. Renaut, J. I. Rey, S. J. Knapp, L. H. Rieseberg, and J. M. Burke. 2014. Chromosomal evolution and patterns of introgression in Helianthus. Genetics, 197(3), 969-979. Barrett, R. D., and D. Schluter. 2008. Adaptation from standing genetic variation. Trends in Ecology and Evolution, 23(1), 38-44. Bartolome, C., X. Maside, and B. Charlesworth. 2002. On the abundance and distribution of transposable elements in the genome of Drosophila melanogaster. Molecular Biology and Evolution, 19(6), 926-937. Barton, N. H., and B. Charlesworth. 1998. Why sex and recombination?. Science, 281(5385), 1986-1990. Baute, G. J., G. L. Owens, D. G. Bock, and L. H. Rieseberg. 2016. Genome-wide GBS data provide a high-resolution view of wild Helianthus diversity, genetic structure and interspecies gene flow. American Journal of Botany, 103(12), 2170-2177. Berg, P. R., B. Star, C. Pampoulie, I. R. Bradbury, P. Bentzen, J. A. Hutchings, and K. S. S. Jakobsen. 2017. Trans-oceanic genomic divergence of Atlantic cod ecotypes is associated with large inversions. Heredity, 119(6), 418. Bergero, R., A. Forrest, E. Kamau, and D. Charlesworth. 2007. Evolutionary strata on the X chromosomes of the dioecious plant Silene latifolia: evidence from new sex-linked genes. Genetics, 175(4), 1945-1954. 135 Berhan, A. M., S. H. Hulbert, L. G. Butler, and J. L. Bennetzen. 1993. Structure and evolution of the genomes of Sorghum bicolor and Zea mays. Theoretical and Applied Genetics, 86(5), 598-604. Blanquart, F., O. Kaltz, S. L. Nuismer, and S. Gandon. 2013. A practical guide to measuring local adaptation. Ecology Letters, 16(9), 1195-1205. Bock, D. G., N. C. Kane, D. P. Ebert, and L. H. Rieseberg. 2014. Genome skimming reveals the origin of the Jerusalem Artichoke tuber crop species: neither from Jerusalem nor an artichoke. New Phytologist, 201(3), 1021-1030. Bolger, A. M., M. Lohse, and B. Usadel. 2014. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics, 30(15), 2114-2120. Bonierbale, M. W., R. L. Plaisted, and S. D. Tanksley. 1988. RFLP maps based on a common set of clones reveal modes of chromosomal evolution in potato and tomato. Genetics, 120(4), 1095-1103. Broman, K. W., H. Wu, S. Sen, and G. A. Churchill. 2003. R/qtl: QTL mapping in experimental crosses. Bioinformatics, 19(7), 889-890. Burger, R., and A. Akerman. 2011. The effects of linkage and gene flow on local adaptation: A two-locus continent-island model. Theoretical Population Biology, 80(4), 272-288. Burke, J. M., Z. Lai, M. Salmaso, T. Nakazato, S. Tang, A. Heesacker, S. J. Knapp, and L. H. Rieseberg. 2004. Comparative mapping and rapid karyotypic evolution in the genus Helianthus. Genetics, 167(1), 449-457. Butlin, R. K. 2005. Recombination and speciation. Molecular Ecology, 14(9), 2621-2635. Carvalho, A. B. 2003. The advantages of recombination. Nature Genetics, 34(2), 128-129. Cavallini, A., L. Natali, A. Zuccolo, T. Giordani, I. Jurman, V. Ferrillo, N. Vitacolonna, V. Sarri, F. Cattonaro, M. Ceccarelli, and P. G. Cionini. 2010. Analysis of transposons and repeat composition of the sunflower (Helianthus annuus L.) genome. Theoretical and Applied Genetics, 120(3), 491-508. Chan, Y. F., M. E. Marks, F. C. Jones, G. Villarreal, M. D. Shapiro, S. D. Brady, A. M. Southwick, D. M. Absher, J. Grimwood, J. Schmutz, R. M. Myers, D. Petrov, B. Jonsson, D. Schluter, M. A. Bell, and D. M. Kingsley. 2010. Adaptive evolution of pelvic reduction in sticklebacks by recurrent deletion of a Pitx1 enhancer. Science, 327(5963), 302-305. Chandler, J. M., C. C. Jan, and B. H. Beard. 1986. Chromosomal differentiation among the annual Helianthus species. Systematic Botany, 354-371. Chang, C. C., C. C. Chow, L. C. Tellier, S. Vattikuti, S. M. Purcell, and J. J. Lee. 2015. Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience, 4(1), 7. 136 Charlesworth, B., and N. H. Barton. 2018. The spread of an inversion with migration and selection. Genetics, 208(1), 377-382. Charlesworth, B., A. Lapid, and D. Canada. 1992. The distribution of transposable elements within and between chromosomes in a population of Drosophila melanogaster. II. Inferences on the nature of selection against elements. Genetics Research, 60(2), 115-130. Charlesworth, D. 2012. Plant sex chromosome evolution. Journal of Experimental Botany, 64(2), 405-420. Charlesworth, D., and B. Charlesworth. 1979. Selection on recombination in clines. Genetics, 91 (3), 581-589. Chen, J., Q. Huang, D. Gao, J. Wang, Y. Lang, T. Liu, B. Li, Z. Bai, J. L. Goicoechea, C. Liang, and C. Chen. 2013. Whole-genome sequencing of Oryza brachyantha reveals mechanisms underlying Oryza genome evolution. Nature Communications, 4(1), 1595. Chen, Z., F. Ai, J. Zhang, X. Ma, W. Yang, W. Wang, Y. Su, M. Wang, Y. Yang, K. Mao, Q. Wang, M. Lascoux, J. Liu, and T. Ma. 2020. Survival in the Tropics despite isolation, inbreeding and asexual reproduction: insights from the genome of the world’s southernmost poplar (Populus ilicifolia). The Plant Journal. 103(1), 430-442. Cheng, F., R. Sun, X. Hou, H. Zheng, F. Zhang, Y. Zhang, B. Liu, J. Liang, M. Zhuang, and D. Y. Liu. 2016. Subgenome parallel selection is associated with morphotype diversification and convergent crop domestication in Brassica rapa and Brassica oleracea. Nature Genetics, 48(10), 1218-1224. Chevin, L. M., G. Martin, and T. Lenormand. 2010. Fisher's model and the genomics of adaptation: restricted pleiotropy, heterogenous mutation, and parallel evolution. Evolution, 64(11), 3213-3231. Chu, D., and L. Wei. 2019. Nonsynonymous, synonymous and nonsense mutations in human cancer-related genes undergo stronger purifying selections than expectation. BMC Cancer, 19(1), 1-12. Cingolani, P., A. Platts, L. L. Wang, M. Coon, T. Nguyen, L. Wang, S. J. Land, X. Lu, and D.M. Ruden. 2012. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly, 6(2), 80-92. Colosimo, P. F., C. L. Peichel, K. Nereng, B. K. Blackman, M. D. Shapiro, D. Schluter, and D. M. Kingsley. 2004. The genetic architecture of parallel armor plate reduction in threespine sticklebacks. PLoS Biology, 2(5), e109. Colosimo, P. F., K. E. Hosemann, S. Balabhadra, G. Villarreal, M. Dickson, J. Grimwood, J. Schmutz, R. M. Myers, D. Schluter, and D. M. Kingsley. 2005. Widespread parallel evolution in sticklebacks by repeated fixation of ectodysplasin alleles. Science, 307(5717), 1928-1933. 137 Coluzzi, M., A. Sabatini, A. Della Torre, M. A. Di Deco, and V. Petrarca. 2002. A polytene chromosome analysis of the Anopheles gambiae species complex. Science, 298(5597), 1415-1418. Conte, G. L., M. E. Arnegard, J. Best, Y. F. Chan, F. C. Jones, D. M. Kingsley, D. Schluter, and C. L. Peichel. 2015. Extent of QTL reuse during repeated phenotypic divergence of sympatric threespine stickleback. Genetics, 201(3), 1189-1200. Coughlan, J. M., and J. H. Willis. 2019. Dissecting the role of a large chromosomal inversion in life history divergence throughout the Mimulus guttatus species complex. Molecular Ecology, 28(6), 1343-1357. Coyne, J. A. 1989. A test of the role of meiotic drive in chromosome evolution. Genetics, 123, 241-243. Coyne, J. A., and H. A. Orr. 2004. Speciation. Sinauer Associates, Inc. Danecek, P., A. Auton, G. Abecasis, C. A. Albers, E. Banks, M. A. DePristo, R. E. Handsaker, G. Lunter, G. T. Marth, S. T. Sherry, G. McVean, R. Durbin, and 1000 Genomes Project Analysis Group. 2011. The variant call format and VCFtools. Bioinformatics, 27(15), 2156-2158. DeGiorgio, M., C. D. Huber, M. J. Hubisz, I. Hellmann, and R. Nielsen. 2016. SweepFinder2: increased sensitivity, robustness and flexibility. Bioinformatics, 32(12), 1895-1897. Delprat, A., B. Negre, M. Puig, and A. Ruiz. 2009. The transposon Galileo generates natural chromosomal inversions in Drosophila by ectopic recombination. PLoS One, 4(11), e7883. DePristo, M. A., E. Banks, R. Poplin, K. V. Garimella, J. R. Maguire, C. Hartl, A. A. Philippakis, G. Del Angel, M. A. Rivas, M. Hanna, and A. McKenna. 2011. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nature Genetics, 43(5), 491. Devos, K. M., S. Chao, Q. Y. Li, M. C. Simonetti, and M. D. Gale. 1994. Relationship between chromosome 9 of maize and wheat homeologous group 7 chromosomes. Genetics, 138(4), 1287-1292. Dixon, J. R., J. Xu, V. Dileep, Y. Zhan, F. Song, V. T. Le, G. G. Yardımcı, A. Chakraborty, D. V. Bann, Y. Wang, and R. Clark. 2018. Integrative detection and analysis of structural variation in cancer genomes. Nature Genetics, 50(10), 1388. Dobzhansky, T. G. 1970. Genetics of the Evolutionary Process. Columbia University Press. Dobzhansky, T., and A. H. Sturtevant. 1938. Inversions in the chromosomes of Drosophila pseudoobscura. Genetics, 23(1), 28. 138 Donovan, L. A., D. R. Rosenthal, M. Sanchez-Velenosi, L. H. Rieseberg, and F. Ludwig. 2010. Are hybrid species more fit than ancestral parent species in the current hybrid species habitats?. Journal of Evolutionary Biology, 23(4), 805-816. Drost, D. R., E. Novaes, C. Boaventura-Novaes, C. I. Benedict, R. S. Brown, T. Yin, G. A. Tuskan, and M. Kirst. 2009. A microarray-based genotyping and genetic mapping approach for highly heterozygous outcrossing species enables localization of a large fraction of the unassembled Populus trichocarpa genome sequence. The Plant Journal, 58(6), 1054-1067. Drummond, E. B. M. 2018. The role of adaptive evolution in the success of an agricultural weed, Helianthus annuus (Doctoral dissertation). University of British Columbia. Dvorak, J., L. Wang, T. Zhu, C. M. Jorgensen, K. R. Deal, X. Dai, M. W. Dawson, H. G. Muller, M. C. Luo, R. K. Ramasamy, and H. Dehghani. 2018. Structural variation and rates of genome evolution in the grass family seen through comparison of sequences of genomes greatly differing in size. The Plant Journal, 95(3), 487-503. Dwivedi, S. L., H. D. Upadhyaya, H. T. Stalker, M. W. Blair, D. J. Bertioli, S. Nielen, and R. Ortiz. 2008. Enhancing crop gene pools with beneficial traits using wild relatives. Plant Breeding Reviews, 30, 179-230. Eanes, W. F., C. Wesley, and B. Charlesworth. 1992. Accumulation of P elements in minority inversions in natural populations of Drosophila melanogaster. Genetics Research, 59(1), 1-9. Fang, B., P. Kemppainen, P. Momigliano, X. Feng, and J. Merila. 2020. On the causes of geographically heterogeneous parallel evolution in sticklebacks. Nature Ecology and Evolution, 4(8), 1105-1115. Fang, Z., T. Pyhajarvi, A. L. Weber, R. K. Dawe, J. C. Glaubitz, J. D. J. S. Gonzalez, C. Ross-Ibarra, J. Doebley, P. L. Morrell, and J. Ross-Ibarra. 2012. Megabase-scale inversion polymorphism in the wild ancestor of maize. Genetics, 191(3), 883-894. Faria, R., P. Chaube, H. E. Morales, T. Larsson, A. R. Lemmon, E. M. Lemmon, M. Rafajlovic, M. Panova, M. Ravinet, K. Johannesson, and A. M. Westram. 2019. Multiple chromosomal rearrangements in a hybrid zone between Littorina saxatilis ecotypes. Molecular Ecology, 28(6), 1375-1393. Farkas, T. E., T. Mononen, A. A. Comeault, and P. Nosil. 2016. Observational evidence that maladaptive gene flow reduces patch occupancy in a wild insect metapopulation. Evolution, 70(12), 2879-2888. Farre, M., D. Micheletti, and A. Ruiz-Herrera. 2013. Recombination rates and genomic shuffling in human and chimpanzee - a new twist in the chromosomal speciation theory. Molecular Biology and Evolution, 30(4), 853-864. 139 Feder, J. L., and P. Nosil. 2009. Chromosomal inversions and species differences: when are genes affecting adaptive divergence and reproductive isolation expected to reside within inversions?. Evolution, 63(12), 3061-3075. Feder, J. L., and P. Nosil. 2010. The efficacy of divergence hitchhiking in generating genomic islands during ecological speciation. Evolution, 64(6), 1729-1747. Feder, J. L., C. A. Chilcote, and G. L. Bush. 1988. Genetic differentiation between sympatric host races of the apple maggot fly Rhagoletis pomonella. Nature, 336(6194), 61. Feder, J. L., S. H. Berlocher, J. B. Roethele, H. Dambroski, J. J. Smith, W. L. Perry, V. Gavrilovic, K. E. Filchak, J. Rull, and M. Aluja. 2003. Allopatric genetic origins for sympatric host-plant shifts and race formation in Rhagoletis. Proceedings of the National Academy of Sciences, 100(18), 10314-10319. Feder, J. L., J. B. Roethele, K. Filchak, J. Niedbalski, and J. Romero-Severson. 2003. Evidence for inversion polymorphism related to sympatric host race formation in the apple maggot fly, Rhagoletis pomonella. Genetics, 163(3), 939-953. Feder, J. L., R. Gejji, T. H. Powell, and P. Nosil. 2011. Adaptive chromosomal divergence driven by mixed geographic mode of evolution. Evolution, 65(8), 2157-2170. Feder, J. L., R. Gejji, S. Yeaman, and P. Nosil. 2012. Establishment of new mutations under divergence and genome hitchhiking. Philosophical Transactions of the Royal Society B: Biological Sciences, 367(1587), 461-474. Felsenstein, J. 1981. Skepticism towards Santa Rosalia, or why are there so few kinds of animals?. Evolution, 35(1), 124-138. Feschotte, C., and E. J. Pritham. 2007. DNA transposons and the evolution of eukaryotic genomes. Annual Review of Genetics, 41, 331-368. Fishman, L., A. Stathos, P. M. Beardsley, C. F. Williams, and J. P. Hill. 2013. Chromosomal rearrangements and the genetics of reproductive barriers in Mimulus (monkey flowers). Evolution, 67(9), 2547-2560. Flagel, L. E., B. K. Blackman, L. Fishman, P. J. Monnahan, A. Sweigart, and J. K. Kelly. 2019. GOOGA: A platform to synthesize mapping experiments and identify genomic structural diversity. PLoS Computational Biology, 15(4), e1006949. Foissac, S., J. Gouzy, S. Rombauts, C. Mathe, J. Amselem, L. Sterck, Y. V. de Peer, P. Rouze, and T. Schiex. 2008. Genome annotation in plants and fungi: EuGene as a model platform. Current Bioinformatics, 3(2), 87-97. Fransz, P., G. Linc, C. R. Lee, S. A. Aflitos, J. R. Lasky, C. Toomajian, H. Ali, J. Peters, P. van Dam, X. Ji, and M. Kuzak. 2016. Molecular, genetic and evolutionary analysis of a paracentric inversion in Arabidopsis thaliana. The Plant Journal, 88(2), 159-178. 140 Garcia-Mas, J., A. Benjak, W. Sanseverino, M. Bourgeois, G. Mir, V. M. González, E. Henaff, F. Camara, L. Cozzuto, E. Lowy, and T. Alioto. 2012. The genome of melon (Cucumis melo L.). Proceedings of the National Academy of Sciences, 109(29), 11872-11877. Gautier, M. 2015. Genome-wide scan for adaptive divergence and association with population-specific covariates. Genetics, 201(4), 1555-1579. Gendrel, A. V., Z. Lippman, C. Yordan, V. Colot, and R. A. Martienssen. 2002. Dependence of heterochromatic histone H3 methylation patterns on the Arabidopsis gene DDM1. Science, 297(5588), 1871-1873. Goebl, A. M., N. C. Kane, D. F. Doak, L. H. Rieseberg, and K. L. Ostevik. 2020. Contrasting selection at multiple life stages maintains divergent adaptation between sunflower ecotypes. bioRxiv, 2020.08.08.242503. Gopinathan, M. C., and C. R. Babu. 1986. Meiotic studies of the F1 hybrid between rice bean (Vigna umbellata) and its wild relative V. minima. Genetica, 71(2), 115-117. Gray, Y. H. 2000. It takes two transposons to tango: transposable-element-mediated chromosomal rearrangements. Trends in Genetics, 16(10), 461-468. Greilhuber, J., and F. Speta. 1976. C-banded karyotypes in the Scilla hohenackeri group, S. persica, and Puschkinia (Liliaceae). Plant Systematics and Evolution, 126(2), 149-188. Griswold, C. K. 2006. Gene flow's effect on the genetic architecture of a local adaptation and its consequences for QTL analyses. Heredity, 96(6), 445-453. Guerrero, R. F., F. Rousset, and M. Kirkpatrick. 2012. Coalescent patterns for chromosomal inversions in divergent populations. Philosophical Transactions of the Royal Society B: Biological Sciences, 367(1587), 430-438. Hahn, M. W. 2019. Molecular population genetics. Sinauer Associates, Inc. Harter, A.V., K. A. Gardner, D. Falush, D. L. Lentz, R. Bye, and L. H. Rieseberg. 2004. Origin of extant domesticated sunflowers in eastern North America. Nature, 430(6996), 201-205. Hartigan, J. A., and M. A. Wong. 1979. Algorithm AS 136: A k-means clustering algorithm. Journal of the Royal Statistical Society: Series C (Applied Statistics), 28(1), 100-108. He, L., and H. K. Dooner. 2009. Haplotype structure strongly affects recombination in a maize genetic interval polymorphic for Helitron and retrotransposon insertions. Proceedings of the National Academy of Sciences, 106(21), 8410-8416. Heiser, C. B. 1958. Three new annual sunflowers (Helianthus) from the southwestern United States. Rhodora, 60(718), 272-283. 141 Heiser, C. B. 1961. Morphological and cytological variation in Helianthus petiolaris with notes on related species. Evolution, 15(2), 247-258. Hermisson, J., and P. S. Pennings. 2005. Soft sweeps: molecular population genetics of adaptation from standing genetic variation. Genetics, 169(4), 2335-2352. Heslop-Harrison, J. 2013. Pollen: development and physiology. Butterworth-Heinemann. Hill, W. G., and A. Robertson. 1966. The effect of linkage on limits to artificial selection. Genetic Research, 8, 269-294. Hoang, D. T., O. Chernomor, A. von Haeseler, B. Q. Minh, and L. S. Vinh. 2018. UFBoot2: Improving the ultrafast bootstrap approximation. Molecular Biology and Evolution, 35(2), 518-522. Hoban, S., J. L. Kelley, K. E. Lotterhos, M. F. Antolin, G. Bradburd, D. B. Lowry, M. L. Poss, L. K. Reed, A. Storfer, and M. C. Whitlock. 2016. Finding the genomic basis of local adaptation: pitfalls, practical solutions, and future directions. The American Naturalist, 188(4), 379-397. Hoffmann, A. A., and L. H. Rieseberg. 2008. Revisiting the impact of inversions in evolution: from population genetic markers to drivers of adaptive shifts and speciation?. Annual Review of Ecology, Evolution, and Systematics, 39, 21-42. Hohenlohe, P. A., S. Bassham, P. D. Etter, N. Stiffler, E. A. Johnson, and W. A. Cresko. 2010. Population genomics of parallel adaptation in threespine stickleback using sequenced RAD tags. PLoS Genetics, 6(2), e1000862. Hollister, J. D., S. Greiner, W. Wang, J. Wang, Y. Zhang, G. K. S. Wong, S. I. Wright, and M. T. Johnson, M.T. 2015. Recurrent loss of sex is associated with accumulation of deleterious mutations in Oenothera. Molecular Biology and Evolution, 32(4), 896-905. Hooper, D. M., and T. D. Price. 2017. Chromosomal inversion differences correlate with range overlap in passerine birds. Nature Ecology and Evolution, 1(10), 1526. Howard, D. J. 1999. Conspecific sperm and pollen precedence and speciation. Annual Review of Ecology and Systematics, 30(1), 109-132. Hu, T. T., P. Pattyn, E. G. Bakker, J. Cao, J. F. Cheng, R. M. Clark, N. Fahlgren, J. A. Fawcett, J. Grimwood, H. Gundlach, and G. Haberer. 2011. The Arabidopsis lyrata genome sequence and the basis of rapid genome size change. Nature Genetics, 43(5), 476. Huang, S., R. Li, Z. Zhang, L. Li, X. Gu, W. Fan, W. J. Lucas, X. Wang, B. Xie, P. Ni, and Y. Ren. 2009. The genome of the cucumber, Cucumis sativus L. Nature Genetics, 41(12), 1275. Huang, K., R. L. Andrew, G. L. Owens, L. L. Ostevik, and L. H. Rieseberg. 2019. Multiple chromosomal inversions contribute to adaptive divergence of a dune sunflower ecotype. bioRxiv, 829622. 142 Huang, K., R. L. Andrew, G. L. Owens, L. L. Ostevik, and L. H. Rieseberg. 2020. Multiple chromosomal inversions contribute to adaptive divergence of a dune sunflower ecotype. Molecular Ecology, 29(14), 2535-2549. Huber, C. D., M. DeGiorgio, I. Hellmann, and R. Nielsen. 2016. Detecting recent selective sweeps while controlling for mutation rate and background selection. Molecular Ecology, 25(1), 142-156. Hubner, S., N. Bercovich, M. Todesco, J. R. Mandel, J. Odenheimer, E. Ziegler, J. S. Lee, G. J. Baute, G. L. Owens, C. J. Grassa, and D. P. Ebert. 2019. Sunflower pan-genome analysis shows that hybridization altered gene content and disease resistance. Nature Plants, 5(1), 54-62. Iijima, T., R. Kajitani, S. Komata, C. P. Lin, T. Sota, T. Itoh, and H. Fujiwara. 2018. Parallel evolution of Batesian mimicry supergene in two Papilio butterflies, P. polytes and P. memnon. Science Advances, 4(4), eaao5416. Irwin, D. E. 2018. Sex chromosomes and speciation in birds and other ZW systems. Molecular Ecology, 27(19), 3831-3851. Jones, R., D. C. Culver, and T. C. Kane. 1992. Are parallel morphologies of cave organisms the result of similar selection pressures? Evolution 46(2), 353-365. Kaiser, V. B., and B. Charlesworth. 2009. The effects of deleterious mutations on evolution in non-recombining genomes. Trends in Genetics, 25(1), 9-12. Kalyaanamoorthy, S., B. Q. Minh, T. K. F. Wong, A. von Haeseler, and L. S. Jermiin. 2017. ModelFinder: Fast model selection for accurate phylogenetic estimates. Nature Methods, 14(6), 587-589. Kane, N. C., N. Gill, M. G. King, J. E. Bowers, H. Berges, J. Gouzy, E. Bachlava, N. B. Langlade, Z. Lai, M. Stewart, and J. M. Burke. 2011. Progress towards a reference genome for sunflower. Botany, 89(7), 429-437. Kawakami, T., P. Dhakal, A. N. Katterhenry, C. A. Heatherington, and M. C. Ungerer. 2011. Transposable element proliferation and genome expansion are rare in contemporary sunflower hybrid populations despite widespread transcriptional activity of LTR retrotransposons. Genome Biology and Evolution, 3, 156-167. Keightley, P. D., and S. P. Otto. 2006. Interference among deleterious mutations favours sex and recombination in finite populations. Nature, 443(7107), 89-92. Keller, L. F. and D. M. Waller. 2002. Inbreeding effects in wild populations. Trends in Ecology and Evolution, 17(5), 230-241. Kemppainen, P., C. G. Knight, D. K. Sarma, T. Hlaing, A. Prakash, Y. N. Maung, P. Somboon, J. Mahanta, and C. Walton. 2015. Linkage disequilibrium network analysis (LDna) gives a 143 global view of chromosomal inversions, local adaptation and geographic structure. Molecular Ecology Resources, 15(5), 1031-1045. Kent, T. V., J. Uzunovic, and S. I. Wright. 2017. Coevolution between transposable elements and recombination. Philosophical Transactions of the Royal Society B: Biological Sciences, 372(1736), 20160458. Kenton, A. 1981. Chromosome evolution in the Gibasis linearis alliance (Commelinaceae). Chromosoma, 84(2), 291-304. Kianian, S. F., and C. F. Quiros. 1992. Generation of a Brassica oleracea composite RFLP map: linkage arrangements among various populations and evolutionary implications. Theoretical and Applied Genetics, 84(5-6), 544-554. Kim, S. C., and L. H. Rieseberg. 1999. Genetic architecture of species differences in annual sunflowers: implications for adaptive trait introgression. Genetics, 153(2), 965-977. King, M. 1993. Species evolution: the role of chromosome change. Cambridge University Press. Kirkpatrick, M., and N. Barton. 2006. Chromosome inversions, local adaptation and speciation. Genetics, 173(1), 419-434. Kirkpatrick, M., and A. Kern. 2012. Where's the money? Inversions, genes, and the hunt for genomic targets of selection. Genetics, 190(4), 1153-1155. Kirubakaran, T. G., H. Grove, M. P. Kent, S. R. Sandve, M. Baranski, T. Nome, M. C. De Rosa, B. Righino, T. Johansen, H. Ottera, and A. Sonesson. 2016. Two adjacent inversions maintain genomic differentiation between migratory and stationary ecotypes of Atlantic cod. Molecular Ecology, 25(10), 2130-2143. Knoll, A., F. Fauser, and H. Puchta. 2014. DNA recombination in somatic plant cells: mechanisms and evolutionary consequences. Chromosome Research, 22(2), 191-201. Konishi, T., and I. Linde-Laursen. 1988. Spontaneous chromosomal rearrangements in cultivated and wild barleys. Theoretical and Applied Genetics, 75(2), 237-243. Korunes, K. L., and M. A. Noor. 2019. Pervasive gene conversion in chromosomal inversion heterozygotes. Molecular Ecology, 28(6), 1302-1315. Krimbas, C. B., and J. R. Powell. 1992. Drosophila inversion polymorphism. CRC Press. Lagercrantz, U. 1998. Comparative mapping between Arabidopsis thaliana and Brassica nigra indicates that Brassica genomes have evolved through extensive genome replication accompanied by chromosome fusions and frequent rearrangements. Genetics, 150(3), 1217-1228. 144 Lai, Z., T. Nakazato, M. Salmaso, J. M. Burke, S. Tang, S. J. Knapp, and L. H. Rieseberg. 2005. Extensive chromosomal repatterning and the evolution of sterility barriers in hybrid sunflower species. Genetics, 171(1), 291-303. Lamichhaney, S., G. Fan, F. Widemo, U. Gunnarsson, D. S. Thalmann, M. P. Hoeppner, S. Kerje, U. Gustafson, C. Shi, H. Zhang, and W. Chen. 2016. Structural genomic changes underlie alternative reproductive strategies in the ruff (Philomachus pugnax). Nature Genetics, 48(1), 84. Lande, R. 1979. Effective deme sizes during long-term evolution estimated from rates of chromosomal rearrangement. Evolution, 33(1Part1), 234-251. Langley, C. H., E. Montgomery, R. Hudson, N. Kaplan, and B. Charlesworth. 1988. On the role of unequal exchange in the containment of transposable element copy number. Genetics Research, 52(3), 223-235. Lawrence, E. J., C. H. Griffin, and I. R. Henderson. 2017. Modification of meiotic recombination by natural variation in plants. Journal of Experimental Botany, 68(20), 5471-5483. Lee, K. M., and G. Coop. 2017. Distinguishing among modes of convergent adaptation using population genomic data. Genetics, 207(4), 1591-1619. Lee, C. R., B. Wang, J. P. Mojica, T. Mandakova, K. V. Prasad, J. L. Goicoechea, N. Perera, U. Hellsten, H. N. Hundley, J. Johnson, and J. Grimwood. 2017. Young inversion with multiple linked QTLs under selection in a hybrid zone. Nature Ecology and Evolution, 1(5), 0119. Lenormand, T. 2002. Gene flow and the limits to natural selection. Trends in Ecology and Evolution, 17(4), 183-189. Lenormand, T., and S. P. Otto. 2000. The evolution of recombination in a heterogeneous environment. Genetics, 156 (1), 423-438. Levin, D. A. 1979. The nature of plant species. Science, 204(4391), 381-384. Lewis, H., and M. R. Roberts. 1956. The origin of Clarkia lingulata. Evolution, 10(2), 126-138. Li, H. 2011. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics, 27(21), 2987-2993. Li, H. 2013. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv preprint arXiv:1303.3997. Li, H., and P. Ralph. 2019. Local PCA shows how the effect of population structure differs along the genome. Genetics, 211(1), 289-304. 145 Li, H., B. Handsaker, A. Wysoker, T. Fennell, J. Ruan, N. Homer, G. Marth, G. Abecasis, R. Durbin, and 1000 Genome Project Data Processing Subgroup. 2009 The Sequence alignment/map (SAM) format and SAMtools. Bioinformatics, 25, 2078-9. Li, S. F., T. Su, G. Q. Cheng, B. X. Wang, X. Li, C. L. Deng, and W. J. Gao. 2017. Chromosome evolution in connection with repetitive sequences and epigenetics in plants. Genes, 8(10), 290. Lisch, D. 2013. How important are transposons for plant evolution?. Nature Reviews Genetics, 14(1), 49-61. Lister, C., D. Jackson, and C. Martin. 1993. Transposon-induced inversion in Antirrhinum modifies nivea gene expression to give a novel flower color pattern under the control of cycloidearadialis. The Plant Cell, 5(11), 1541-1553. Lowry, D. B., and J. H. Willis. 2010. A widespread chromosomal inversion polymorphism contributes to a major life-history transition, local adaptation, and reproductive isolation. PLoS Biology, 8(9), e1000500. Lu, J., T. Tang, H. Tang, J. Huang, S. Shi, and C. I. Wu. 2006. The accumulation of deleterious mutations in rice genomes: a hypothesis on the cost of domestication. Trends in Genetics, 22(3), 126-131. Lu, K., L. Wei, X. Li, Y. Wang, J. Wu, M. Liu, C. Zhang, Z. Chen, Z. Xiao, H. Jian, and F. Cheng. 2019. Whole-genome resequencing reveals Brassica napus origin and genetic loci involved in its improvement. Nature Communications, 10(1), 1-12. Lysak, M. A., and C. Lexer. 2006. Towards the era of comparative evolutionary genomics in Brassicaceae. Plant Systematics and Evolution, 259(2-4), 175-198. Lysak, M. A., A. Berr, A. Pecinka, R. Schmidt, K. McBreen, and I. Schubert. 2006. Mechanisms of chromosome number reduction in Arabidopsis thaliana and related Brassicaceae species. Proceedings of the National Academy of Sciences, 103(13), 5224-5229. Lysak, M. A., K. Cheung, M. Kitschke, and P. Bures. 2007. Ancestral chromosomal blocks are triplicated in Brassiceae species with varying chromosome number and genome size. Plant Physiology, 145(2), 402-410. Ma, J., and J. L. Bennetzen. 2006. Recombination, rearrangement, reshuffling, and divergence in a centromeric region of rice. Proceedings of the National Academy of Sciences, 103(2), 383-388. MacPherson, A., and S. L. Nuismer. 2017. The probability of parallel genetic evolution from standing genetic variation. Journal of Evolutionary Biology, 30(2), 326-337. Mandakova, T., and M. A. Lysak. 2008. Chromosomal phylogeny and karyotype evolution in x=7 crucifer species (Brassicaceae). The Plant Cell, 20(10), 2559-2570. 146 Mandakova, T., V. Singh, U. Kraemer, and M. A. Lysak. 2015. Genome structure of the heavy metal hyperaccumulator Noccaea caerulescens and its stability on metalliferous and nonmetalliferous soils. Plant Physiology, 169(1), 674-689. Marie-Nelly, H., M. Marbouty, A. Cournac, J. F. Flot, G. Liti, D. P. Parodi, S. Syan, N. Guillén, A. Margeot, C. Zimmer, and R. Koszul. 2014. High-quality genome (re)assembly using chromosomal contact data. Nature Communications, 5(1), 1-10. Marsden, C. D., D. Ortega-Del Vecchyo, D. P. O’Brien, J. F. Taylor, O. Ramirez, C. Vila, T. Marques-Bonet, R. D. Schnabel, R. K. Wayne, and K. E. Lohmueller. 2016. Bottlenecks and selective sweeps during domestication have increased deleterious genetic variation in dogs. Proceedings of the National Academy of Sciences, 113(1), 152-157. McGaugh, S. E., and M. A. Noor. 2012. Genomic impacts of chromosomal inversions in parapatric Drosophila species. Philosophical Transactions of the Royal Society B: Biological Sciences, 367(1587), 422-429. McKenna, A., M. Hanna, E. Banks, A. Sivachenko, K. Cibulskis, A. Kernytsky, K. Garimella, D. Altshuler, S. Gabriel, M. Daly, and M. A. DePristo. 2010. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome research, 20(9), 1297-1303. Messer, P. W., and R. A. Neher. 2012. Estimating the strength of selective sweeps from deep population diversity data. Genetics, 191(2), 593-605. Michel, A. P., S. Sim, T. H. Powell, M. S. Taylor, P. Nosil, and J. L. Feder. 2010. Widespread genomic divergence during sympatric speciation. Proceedings of the National Academy of Sciences, 107(21), 9724-9729. Montgomery, E., B. Charlesworth, and C. H. Langley. 1987. A test for the role of natural selection in the stabilization of transposable element copy number in a population of Drosophila melanogaster. Genetics Research, 49(1), 31-41. Morrell, P. L., E. S. Buckler, and J. Ross-Ibarra. 2012. Crop genomics: advances and applications. Nature Reviews Genetics, 13(2), 85-96. Moyers, B. T. 2015. The landscape of divergence in silverleaf sunflowers (Doctoral dissertation). University of British Columbia. Moyers, B. T., and L. H. Rieseberg. 2016. Remarkable life history polymorphism may be evolving under divergent selection in the silverleaf sunflower. Molecular Ecology, 25(16), 3817-3830. Nadeau, N. J., A. Whibley, R. T. Jones, J. W. Davey, K. K. Dasmahapatra, S. W. Baxter, M. A. Quail, M. Joron, R. H. Ffrench-Constant, M. L. Blaxter, and J. Mallet. 2012. Genomic islands of divergence in hybridizing Heliconius butterflies identified by large-scale targeted sequencing. Philosophical Transactions of the Royal Society B: Biological Sciences, 367(1587), 343-353. 147 Nair, K.P., 2019. Utilizing crop wild relatives to combat global warming. Advances in Agronomy, 153, 175-258. Navarro, A., and N. H. Barton. 2003. Accumulating postzygotic isolation genes in parapatry: a new twist on chromosomal speciation. Evolution, 57(3), 447-459. Nguyen, L.-T., H. A. Schmidt, A. von Haeseler, and B. Q. Minh. 2015. IQ-TREE: A fast and effective stochastic algorithm for estimating maximum likelihood phylogenies. Molecular Biology and Evolution, 32(1), 268-274. Nicolas, M., G. Marais, V. Hykelova, B. Janousek, V. Laporte, B. Vyskot, D. Mouchiroud, I. Negrutiu, D. Charlesworth, and F. Moneger. 2004. A gradual process of recombination restriction in the evolutionary history of the sex chromosomes in dioecious plants. PLoS Biology, 3(1), e4. Noor, M. A., K. L. Grams, L. A. Bertucci, and J. Reiland. 2001. Chromosomal inversions and the reproductive isolation of species. Proceedings of the National Academy of Sciences, 98(21), 12084-12088. Nosil, P., D. J. Funk, and D. Ortiz-Barrientos. 2009. Divergent selection and heterogeneous genomic divergence. Molecular Ecology, 18(3), 375-402. Oneal, E., D. B. Lowry, K. M. Wright, Z. Zhu, and J. H. Willis. 2014. Divergent population structure and climate associations of a chromosomal inversion polymorphism across the Mimulus guttatus species complex. Molecular Ecology, 23(11), 2844-2860. Orr, H. A. 1998. Testing natural selection vs. genetic drift in phenotypic evolution using quantitative trait locus data. Genetics, 149(4), 2099-2104. Orr, H. A. 2005. The probability of parallel evolution. Evolution, 59(1), 216-220. Ortiz-Barrientos, D., J. Reiland, J. Hey, and M. A. Noor. 2002. Recombination and the divergence of hybridizing species. Genetica. 116, 167-78. Ortiz-Barrientos, D., J. Engelstadter, and L. H. Rieseberg. 2016. Recombination rate evolution and the origin of species. Trends in Ecology and Evolution, 31(3), 226-236. Ostevik, K. L. 2016. The ecology and genetics of adaptation and speciation in dune sunflowers (Doctoral dissertation). University of British Columbia. Ostevik, K. L., R. L. Andrew, S. P. Otto, and L. H. Rieseberg. 2016. Multiple reproductive barriers separate recently diverged sunflower ecotypes. Evolution, 70(10), 2322-2335. Ostevik, K. L., K. Samuk, and L. H. Rieseberg. 2019. Ancestral reconstruction of sunflower karyotypes reveals dramatic chromosomal evolution. bioRxiv, 737155. 148 Ostevik, K. L., K. Samuk, and L. H. Rieseberg. 2020. Ancestral reconstruction of karyotypes reveals an exceptional rate of nonrandom chromosomal evolution in sunflower. Genetics, 214(4), 1031-1045. Ou, S., W. Su, Y. Liao, K. Chougule, J. R. Agda, A. J. Hellinga, C. S. B. Lugo, T. A. Elliott, D. Ware, T. Peterson, and N. Jiang. 2019. Benchmarking transposable element annotation methods for creation of a streamlined, comprehensive pipeline. Genome Biology, 20(1), 1-18. Owens, G. L., G. J. Baute, and L. H. Rieseberg. 2016. Revisiting a classic case of introgression: hybridization and gene flow in Californian sunflowers. Molecular Ecology, 25(11), 2630-2643. Paul, J. R., S. N. Sheth, and A. L. Angert. 2011. Quantifying the impact of gene flow on phenotype-environment mismatch: a demonstration with the scarlet monkeyflower Mimulus cardinalis. The American Naturalist, 178(S1), S62-S79. Pearce, R. J., H. Pota, M. S. B. Evehe, E. H. Ba, G. Mombo-Ngoma, A. L. Malisa, R. Ord, W. Inojosa, A. Matondo, D. A. Diallo, W. Mbacham, and C. Roper. 2009. Multiple origins and regional dispersal of resistant dhps in African Plasmodium falciparum malaria. PLoS Medicine, 6(4), e1000055. Pennings, P. S., and J. Hermisson. 2006. Soft sweeps III: the signature of positive selection from recurrent mutation. PLoS Genetics, 2(12), e186. Poland, J. A., P. J. Brown, M. E. Sorrells, and J. L. Jannink. 2012. Development of high-density genetic maps for barley and wheat using a novel two-enzyme genotyping-by-sequencing approach. PLoS One, 7(2), e32253. Prevosti, A., G. Ribo, L. Serra, M. Aguade, J. Balana, M. Monclus, and F. Mestres. 1988. Colonization of America by Drosophila subobscura: experiment in natural populations that supports the adaptive role of chromosomal-inversion polymorphism. Proceedings of the National Academy of Sciences, 85(15), 5597-5600. Purcell, S., B. Neale, K. Todd-Brown, L. Thomas, M. A. Ferreira, D. Bender, J. Maller, P. Sklar, P. I. De Bakker, M. J. Daly, and P. C. Sham. 2007. PLINK: a tool set for whole-genome association and population-based linkage analyses. The American Journal of Human Genetics, 81(3), 559-575. Pylkov, K. V., L. A. Zhivotovsky, and M. W. Feldman. 1998. Migration versus mutation in the evolution of recombination under multilocus selection. Genetics Research, 71(3), 247-256. Qin, C., C. Yu, Y. Shen, X. Fang, L. Chen, J. Min, J. Cheng, S. Zhao, M. Xu, Y. Luo, and Y. Yang. 2014. Whole-genome sequencing of cultivated and wild peppers provides insights into Capsicum domestication and specialization. Proceedings of the National Academy of Sciences, 111(14), 5135-5140. Quinlan, A. R. 2014. BEDTools: the Swiss-army tool for genome feature analysis. Current Protocols in Bioinformatics, 47(1), 11-12. 149 Raduski, A. R., L. Rieseberg, and J. Strasburg. 2010. Effective population size, gene flow, and species status in a narrow endemic sunflower, Helianthus neglectus, compared to its widespread sister species, H. petiolaris. IJMS, 11, 492-506. Rajakaruna, N., M. Siddiqi, J. Whitton, B. Bohm, and A. Glass. 2003. Differential responses to Na+/K+ and Ca2+/Mg2+ in two edaphic races of the Lasthenia californica (Asteraceae) complex: A case for parallel evolution of physiological traits. New Phytologist, 157(1), 93-103. Rausher, M. D., and L. F. Delph. 2015. Commentary: when does understanding phenotpyic evolution require identification of underlying genes? Evolution, 69(7), 1655-1664. Renaut, S., and L. H. Rieseberg. 2015. The accumulation of deleterious mutations as a consequence of domestication and improvement in sunflowers and other compositae crops. Molecular Biology and Evolution, 32(9), 2273-2283. Renaut, S., C. J. Grassa, S. Yeaman, B. T. Moyers, Z. Lai, N. C. Kane, J. E. Bowers, J. M. Burke, and L. H. Rieseberg. 2013. Genomic islands of divergence are not affected by geography of speciation in sunflowers. Nature Communications, 4(1), 1-8. Renaut, S., H. C. Rowe, M. C. Ungerer, and L. H. Rieseberg. 2014. Genomics of homoploid hybrid speciation: diversity and transcriptional activity of long terminal repeat retrotransposons in hybrid sunflowers. Philosophical Transactions of the Royal Society B: Biological Sciences, 369(1648), 20130345. Rieseberg, L. H. 2001. Chromosomal rearrangements and speciation. Trends in Ecology and Evolution, 16(7), 351-358. Rieseberg, L. H., and J. M. Burke. 2001. The biological reality of species: gene flow, selection, and collective evolution. Taxon, 50(1), 47-67. Rieseberg, L. H., C. Van Fossen, and A. M. Desrochers. 1995. Hybrid speciation accompanied by genomic reorganization in wild sunflowers. Nature, 375(6529), 313. Rieseberg, L. H., A. M. Desrochers, and S. J. Youn. 1995. Interspecific pollen competition as a reproductive barrier between sympatric species of Helianthus (Asteraceae). American Journal of Botany, 82(4), 515-519. Rieseberg, L. H., O. Raymond, D. M. Rosenthal, Z. Lai, K. Livingstone, T. Nakazato, J. L. Durphy, A. E. Schwarzbach, L. A. Donovan, and C. Lexer. 2003. Major ecological transitions in wild sunflowers facilitated by hybridization. Science, 301(5637), 1211-1216. Rodriguez, S., E. Perera, B. Maestra, M. Diez, and T. Naranjo. 2000. Chromosome structure of Triticum timopheevii relative to T. turgidum. Genome, 43(6), 923-930. Roesti, M., S. Gavrilets, A. P. Hendry, W. Salzburger, and D. Berner. 2014. The genomic signature of parallel adaptation from shared genetic variation. Molecular Ecology, 23(16), 3944-3956. 150 Rogers, C. E., T. E. Thompson, and G. J. Seiler. 1982. Sunflowers species of the United States. National Sunflower Association. Rousset, F., M. Kirkpatrick, and R. F. Guerrero. 2014. Matrix inversions for chromosomal inversions: A method to construct summary statistics in complex coalescent models. Theoretical Population Biology, 97, 1-10. Rundle, H., L. Nagel, J. Boughman, and D. Schluter. 2000. Natural selection and parallel speciation in sympatric sticklebacks. Science, 287(5451), 306-308. Savolainen, O., M. Lascoux, and J. Merila. 2013. Ecological genomics of local adaptation. Nature Reviews Genetics, 14(11), 807-820. Schluter, D. 2001. Ecology and the origin of species. Trends in Ecology and Evolution, 16(7), 372-380. Schluter, D., E. A. Clifford, M. Nemethy, and J. S. McKinnon. 2004. Parallel evolution and inheritance of quantitative traits. The American Naturalist, 163(6), 809-822. Schmieder, R., and R. Edwards. 2011. Quality control and preprocessing of metagenomic datasets. Bioinformatics, 27(6), 863-864. Schultz, J., and H. Redfield. 1951. Interchromosomal effects on crossing over in Drosophila. Cold Spring Harbor Symposia on Quantitative Biology, 16, 175-197. Schwander, T., R. Libbrecht, and L. Keller. 2014. Supergenes and complex phenotypes. Current Biology, 24(7), R288-R294. Searle, J. B. 1993. Chromosomal hybrid zones in eutherian mammals. Hybrid zones and the evolutionary process, 309-353. Sedlazeck, F. J., P. Rescheneder, and A. Von Haeseler. 2013. NextGenMap: fast and accurate read mapping in highly polymorphic genomes. Bioinformatics, 29(21), 2790-2791. Servedio, M. R. 2000. Reinforcement and the genetics of nonrandom mating. Evolution, 54(1), 21-29. Servedio, M. R. 2009. The role of linkage disequilibrium in the evolution of premating isolation. Heredity, 102(1), 51. Smith, J. M., and J. Haigh. 1974. The hitch-hiking effect of a favourable gene. Genetics Research, 23(1), 23-35. Sniegowski, P. D., and B. Charlesworth. 1994. Transposable element numbers in cosmopolitan inversions from a natural population of Drosophila melanogaster. Genetics, 137(3), 815-827. 151 Staton, S. E., B. H. Bakken, B. K. Blackman, M. A. Chapman, N. C. Kane, S. Tang, M. C. Ungerer, S. J. Knapp, L. H. Rieseberg, and J. M. Burke. 2012. The sunflower (Helianthus annuus L.) genome reflects a recent history of biased accumulation of transposable elements. The Plant Journal, 72(1), 142-153. Stein, J., C. L. Quarin, E. J. Martinez, S. C. Pessino, and J. P. A. Ortiz. 2004. Tetraploid races of Paspalum notatum show polysomic inheritance and preferential chromosome pairing around the apospory-controlling locus. Theoretical and Applied Genetics, 109(1), 186-191. Stephens, J. D., W. L. Rogers, C. M. Mason, L. A. Donovan, and R. L. Malmberg. 2015. Species tree estimation of diploid Helianthus (Asteraceae) using target enrichment. American Journal of Botany, 102(6), 910-920. Stern, D. L. 2013. The genetic causes of convergent evolution. Nature Reviews Genetics, 14(11), 751-764. Storz, J. F. 2016. Causes of molecular convergence and parallelism in protein evolution. Nature Reviews Genetics, 17(4), 239. Storz, J. F., B. A. Payseur, and M. W. Nachman. 2004. Genome scans of DNA variability in humans reveal evidence for selective sweeps outside of Africa. Molecular Biology and Evolution, 21(9), 1800-1811. Strasburg, J. L., N. C. Kane, A. R. Raduski, A. Bonin, R. Michelmore, and L. H. Rieseberg. 2011. Effective population size is positively correlated with levels of adaptive divergence among annual sunflowers. Molecular Biology and Evolution, 28(5), 1569-1580. Sturtevant, A. H. 1921. A case of rearrangement of genes in Drosophila. Proceedings of the National Academy of Sciences, 7(8), 235. Sun, Y., J. Svedberg, M. Hiltunen, P. Corcoran, and H. Johannesson. 2017. Large-scale suppression of recombination predates genomic rearrangements in Neurospora tetrasperma. Nature Communications, 8(1), 1140. Sybenga, J. 1975. Meiotic configurations. Springer-Verlag. Szinay, D., E. Wijnker, R. van den Berg, R. G. Visser, H. de Jong, and Y. Bai. 2012. Chromosome evolution in Solanum traced by cross-species BAC-FISH. New Phytologist, 195(3), 688-698. Tajima, F. 1989. Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics, 123(3), 585-595. Tang, H., E. Lyons, and C. D. Town. 2015. Optical mapping in plant comparative genomics. GigaScience, 4(1), s13742-015. 152 Tanksley, S. D., M. W. Ganal, J. P. Prince, M. C. De Vicente, M. W. Bonierbale, P. Broun, T. M. Fulton, J. J. Giovannoni, S. Grandillo, and G. B. Martin. 1992. High density molecular linkage maps of the tomato and potato genomes. Genetics, 132(4), 1141-1160. Tarasov, A., A. J. Vilella, E. Cuppen, I. J. Nijman, and P. Prins. 2015. Sambamba: fast processing of NGS alignment formats. Bioinformatics, 31(12), 2032-2034. Taylor, J., and D. Butler. 2017. R package ASMap: efficient genetic linkage map construction and diagnosis. arXiv, 1705.06916. Tishkoff, S. A., F. A. Reed, A. Ranciaro, B. F. Voight, C. C. Babbitt, J. S. Silverman, K. Powell, H. M. Mortensen, J. B. Hirbo, M. Osman, M. Ibrahim, S. A. Omar, G. Lema, T. B. Nyambo, J. Ghori, S. Bumpstead, J. K Pritchard, G. A. Wray, and P. Deloukas. 2007. Convergent adaptation of human lactase persistence in Africa and Europe. Nature Genetics, 39(1), 31-40. Todesco, M., G. L. Owens, N. Bercovich, J. S. Legare, S. Soudi, D. O. Burge, K. Huang, K. L. Ostevik, E. B. M. Drummond, I. Imerovski, K. Lande, M. A. Pascual, W. Cheung, S. E. Staton, S. Munos, R. Nielsen, L. A. Donovan, J. M. Burke, S. Yeaman, and L. H. Rieseberg. 2019. Massive haplotypes underlie ecotypic differentiation in sunflowers. bioRxiv, 790279. Todesco, M., G. L. Owens, N. Bercovich, J. S. Legare, S. Soudi, D. O. Burge, K. Huang, K. L. Ostevik, E. B. M. Drummond, I. Imerovski, K. Lande, M. A. Pascual, M. Nanavati, M. Jahani, W. Cheung, S. E. Staton, S. Munos, R. Nielsen, L. A. Donovan, J. M. Burke, S. Yeaman, and L. H. Rieseberg. 2020. Massive haplotypes underlie ecotypic differentiation in sunflowers. Nature, 584(7822), 602-607. Tong, C., H. Li, Y. Wang, X. Li, J. Ou, D. Wang, H. Xu, C. Ma, X. Lang, G. Liu, and B. Zhang. 2016. Construction of high-density linkage maps of Populus deltoides × P. simonii using restriction-site associated DNA sequencing. PLoS One, 11(3), e0150692. Trickett, A. J., and R. K. Butlin. 1994. Recombination suppressors and the evolution of new species. Heredity, 73(4), 339. Tsukahara, S., A. Kawabe, A. Kobayashi, T. Ito, T. Aizu, T. Shin-i, A. Toyoda, A. Fujiyama, Y. Tarutani, and T. Kakutani. 2012. Centromere-targeted de novo integrations of an LTR retrotransposon of Arabidopsis lyrata. Genes and development, 26(7), 705-713. Turner, T. L., M. W. Hahn, and S. V. Nuzhdin. 2005. Genomic islands of speciation in Anopheles gambiae. PLoS Biology, 3(9), e285. Tuttle, E. M., A. O. Bergland, M. L. Korody, M. S. Brewer, D. J. Newhouse, P. Minx, M. Stager, A. Betuel, Z. A. Cheviron, and R. A. W. C. Gonser. 2016. Divergence and functional degradation of a sex chromosome-like supergene. Current Biology, 26(3), 344-350. Twyford, A. D., and J. Friedman. 2015. Adaptive divergence in the monkey flower Mimulus guttatus is maintained by a chromosomal inversion. Evolution, 69(6), 1476-1486. 153 Via, S. 2012. Divergence hitchhiking and the spread of genomic isolation during ecological speciation-with-gene-flow. Philosophical Transactions of the Royal Society B: Biological Sciences, 367(1587), 451-460. Via, S., and D. J. Hawthorne. 2002. The genetic architecture of ecological specialization: correlated gene effects on host use and habitat choice in pea aphids. The American Naturalist, 159(S3), S76-S88. Vitti, J. J., S. R. Grossman, and P. C. Sabeti. 2013. Detecting natural selection in genomic data. Annual Review of Genetics, 47, 97-120. Wang, J., J. K. Na, Q. Yu, A. R. Gschwend, J. Han, F. Zeng, R. Aryal, R. VanBuren, J. E. Murray, and R. W. Navajas-Perez. 2012. Sequencing papaya X and Yh chromosomes reveals molecular basis of incipient sex chromosome evolution. Proceedings of the National Academy of Sciences, 109(34), 13710-13715. Wang, J., Y. Wurm, M. Nipitwattanaphon, O. Riba-Grognuz, Y. C. Huang, D. Shoemaker, and L. Keller. 2013. A Y-like social chromosome causes alternative colony organization in fire ants. Nature, 493(7434), 664-668. Weigand, H., and F. Leese. 2018. Detecting signatures of positive selection in non-model species using genomic data. Zoological Journal of the Linnean Society, 184(2), 528-583. Weir, B. S. 1996. Genetic data analysis II. Sinauer. Wellenreuther, M., and L. Bernatchez. 2018. Eco-evolutionary genomics of chromosomal inversions. Trends in Ecology and Evolution, 33(6), 427-440. White, M. J. D. 1973. Animal Cytology and Evolution. Cambridge University Press. Whitney, K. D., R. A. Randell, and L. H. Rieseberg. 2006. Adaptive introgression of herbivore resistance traits in the weedy sunflower Helianthus annuus. The American Naturalist, 167(6), 794-807. Whitney, K. D., R. A. Randell, and L. H. Rieseberg. 2010. Adaptive introgression of abiotic tolerance traits in the sunflower Helianthus annuus. New Phytologist, 187(1), 230-239. Wickham, H. 2016. ggplot2: elegant graphics for data analysis. Springer. Wright, S. I., N. Agrawal, and T. E. Bureau. 2003. Effects of recombination rate and gene density on transposable element distributions in Arabidopsis thaliana. Genome Research, 13(8), 1897-1903. Wright, S. I., I. V. Bi, S. G. Schroeder, M. Yamasaki, J. F. Doebley, M. D. McMullen, and B. S. Gaut. 2005. The effects of artificial selection on the maize genome. Science, 308(5726), 1310-1314. 154 Wright, A. E., R. Dean, F. Zimmer, and J. E. Mank. 2016. How to make a sex chromosome. Nature Communications, 7(1), 1-8. Wu, C. I. 2001. The genic view of the process of speciation. Journal of Evolutionary Biology, 14(6), 851-865. Wu, F., and S. D. Tanksley. 2010. Chromosomal evolution in the plant family Solanaceae. BMC Genomics, 11(1), 182. Wu, F., N. T. Eannetta, Y. Xu, R. Durrett, M. Mazourek, M. M. Jahn, and S. D. Tanksley. 2009. A COSII genetic map of the pepper genome provides a detailed picture of synteny with tomato and new insights into recent chromosome evolution in the genus Capsicum. Theoretical and Applied Genetics, 118(7), 1279-1293. Xu, L., G. Auer, V. Peona, A. Suh, Y. Deng, S. Feng, G. Zhang, M. P. Blom, L. Christidis, and M. S. Irestedt. 2019. Dynamic evolutionary history and gene content of sex chromosomes across diverse songbirds. Nature Ecology and Evolution, 3(5), 834. Yang, L., D. H. Koo, Y. Li, X. Zhang, F. Luan, M. J. Havey, and Y. J. Weng. 2012. Chromosome rearrangements during domestication of cucumber as revealed by high-density genetic mapping and draft genome assembly. The Plant Journal, 71(6), 895-906. Yang, Y., T. Ma, Z. Wang, Z. Lu, Y. Li, C. Fu, X. Chen, M. Zhao, M. S. Olson, and J. Liu. 2018. Genomic effects of population collapse in a critically endangered ironwood tree Ostrya rehderiana. Nature Communications, 9(1), 1-9. Yang, Z., X. Ge, Z. Yang, W. Qin, G. Sun, Z. Wang, Z. Li, J. Liu, J. Wu, Y. Wang, and L. Lu. 2019. Extensive intraspecific gene order and gene structural variations in upland cotton cultivars. Nature Communications, 10(1), 1-13. Yeaman, S. 2013. Genomic rearrangements and the evolution of clusters of locally adaptive loci. Proceedings of the National Academy of Sciences, 110(19), e1743-e1751. Yeaman, S., and S. P. Otto. 2011. Establishment and maintenance of adaptive genetic divergence under migration, selection, and drift. Evolution: International Journal of Organic Evolution, 65(7), 2123-2129. Yeaman, S., and M. C. Whitlock. 2011. The genetic architecture of adaptation under migration-selection balance. Evolution, 65(7), 1897-1911. Yeaman, S., A. C. Gerstein, K. A. Hodgins, and M. C. Whitlock. 2018. Quantifying how constraints limit the diversity of viable routes to adaptation. PLoS Genetics, 14(10), e1007717. Yogeeswaran, K., A. Frary, T. L. York, A. Amenta, A. H. Lesser, J. B. Nasrallah, and M. E. S. D. Nasrallah. 2005. Comparative genome analyses of Arabidopsis spp.: inferring chromosomal rearrangement events in the evolutionary history of A. thaliana. Genome Research, 15(4), 505-515. 155 Yu, C., J. Zhang, and T. Peterson. 2011. Genome rearrangements in maize induced by alternative transposition of reversed Ac/Ds termini. Genetics, 188(1), 59-67. Yu, G., L. G. Wang, Y. Han, and Q. Y. He. 2012. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics: a journal of integrative biology, 16(5), 284-287. Zapata, L., J. Ding, E. M. Willing, B. Hartwig, D. Bezdan, W. B. Jiao, V. Patel, G. V. James, M. Koornneef, and K. S. Schneeberger. 2016. Chromosome-level assembly of Arabidopsis thaliana Ler reveals the extent of translocation and inversion polymorphisms. Proceedings of the National Academy of Sciences, 113(28), e4052-e4060. Zhang, J., and T. Peterson. 2004. Transposition of reversed Ac element ends generates chromosome rearrangements in maize. Genetics, 167(4), 1929-1937. Zhang, J. Q., I. Imerovski, K. Borkowski, K. Huang, D. Burge, and L. H. Rieseberg. 2019. Intraspecific genetic divergence within Helianthus niveus and the status of two new morphotypes from Mexico. American Journal of Botany, 106(9), 1229-1239. Zheng, X., D. Levine, J. Shen, S. M. Gogarten, C. Laurie, and B. S. Weir. 2012. A high-performance computing toolset for relatedness and principal component analysis of SNP data. Bioinformatics, 28(24), 3326-3328. Zhou, Y., A. Minio, M. Massonnet, E. Solares, Y. Lv, T. Beridze, and B. S. D. Gaut. 2019. The population genetics of structural variants in grapevine domestication. Nature Plants, 5(9), 965-979. Ziolkowski, P. A., G. Blanc, and J. Sadowski. 2003. Structural divergence of chromosomal segments that arose from successive duplication events in the Arabidopsis genome. Nucleic Acids Research, 31(4), 1339-1350. Zluvova, J., B. Janousek, I. Negrutiu, and B. Vyskot. 2005. Comparison of the X and Y chromosome organization in Silene latifolia. Genetics, 170(3), 1431-1434. 156 Appendices Appendix A - Chapter 3 supplementary materials 157 A.1 Location and ecotype assignment of each population, as well as the population genotype frequencies of putative inversions. Genotypes are based on k-means cluster assignment in PCA. The genotype frequencies of each population are presented as cluster0/cluster1/cluster2 Population Ecotype† Latitude Longitude pet05.01 pet07.01 pet09.01 pet09.02 pet11.01 pet14.01 pet17.01 970 N 37.66277 -105.6251 0/0.4/0.6 0.2/0/0.8 1/0/0 0/0/1 1/0/0 0/0/1 0.2/0.6/0.2 1003 D 37.77328 -105.5756 0.6/0.4/0 1/0/0 0/0.2/0.8 0/0.4/0.6 0/0/1 0.2/0/0.8 0/0/1 1033 D 37.78696 -105.5682 1/0/0 1/0/0 0/0.4/0.6 0/0.6/0.4 0/0.2/0.8 0/0/1 0/0/1 1063 N 37.77438 -105.5979 0/0/1 0.2/0.4/0.4 0.6/0.4/0 0/0/1 0.2/0.4/0.4 0/0/1 0/0.4/0.6 1094 I 37.81933 -105.6165 0.4/0.6/0 1/0/0 0.4/0.4/0.2 0.2/0.4/0.4 0/0/1 0/0.2/0.8 0/0.4/0.6 1117 D 37.81574 -105.5975 0.8/0.2/0 1/0/0 0/0.2/0.8 0.2/0.4/0.4 0/0/1 0/0.2/0.8 0/0/1 1147 N 37.83565 -105.6029 0.2/0.6/0.2 0.8/0.2/0 0.4/0.4/0.2 0/0.2/0.8 0/0.2/0.8 0/0/1 0/0/1 1180 D 37.77275 -105.5526 1/0/0 1/0/0 0.2/0.4/0.4 0/0.4/0.6 0/0/1 0/0.4/0.6 0/0.2/0.8 1240 D 37.78629 -105.5307 0.8/0.2/0 1/0/0 0/0.4/0.6 0.2/0/0.8 0/0/1 0/0.2/0.8 0/0/1 1270 D 37.76442 -105.524 0.8/0.2/0 0.8/0.2/0 0/0.4/0.6 0/0.4/0.6 0/0/1 0/0/1 0/0/1 1300 D 37.74512 -105.546 1/0/0 1/0/0 0/0.2/0.8 0/0/1 0.2/0/0.8 0/0.4/0.6 0/0.2/0.8 1331 I 37.72637 -105.5531 0.6/0.2/0.2 0.6/0.4/0 0.2/0.8/0 0/0.4/0.6 0/0.2/0.8 0/0/1 0/0/1 1363 N 37.71617 -105.5331 0/0/1 0.4/0.2/0.4 0.8/0.2/0 0/0/1 0.8/0.2/0 0/0/1 0/0.8/0.2 1547 D 37.76125 -105.5696 0.8/0.2/0 0.4/0.6/0 0/0.6/0.4 0/0/1 0/0/1 0/0/1 0/0/1 1701 D 37.80392 -105.5242 1/0/0 1/0/0 0.2/0.4/0.4 0/0.2/0.8 0/0/1 0/0.2/0.8 0/0/1 1731 D 37.80782 -105.5526 1/0/0 0.8/0.2/0 0/0.4/0.6 0/0.6/0.4 0/0/1 0/0.4/0.6 0/0/1 1761 I 37.81448 -105.5317 0.6/0.2/0.2 0.6/0.4/0 0.6/0.4/0 0/0/1 0/0.4/0.6 0/0/1 0/0.4/0.6 1791 N 37.8131 -105.5167 0.2/0.2/0.6 0.6/0.4/0 0.8/0.2/0 0/0/1 0.2/0.8/0 0/0/1 0/0.4/0.6 2001 N 37.75662 -105.507 0.2/0.2/0.6 0.4/0.2/0.4 0.4/0.2/0.4 0/0.6/0.4 0/0.6/0.4 0/0/1 0.2/0.8/0 2250 N 37.672 -105.592 0/0.2/0.8 0/0.2/0.8 0.8/0.2/0 0/0/1 0.6/0.4/0 0/0/1 0/0.2/0.8 †N: non-dune, D: dune, I: intermediate. 158 A.2 Characterization of MDS outlier regions. (a) Genome plot of corresponding MDS values across 17 reference chromosomes. Each dot represents a window of 50 SNPs, and outlier windows are highlighted in red. (b) PCA with SNPs in the region. Three clusters identified using k-means clustering correspond to two homozygote groups (blue and red) and a heterozygote group (purple). (c) Heterozygosity for each of the groups identified in PCA. (d) LD plot for corresponding chromosome. Upper triangle with all individuals and lower triangle with only individuals homozygous for the more common orientation. SNPs were summarized and the second highest R2 values were presented in 1 Mbp windows. Purple bars represent the range of the inversion. 159 (A.2 continued) 160 (A.2 continued) 161 (A.2 continued) 162 A.3 LD plots for all chromosomes. Result based on all individuals is shown in upper triangle. For the chromosomes with regions of MDS outliers (named by the MDS coordinate and direction where the region was identified), calculation with only individuals homozygous for the more common orientation is shown in lower triangle. SNPs were summarized and the second highest R2 values were presented in 1 Mbp windows. Purple bars represent the location of the outlier region 163 (A.3 continued) 164 (A.3 continued) 165 (A.3 continued) 166 A.4 Genetic map of non-dune ecotype compared to the HA412HOv2 reference genome. Marker distances on HA412 are based their physical position, assuming an average recombination rate of 0.5 cM/Mb to make the linkage groups comparable. Linkage groups are named and color-coded based on their the synteny blocks with Helianthus annuus chromosomes according to Ostevik et al. (2019). Homologous markers are connected by lines. 167 (A.4 continued) 168 (A.4 continued) 169 (A.4 continued) 170 (A.4 continued) 171 (A.4 continued) 172 (A.4 continued) 173 A.5 Genetic map of dune ecotype compared to the HA412HOv2 reference genome. Marker distances on HA412 are based their physical position, assuming an average recombination rate of 0.5 cM/Mb to make the linkage groups comparable. Linkage groups are named and color-coded based on their the synteny blocks with Helianthus annuus chromosomes according to Ostevik et al. (2019). Homologous markers are connected by lines. 174 (A.5 continued) 175 (A.5 continued) 176 (A.5 continued) 177 (A.5 continued) 178 (A.5 continued) 179 (A.5 continued) 180 A.6 Genome-environment association for coverage variables (arcsine-square-root-transformed). Bayes factors (BFis, in deciban unit) was estimated using importance sampling estimator approach in BayPass. SNPs on different reference chromosomes are represented in alternate grey colors. The locations and BFis values of 7 putative inversions are indicated by red solid bars. Red horizontal dashed lines represent 1% significance thresholds computed from simulated samples. 181 (A.6 continued) 182 A.7 Genome-environment association for soil variables. Bayes factors (BFis, in deciban unit) was estimated using importance sampling estimator approach in BayPass. SNPs on different reference chromosomes are represented in alternate grey colors. The locations and BFis values of 7 putative inversions are indicated by red solid bars. Red horizontal dashed lines represent 1% significance thresholds computed from simulated samples. 183 (A.7 continued) 184 (A.7 continued) 185 (A.7 continued) 186 (A.7 continued) 187 Appendix B - Chapter 4 supplementary figures B.1 LD plots for all inversions. Upper triangle was based on all individuals from populations polymorphic for the inversion and lower triangle was based on individuals from populations homozygous for the more common orientation. Pairwise R2 between SNPs were summarized and the average R2 values were presented in 500-kbp windows. Purple bars represent the location of the inversion. Names for the inversions are consistent with those in Todesco et al. (2020). 188 (B.1 continued) 189 B.2 Genomic distributions of LTR-RTs in the three genome assemblies of sunflower. (a) Ha412HOv2.0, (b) XRQv2, (c) PSC8. Density of LTR-RTs was calculated in 500 kbp bins per chromosome. Red bars indicate the locations of inversions ann01.01 and ann05.01. [binary] 100 150 200 250Mbp0.250.500.75LTR−RT densityHanXRQChr17HanXRQChr16HanXRQChr15HanXRQChr14HanXRQChr13HanXRQChr12HanXRQChr11HanXRQChr10HanXRQChr09HanXRQChr08HanXRQChr07HanXRQChr06HanXRQChr05HanXRQChr04HanXRQChr03HanXRQChr02HanXRQChr010 50 100 150 200 250Mbp0.000.250.500.75LTR−RT density(a) (b)[binary] 100 150 200 250Mbp0.000.250.500.75LTR−RT density(c) 190 B.3 Distribution of window-based π0/π4 in each species. (a-b) Helianthus annuus, (c-d) H. argophyllus, (e-f) H. petiolaris. The ratio of π0 and π4 was calculated in sliding windows of 500 kbp. (a), (b) and (c) are distributions for all windows across the genomes; (b), (d) and (f) are distributions for windows with values < 2. 05001000150020000 10 20 30π0 π4Count(a)0501001502000.0 0.5 1.0 1.5 2.0π0 π4Count(b)0500100015000 5 10 15 20π0 π4Count(c)0501001500.0 0.5 1.0 1.5 2.0π0 π4Count(d)030060090012000.0 2.5 5.0 7.5 10.0π0 π4Count(e)0501001502000.0 0.5 1.0 1.5 2.0π0 π4Count(f) 191 B.4 Comparison of π0/π4 within inversions to genome-wide average for regions with a similar recombination rate. Windows of each recombination rate category were compared separately. P-values are Welch two sample t-test on log-transformed π0/π4 values. 192 B.5 Comparison of Pnonsense/P0 within inversions to genome-wide average for regions with a similar recombination rate. Windows of each recombination rate category were compared separately. P-values are Welch two sample t-test on log-transformed Pnonsense/P0 values. 193 B.6 Comparison of π0/π4 and Pnonsense/P0 between populations for inversion pet09.01. (a) Distribution map of inversion genotypes in populations used in the analyses. The statistics were calculated for samples from monomorphic populations (all 1/1 genotype) and polymorphic populations (with multiple genotypes). (b) Comparison of π0/π4 between monomorphic populations (green) and polymorphic populations (orange), as well as samples in the polymorphic populations that have the same inversion orientation (1/1) as the homozygous group (red). (c) Comparison of Pnonsense/P0 for the same groups of samples. The control is a region of the same size of the inversion randomly chosen from chromosome 4. P-values are Welch two sample t-tests on log-transformed statistics between samples from monomorphic populations and samples from polymorphic populations. 28323640−115 −110 −105 −100 −95LongitudeLatitudeGenotype0/00/11/1(a)●●●●●● ●●●●●p=0.000143 p=3.4e−05−1.7−1.5−1.3control inversionlog(π0π 4)(b)●●●●●●●●p=0.000162 p=6.8e−13−5−4−3control inversionlog(PnonsenseP 0)MonomorphicPolymorphicPolymorphic (same orientation)(c) 194 B.7 Comparison of π0/π4 and Pnonsense/P0 between populations for inversion pet11.01. (a) Distribution map of inversion genotypes in populations used in the analyses. The statistics were calculated for samples from monomorphic populations (all 1/1 genotype) and polymorphic populations (with multiple genotypes). (b) Comparison of π0/π4 between monomorphic populations (green) and polymorphic populations (orange), as well as samples in the polymorphic populations that have the same inversion orientation (1/1) as the homozygous group (red). (c) Comparison of Pnonsense/P0 for the same groups of samples. The control is a region of the same size of the inversion randomly chosen from chromosome 4. P-values are Welch two sample t-tests on log-transformed statistics between samples from monomorphic populations and samples from polymorphic populations. 28323640−115 −110 −105 −100 −95LongitudeLatitudeGenotype0/00/11/1(a)●●●●p=0.461 p=1.24e−08−1.7−1.5−1.3control inversionlog(π0π 4)(b)●●●●●●●●●●p=5.52e−08 p=0.644−5−4−3control inversionlog(PnonsenseP 0)MonomorphicPolymorphicPolymorphic (same orientation)(c) 195 Appendix C - Chapter 5 supplementary figures C.1 Sample information by individual for chapter 5. Sample and population names are consistent with those in Todesco et al. (2020). Information of each population are described in Todesco et al. (2020). State code: CO - Colorado; TX - Texas; AZ - Arizona; NM - New Mexico; UT - Utah. Sample Population State Ecotype GSD1009 GSD CO Dune GSD1039 GSD CO Dune GSD1085 GSD CO nonDune† GSD1139 GSD CO Dune GSD1169 GSD CO nonDune† GSD1184 GSD CO Dune GSD1258 GSD CO Dune GSD1276 GSD CO Dune GSD1329 GSD CO Dune GSD1375 GSD CO nonDune† GSD1503 GSD CO nonDune† GSD1573 GSD CO Dune GSD1707 GSD CO Dune GSD1732 GSD CO Dune GSD1795 GSD CO nonDune† GSD2008 GSD CO nonDune† GSD2031 GSD CO nonDune† GSD2067 GSD CO Dune GSD2252 GSD CO nonDune† GSD983 GSD CO nonDune† MON014 Mon TX Dune MON069 Mon TX Dune MON106 Mon TX Dune MON151 Mon TX Dune MON220 Mon TX Dune MON263 Mon TX nonDune‡ MON308 Mon TX nonDune‡ MON365 Mon TX nonDune‡ MON401 Mon TX nonDune‡ MON459 Mon TX nonDune‡ MON520 Mon TX Dune MON564 Mon TX Dune MON606 Mon TX nonDune‡ MON658 Mon TX nonDune‡ MON713 Mon TX nonDune‡ MON768 Mon TX nonDune‡ MON818 Mon TX nonDune‡ MON862 Mon TX Dune PET0301 PET_01 AZ nonDune PET0302 PET_01 AZ nonDune PET0303 PET_01 AZ nonDune PET0304 PET_01 AZ nonDune PET0305 PET_01 AZ nonDune PET0306 PET_01 AZ nonDune PET0307 PET_01 AZ nonDune PET0308 PET_01 AZ nonDune PET0309N PET_01 AZ nonDune PET0310 PET_01 AZ nonDune 196 Sample Population State Ecotype PET0311 PET_02 AZ nonDune PET0312 PET_02 AZ nonDune PET0313 PET_02 AZ nonDune PET0314 PET_02 AZ nonDune PET0315 PET_02 AZ nonDune PET0316 PET_02 AZ nonDune PET0317 PET_02 AZ nonDune PET0318 PET_02 AZ nonDune PET0319 PET_02 AZ nonDune PET0320 PET_02 AZ nonDune PET0321 PET_03 AZ nonDune PET0322 PET_03 AZ nonDune PET0323 PET_03 AZ nonDune PET0324 PET_03 AZ nonDune PET0325 PET_03 AZ nonDune PET0326 PET_03 AZ nonDune PET0327 PET_03 AZ nonDune PET0328 PET_03 AZ nonDune PET0329 PET_03 AZ nonDune PET0330 PET_03 AZ nonDune PET0331 PET_04 AZ nonDune PET0332 PET_04 AZ nonDune PET0333 PET_04 AZ nonDune PET0334 PET_04 AZ nonDune PET0335 PET_04 AZ nonDune PET0336 PET_04 AZ nonDune PET0337 PET_04 AZ nonDune PET0338 PET_04 AZ nonDune PET0339 PET_04 AZ nonDune PET0340 PET_04 AZ nonDune PET0341 PET_05 AZ nonDune PET0342 PET_05 AZ nonDune PET0343 PET_05 AZ nonDune PET0344 PET_05 AZ nonDune PET0345 PET_05 AZ nonDune PET0346 PET_05 AZ nonDune PET0347 PET_05 AZ nonDune PET0348 PET_05 AZ nonDune PET0349 PET_05 AZ nonDune PET0350 PET_05 AZ nonDune PET0351 PET_06 NM nonDune PET0352 PET_06 NM nonDune PET0353 PET_06 NM nonDune PET0354 PET_06 NM nonDune PET0355 PET_06 NM nonDune PET0356 PET_06 NM nonDune PET0357 PET_06 NM nonDune PET0358 PET_06 NM nonDune PET0359 PET_06 NM nonDune PET0360 PET_06 NM nonDune PET0361N PET_07 NM nonDune PET0362 PET_07 NM nonDune PET0363 PET_07 NM nonDune PET0364 PET_07 NM nonDune PET0365 PET_07 NM nonDune PET0366 PET_07 NM nonDune PET0367 PET_07 NM nonDune PET0368 PET_07 NM nonDune PET0369 PET_07 NM nonDune PET0370 PET_07 NM nonDune PET0371 PET_08 CO nonDune† PET0372 PET_08 CO nonDune† PET0373 PET_08 CO nonDune† PET0374 PET_08 CO nonDune† PET0375 PET_08 CO nonDune† PET0376 PET_08 CO nonDune† 197 Sample Population State Ecotype PET0377 PET_08 CO nonDune† PET0378 PET_08 CO nonDune† PET0379 PET_08 CO nonDune† PET0380 PET_08 CO nonDune† PET0381 PET_09 CO nonDune† PET0382 PET_09 CO nonDune† PET0383 PET_09 CO nonDune† PET0384 PET_09 CO nonDune† PET0385 PET_09 CO nonDune† PET0386 PET_09 CO nonDune† PET0387 PET_09 CO nonDune† PET0388 PET_09 CO nonDune† PET0391N PET_10 CO nonDune† PET0392N PET_10 CO nonDune† PET0393 PET_10 CO nonDune† PET0394 PET_10 CO nonDune† PET0395 PET_10 CO nonDune† PET0396 PET_10 CO nonDune† PET0397 PET_10 CO nonDune† PET0398 PET_10 CO nonDune† PET0399 PET_10 CO nonDune† PET0400 PET_10 CO nonDune† PET0401 PET_11 CO nonDune† PET0402 PET_11 CO nonDune† PET0403 PET_11 CO nonDune† PET0404 PET_11 CO nonDune† PET0405 PET_11 CO nonDune† PET0406 PET_11 CO nonDune† PET0407 PET_11 CO nonDune† PET0409 PET_11 CO nonDune† PET0410 PET_11 CO nonDune† PET0412 PET_12 AZ nonDune PET0413 PET_12 AZ nonDune PET0414 PET_12 AZ nonDune PET0415 PET_12 AZ nonDune PET0416 PET_12 AZ nonDune PET0417 PET_12 AZ nonDune PET0418 PET_12 AZ nonDune PET0419 PET_12 AZ nonDune PET0420 PET_12 AZ nonDune PET0421 PET_13 UT nonDune PET0422 PET_13 UT nonDune PET0423 PET_13 UT nonDune PET0424 PET_13 UT nonDune PET0425 PET_13 UT nonDune PET0427 PET_13 UT nonDune PET0428 PET_13 UT nonDune PET0429 PET_13 UT nonDune PET0430 PET_13 UT nonDune PET0609 PET_32 AZ nonDune PET0610 PET_32 AZ nonDune PET0611 PET_32 AZ nonDune PET0612 PET_32 AZ nonDune PET0613 PET_32 AZ nonDune PET0614 PET_32 AZ nonDune PET0615 PET_32 AZ nonDune PET0616 PET_32 AZ nonDune PET0617 PET_32 AZ nonDune PET0800 PET_32 AZ nonDune PET0618 PET_33 AZ nonDune PET0619 PET_33 AZ nonDune PET0620 PET_33 AZ nonDune PET0621 PET_33 AZ nonDune PET0622 PET_33 AZ nonDune PET0623 PET_33 AZ nonDune PET0624 PET_33 AZ nonDune 198 Sample Population State Ecotype PET0625 PET_33 AZ nonDune PET0626 PET_33 AZ nonDune PET0627 PET_33 AZ nonDune PET0628 PET_34 AZ nonDune PET0629 PET_34 AZ nonDune PET0630 PET_34 AZ nonDune PET0631 PET_34 AZ nonDune PET0632 PET_34 AZ nonDune PET0633 PET_34 AZ nonDune PET0634 PET_34 AZ nonDune PET0635 PET_34 AZ nonDune PET0636 PET_34 AZ nonDune PET0637 PET_34 AZ nonDune PET0638 PET_35 AZ nonDune PET0639 PET_35 AZ nonDune PET0640 PET_35 AZ nonDune PET0641 PET_35 AZ nonDune PET0642 PET_35 AZ nonDune PET0643 PET_35 AZ nonDune PET0644 PET_35 AZ nonDune PET0645 PET_35 AZ nonDune PET0646 PET_35 AZ nonDune PET0647 PET_35 AZ nonDune PET0718 PET_43 NM nonDune PET0719 PET_43 NM nonDune PET0720 PET_43 NM nonDune PET0721 PET_43 NM nonDune PET0722 PET_43 NM nonDune PET0723 PET_43 NM nonDune PET0724 PET_43 NM nonDune PET0725 PET_43 NM nonDune PET0726 PET_43 NM nonDune PET0727 PET_43 NM nonDune PET0748 PET_46 TX nonDune‡ PET0749N PET_46 TX nonDune‡ PET0750 PET_46 TX nonDune‡ PET0751 PET_46 TX nonDune‡ PET0752 PET_46 TX nonDune‡ PET0753 PET_46 TX nonDune‡ PET0754 PET_46 TX nonDune‡ PET0755N PET_46 TX nonDune‡ PET0756N PET_46 TX nonDune‡ PET0757 PET_46 TX nonDune‡ PET0758 PET_47 TX Dune PET0759 PET_47 TX Dune PET0760 PET_47 TX Dune PET0761 PET_47 TX Dune PET0762 PET_47 TX Dune PET0763 PET_47 TX Dune PET0764 PET_47 TX Dune PET0765 PET_47 TX Dune PET0766 PET_47 TX Dune PET0767 PET_47 TX Dune PET0768 PET_48 TX nonDune‡ PET0769 PET_48 TX nonDune‡ PET0770 PET_48 TX nonDune‡ PET0774N PET_48 TX nonDune‡ PET0775 PET_48 TX nonDune‡ PET0776 PET_48 TX nonDune‡ PET0777 PET_48 TX nonDune‡ PET0778 PET_49 TX Dune PET0779N PET_49 TX Dune PET0780N PET_49 TX Dune PET0781 PET_49 TX Dune PET0783 PET_49 TX Dune PET0784 PET_49 TX Dune 199 Sample Population State Ecotype PET0785 PET_49 TX Dune PET0786 PET_49 TX Dune PET0787 PET_49 TX Dune PET0788 PET_50 TX nonDune‡ PET0789 PET_50 TX nonDune‡ PET0790 PET_50 TX nonDune‡ PET0791 PET_50 TX nonDune‡ PET0792 PET_50 TX nonDune‡ PET0793 PET_50 TX nonDune‡ PET0794 PET_50 TX nonDune‡ PET0795 PET_50 TX nonDune‡ PET0796 PET_50 TX nonDune‡ PET0797 PET_50 TX nonDune‡ †Excluded from non-dune group in the analyses with MON system. ‡Excluded from non-dune group in the analyses with GSD system. 200 C.2 Maximum likelihood trees of parallel regions. (a) Ha412HOChr04:91990564-139990182, (b) Ha412HOChr06:101167874-106607943, (c) Ha412HOChr09:107701884-116410572, (d) Ha412HOChr11:5890343-5916951, (e) Ha412HOChr12:96918451-97184453, (f) Ha412HOChr13:105437209-105575210, (g) Ha412HOChr16:140217167-142747183, (h) Ha412HOChr17:19185748-19215393, (i) Ha412HOChr17:190415052-191878407. Samples were chosen from each of the dune populations (GSD_dune and MON_dune), the non-dune populations in the proximity of each dune system (GSD_nondune, MON_nondune), H. petiolaris sp. petiolaris (petpet) and perennial outgroup (GRO_2043 and DIV_1956). The trees were constructed using the SNPs within each region and analyzed using IQ-TREE. Nodes with bootstrap values > 70 are marked by turquoise circles. Note that branch length is not of the same scale among different trees. (a) (b)GSD1009GSD1039GSD1732GSD1276GSD1707PET0393PET0394PET0397PET0395PET0396MON014MON862MON069MON106MON520PET0718PET0720PET0725PET0722PET0726DIV_1956GRO_2043PET0505PET0512PET0554PET0539PET0523100100100100100100100100100100100975458100100100100100100100100100100GroupaaaaaaGSD_duneGSD_nonduneMON_duneMON_nonduneoutgrouppetpetHa412HOChr11_16016058_62368169(f)(d)(g)(e)(c)(h) (i)
Thesis/Dissertation
eng
Botany
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
The genomics of adaptation and speciation in wild sunflowers
Text