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Adaptive introgression from Populus balsamifera (balsam poplar) in P. trichocarpa (black cottonwood) Suarez-Gonzalez, Adriana 2017

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ADAPTIVE INTROGRESSION FROM POPULUS BALSAMIFERA (BALSAM POPLAR) IN P. TRICHOCARPA (BLACK COTTONWOOD) by  Adriana Suarez-Gonzalez  B.Sc., Universidad Industrial de Santander, 2007 M.Sc., University of Winnipeg, 2011  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 2017  © Adriana Suarez-Gonzalez, 2017 ii  Abstract Hybridization is a widespread phenomenon that has shaped the genome of many lineages. In natural hybrid zones, back-crossing of early-generation hybrids over subsequent generations can result in introgression which can introduce adaptive genetic variation. Populus has emerged as a model genus for population genomic studies of adaptation due to porous species barriers and a wealth of genomic resources available. Populus trichocarpa and P. balsamifera are sibling poplar species ecologically divergent and adapted to strongly contrasting environments. In this thesis, I provide evidence for adaptive introgression in P. trichocarpa and P. balsamifera, by implementing local ancestry analyses together with functional, phenotypic and selection tests. Based on a local ancestry analysis across the whole genome, I detected asymmetric patterns of introgression with stronger signals of introgression from P. balsamifera to P. trichocarpa than vice versa. There was no overlap between the introgressed regions in P. trichocarpa compared to those in P. balsamifera or the enriched GO and Pfam terms in the introgressed regions. In admixed P. trichocarpa individuals, candidate regions for adaptive introgression showed strong signals of selection and were enriched for genes that may play crucial roles for survival and adaptation. These analyses also revealed overrepresentation of P. balsamifera introgression in subtelomeric regions and possible protection of the sex-determining regions from interspecific gene flow. An admixture mapping analysis and phenotypic tests provided strong support that introgressive hybridization is a driver of clinal adaptation in P. trichocarpa and suggest that the northern range extension of P. trichocarpa depends, at least in part, on introgression of alleles from P. balsamifera. A number of genomic regions showing unusually high levels of P. balsamifera introgression were significantly associated with putatively adaptive trait combinations, in particular, regions on chromosome 9 and iii  chromosome 15. Overall, these results contribute to our understanding of introgression as a source of genetic variation associated with adaptive characters that may allow improved survival in new environments. To my knowledge, this is the first fine-scale study on natural hybrids of tree species with such a comprehensive view of the effects of admixture in adaptation.  iv  Preface I designed and analyzed this research as well as wrote this thesis and three manuscripts. CJ Douglas and QCB Cronk provided guidance with the planning of the project and contributed to writing all the content.   Chapter two has been published as:  Suarez-Gonzalez A, Hefer CA, Christe C, Corea O, Lexer C, Cronk QCB, and Douglas CJ (2016) Genomic and functional approaches reveal a case of adaptive introgression from Populus balsamifera (balsam poplar) in P. trichocarpa (black cottonwood). Molecular Ecology, 25, 2427-2442. I designed, analyzed and wrote the research. CJ Douglas, QCB Cronk and C Lexer provided assistance for the study design and valuable input towards the final manuscript. C Christe provided assistance in the development of the scripts to implement RASPberry. O Correa generated the gene co-expression network. C Hefer performed SNP calling and RNAseq bioinformatics analyses. All authors approved the final version.  Chapter three has been submitted for publication as:  Suarez-Gonzalez A, Hefer CA, Lexer C, Cronk QCB, and Douglas CJ. Scale and direction of adaptive introgression between black cottonwood (Populus trichocarpa) and balsam poplar (Populus balsamifera).  I designed, analyzed and wrote the research. CJ Douglas, QCB Cronk and C Lexer provided assistance for the study design and valuable input towards the final manuscript. C Hefer performed SNP calling. All authors approved the final version.   Chapter four will be submitted for publication as:  v  Suarez-Gonzalez A, Hefer CA, Lexer C, Cronk QCB, and Douglas CJ. Introgression from Populus balsamifera underlies adaptation in P. trichocarpa I designed, analyzed and wrote the research. CJ Douglas, QCB Cronk and C Lexer provided assistance for the study design and valuable input towards the final manuscript. C Hefer performed SNP calling.   vi  Table of Contents Abstract....................................................................................................................................... ii Preface ....................................................................................................................................... iv Table of Contents ....................................................................................................................... vi List of Tables .............................................................................................................................. xii List of Figures ........................................................................................................................... xiii List of Abbreviations .................................................................................................................. xv Acknowledgements ................................................................................................................. xvii Dedication ................................................................................................................................ xix Chapter 1: Introduction ................................................................................................................ 1 1.1 Species boundaries ................................................................................................................ 1 1.2 Hybridization and introgression ............................................................................................ 2 1.3 Differential introgression ...................................................................................................... 3 1.4 Hybridization: Evolutionary novelty or a destructive force? ................................................ 4 1.4.1 Examples of adaptive introgression in animals ................................................................. 5 1.4.2 Examples of adaptive introgression in plants ................................................................... 6 1.5 Poplar as a model organism .................................................................................................. 7 1.5.1 Populus trichocarpa and Populus balsamifera .................................................................. 8 1.5.2 Genetic diversity, phenotypic clines and admixture from P. balsamifera in P. trichocarpa ..................................................................................................................................... 9 1.6 Research questions ............................................................................................................. 10 1.6.1 Targeted approach to identify adaptive introgression ................................................... 11 vii  1.6.2 Whole genome approach to identify the scale and direction of introgression .............. 11 1.6.3 Whole genome approach to determine the effects of introgression in clinal trait variation ....................................................................................................................................... 12 Chapter 2: Genomic and functional approaches reveal a case of adaptive introgression from Populus balsamifera (balsam poplar) in P. trichocarpa (black cottonwood) ................................. 13 2.1 Introduction ......................................................................................................................... 13 2.2 Materials and methods ....................................................................................................... 18 2.2.1 Samples ........................................................................................................................... 18 2.2.2 Sequencing, read mapping and variant calling ............................................................... 19 2.2.3 Inference of local ancestry .............................................................................................. 20 2.2.4 Characterization of introgressed regions and tests of selection in pure species and admixed individuals ..................................................................................................................... 21 2.2.5 Haplotype distribution of candidate genes for local adaptation .................................... 22 2.2.6 Gene expression and phenotypic analysis in admixed P. trichocarpa individuals .......... 23 2.3 Results ................................................................................................................................. 24 2.3.1 Local ancestry analysis revealed three introgressed regions ......................................... 24 2.3.2 Selection in pure P. balsamifera and P. trichocarpa ....................................................... 26 2.3.3 Haplotype distribution of candidate introgressed genes on P. trichocarpa ................... 30 2.3.4 Evidence for adaptive introgression based on GO, gene expression, and phenotypic analysis ........................................................................................................................................ 33 2.4 Discussion ............................................................................................................................ 37 2.4.1 A telomeric region on chromosome 15 is a hot spot for introgression .......................... 37 viii  2.4.2 Evidence that introgressed P. balsamifera alleles are functionally different from P. trichocarpa variants ..................................................................................................................... 38 2.4.3 Introgression and functional divergence in paralogous genes ....................................... 42 Chapter 3: Scale and direction of adaptive Introgression between black cottonwood (Populus trichocarpa) and balsam poplar (Populus balsamifera) ............................................................... 44 3.1 Introduction ......................................................................................................................... 44 3.2 Materials and methods ....................................................................................................... 46 3.2.1 Samples ........................................................................................................................... 46 3.2.2 Sequencing, read mapping and variant calling ............................................................... 48 3.2.3 Inference of local ancestry .............................................................................................. 48 3.2.4 Climate of tree origin in admixed individuals ................................................................. 49 3.2.5 Enrichment analysis ........................................................................................................ 49 3.2.6 Tests of selection in the introgressed regions from the pure species individuals .......... 50 3.2.7 Levels of introgression in sex-determining regions ........................................................ 50 3.3 Results ................................................................................................................................. 51 3.3.1 Local ancestry in RASPberry vs previous admixture analysis.......................................... 51 3.3.2 Introgression patterns are strongly asymmetric between the two poplar species ........ 51 3.3.3 Subtelomeric enrichment of introgressed regions ......................................................... 55 3.3.4 Introgressed regions show enrichment for disease resistance genes ............................ 56 3.3.5 Introgressed regions in pure individuals are under selection ......................................... 58 3.3.6 Are sex-determining regions protected from interspecific introgression? .................... 58 3.4 Discussion ............................................................................................................................ 59 ix  3.4.1 Direction and scale of adaptive introgression into P. trichocarpa in relation to environment ................................................................................................................................ 59 3.4.2 Implications for future climates ...................................................................................... 61 3.4.3 Disease resistance genes and structural properties may explain subtelomeric enrichment of introgressed regions ............................................................................................ 62 3.4.4 Possible protection of the sex-determining regions from interspecific gene flow ........ 64 Chapter 4: Introgression from Populus balsamifera underlies adaptation and range boundaries in P. trichocarpa ............................................................................................................................ 66 4.1 Introduction ......................................................................................................................... 66 4.2 Methods .............................................................................................................................. 68 4.2.1 Samples ........................................................................................................................... 68 4.2.2 Ancestries and phenotypes ............................................................................................. 69 4.2.3 Admixture mapping......................................................................................................... 70 4.2.4 Phenotypic analysis of associations from admixture mapping ....................................... 71 4.2.5 Climate of tree origin in admixed and pure individuals .................................................. 72 4.2.6 Enrichment analysis ........................................................................................................ 73 4.3 Results ................................................................................................................................. 73 4.3.1 Introgressed regions from P. balsamifera are associated with trait variation in P. trichocarpa ................................................................................................................................... 73 4.3.2 Admixture drives adaptively relevant trait variation across a latitudinal gradient ........ 74 4.3.3 An introgressed haplotype on chromosome 9 restores parental phenotype in admixed P. trichocarpa ............................................................................................................................... 75 x  4.3.4 Introgression in the telomeric region of chromosome 15 confirms signals of adaptive introgression ................................................................................................................................ 80 4.4 Discussion ............................................................................................................................ 80 4.4.1 Admixture mapping connects phenotypic traits with genomic regions ......................... 80 4.4.2 Introgression extends the species range of P. trichocarpa by driving clinal adaptation of phenology traits ........................................................................................................................... 82 4.4.3 An introgressed haplotype on chromosome 9 restores parental phenotype at certain latitudes ....................................................................................................................................... 83 4.4.4 Adaptive introgression increases plant disease resistance ............................................ 84 4.4.5 Candidate genes for adaptive introgression ................................................................... 85 4.4.6 Introgressed alleles in a telomeric region of chromosome 15 create a transgressive phenotype in P. trichocarpa background .................................................................................... 86 Chapter 5: Conclusion ................................................................................................................ 88 5.1 Summary ............................................................................................................................. 88 5.2 Genome-wide patterns of introgression ............................................................................. 89 5.2.1 Subtelomeric enrichment of adaptively introgressed regions ....................................... 89 5.2.2 Possible protection of the sex-determining regions from interspecific gene flow ........ 90 5.3 Introgression into P. balsamifera: Implications for future climates ................................... 90 5.4 Introgression is a driver of adaptation in P. trichocarpa northern range ........................... 91 5.4.1 Introgression drives clinal adaptation of phenology traits ............................................. 92 5.4.2 An introgressed haplotype on chromosome 9 restores parental phenotype at certain latitudes ....................................................................................................................................... 93 xi  5.4.3 Introgressed alleles in a telomeric region of chromosome 15 create a transgressive phenotype in P. trichocarpa background .................................................................................... 94 5.5 Future directions and applications of this research ............................................................ 94 5.5.1 Candidate genes in the introgressed region ................................................................... 95 5.5.1.1 PRR5 and COMT1 are strong candidate genes for adaptive introgression in the telomeric region on chromosome 15 ...................................................................................... 95 5.5.1.2 NRT2 is a strong candidate gene for adaptive introgression on chromosome 9 ... 96 5.5.1.3 Candidate genes associated with oxidative stress in an introgressed region on chromosome 3 ......................................................................................................................... 97 5.5.1.4 Candidate genes associated with disease resistance in an introgressed region on chromosome 7 ......................................................................................................................... 97 Bibliography .............................................................................................................................. 99 Appendices .............................................................................................................................. 121 Appendix A Supplementary information for chapter 2 ................................................................. 121 Appendix B Supplementary information for chapter 3 ................................................................. 136 Appendix C Supplementary information for chapter 4 ................................................................. 140  xii  List of Tables Table 2-1. List of three introgressed regions ....................................................................................... 26 Table 2-2. Alpha estimates for three introgressed P. balsamifera regions ......................................... 30 Table 3-1 Summary of the Populus balsamifera introgressed regions ................................................ 53 Table 4-1 Summary of the results from admixture mapping. ............................................................. 74 Table 4-2 List of the P. balsamifera introgressed regions ................................................................... 76 Table 4-3 List of traits that showed significant associations with introgressed haplotypes ............... 77 Table A-1. List of P. trichocarpa and P. balsamifera accessions used in this study. .......................... 128 Table A-2. Parameters for the RASPberry model .............................................................................. 131 Table A-3. Quantitative reverse transcription PCR (qTt-PCR) ............................................................ 132 Table A-4. Candidate genes for local adaptation ............................................................................... 132 Table A-5. List of GO terms enriched in the introgressed regions ..................................................... 133 Table A-6. List of genes in introgressed region B on chromosome 15 that showed significantly different levels of expression ............................................................................................................. 134 Table A-7. Gene expression levels in different genotypes of COMT1 ............................................... 135 Table B-1. List of environmental variables used in a principal component analysis.. ....................... 139 Table C-1. List of traits used in the admixture mapping analysis in P. trichocarpa. .......................... 140 Table C-2. P-values of ANOVAs of 16 traits in pure and admixed P. trichocarpa. ............................. 142   xiii  List of Figures Figure 2-1. Geographic distribution of admixed and pure individuals used in the targeted local ancestry analysis .................................................................................................................................. 16 Figure 2-2. Proportion of P. balsamifera ancestry ............................................................................... 25 Figure 2-3. Tajima’s D analysis across chromosome 15 ....................................................................... 28 Figure 2-4. Linkage disequilibrium (LD) plot of the first 200-kb of chromosome 15 ........................... 29 Figure 2-5. COMT1 alleles and geographic distribution ...................................................................... 32 Figure 2-6. COMT1 (A) and TTG1 (B) gene expression levels .............................................................. 35 Figure 2-7. Boxplot diagrams depicting chlorophyll concentration index and leaf nitrogen content 36 Figure 3-1 Geographic distribution of admixed and pure individuals used in the whole genome local ancestry analysis .................................................................................................................................. 47 Figure 3-2 Proportion of admixed ancestry across 19 chromosomes ................................................. 54 Figure 3-3 Relationship between levels of admixture and environmental variables. ......................... 55 Figure 3-4 Relationship between the levels of P. balsamifera introgressed ancestry and distance to the telomeres ....................................................................................................................................... 56 Figure 3-5 Chromosome diagrams depicting introgressed regions (arrows) enriched for Pfam terms associated with R proteins ................................................................................................................... 57 Figure 3-6 Average Tajima’s D within each introgressed region and in windows without introgressed blocks ................................................................................................................................................... 60 Figure 4-1 Geographic distribution of admixed P. trichocarpa individuals (circles) used in admixture mapping ............................................................................................................................................... 70 Figure 4-2 Relationship between trait variation and geoclimate variables ......................................... 78 xiv  Figure 4-3 Trait variation in admixed and pure P. trichocarpa ............................................................ 79 Figure 4-4 Variation in the chlorophyll content index in admixed and pure P. trichocarpa and P. balsamifera individuals ........................................................................................................................ 81 Figure A-1. Neighbor-joining (NJ) trees ............................................................................................. 121 Figure A-2. Proportion of P. balsamifera ancestry ............................................................................ 122 Figure A-3. Neighbor-joining (NJ) tree of parental lineages .............................................................. 123 Figure A-4. Linkage disequilibrium (LD) decay with distance ............................................................ 124 Figure A-5. Nucleotide diversity (π) levels ......................................................................................... 125 Figure A-6. Partial protein alignment of different COMT1 homologs. .............................................. 125 Figure A-7. Relative gene expression (based on 2–ΔCT) of COMT1 .................................................. 126 Figure A-8. Box plot of the mean-centered FPKM values .................................................................. 126 Figure A-9. LD plot of COMT1 ............................................................................................................ 127 Figure A-10. Three-dimensional protein model of P. trichocarpa COMT1 ........................................ 128 Figure B-1. Genome-wide ancestry analysis ...................................................................................... 136 Figure B-2. Average width of P. balsamifera introgressed blocks ..................................................... 136 Figure B-3. Average P. balsamifera (A) and P. trichocarpa (B) introgressed ancestry ...................... 137 Figure B-4 Correlation between the levels of P. trichocarpa introgressed ancestry and distance to the telomeres ..................................................................................................................................... 137 Figure B-5. Proportion of introgressed ancestry across chromosomes that showed SNPs significantly associated with sex ............................................................................................................................ 138 Figure B-6. Loading of the temperature and moisture variables used in the PCA ............................ 138 Figure C-1. Haplotype blocks ............................................................................................................. 140 xv  List of Abbreviations ANAC NAC domain containing protein  ANOVA  Analysis of variance ATP Adenosine Triphosphate CCI Chlorophyll Concentration Index  CCT CONSTANS, CO-like, and TOC1 COMT CAFFEIC ACID 3-O-METHYLTRANSFERASE  DAY Maximum day length Dn Number of nonsynonymous substitutions per non-synonymous site  DNA  Deoxyribonucleic acid Ds Number of synonymous substitutions per synonymous site FAR Far-red-impaired response FHY Far-red elongated hypocotyls FPKM Fragments Per Kilobase of transcript per Million mapped reads FST Fixation index GO Gene Ontology kb Kilobase L238 Leucine238 LD Linkage disequilibrium  LRR_1 Leucine-rich repeat protein family MAF  Minor allele frequency Mb Megabase xvi  MAP Mean annual precipitation MAT Mean annual temperature NBS-LRRs Nucleotide binding sites and leucine-rich repeat nsSNP Nonsynonymous single nucleotide polymorphism  NFFD Number of frost-free days P287Q Proline287Glutamine PCA Principal component analysis Pfam Protein family PRR5 PSEUDORESPONSE REGULATOR5 R396W Arginine396Tryptophan RNA Ribonucleic acid SD Standard deviation SDR Sex-determining region SNP Single-nucleotide polymorphism SRA Sequence read archive TIR Toll/interleukin-1 receptor TTG Transparent testa glabra NRT Nitrate transporter sSNP Synonymous single nucleotide polymorphism   xvii  Acknowledgements Undertaking this Ph.D. was definitely an exciting and a truly life-changing journey. My experience extended well beyond the development of this thesis and allowed me to grow not only professionally but also personally. The support and guidance that I received for my research and non-research activities were invaluable. First and foremost, I would like to thank Carl Douglas. His support was key for the development of my research project and my professional development. Working with him was a life experience and allowed me to grow as a scientist and mentor. His passion for research and mentoring together with his environmental-friendly lifestyle and enthusiasm for outdoors activities was inspiring and these memories of him will always remain with us.  I also would like to give special thanks to Christian Lexer who invited me to his lab at the University of Fribourg and later at the University of Vienna. His guidance and expertise on evolutionary genetics allowed me to implement innovative genomic tools as well as to develop relevant and interesting research questions. I am also grateful for Camille’s assistance with the Raspberry analysis. Camille Christe, a former Ph.D. student in Christian’s lab, was an amazing Swiss host and our lunches in the botanical garden were a great place to get inspired and recharge energies.    I would like to express huge thanks to Quentin Cronk for welcoming in his lab for the last term of my Ph.D. His prompt feedback and guidance allowed me to organize my research chapters, see the big picture and finalize my thesis as scheduled.  I thank the POPCAN team, especially Armando Geraldes and Charles Hefer, who were always happy to meet, discuss, provide suggestion and ask tough questions. xviii  I would also like to thank my committee members— Robert Guy and Keith Adams —who provided valuable feedback, comments, and suggestions on this research.  Several volunteers contributed to data collection, in particular Judy Wu who assisted in the collection of chlorophyll concentration index during the summer of 2015. Joe Liu, Clarence Yeung, Sanaz Hamzeh, Manpreet Takhi, and Melina Huang helped in laboratory analysis.  My fellow graduate students in the Botany Department provided friendship, support and fun moments. Of those students, I would like to particularly thank Shumin Wang, Lan Tran, Anna Bazzicalupo, Ludo Le Renard and Marybel Soto. I offer my enduring gratitude to the UBC and SCWIST community for their support in my outreach, mentorship and innovation initiatives. I owe particular thanks to Andrea Lloyd, Zaira Petruf, Po On Yeung, Silvia Moreno, Mairin Kerr, Laura Supper, Catherine Hoffman, Fariba Pacheleh, Christin Wiedemannand, Stefanie Butland, the BioTAP team and the GenXys team.  I am forever grateful to my mom, dad and Dianita (“la mamita”), who gave me the freedom of choosing my career and supported me all the way. Thank you for encourage me to work hard for my dreams. Felixito, you were my most important support during this journey, especially during the lows of this roller coaster ride. I love you from the bottom of my hypothalamus.  My work was partially supported by a UBC-Affiliated Fellowship, a ThinkSwiss Fellowship, the Biodiversity Research Integrative Training & Education (BRITE) program, a Genome Canada Large-Scale Applied Research Program (POPCAN, project 168BIO) and a Natural Sciences and Engineering Research Council of Canada Discovery Grant (RGPIN 36485-12). xix  Dedication I dedicate this thesis to Sarita Gutierrez-Suarez and her generation. Use your lives wisely acknowledging that you are not apart from nature but a part of nature. Use your intelligence to generate and implement innovative ideas that will allow you to live a happy life in harmony with the environment.  1  Chapter 1: Introduction 1.1 Species boundaries Our understanding of the nature of species boundaries and barriers to gene flow between species has changed greatly over the last decades. In 1942, Ernst Mayr developed the classic Biological Species Concept (BSC) focusing on the importance of genome-wide reproductive isolation and assuming species genomes behave as cohesive or coadapted units (Mayr 1942). Although a widely used framework for empirical studies identifying the phenotypes and genotypes driving gene exchange (Coyne & Orr 2004), the BSC has been often questioned. Mallet (2008) argued that Mayr’s BSC failed to recognize the importance of continuity between species, particularly introgression in hybrids, and to acknowledge the maintenance of divergence between lineages in spite of gene flow (Mallet 2008). A ‘genic view’ of the process of speciation extended the BSC and defined species as groups adapted to different environments with a modest number of loci controlling species isolation (Wu 2001). According to this ‘genic view’ of speciation, alleles at other loci in the genome may or may not be differentiated but genes controlling differential adaptive characters will not move freely between species. In the past decade, speciation theory has shifted from the traditional geographic modes of speciation to include a ‘divergence with gene flow’ framework, in which the ‘speciation continuum’ ranges from weakly to completely isolated species (Nosil et al. 2009; Feder et al. 2012). Species boundaries have also been characterized as being porous or semipermeable, with the latter implying that differential introgression results from a selective process (Harrison & Larson 2014).  2  1.2 Hybridization and introgression Hybridization has been defined as the successful mating of individuals from two populations or groups of populations that are differentiated on the basis of one or more heritable characters. When parental taxa hybridize, a hybrid F1 progeny is formed. Subsequent crosses between F1 individuals or backcrosses to parental taxa result in individuals and populations with varying degrees of admixture. In early generation and later generation backcrosses, recombination breaks up parental allele combinations and with additional generations of recombination, the size of ancestry blocks and the linkage disequilibrium between neighboring loci decay (Macleod et al. 2005; Pfaff et al. 2000).  Introgression, defined as the stable integration of genomic regions of one parental species into the genome of another, is a natural process that occurs through hybridization and repeated backcrossing (Rieseberg & Wendel 1993). Although stochastic processes such as genetic drift are main drivers of introgressive hybridization, natural selection also plays an important role when adaptively important alleles are exchanged across species boundaries. Loci associated with advantageous traits are expected to introgress more frequently (“adaptive introgression”; e.g., see (Whitney et al. 2006; Pardo-Diaz et al. 2012; Hedrick 2013); while alleles that contribute to local adaptation or reproductive isolation will introgress little or not at all (Barton 1979; Shaw & Mullen 2011). Hybridization promotes new adaptations when sets of multiple linked mutations associated with adaptive traits and previously tested by natural selection on the original parental background are introduced (Arnold 2004; Abbott et al. 2013). Furthermore, introgressed alleles could result in favorable gene combinations in the genomic background and accelerate adaptation (Barton 2001). 3  1.3 Differential introgression The permeability of species boundaries to introgression depends in large part on the genomic distribution and number of loci associated with reproductive isolation (Barton & Hewitt 1985; Harrison 1990). The possibility of introgression increases considerably if isolation is due to a small number of clustered loci, and will depend not only on the fitness effects of the alien allele in the genetic background of the recurrent parent but also on the genomic location of the locus. The likelihood of successful introgression is low when a large number of loci that contribute to isolation are evenly dispersed.  Hybrid zones are valuable tools to examine patterns of differential introgression (Lexer et al. 2004) and have been described as clines or “tension zones” maintained by a balance between dispersal and selection against hybrids (Barton & Hewitt 1985). Although many hybrid zones are often represented as a narrow zone along the edges of the parental species distributions, species with a patchy distribution where their ranges overlap will form mosaic hybrid zones (Ross & Harrison 2002). These contact zones may occur in different spatial and temporal contexts such as in abrupt parapatric boundaries vs locally adapted populations, or during secondary contact after a period of geographic isolation vs. continuous contact with divergent selection (Harrison 1993; Abbott et al. 2013). Natural hybrid zones arising from secondary contact of previously allopatric populations, in particular, often display differential patterns of introgression (Barton & Hewitt 1985; Harrison 1990; Harrison & Larson 2014). In fact, differential introgression seems to be the norm when multiple loci are studied, and in some cases, asymmetric introgression is also detected (Harrison & Larson 2014). The environmental context in which hybridization occurs together with 4  dispersion distance and processes of natural selection are key factors underlying the geographic patterns and spatial scale of introgression.  Genome-wide studies have mainly focused on identifying genomic regions with restricted introgression and associated with speciation phenotypes (e.g. (Lawniczak et al. 2010; Hohenlohe et al. 2010). Comparisons of species that are ecologically distinct but morphologically indistinguishable have documented that divergence is not restricted to a few discrete regions but rather that “divergence islands” are widespread across the genome (e.g. (Michel et al. 2010; Ellegren et al. 2012; Renaut et al. 2013). A clear pattern detected by these studies is that introgression is suppressed in regions with reduced recombination such as sex chromosomes (Lawniczak et al. 2010; Carneiro et al. 2014; Ellegren et al. 2012) and regions adjacent to centromeres (Noor et al. 2001; Rieseberg 2001; Navarro & Barton 2003). Genomic patterns of introgression are less understood and more empirical data are needed to detect if certain genomic regions (e.g. telomeres) or genes from certain biological terms are more prone to be introgressed than others.  1.4 Hybridization: Evolutionary novelty or a destructive force?  The evolutionary importance of natural hybridization and introgression has been a matter of great debate (Seehausen 2004). In some cases, hybridization may act as a destructive force producing hybrids with reduced fitness (Todesco et al. 2016). When hybrids are not completely sterile and hybridization persists, outbreeding depression may arise from epistatic incompatibilities (Coyne & Orr 2004; Costa e Silva et al. 2012). The strength of this evolutionary process is likely to have a greater impact when hybridization occurs between divergent lineages (Burke & Arnold 2001), where changes in genomic sequence, structure, and location result in incompatibilities in interspecific hybrids. 5  In turn, introgression can also be a source of evolutionary novelty when hybridization aids the transfer of advantageous characters. Novel genetic variation, including new gene combinations, can be introduced by introgression at a faster rate than through mutation alone and results in adaptive introgression when adaptive introgressed alleles are maintained over time by natural selection (Twyford & Ennos 2012; Slarkin 1985; Rieseberg et al. 2003). In some cases, plant hybrids may acquire transgressive phenotypes, which are new adaptive traits that increase their fitness compared to parental species or allow hybrids to colonize new environments (Yakimowski & Rieseberg 2014; Rieseberg et al. 2003). Particularly in rapidly changing environments, adaptive introgression can be an important source of genetic variation, when standing genetic variation and mutation alone may only offer limited potential for adaptation (Hedrick 2013; Hamilton & Miller 2015). A number of reports in plants and animals have provided evidence that adaptive introgression has been key for adaptation to biotic and abiotic environments. 1.4.1 Examples of adaptive introgression in animals Evidence of adaptive introgression in animals include: - introgression of a region linked to mimetic red patterns in Heliconius butterflies (Pardo-Diaz et al. 2012), - acquired resistance to anticoagulant pesticides in European house mouse (Mus musculus domesticus) from a closely related species [M. spretus, (Song et al. 2011)], - introgression of a suite of insecticide resistance alleles in African malaria vectors (Anopheles) coincident with the start of a major insecticide-treated bed net distribution (Norris et al. 2015), and  6  - Neanderthal and Denisovan introgression that has allowed humans to adapt rapidly to a variety of new environmental conditions (Racimo et al. 2015). Reports of adaptive introgression from the admixture between archaic and modern human groups include: o the human leukocyte antigen (HLA), a key regulator of the immune system which shows signatures of balancing selection (Abi-Rached et al. 2011), o the OAS (2ʹ-5ʹ-oligoadenylate synthetase) gene cluster linked to innate immune response to viral infection and that has been subjected to two separate archaic introgression events (Mendez et al. 2013), o alleles from HYAL2 (hyaluronoglucosaminidase 2) associated with response to ultraviolet-B irradiation that were lost in the ancestors of Eurasians after migrating out of Africa but regained through admixture with Neanderthals (Sankararaman et al. 2014), and  o introgression of alleles in the EPAS1 (endothelial PAS domain protein 1) gene in Tibetans which are associated with significantly reduced haemoglobin levels and possibly adaptation to high-altitude hypoxia (Huerta-Sanchez et al. 2014).  1.4.2 Examples of adaptive introgression in plants Introgression of adaptive genetic variation has also been documented in plants. In sunflowers, the introgression of only three morphological quantitative trait loci (QTL) from Helianthus debilis into a H. annuus background was sufficient to recover the phenotype of H. a. texanus, a natural hybrid of these two species (Kim & Rieseberg 1999). The combination of traits gained from each parental species, including the shape of bracts surrounding the flower head and the shape of the fruits from H. annuus or stem speckling and fruit size from H. debilis, results in a unique phenotype. In addition, introgression from H. debilis into a H. a. annuus genetic background 7  resulted in increased herbivore resistance of H. a. texanus suggesting that introgression of biotic resistance traits was important in the adaptation of this hybrid species.  Adaptive trait transfer has also been reported in the flood-tolerant Iris fulva and the dry-adapted Iris brevicaulis. In artificial backcrosses of these two species, the ability to survive extreme flooding conditions was strongly influenced by the presence of introgressed I. fulva alleles located throughout the genome (Martin et al. 2006).  In long-lived tree species, a number of studies have reported the contribution of interspecific hybridization to local adaptation [e.g. spruce (de Lafontaine et al. 2015; Hamilton et al. 2014), Eucalyptus (Larcombe et al. 2015), and oak (Abadie et al. 2012)]. However, some of these studies focus on the nature and strength of reproductive isolation. Reports documenting fine-scale introgression patterns or identifying adaptive introgression at the gene level have mainly focused on taxa with short generation times and have not been carried out in tree species.  1.5 Poplar as a model organism Natural hybrid zones provide evolutionary laboratories to study the transfer of adaptive genetic variation by introgression (Lexer et al. 2004; Buerkle & Lexer 2008). Forest tree species are attractive organisms to study adaptive introgression because of their extensive natural hybrid zones, large ranges across geographical and climatic clines as well as substantial trait variation (Savolainen et al. 2007; Soolanayakanahally et al. 2009; Keller et al. 2011; McKown et al. 2013). Populus in particular has emerged as a model for population genomic studies of adaptation (Weigel & Nordborg 2015) due to porous species barriers and a wealth of genomic resources available including a sequenced genome and annotated gene models (Cronk 2005; Jansson & Douglas 2007; Tuskan et al. 2006). Forest trees from this genus are major components of the forest ecosystems of 8  northern North America and are economically important, being wood pulp sources and potential feedstock for cellulosic ethanol production (Jansson & Douglas 2007; Porth & El-Kassaby 2015). 1.5.1 Populus trichocarpa and Populus balsamifera Populus trichocarpa and P. balsamifera are sibling poplar species of the section Tacamahaca that diverged in allopatry rather recently (~76 Ka) during Pleistocene glaciations (Levsen et al. 2012); [see (Ismail et al. 2012) for an alternative - older - estimate]. Despite their recent divergence and morphological similarity, with some minor differences in flower and fruit morphology, these species are ecologically divergent and adapted to strongly contrasting environments.  Populus trichocarpa is distributed throughout the western US and Canada, from northern California to southern Alaska, and is adapted to relatively humid, moist, and mild conditions west of the Rocky Mountains. P. balsamifera, on the other hand, is largely a boreal species distributed from Alaska to Newfoundland, with high frost tolerance and adapted to environments where there can be huge annual temperature differences (-62˚C to 44˚C) and generally less annual precipitation (Richardson et al. 2014; Geraldes et al. 2014). Along its range, P. trichocarpa exhibits moderate levels of population structure and high levels of gene flow promoted by obligate outcrossing (i.e. dioecy) and wind pollination/seed dispersal (Slavov et al. 2012; Geraldes et al. 2014). Despite this among-population connectivity, common garden tests show variation in several adaptive traits (e.g. phenology, growth, and disease susceptibility) suggesting that selection plays an important role shaping the genetic and phenotypic distribution of this species (La Mantia et al. 2013; McKown et al. 2013; McKown et al. 2014b).  9  1.5.2 Genetic diversity, phenotypic clines and admixture from P. balsamifera in P. trichocarpa The POPCAN project at UBC has used a collection of ~500 P. trichocarpa individuals from BC, Oregon and Washington established by the BC Ministry of Forests, to characterize genetic and trait diversity. The project initially used a 34K SNP chip and transcriptomes, as well as an extensive array of phenotypic measurements to characterize phenology, growth, gas exchange, leaf traits, disease susceptibility and wood properties variation. McKnown et al. (2013) showed that many of these traits, including those related to phenology, biomass, and ecophysiology, have high levels of heritability and are strongly correlated with geoclimate variables such as latitude, day length, and temperature.  To further understand the genetic basis of this local adaptation, Geraldes et al. (2014) implemented FST outlier approaches and found outlier SNPs in 396 genes. In a parallel study, McKnown et al. (2014) found that 88 of these genes with FST outlier SNPs also had SNPs associated with 30 phenotypic traits. Overall, these recent studies confirm the potential of using Populus as a model system to study adaptation (Cronk 2005; Brunner et al. 2004; Jansson & Douglas 2007) and reflect the importance of diversifying selection in the geographic distribution of genetic and phenotypic trait variability among P. trichocarpa populations.  Geraldes et al. (2014) showed that introgression from P. balsamifera plays a role in shaping the geographically and climatically associated patterns of genetic variation of P. trichocarpa across much of its range. This admixture, geographically limited to contact zones located in Alaska, northwestern Canada and the Canadian Rockies (Viereck & Foote 1970; Geraldes et al. 2014), may be driving adaptive processes if introgressed alleles are functionally different and linked to adaptive phenotypic traits. For example, introgression of P. balsamifera genes associated with faster growth 10  rates that compensate for short growing seasons at higher latitudes may allow admixed P. trichocarpa individuals to colonize new environments in northern and eastern regions. This could also be the case if introgressed P. balsamifera genes are associated with cold or drought tolerance (Soolanayakanahally et al. 2009).  1.6 Research questions Introgression between recently diverged species with incomplete reproductive barriers is a widespread process, and although it can provide novel genetic recombinants to promote adaptation to new environments (Arnold 2006), still most studies on interspecific hybridization focus on the nature and strength of reproductive isolation. Furthermore, reports on fine-scale patterns of introgression have mainly focused on taxa with short generation spans and have not been carried out in tree species. Clarifying the magnitude and impact of introgressed genes contributing to functionally relevant variation in trees has great potential for forest ecology, management, and restoration efforts in the face of climate change (Lexer et al. 2004; Whitham et al. 2006). Previous studies from the POPCAN project provided fundamental information about the variation in adaptive traits and the genetic basis of adaptation across the P. trichocarpa distribution. However, many questions remain to be answered about the role of admixture from P. balsamifera and the effects of other regions of the genome not included in the SNP chip used in these studies. In this thesis, my aim is to better understand the effect of admixture between P. trichocarpa and P. balsamifera on adaptation and identify candidate genes for adaptive introgression by implementing a targeted and whole genome approach using population-wide genome resequencing, phenotypic and gene expression data. 11  1.6.1 Targeted approach to identify adaptive introgression For the targeted approach, I implemented a local ancestry analysis and used genomic and functional tests to detect adaptive introgression. I focused on two genomic regions from chromosomes 15 and 6 containing several candidate genes for local adaptation. Chromosome 12 had a paralog block of the chromosome 15 candidate genes but shows no signatures of selection and was also included in the analysis.  My main goals for this targeted analysis were to (i) identify hot spots of introgression (i.e. regions that exhibited excess introgression relative to chromosomal averages) and test if candidate regions for adaptation in P. trichocarpa have alleles introgressed from P. balsamifera, (ii) implement genomic and gene ontology enrichment tests in introgressed regions to detect signals of selection and overrepresented biological terms respectively, (iii) analyze gene expression levels to test if introgressed alleles could function differently from P. trichocarpa alleles, and (iv) compare phenotypes of admixed vs. pure P. trichocarpa individuals to explore the potential functional effects of introgressed P. balsamifera alleles.  1.6.2 Whole genome approach to identify the scale and direction of introgression I expanded the local ancestry analysis to the whole genomes of both parents, P. trichocarpa and P. balsamifera, to provide a comprehensive, unbiased view of introgression patterns and to identify additional candidate regions for adaptive introgression genome-wide. I addressed the following questions: (i) are the introgression patterns asymmetric between the two parental poplar species? (ii) is there enrichment in the introgressed regions for certain biological terms, in particular, those related to disease resistance? and (iii) are certain genomic regions more prone to (e.g. subtelomeres) or protected from (e.g. sex-determining regions) interspecific introgression? 12  1.6.3 Whole genome approach to determine the effects of introgression in clinal trait variation Previous studies on trait variation showed high levels of heritability on a suite of phenology, biomass, disease resistance, and ecophysiology traits some of which were highly correlated with geoclimate variables such as latitude, day length, and temperature. To explore the role of admixture in the genetic architecture of these phenotypic traits, I implemented admixture mapping as well as other analyses of associations between phenotype and genetic and geo-climatic variables. The latter were used to detect the specific effects of regions with unusually high levels of P. balsamifera introgression on trait variation in P. trichocarpa. In addition, these analyses were used to confirm previous findings based on the targeted genomic approach.  13  Chapter 2: Genomic and functional approaches reveal a case of adaptive introgression from Populus balsamifera (balsam poplar) in P. trichocarpa (black cottonwood) 2.1 Introduction Introgression between recently diverged species with incomplete reproductive barriers is a widespread process that can provide novel genetic recombinants and promote adaptation to new environments (Arnold 2006). Although stochastic processes such as genetic drift are main drivers underlying the geographic distribution of genetic regions introduced by introgressive hybridization, natural selection also plays an important role when adaptive genes are exchanged across species boundaries (Barton & Gale 1993; The Heliconius Genome Consortium 2012; Hamilton et al. 2013; Abbott et al. 2013). For instance, hybridization can contribute to adaptive genetic variation if recombination leads to the introgression of genomic regions with modular, or cassette-like variation, i.e. multiple linked mutations across blocks of genes, associated with adaptive traits (Abbott et al. 2013).  Forest tree species are attractive targets to study adaptive introgression largely because they exhibit extensive natural hybrid zones (Lexer et al. 2004), but also because of their large ranges across geographical and climatic clines, as well as substantial trait variation (Savolainen et al. 2007; Soolanayakanahally et al. 2009; Keller et al. 2011; McKown et al. 2013). Research on the contribution of interspecific hybridization and introgression to local adaptation is rapidly expanding in long-lived tree species [e.g. spruce (de Lafontaine et al. 2015; Hamilton et al. 2014), Eucalyptus (Larcombe et al. 2015), and oak (Abadie et al. 2012)]. However, studies documenting fine-scale introgression patterns within and around target genes, and identifying adaptive introgression at the gene level have mainly focused on taxa with short generation spans in both plants and animals 14  [(Whitney et al. 2015; Martin et al. 2006; Wang et al. 2014; The Heliconius Genome Consortium 2012; Norris et al. 2015) but see (Martinsen et al. 2001; Lind-Riehl et al. 2014; Racimo et al. 2015)], and have not been carried out in tree species. The characterization of population-wide genomic diversity in keystone or foundation forest tree species will enable the study of species complexes involving ecologically divergent yet hybridizing taxa (Lexer et al. 2004; Buerkle & Lexer 2008), to explicitly test for adaptive introgression at a fine genomic scale. Such genomic scans could be used to identify genomic regions that are more prone to introgression than others, potentially underlying adaptive processes. Clarifying the magnitude and impact of introgressed genes contributing to functionally relevant variation in trees has great potential for forest ecology, management, and restoration efforts in the face of climate change (Lexer et al. 2004; Whitham et al. 2006).  Buerkle and Lexer (2008) proposed using admixture mapping, a gene mapping method for traits that differ in frequency across populations, to reveal genomic regions associated with adaptation. Recent applications of this approach to trees have provided a first glimpse of the genetic architecture of ecologically important traits (Caseys et al. 2015; Lindtke et al. 2013), but much higher marker densities and more highly recombinant mapping populations are required to obtain precise map locations of candidate genes. Based on the large-scale characterization of genomic diversity in wild populations of Populus, trees of this genus have emerged as an important model for population genomic studies of adaptation (Weigel & Nordborg 2015). Here, I used such data from P. trichocarpa, P. balsamifera and their hybrids, including single nucleotide polymorphism (SNP) data for hundreds of individuals (Evans et al. 2014; Slavov et al. 2012; Geraldes et al. 2014), to detect introgressed regions at a fine scale using three target chromosomes. I also implemented functional and phenotypic tests to determine if introgressed regions are associated 15  with adaptation, using extensive data on phenotypic and transcriptome diversity in many of the same individuals grown in a common garden (McKown et al. 2103; McKown et al. 2014; Corea et al. in preparation).  Populus trichocarpa and P. balsamifera are sibling poplar species of the section Tacamahaca that diverged in allopatry rather recently (~76 Ka) during Pleistocene glaciations (Levsen et al. 2012); [see (Ismail et al. 2012) for an alternative - older - estimate]. They are major components of the forest ecosystems of northern North America and are economically important forest trees, being wood pulp sources and potential feedstock for cellulosic ethanol production (Jansson & Douglas 2007; Porth & El-Kassaby 2015). Despite their recent divergence and morphological similarity, with some minor differences in flower and fruit morphology, the species are ecologically divergent and adapted to strongly contrasting environments. P. trichocarpa is distributed throughout the western US and Canada, from northern California to southern Alaska, and is adapted to relatively humid, moist, and mild conditions west of the Rocky Mountains. P. balsamifera, on the other hand, is largely a boreal species distributed from Alaska to Newfoundland, with high frost tolerance and is adapted to environments where there can be huge annual temperature differences (-62˚C to 44˚C) and generally less annual precipitation (Richardson et al. 2014).  Recently, Geraldes et al. (2014) showed that introgression from P. balsamifera plays a role in shaping the geographically and climatically associated patterns of genetic variation of P. trichocarpa across much of its range. This admixture, geographically limited to contact zones located in Alaska, northwestern Canada and the Canadian Rockies (Figure 2-1) (Viereck & Foote 1970; Geraldes et al. 2014), may be driving adaptive processes if introgressed alleles are 16  functionally different and linked to adaptive phenotypic traits. For example, introgression of P. balsamifera genes associated with faster growth rates that compensate for short growing seasons at higher latitudes may allow admixed P. trichocarpa individuals to colonize new environments in northern and eastern regions. This could also be the case if introgressed P. balsamifera genes are associated with cold or drought tolerance (Soolanayakanahally et al. 2009).   Figure 2-1. Geographic distribution of admixed and pure individuals used in the targeted local ancestry analysis across the contact zones between P. trichocarpa and P. balsamifera. Ranges of P. trichocarpa and P. balsamifera are shown in red and blue, respectively (Little 1971).   Previous studies based on a 34K SNP genotyping array in ~500 P. trichocarpa individuals from the Pacific Northwest (Geraldes et al. 2013), found 88 genes with FST outlier SNPs (Geraldes et al. 2014) associated with 30 phenotypic traits (McKown et al. 2014b) putatively underlying adaptive processes. Two unique sets of SNPs in candidate genes, located on chromosome 6 and chromosome 15 respectively, had FST values that were among the 10 highest - genome-wide - and were strongly correlated with geoclimate variables (Geraldes et al. 2014). In addition, these SNPs were in high linkage disequilibrium (LD) with other candidate SNPs on the same chromosome, and had multiple 17  trait associations based on a genome-wide association analysis (McKown et al. 2014b). In this study, I tested if introgression from P. balsamifera was a source of any signals of local adaptation found in P. trichocarpa populations. On chromosome 6, adjacent genes FAR1 (Potri.006G020600) and FHY3 (Potri.006G020700), encoding transcription factors related to the far-red light response, had SNPs with the highest FST genome-wide, which are in high LD (Geraldes et al. 2014). On chromosome 15, a genomic region of ~600-kb showed strong patterns of structure and association with latitude, environmental variables (Geraldes et al. 2014) and phenology, biomass and ecophysiology traits (McKown et al. 2014b). This ‘gene block’ had candidate SNPs in high pairwise LD, including those from a gene encoding a phenylpropanoid enzyme (COMT1, Potri.015G003100) and from transcription factors putatively involved in light and developmentally regulated gene expression (TTG1, Potri.015G002600; PRR5, Potri.015G002300; ANAC062, Potri.015G004100) (McKown et al. 2014b). Interestingly, a paralog region of this genomic block found on chromosome 12, that was produced by a whole genome duplication event in the Salicoid lineage (Tuskan et al. 2006), did not show signals of population structure or association with phenotypic traits, suggesting that these gene paralogs have followed different evolutionary trajectories.  Based on these results, I focused the current study on data obtained from population-wide genome resequencing of chromosomes 15 and 6 in P. trichocarpa and P. balsamifera, and of chromosome 12, which had a paralog block of the chromosome 15 candidate genes showing no signatures of selection. I (i) identified hot spots of introgression (i.e. regions that exhibited excess introgression relative to chromosomal averages) and tested if candidate regions for adaptation in P. trichocarpa have alleles introgressed from P. balsamifera, (ii) implemented genomic and gene ontology enrichment tests in introgressed regions to detect signals of selection and 18  overrepresented biological terms respectively, (iii) analyzed gene expression levels to test if introgressed alleles could function differently from P. trichocarpa alleles, and (iv) compared phenotypes of admixed vs. pure P. trichocarpa individuals to explore the potential functional effects of introgressed alleles.  2.2 Materials and methods 2.2.1 Samples  Populus trichocarpa accessions used in this study were collected by the British Columbia Ministry of Forests, Lands and Natural Resource Operations (Xie et al. 2009), and outplanted in a common garden at the University of British Columbia (McKown et al. 2013). These individuals comprised 28 “drainages” (i.e., topographic units separated by watershed barriers) spanning 14° in latitude (45.6°–59.6°) from throughout the species range. For P. balsamifera, I used accessions from 46 provenances throughout the species range obtained from the Agriculture and Agri-Food Canada AgCanBaP collection (Soolanayakanahally et al. 2009). For local ancestry analysis in RASPberry (Wegmann et al. 2011), 50 reference individuals and 68 admixed individuals (36 P. trichocarpa individuals with P. balsamifera admixture, and 32 P. balsamifera individuals with P. trichocarpa admixture) were selected from the sympatric zone between P. trichocarpa and P. balsamifera as well as from allopatric populations (Figure 2-1). These 118 individuals were selected from a collection of 435 P. trichocarpa and 448 P. balsamifera genotypes, using a previous genome-wide admixture analysis (Geraldes et. al., in preparation). Additional sample information and data generated in this chapter and previous studies can be found in Table A-1, Appendix.  19  2.2.2 Sequencing, read mapping and variant calling  Sampled leaves were flash frozen in liquid nitrogen, or silica gel dried and lyophilized.  Between 50 and 100 mg of leaf tissue were ground in a Precellys 24 tissue homogenizer (Bertin Technologies, Montigny-le-Bretonneux, France), and DNA extracted with the Qiagen DNeasy Plant Mini Kit (Qiagen, Valencia, CA). Protocol modifications include the use of 12uL of RNA A applied to each sample, and an additional centrifugation for 2 minutes at 20,000g performed after cell lysis to remove tissue debris. All solution volumes were increased by 1.5x in each step of the manufacturer’s protocol. DNA quantitation and purity assessment were performed using a NanoDrop spectrophotometer (Termo Fisher Scientific, Wilmington, DE) and a Qubit fluorometer (Invitrogen, Carlsbad, CA). High quality purified genomic DNA was used to prepare multiplexed sequencing libraries according the Standard Operating Protocols at the Genome Sciences Centre, Vancouver, BC.  Sequences (100bp paired-end reads) were obtained at estimated 30X or 15X coverage of the Nisqually-1 reference genome on an Illumina HiSeq at the Genome Sciences Centre. Three different sample sets were used to call SNPs. For gene sequence analyses, SNPs were called for three genes (COMT1, PRR5, and TTG1) among 302 P. trichocarpa genotypes and for one gene (COMT1) among 243 P. balsamifera genotypes. For local ancestry analysis SNPs were called in three chromosomes (6, 12, 15) for 118 individuals. For phenotypic and gene expression analysis, 161 P. trichocarpa individuals were classified in genotypic categories using SNPs in a telomeric region of chromosome 15. The genotypes were sequenced at an expected coverage ranging from 15X to 30X using the Illumina HiSeq2000 platform. Short reads from the sequencing libraries were independently aligned to the P. trichocarpa version 3 (v3.0) genome using BWA (version 0.6.1-r104) with default parameters. Mate pair metadata was corrected and duplicated molecules were marked 20  using the FixMateInformation and MarkDuplicates methods in the Picard package (http://picard.sourceforge.net). Reads present in areas surrounding InDels were re-aligned using the IndelRealigner method from GATK (version v1.5-25-gf46f7d0). Next, SNPs and small indels were called independently using the UnifiedGenotyper method from GATK. SNPs were then filtered to exclude variants within 3bp of any identified variants, having a mapping quality less than 5, and a variant quality less than 30. Each SNP was annotated using SNPeff (Cingolani et al. 2012) with version 3 of the P. trichocarpa genome. All raw sequencing data and alignment information have been deposited on SRA (Accession: PRJNA298917 ID: 298917).  2.2.3 Inference of local ancestry The probabilities for each of the possible ancestral configurations (P. balsamifera, P. trichocarpa or mixed ancestry) were estimated in every SNP across three chromosomes (6, 12 and 15), in 50 reference individuals and 68 admixed individuals using RASPberry, a software that implements a reliable Hidden Markov model (HMM) for admixture (Price et al. 2009).  For RASPberry analyses, SNPs with missing data in the parental genotypes were removed using Plink 1.07 (Purcell et al. 2007), the parental genotypes (50) were then phased with fastphase (Scheet & Stephens 2006) by creating the input files with FCGENE (Roshyara & Scholz 2014), and ancestries for each admixed individual were estimated in ADMIXTURE (Alexander et al. 2009). To select the parameters in RASPberry that best fit my data, I ran a number of parameter combinations - including time since admixture and recombination rate – using a 971k SNPs genome-wide data set and selected the model with the highest log likelihood value (Table A-2, Appendix). Using the 971k SNPs data set for this preliminary analysis allowed me to both estimate individual ancestries based on a genome-wide approach and to run different parameter combinations relatively fast. For local 21  ancestry analyses based on the whole sequence of chromosome 6, 12 and 15, only SNP ancestries with probabilities >95% in one of the three categories were considered. The proportion of P. balsamifera ancestry in P. trichocarpa admixed individuals was calculated, for each SNP, by counting sites with P. balsamifera ancestry as two, and sites with mixed ancestry as one. I detected regions with unusually high levels of introgression in admixed P. trichocarpa individuals using a sliding window approach, and a significance cut-off of three standard deviations (SD) from the weighted mean across the three chromosomes based on SNP density per window. I used 100-kb, 500-kb and 1-Mb windows with steps of 20-kb, 100-kb and 200-kb respectively. The same procedure was implemented to estimate levels of P. trichocarpa ancestry in P. balsamifera admixed individuals. 2.2.4 Characterization of introgressed regions and tests of selection in pure species and admixed individuals First, I performed Gene Ontology enrichment analysis by comparing the list of introgressed genes with the list of all poplar genes (41,335) and the best-annotated orthologs in Arabidopsis thaliana (based on Popgenie annotation; http://popgenie.org/), using the DAVID online database (Huang et al. 2008). Second, I estimated Tajima’s D values across chromosome 6, 12 and 15 for the 50 pure species individuals, using 50-kb windows in VCFtools v0.1.12b (Danecek et al. 2011). For introgressed regions with positive Tajima’s D values, I performed neighbor-joining analysis with 1000 bootstrap replicates using MEGA (Tamura et al. 2007). Third, I calculated levels of nucleotide diversity (π) for the pure species individuals across the three chromosomes using 50-kb windows in VCFtools v0.1.12b. Fourth, I estimated the average proportion of amino acid substitutions driven by directional selection (alpha) in pure species individuals for all introgressed regions including a 22  section with the lowest nucleotide diversity (π) level, using Smith and Eyre-Walker’s alpha (Smith & Eyre-Walker 2002), and generated confidence intervals by randomly selecting genes with replacement (bootstrapping) 1000 times in R v. 3.2.1 (R Development Core Team, http://www.r-project.org). Fifth, I estimated pairwise linkage disequilibrium (LD) as r2 in the pure species and a set of admixed individuals, and detected haplotype blocks in the pure species using Haploview version 4.2 (Barrett et al. 2005) and the same phased genotypes used in RASPberry analysis. The decay of LD with physical distance was estimated following Remington et al. (2001) with expected values, E(r2), computed using the formula from Hill & Weir (1998) in an R script (http://www.r-project.org/).  2.2.5 Haplotype distribution of candidate genes for local adaptation For three candidate genes on chromosome 15 (PRR5, Potri.015G002300; TTG1, Potri.015G002600; COMT1, Potri.015G003100) that showed signatures of selection in previous studies (McKown et al. 2014b; Geraldes et al. 2014), I estimated the FST value per SNP using Arlequin 3.5.1.2 (Excoffier et al. 2005), in 302 accessions from 28 drainages across much of P. trichocarpa range. These FST values were compared to P. trichocarpa whole genome average FST (mean=0.05; 99th percentile = 0.18) following Geraldes et al. (2014). Based on the coding regions of each gene, I inferred haplotypes using PHASE 2.1 (Stephens et al. 2001) and mapped the haplotype distributions. For the COMT1 gene, I also estimated allele frequencies across 46 P. balsamifera provenances (484 individuals). Finally, I analyzed a protein alignment of COMT2, a paralog of COMT1 encoded on chromosome 12, from P. balsamifera and P. trichocarpa genotypes, and a protein alignment of different homologous sequences retrieved from Phytozome (http://www.phytozome.net).  23  2.2.6 Gene expression and phenotypic analysis in admixed P. trichocarpa individuals For phenotypic analyses, I used data from McKown et al. (2013). Gene expression data were obtained from a population-wide RNAseq dataset based on a subset of the P. trichocarpa accessions from trees outplanted in the common garden at the University of British Columbia (Corea et al. in preparation). For both phenotypic and gene expression data, I focused on one introgressed region on chromosome 15 and on individuals from northern and central parts of the P. trichocarpa range. By excluding genotypes from southern locations, I aimed to remove the latitudinal variation inherent in some phenotypic traits of P. trichocarpa (McKown et al. 2013), and targeted locations close to the contact zone. I used published phenotypic data from 146 P. trichocarpa genotypes (McKown et al. 2013) and RNAseq data, as FPKM (Fragments Per Kilobase of transcript per Million mapped reads), from a subset (33) of those P. trichocarpa genotypes. Each of the 146 P. trichocarpa individuals was classified into one of three genotypic categories: homozygotes for P. balsamifera haplotypes (bb), heterozygotes (bt) and homozygotes for P. trichocarpa haplotypes (tt), based on phased genomic sequences of one introgressed region on chromosome 15 (Figure A-1, Appendix). Nine phenotypic traits and expression levels of 134 genes were then compared among the three genotypic categories (bb, bt, and tt) using ANOVAs with adjusted p-values with Bonferroni correction, and Tukey tests for multiple comparisons. For one candidate gene, PRR5, I used a gene co-expression network analysis for the xylem RNAseq dataset to identify the top 25 co-expressed genes, and compared the average mean-centered expression levels of these genes between the three genotypic categories (bb, bt, and tt) using an ANOVA. Finally, I validated the RNAseq data using quantitative reverse transcription PCR (qRT-PCR) in COMT1, a selected candidate gene (Table A-3, Appendix).  24  2.3 Results 2.3.1 Local ancestry analysis revealed three introgressed regions Using 186,376 SNPs across chromosome 6, 12 and 15, I detected three regions with P. balsamifera ancestry in admixed P. trichocarpa individuals: two on chromosome 15 and one on chromosome 6. These regions were above a significance cutoff of three Standard Deviations (SD) from the mean P. balsamifera ancestry (0.096, SD 0.066, weighted by SNP density), using 50-kb, 100-kb and 1-Mb window analysis. This was consistent with genome-wide estimates based on a 971k SNP data set, where the mean P. balsamifera ancestry was 0.13 (SD 0.036). Across the remaining regions of P. trichocarpa chromosomes 6 and 15, and across the entire P. trichocarpa chromosome 12, levels of P. balsamifera ancestry were for the most part low and did not surpass the significance cutoff. For downstream analyses, I used the results based on 100-kb windows (Figure 2-2), since this window size provided the best compromise between large numbers of windows with missing data (50-kb windows) and low resolution (1-Mb windows) (Figure A-2, Appendix).  On chromosome 15, a large introgressed segment (880-kb) was located in a telomeric region (peak B in Figure 2-2) and included candidate genes PRR5, TTG1, and COMT1 as well as another 131 genes (Table 2-1; Table A-4, Appendix). A second 580-kb introgressed region on chromosome 15 was located 13.34 Mb from the start of the chromosome and included 84 genes (peak C in Figure 2-2). 25   Figure 2-2. Proportion of P. balsamifera ancestry in admixed P. trichocarpa individuals across chromosomes 6, 12, and 15 based on sliding window analysis (size: 100-kb, step: 20-kb). Introgressed regions – peaks A, B, C -have P. balsamifera ancestry higher than 3 standard deviations from the weighted mean across chromosomes 6, 12 and 15 based on SNP density per window (broken line). Asterisks represent the chromosomal position of candidate genes: FAR1 (Potri.006G020600) and FHY3 (Potri.006G020700) on chromosome 6, and COMT1 (Potri.015G003100), TTG1 (Potri.015G002600), PRR5 (Potri.015G002300) ANAC062 (Potri.015G004100) on chromosome 15. On chromosome 12, the asterisk represents the position of the COMT1 paralog, COMT2 (Potri.012G006400).   On chromosome 6, a 580-kb introgressed region comprised 67 genes and was located at 3.36 Mb from the start of the chromosome (peak A in Figure 2-2), downstream of candidate genes for adaptation (Table 2-1; Table A-4, Appendix). Local ancestry analysis of P. balsamifera admixed individuals did not reveal introgression from P. trichocarpa. 26  Table 2-1. List of three introgressed regions (A, B, C – see Figure 2-2) from P. balsamifera in P. trichocarpa on chromosomes (Chr) 6 and 15. Genes in region B with signals of selection and associated with enriched GO terms (11) and protein groups (3) are shown.  Chr Region Genomic coordinates # of Genes Genes under selection and associated with enriched GO terms   start end  6 A 3360000 3940000 67  15 B 0 880000 134 Potri.015G000200 §      Potri.015G001200 §      Potri.015G002200 §      Potri.015G002300 §      Potri.015G002900 ¶      Potri.015G004100 ¶ 15 C 13340000 13920000 84  § Genes showing relatively high levels of Tajima’s D ¶ Genes showing signals of positive selection (alpha) Potri.015G000200 (ABCB19); Potri.015G001200 (Uncharacterized); Potri.015G002200 (Sigma 54); Potri.015G002300 (PRR5); Potri.015G002900 (NAC147); Potri.015G004100 (NAC062)  2.3.2 Selection in pure P. balsamifera and P. trichocarpa The three introgressed regions revealed different patterns of Tajima’s D and alpha estimates in the parental genotypes. A section of introgressed region B (telomeric introgressed region of chromosome 15) that included candidate gene PRR5 revealed positive values of Tajima’s D in P. balsamifera but not in P. trichocarpa, with estimates greater than the neutral expectation (Figure 2-3). In P. balsamifera the first two 50-kb windows of this region showed Tajima’s D estimates above the 1% of the distribution (0 to 49-kb = 2.58; 50 to 99-kb = 3.18), and the following window showed Tajima’s D estimates above the 5% of the distribution (100 to 149-kb = 2.14). A neighbor-joining (NJ) tree based on the first 880-kb of chromosome 15 revealed two haplotype clusters in P. balsamifera, both of which occur in populations from the northwestern and central parts of the species range (Figure A-3, Appendix).  Region C on chromosome 15 also showed positive values of Tajima’s D higher than the 1% of the distribution, in both P. trichocarpa and P. balsamifera, but only in one 50-kb window (13.45 27  Mb to 13.50 Mb) (Figure 2-3), comprising three genes. Tajima’s D values in introgressed region A on chromosome 6 did not deviate from neutral expectations in either P. trichocarpa or P. balsamifera. Region B on chromosome 15 exhibited extended LD decay in both admixed and pure P. balsamifera individuals. In admixed individuals, average LD in this region did not decay below 2kb until past 10 kb, as it did in the second introgressed region (region C) and across the remainder of chromosome 15 (r2 decay to 0.2 at 2 kb and 5 kb respectively) (Figure A-4, Appendix). In pure P. trichocarpa and P. balsamifera LD decay also was extended in region B, with average LD not dropping below 2kb for 5 kb and 4 kb, respectively. LD decay in introgressed regions A and C on chromosomes 6 and 15, respectively, was similar to that in the chromosome-wide average (2 kb; Figure A-4, Appendix). In pure P. balsamifera, a haplotype block at the start of chromosome 15 extended for 130 kb and was followed by a shorter block of 26 kb that included candidate genes PRR5 and TTG1 (Figure 2-4A). In pure P. trichocarpa, the haplotype block at the start of chromosome 15 extended for only 36 kb, and other blocks downstream were not longer than 8-kb (Figure 2-4B). In addition to positive values of Tajima’s D and relatively long haplotype blocks in pure P. balsamifera individuals, the first ~200 kb of chromosome 15 from region B (i.e. P. balsamifera introgressed region B in P. trichocarpa individuals) showed the highest levels of nucleotide diversity compared to levels across the three chromosomes examined (Figure A-5, Appendix). Levels of nucleotide diversity (π) in the first 160 kb, comprising 25 genes including PRR5, were in the top 5% of the distribution in P. balsamifera. However, the contiguous downstream region had the lowest P. balsamifera π values (lower than the bottom 5% of the distribution). 28   Figure 2-3. Tajima’s D analysis across chromosome 15 in Populus balsamifera and P. trichocarpa individuals calculated in 50-kb windows. (Top) Tajima’s D values across chromosome 15 are represented by blue and red continuous lines for P. balsamifera and P. trichocarpa respectively, and introgressed regions B and C are shown (see Figure 2-2). Values representing the top 95% of the distribution are shown in blue and red dashed lines for P. balsamifera and P. trichocarpa respectively. (Bottom) Tajima’s D values in introgressed region B of chromosome 15 (telomeric region from 0 to 880-kb) with straight solid blue and red lines representing the top 99% of the distribution for P. balsamifera and P. trichocarpa respectively. The asterisk represents the chromosomal position of candidate gene PRR5 (Potri.015G002300).   29   Figure 2-4. Linkage disequilibrium (LD) plot of the first 200-kb of chromosome 15 introgressed region in pure P. trichocarpa (B) and P. balsamifera (A). Red areas represent pairs of SNPs with high levels of LD (D’=1, LOD ≥2) and blue areas represent pairs of SNPs with LD comparisons with a low estimation confidence (LOD < 2). Dark triangles represent haplotype blocks based on Gabriel et al. (2002). In P. balsamifera a haplotype block at the start of chromosome 15 extends 130 -kb but in P. trichocarpa it extends to only 38-kb.   30  Alpha estimates of this 110-kb region (from 183 kb to 280 kb on chromosome 15), comprising 14 genes including COMT1 and ANAC062, revealed signals of positive selection in 46% and 41% of all amino acid substitutions in P. trichocarpa and P. balsamifera respectively (Table 2-2). The alpha estimate for the entire telomeric introgressed region on chromosome 15 (region B) was not significantly different from zero, and neither was that for the introgressed region on chromosome 6 (region A). For the second introgressed region on chromosome 15 (region C), synonymous and non-synonymous substitutions were not detected (Ds and Dn = 0) and alpha was undefined (Table 2-2). Table 2-2. Alpha estimates for three introgressed P. balsamifera regions in P. trichocarpa, one on chromosome (Chr) 06 (region A) and two on chromosome 15 (B and C).  Chr Region Species # of Genes alpha CI 6 A P. balsamifera 67 0.16 -0.6286   P. trichocarpa 67 0.16 -0.6271 15 B P. balsamifera 128 -0.47 -1.3095   P. trichocarpa 128 -0.38 -1.29 15 B† P. balsamifera 14 0.41 0.1285 - 2.5087*   P. trichocarpa 14 0.46 0.1625 - 2.2363* 15 C P. balsamifera 83 NA NA     P. trichocarpa 83 NA NA CI: Bootstrap confidence intervals †Genomic region with lowest levels of nucleotide diversity (p) in P. balsamifera (< the bottom 5% of the distribution) *alpha estimate significantly different from zero NA: No polymorphism data; alpha is undefined  2.3.3 Haplotype distribution of candidate introgressed genes on P. trichocarpa  In COMT1 from P. trichocarpa, I identified 171 SNPs with an MAF (minor allele frequency) > 0.01, eight of which were in the coding region. The highest FST value in the coding region was for a synonymous SNP located in the first exon of the gene (amino acid position A84, FST = 0.3), and the second largest FST value was found in a nonsynonymous SNP (nsSNP, P287Q, FST =0.23) (Figure 2-31  5A), both of which were higher than the 99th percentile of the genome-wide distribution (0.18) based on a previous analysis (Geraldes et al. 2014).  The P287Q polymorphism is located in the O-methyltransferase domain of the COMT1 enzyme, and the P287 variant was strictly conserved in all homologs including COMT2, a paralog of COMT1 encoded on Populus chromosome 12, and in COMT homologs from other species (Figure A-6, Appendix). In P. trichocarpa, this site was polymorphic with the alternative Q287 variant occurring at a relatively low frequency (8.6%), and restricted to northern and interior populations (Figure 2-5B). In P. balsamifera, the Q287 allele was almost fixed (95%) and the alternative P287 variant only occurred in admixed individuals with P. trichocarpa and P. deltoides (Geraldes et. al., in preparation) (Figure 2-5C). These results support the finding of signals for positive selection in COMT1 and flanking genes (110-kb region from 170-kb to 280-kb on chromosome 15), and suggest that the rare Q287 COMT1 variant selected for in P. balsamifera, but not found in any other homologous COMT genes, introgressed to P. trichocarpa in regions close to the contact zone with P. balsamifera.  In TTG1 I found 20 SNPs with an MAF >0.01, five of which were in the coding region. The highest FST values in the coding region were for two sSNPs (L238-G/T, L323-A/C FST= 0.25), with T238/C323 alleles restricted to the north and interior. In PRR5 I found 75 SNPs with an MAF >0.01, 12 of which were in the coding region and five of which were nsSNPs. The highest FST value in the coding region was found in nsSNP R396W (FST =0.24), where R396 was the most common allele and W396 was restricted to the north and interior with an allele frequency of 9.1%.   32   Figure 2-5. COMT1 alleles and geographic distribution in P. trichocarpa and P. balsamifera populations. (A) Gene model of COMT1 with arrows depicting SNPs with the top Fst values in the coding region, which were higher than the 99th percentile of the genome-wide distribution (0.18) based on a previous analysis (Geraldes et al. 2014). The green arrow shows nsSNP P287Q (FST=0.23), the yellow arrow represents sSNP A84 (FST= 0.3) and black arrows depict SNPs in the non-coding region (5’UTR and introns). (B) Geographic distribution of COMT1 haplotypes from 28 drainages in Populus trichocarpa, based on the nsSNPs with the highest Fst value (P287Q). (C) Geographic distribution of COMT1 haplotypes from 46 drainages in P. balsamifera. P. balsamifera individuals with P287 COMT1 alleles (easternmost population) are admixed with P. deltoides.    33  2.3.4 Evidence for adaptive introgression based on GO, gene expression, and phenotypic analysis  GO term enrichment analysis of the three introgressed regions (region A on chromosome 6; region B and C on chromosome 15) revealed 11 GO terms and three protein groups overrepresented in  region A on chromosome 6 (14 genes) and region B on chromosome 15 (25 genes) (Table A-5, Appendix). From the total 39 genes associated with these biological processes, four genes - including PRR5 - were found in a genomic region showing an excess of intermediate-frequency alleles in P. balsamifera, and two genes, namely NAC147 and ANAC062, were located in a region with signals of positive selection in both P. balsamifera and P. trichocarpa (Table 2-1). The signals of selection and overrepresentation of genes that may play roles in adaptive traits (e.g. RNA processing, response to far-red light, ATPase activity) further supports the potential of the telomeric region of chromosome 15 in adaptive introgression. In turn, genes in the second introgressed region (C) of chromosome 15 were not overrepresented in any of the 75,000 functional terms tested.  Based on gene expression analysis in the 880-kb introgressed region B, I identified seven genes - including TTG1 and COMT1 - out of 134 in which the level of expression was significantly different in admixed individuals with P. balsamifera haplotypes (bb and bt), compared to P. trichocarpa genotypes (tt) in both xylem and leaf samples (Table A-6, Appendix). TTG1 and COMT1 were among the most highly expressed genes and for both the highest expression was found in those homozygous for P. balsamifera haplotypes (bb), followed by heterozygotes (bt), with those homozygous for P. trichocarpa haplotypes (tt) showing the lowest expression (Figure 2-6). This 34  variation in expression, especially pronounced for COMT1 and validated using qRT-PCR (Figure A-7; Table A-7, Appendix), suggests that introgressed alleles are functionally distinct at the level of gene expression.  In PRR5 I found that the mean-centered FPKM from the top 25 co-expressed genes was significantly lower in P. trichocarpa individuals homozygous for P. balsamifera PRR5 alleles (bb, W396W), compared to either individuals that were heterozygotes (bt, W396R), or homozygotes for P. trichocarpa PRR5 alleles (tt, R396R) (Figure A-8, Appendix). Thus, the introgressed W396-PRR5 variant from P balsamifera may encode a functionally distinct protein with respect to its ability to regulate transcription of direct or indirect target genes in P. trichocarpa. Analyses of the phenotypic variation, based on a suite of nine phenology, biomass, and ecophysiology traits, some measured in multiple years, also revealed significant differences between admixed individuals with P. balsamifera haplotypes (bb and bt) and P. trichocarpa genotypes (tt). Admixed individuals with P. balsamifera haplotypes (bb and bt) in the first 880-kb of chromosome 15 (region B, Figure 2-2) exhibited higher chlorophyll concentration index across two years (2009 and 2011, Figure 2-7A, B), and higher leaf nitrogen content in 2009 (Figure 2-7C). Leaf nitrogen content was not significantly different among genotypes in 2010, probably due to a smaller sample size; in this year some trees set bud before measurements took place (McKown, personal communication).  My study revealed an approximately 880-kb genomic region on chromosome 15 with strong evidence for introgression from P. balsamifera into P. trichocarpa populations at higher frequencies than the genomic background, and provides evidence that this is an example of adaptive introgression. In contrast, a paralog block of duplicated genes on chromosome 12 showed no signs 35  of introgression or signatures of selection, suggesting alternative evolutionary trajectories of these duplicated genes in Populus.  Figure 2-6. COMT1 (A) and TTG1 (B) gene expression levels in leaf and xylem of P. trichocarpa individuals with different haplotypes for introgressed region B from P. balsamifera. Gene expression is presented as FPMK (Fragments Per Kilobase of transcript per Million mapped reads) based on RNAseq data. Genotypes are based on the first 880-kb of chromosome 15 (see Figure 2-2): bb, homozygotes for the P. balsamifera introgressed region B [six samples from three genotypes]; bt, individuals heterozygous for the introgressed region [10 samples from five genotypes]; tt, individuals homozygous for P. trichocarpa haplotypes in the same region [65 samples from 24 genotypes]. The standard error (SE) within each genotypic class (bb, bt, tt) is represented by the bars. Shared letters above columns indicate that the average gene expression levels were not significantly different between those genotypes (p<0.05).  36   Figure 2-7. Boxplot diagrams depicting chlorophyll concentration index and leaf nitrogen content in P. trichocarpa individuals from northern and interior locations, with different haplotypes for introgressed region B from P. balsamifera (first 880-kb of chromosome 15, see Figure 2-2). bb, homozygotes for the P. balsamifera introgressed region B; bt, individuals heterozygous for the introgressed region; tt, individuals homozygous for P. trichocarpa haplotypes in the same region. (A) leaf chlorophyll concentration index measured in 2009 [sample size: tt = 73, bt = 20, bb = 7], (B) leaf chlorophyll concentration index measured in 2011 [tt = 47, bt = 9, bb = 3], (C) leaf nitrogen content measured in 2011 [tt = 100, bt = 30, bb = 8]. Each box shows the lower quartile, median and upper quartile values and the whiskers show the range of the phenotypic variation. Shared letters above columns indicate when differences between genotypes were not significant (p<0.05). Data from (McKown et al. 2013).   37  2.4 Discussion My analyses suggest that specific introgressed P. balsamifera alleles on chromosome 15 are under selection, are functionally distinct, and are correlated with the expression of adaptive traits in P. trichocarpa individuals that harbor them. Admixed individuals with these introgressed P. balsamifera haplotypes are restricted to northern and central areas of the geographical range of P. trichocarpa and have been assigned to distinct climate clusters (Porth et al. 2015). Based on historical climate data, northern and north-central regions have significantly lower mean annual temperature (MAT: 4.2 ˚C), number of frost-free days (NFFD: 175 d), and mean annual precipitation (MAP: 744 mm) than coastal and southern regions (MAT: 9.5˚C, NFFD: 287 d, MAP 2805 mm), suggesting potential niche differentiation between populations with and without the introgressed region.  2.4.1 A telomeric region on chromosome 15 is a hot spot for introgression Local ancestry analysis revealed three introgressed regions, one of which was located in a telomeric region of chromosome 15 (region B), showed high LD levels and signals of selection in both pure species, and included several candidate genes for local adaptation identified by previous association studies (McKown et al. 2014b) and FST outlier tests (Geraldes et al. 2014) (Table A-4, Appendix). To explain these results I propose two scenarios: recent hybridization and adaptive introgression, which are not mutually exclusive.  Region B on chromosome 15 was the largest among the three introgressed regions and had LD levels higher than the genomic background, which may indicate recent admixture with insufficient time for recombination to have reduced LD within the introgressed region. The micro-evolutionary processes at work in the hybrid zone may not – or not yet – have impacted adjacent or 38  allopatric populations of the hybridizing species (Harrison & Larson 2014), also explaining the low frequencies of introgressed alleles in candidate genes (e.g. 8.6% COMT1-Q287, 9.1% PRR5-W396) in P. trichocarpa, and the relatively high levels of divergence still present in parental lineages (Figure A-3, Appendix). However in pure P. balsamifera, the presence of haplotype blocks in the telomeric region of chromosome 15 (first 150-kb), the excess of intermediate-frequency alleles and the segregation of two distinct haplotypes, all of which could be signatures of balancing selection (Vitti et al. 2013), lead me to an alternative scenario: two types of haplotype blocks selected for in pure P. balsamifera introgressed as a modular, or cassette-like block of genes into P. trichocarpa, and these now underlie adaptive processes in the P. trichocarpa admixed individuals as well as in the P. balsamifera donor species.  In line with the latter adaptive introgression scenario, the telomeric region of chromosome 15: (i) is enriched for genes that may play crucial roles for survival and adaptation, such as those related to response to far-red light, RNA processing, and ATPase activity, (ii) contains a section (from 200 kb to 280 kb) showing signals of positive selection in both pure P. balsamifera and P. trichocarpa, and (iii) includes introgressed alleles affecting P. trichocarpa phenotypes according to functional as well as phenotypic analyses.  2.4.2 Evidence that introgressed P. balsamifera alleles are functionally different from P. trichocarpa variants The introgressed region B on chromosome 15 is enriched for 11 GO terms and three protein groups with overrepresentation of genes that may play crucial roles for survival and adaptation, such as those related to response to far-red light, RNA processing and ATPase activity. Four of the genes associated with these biological processes, including PRR5, were found in a section of the 39  introgressed region that showed signals of excess intermediate-frequency alleles in P. balsamifera, and two other genes (NAC147 and ANAC062) were located in a region with signals of positive selection in both P. balsamifera and P. trichocarpa. A third candidate gene that showed signals of purifying selection in both pure P. trichocarpa and P. balsamifera genotypes is COMT1, which encodes a phenylpropanoid enzyme. The functions of candidate genes PRR5 and COMT1 and evidence for functional differentiation of introgressed alleles of PRR5 and COMT1 are discussed below. Candidate gene PRR5. The Populus PSEUDORESPONSE REGULATOR5 (PRR5) in the introgressed region encodes a transcriptional regulator that is an important component of the circadian clock mechanism, based on studies in Arabidopsis (Nakamichi et al. 2010; Wang et al. 2010). In Populus, PRR5 is upregulated at the onset of short days and it may play a crucial role in the timing of the onset of bud dormancy in P. trichocarpa (Ruttink et al. 2007; Ko et al. 2011). Furthermore, a transcriptomic analysis of an Arabidopsis prr9, prr7, prr5 triple mutant suggested that these pseudo-response regulators negatively affect the expression of genes encoding enzymes associated with chlorophyll biosynthetic pathways (Fukushima et al. 2009).  Introgressed PRR5 P. balsamifera variants have a nsSNP W396 substitution located between the pseudoreceiver domain and the CCT motif, in a highly conserved 44-amino acid region shown to be essential for PRR5 transcription repressor activity in Arabidopsis (Nakamichi et al. 2010). This position in P. trichocarpa alleles, as in other PRR genes from other species including Arabidopsis, is usually occupied by an amino acid with a positively charged R-group (R or H). It is possible that the P. balsamifera W396 PRR5 allele, with a nonpolar amino acid at that site, represses target genes in a different manner compared to common P. trichocarpa alleles, which instead have a polar amino 40  acid at that site (R396). In P. trichocarpa, I found that the levels of expression of the top 25 genes co-expressed with PRR5 are significantly lower in P. trichocarpa individuals homozygotes for P. balsamifera PRR5 alleles (WW) compared to either individuals that were heterozygotes (WR) or homozygotes for P. trichocarpa PRR5 alleles (RR) (Figure A-8, Appendix). This finding suggests that the introgressed W396 protein variant is functionally distinct since it appears to differ in its transcriptional regulatory activity in the clock-regulated gene network.  Analysis of phenotype partitioning in admixed P. trichocarpa individuals revealed that those with P. balsamifera haplotypes on the telomeric region of chromosome 15, including the PRR5 W396 allele, showed higher chlorophyll concentration index - which is associated with chlorophyll levels - and higher leaf nitrogen content compared to those without P. balsamifera alleles (Figure 2-6). Previous studies in Picea abies (Norway spruce) (Oleksyn et al. 1998) and P. balsamifera (Soolanayakanahally et al. 2009) showed that higher chlorophyll and leaf nitrogen content are associated with higher photosynthetic rates, and in P. balsamifera these traits were also correlated with faster growth and higher carbon acquisition. In P. trichocarpa (McKown et al. 2013) and P. balsamifera (Soolanayakanahally et al. 2009), photosynthetic rates are also positively correlated with latitude; where faster growth and higher carbon acquisition may counteract shorter growing seasons at higher latitudes. My analysis of the genetic effects of introgressed balsam alleles on P. trichocarpa phenotype (higher chlorophyll and leaf nitrogen content) suggests that admixed individuals may grow and acquire carbon faster than pure P. trichocarpa individuals from the same provenances.  Candidate gene COMT1. A second candidate gene that showed signals of purifying selection in both pure P. trichocarpa and P. balsamifera genotypes is COMT1 (CAFFEIC ACID 3-O-41  METHYLTRANSFERASE1), which encodes a key phenylpropanoid enzyme required for the biosynthesis of sinapyl alcohol and metabolites derived from sinapyl alcohol, and that could be involved in lignification or pathogen defense (Barakat et al. 2011). In COMT1, I also found evidence for functionally distinct P. balsamifera COMT1 alleles based on gene expression differences in both leaf and xylem (Figure 2-6). COMT1 expression in P. trichocarpa admixed individuals homozygous for P. balsamifera introgressed alleles (bb) is high, in tt individuals low, and in bt individuals intermediate, suggesting that P. balsamifera alleles are functionally different from P. trichocarpa alleles and have the potential to affect the phenotype of admixed individuals. In COMT1 I found one SNP in the promoter region in tight linkage with a nsSNP (P287Q) that differentiates P. balsamifera from P. trichocarpa COMT1 alleles (Figure A-9, Appendix), and that could potentially be a cis-regulatory element causing the difference in expression.  The introgressed COMT1 Q287 allele is restricted to northern P. trichocarpa populations in close proximity to contact zones but is nearly fixed and under selection in P. balsamifera (Figure 2-5). The unusual P. balsamifera COMT1 Q287 variant - P287 was strictly conserved in all COMT homologs (Figure A-6, Appendix) - may affect the enzyme activity, with a substitution of a nonpolar amino acid (proline-P) for a polar amino acid (glutamine-Q), at a site in close proximity to conserved catalytic histidine (H) and glutamic acid (E) residues (Zubieta et al. 2002) (Figure A-10, Appendix). This could result in a competitive disadvantage to individuals with introgressed Q287 alleles at lower latitudes, where both temperature and precipitation are higher. It is also possible that Q287-COMT1 variants have lower enzyme efficacy and that an increase in gene expression is needed to overcome this change in enzyme activity. These results suggest that functional differences between 42  P. balsamifera and P. trichocarpa COMT1 alleles may contribute to differences in fitness in specific environments.  Overall, my results suggest that PRR5 and COMT1 are good candidate genes to explain the signals of selection found in the telomeric introgressed region B of chromosome 15, consistent with the introduction of modular, or cassette-like variation into P. trichocarpa, where multiple linked introgressed alleles such as PRR5 R396W and COMT P287Q are associated with adaptive traits (Abbott et al. 2013). However, since this region comprises 134 genes, a plausible alternative scenario is that PRR5, COMT1, and other candidate genes are hitchhiking and the actual targets (or target) of selection remain to be uncovered.  2.4.3 Introgression and functional divergence in paralogous genes While genes within block B on the chromosome 15 telomeric region are under selection, are associated with adaptation, and have apparently adaptively introgressed into northern and central P. trichocarpa populations (Geraldes et al. 2014; McKown et al. 2014b), a highly syntenic block of paralogous duplicated genes on chromosome 12, derived from the whole genome duplication event in the Salicoid lineage (Tuskan et al. 2006), showed neither evidence for introgression nor signatures of selection. This is consistent with the lack of signatures of adaptation for the chromosome 12 paralogs in previous studies (Geraldes et al. 2014; McKown et al. 2014b). Interestingly, chromosome 12 paralogs - such as TTG2 and COMT2 - and candidate genes on chromosome 15 - such as TTG1 and COMT1 - have different expression patterns (Sjödin et al. 2009; Hefer et al. 2015). For example, while the expression pattern of the COMT2 paralog on chromosome 12 clearly implicates this gene in developmental lignification (Barakat et al. 2011), the chromosome 15 paralog COMT1 is not highly expressed in developing xylem but is strongly induced 43  by herbivory (Barakat et al. 2011). These data suggest potential sub- or neo-functionalization of paralogs on chromosomes 15 and 12, which may have facilitated the co-opting of certain chromosome 15 paralogs for new roles in adaptation. A plausible scenario is that further microevolution of chromosome 15 paralogs such as COMT1, PRR5, and TTG1 contributed to local adaptation in P. balsamifera and that such alleles subsequently introgressed into P. trichocarpa populations, adapting them to transitional environments at the northern and north-central extremes of this species’ range.       44  Chapter 3: Scale and direction of adaptive Introgression between black cottonwood (Populus trichocarpa) and balsam poplar (Populus balsamifera)  3.1 Introduction Hybridization, the interbreeding between individuals of different varieties or species, is a widespread phenomenon in plants (Ellstrand et al. 1999) and animals (Dowling & Secor 1997) that has shaped the genome of many lineages. In natural hybrid zones, back-crossing of early-generation hybrids over subsequent generations can result in introgression, where genetic material from one of the parental species is incorporated into another species. Introgression can introduce novel genetic variation including new gene combinations at a faster rate than through mutation alone and result in adaptive introgression when adaptive alleles are maintained over time by natural selection (Twyford & Ennos 2012; Slarkin 1985; Rieseberg et al. 2003). Adaptive introgression has been key for adaptation to biotic and abiotic environments. Examples in plants and animals include increased herbivore resistance in sunflower (Whitney et al. 2006), flood tolerance in Iris (Martin et al. 2006), mimetic wing patterns in butterflies (Pardo-Diaz et al. 2012), and altitude adaptation in Tibetans (Huerta-Sanchez et al. 2014). Adaptive introgression may be particularly important in rapidly changing environments, where standing genetic variation and mutation alone may offer limited potential for adaptation (Hedrick 2013; Hamilton & Miller 2015). Natural hybrid zones provide evolutionary laboratories to identify chromosomal regions that introgressed significantly more frequently than expected (Lexer et al. 2004; Buerkle & Lexer 2008). Forest tree species with extensive natural hybrid zones, large ranges across geographical and climatic clines as well as substantial trait variation (Savolainen et al. 2007; Soolanayakanahally et al. 2009; Keller et al. 2011; McKown et al. 2013) are attractive for the study of adaptive introgression. 45  Populus spp. in particular, have emerged as models for population genomic studies of adaptation (Weigel & Nordborg 2015) due to porous species barriers and a wealth of genomic resources available (Cronk 2005; Jansson & Douglas 2007; Tuskan et al. 2006). Populus trichocarpa and P. balsamifera are sibling poplar species of the section Tacamahaca that diverged in allopatry rather recently and hybridize freely where their overlap (Viereck & Foote 1970). Despite their recent divergence [~76 Ka (Levsen et al. 2012); but see (Ismail et al. 2012)] and morphological similarity, these species are ecologically divergent and adapted to strongly contrasting environments. Populus balsamifera is a boreal species distributed from Alaska to Newfoundland, with high frost tolerance and adapted to a very large temperature range (-62˚C to 44˚C) as well as to moderate annual precipitation. Populus trichocarpa, on the other hand, is distributed throughout the western US and Canada, from northern California to southern Alaska, and is adapted to relatively humid, moist (riparian or higher precipitation), and milder conditions (Richardson et al. 2014; Geraldes et al. 2014). In Chapter 2, I explored fine-scale introgression patterns within and around target genes in three chromosomes of P. trichocarpa and P. balsamifera to detect candidate regions for adaptive introgression. This local ancestry analysis revealed a subtelomeric region on chromosome 15 with P. balsamifera ancestry, containing genes potentially associated with higher chlorophyll levels and leaf nitrogen content. Admixed P. trichocarpa individuals with these particular P. balsamifera alleles may grow and acquire carbon faster than pure P. trichocarpa individuals to counteract shorter growing seasons at higher latitudes (Oleksyn et al. 1998; Soolanayakanahally et al. 2009; McKown et al. 2013). These results, based on functional and phenotypic tests, showed the potential of local 46  ancestry analyses to identify strong candidates for adaptive introgression (Suarez-Gonzalez et al. 2016).  My targeted local ancestry analysis (Chapter 2), together with other documented empirical cases (Martin et al. 2006; Pardo-Diaz et al. 2012; Huerta-Sanchez et al. 2014), show the importance of adaptive introgression. However little is known about the extent and direction of introgression across the entire genome, and the genomic architecture of introgression at a fine genomic scale. Here I expand the local ancestry analysis to the whole genome of P. trichocarpa and P. balsamifera using a population-wide resequencing approach and include additional admixed individuals to address the following questions: (i) are the introgression patterns asymmetric between the two parental poplar species? (ii) is there enrichment for certain biological terms, in particular, those related to disease resistance, in the introgressed regions? and (iii) are certain genomic regions more prone to (e.g. subtelomeric regions) or protected from (e.g. sex-determining regions) interspecific introgression? 3.2 Materials and methods 3.2.1 Samples  Populus trichocarpa accessions used in this study were collected by the British Columbia Ministry of Forests, Lands and Natural Resource Operations (Xie et al. 2009), and outplanted in a common garden at the University of British Columbia (McKown et al. 2013). These individuals comprised 28 “drainages” (i.e., topographic units separated by watershed barriers) spanning 14° in latitude (45.6°–59.6°) from throughout the species range. For P. balsamifera, I used accessions from 46 provenances throughout the species range obtained from the Agriculture and Agri-Food Canada AgCanBaP collection (Soolanayakanahally et al. 2009). For local ancestry analysis in RASPberry 47  (Wegmann et al. 2011), 50 reference individuals and 161 admixed individuals (129 P. trichocarpa individuals with P. balsamifera admixture, and 32 P. balsamifera individuals with P. trichocarpa admixture) were selected from the sympatric zone between P. trichocarpa and P. balsamifera as well as from allopatric populations (Figure 3-1). These 211 individuals were selected from a collection of 435 P. trichocarpa and 448 P. balsamifera genotypes, using a previous genome-wide admixture analysis based on a genome-wide dataset of 971k SNPs (Geraldes et al. in preparation). Additional sample information can be found in Table A-1, Appendix.  Figure 3-1 Geographic distribution of admixed and pure individuals used in the whole genome local ancestry analysis across the contact zones between P. trichocarpa and P. balsamifera. Ranges of P. trichocarpa and P. balsamifera are shown in red and blue, respectively (Little 1971).   48  3.2.2 Sequencing, read mapping and variant calling  For local ancestry analysis, SNPs were called in the whole genome for 211 individuals. Each of the genotypes was sequenced at an expected coverage ranging from 15x to 30x using the Illumina HiSeq2000 platform. Short reads from the sequencing libraries were independently aligned to the P. trichocarpa version 3 (v3.0) genome using BWA (version 0.6.1-r104) with default parameters and following the same pipeline as in chapter 2. All the raw sequencing data and alignment information have been deposited on SRA (PRJNA276056).  3.2.3 Inference of local ancestry I estimated the probabilities for each of the possible ancestral configurations (P. balsamifera, P. trichocarpa or mixed ancestry) in every SNP across the whole genome of 161 admixed individuals using RASPberry, a software that implements a reliable Hidden Markov model (HMM) for admixture (Price et al. 2009), following the same pipeline and parameters as in the targeted local ancestry analysis (Chapter 2). Briefly, SNPs with missing data in the parental genotypes were removed using Plink 1.07 (Purcell et al. 2007), the parental genotypes (50) were then phased with fastphase (Scheet & Stephens 2006) by creating the input files with FCGENE (Roshyara & Scholz 2014), and ancestries for each admixed individual were estimated in ADMIXTURE (Alexander et al. 2009).  To determine the ancestral configurations of each SNP in one of the three categories, ancestries were considered for probabilities >95%. The proportion of introgressed ancestry in admixed individuals was calculated, for each SNP, by counting sites with introgressed ancestry as two and sites with mixed ancestry as one. I detected candidate regions for adaptive introgression (i.e. regions with unusually high levels of introgression) in admixed individuals using a sliding 49  window approach and a significance cut-off of three standard deviations (SD) from the weighted mean across the three chromosomes based on SNP density per window. I used windows of 100-kb with steps of 20-kb since this window size provided the best compromise between large numbers of windows with missing data (50-kb windows) and low resolution (1-Mb windows) (Suarez-Gonzalez et al. 2016).  To determine if the levels of introgression were correlated with certain structural features of the genome, the Pearson correlation coefficient (r) was calculated between admixed ancestry values and distance to telomeres. I also explored the levels of introgression in predicted centromeric regions (Pinosio et al. 2016). 3.2.4 Climate of tree origin in admixed individuals To determine if introgression was associated with climate, I compiled 23 climate variables from ClimateNA (Wang 2012) based on 1971–2000 and performed a principal component analysis (PCA) using the function prcomp in R. Eight of the variables were associated with moisture and 15 were associated with temperature (Table B-1, Appendix). 3.2.5 Enrichment analysis To detect overrepresented biological terms in introgressed regions, in particular those related to disease resistance, I performed enrichment tests for various terms including Gene Ontology (GO) and Protein Family (Pfam) using Popgenie (Sjödin et al. 2009). Since NBS-LRRs (nucleotide binding sites and leucine-rich repeat) are the largest class of disease resistance genes and play critical roles in plant defense (Dangl & Jones 2001), I searched for protein families (Pfam) associated with NBS-LRR domains using the keyword search function in the Pfam database (Finn et al. 2016). I compared these Pfam codes associated with NBS-LRRs domains with the enriched Pfam 50  codes in introgressed regions. In addition, I performed an enrichment analysis in a P. balsamifera introgressed region showing signals of positive selection. 3.2.6 Tests of selection in the introgressed regions from the pure species individuals  I estimated Tajima’s D values across all the introgressed regions for the 50 pure species individuals, using 20-kb windows in VCFtools v0.1.12b (Danecek et al. 2011). Average Tajima’s D was estimated within each introgressed region and compared with the average from windows without introgressed peaks using ANOVA. To detect windows with Tajima’s D values significantly different from neutral expectations, I estimated the top and bottom 5% of the distribution across the whole genome. The average proportion of amino acid substitutions driven by directional selection (alpha; values range between -∞ and 1) was also estimated in pure species individuals for all introgressed regions using Smith and Eyre-Walker’s alpha (Smith & Eyre-Walker 2002), and generating confidence intervals by randomly selecting genes with replacement (bootstrapping) 1000 times in R v. 3.2.1 (R Development Core Team, http://www.r-project.org).  3.2.7 Levels of introgression in sex-determining regions To determine if sex-associated regions are protected from introgression, I plotted the location of SNPs significantly associated with sex in Populus (Geraldes et al. 2015) with the levels of admixed ancestry from RASPberry across six chromosomes (4, 5, 9, 14, 18, 19). Geraldes et al. (2015) mapped the sex-linked region to these six chromosomes and two unmapped scaffolds in version 3.0, likely due to genome misassembly.  51  3.3 Results 3.3.1 Local ancestry in RASPberry vs previous admixture analysis Eleven of the 129 individuals initially considered as admixed P. trichocarpa (Geraldes et al. in preparation) did not show P. balsamifera ancestry with the RASPberry analysis and were removed from downstream analyses. These 11 individuals could have been incorrectly classified as admixed based on the previous admixture analysis (Geraldes et al. in preparation) or have so little P. balsamifera introgression that it is undetectable with RASPberry (Figure B-1, Appendix; Q values for P. balsamifera ancestry = 0.011-0.003). The remaining 118 admixed P. trichocarpa showed 0.049% to 15.184% of P. balsamifera alleles across the whole genome based on RASPberry analysis. These estimates were in line with previous admixture analysis, but RASPberry estimates (mean: 0.0559, SD 0.0399) were, for the most part, lower than previous Q-values estimates (mean 0.0682, SD: 0.0562) based on a genome-wide analysis. Admixture levels above 1% were geographically limited to interior and northern populations of P. trichocarpa (Figure 3-1). From the 32 individuals initially considered as admixed P. balsamifera, 11 were early generation hybrids (P. trichocarpa ancestry estimates above 20%) and were not included in downstream calculations (Figure B-1, Table A-1, Appendix). Using introgressed individuals exclusively (P. trichocarpa admixture levels between 6.161% and 18.164%) allowed me to detect introgressed blocks.   3.3.2 Introgression patterns are strongly asymmetric between the two poplar species  In admixed P. trichocarpa, 2.3% of the genome showed candidate regions for adaptive introgression from P. balsamifera (total size of introgressed peaks: 9Mb; genome size: 394.5 Mb; Table 3-1), while in admixed P. balsamifera, only 1.04% of the genome showed candidate regions for adaptive introgression from P. trichocarpa (total size of introgressed peaks: 4.1Mb; Table 3-1). 52  Overall levels of genomic admixture were higher in admixed P. balsamifera individuals (mean P. trichocarpa ancestry: 0.1157, SD 0.0666, weighted by SNP density), compared to that in admixed P. trichocarpa individuals (mean P. balsamifera ancestry 0.0559, SD 0.0399), suggesting the former are earlier generation hybrids.  Across the whole genome (1,168,955 SNPs examined) of 118 admixed P. trichocarpa individuals, I detected 19 candidate regions for adaptive introgression (1107 genes) with P. balsamifera ancestry (Figure 3-2, Table 3-1). These regions included candidate genes for adaptive introgression previously identified by the targeted local ancestry analysis (Chapter 2). The 19 regions, occurring across 11 chromosomes, showed P. balsamifera ancestry peaks with a height (i.e. percentage of P. balsamifera ancestry) ranging from 0.1778 to 0.2733 and width ranging from 140 kb to 1.02 Mb.  Local ancestry analysis in 21 P. balsamifera admixed individuals revealed nine candidate regions for adaptive introgression (545 genes), in five chromosomes, with P. trichocarpa ancestry (Figure 3-2, Table 3-1). These introgressed regions showed P. trichocarpa ancestry peaks with height ranging from 0.3262 to 0.4273 and width ranging from 160 kb to 880 kb.  The average width of the candidate regions for adaptive introgression was slightly larger in blocks with P. balsamifera ancestry - in admixed P. trichocarpa individuals - (mean 473.68 kb, SD: 252.371) compared to those with P. trichocarpa ancestry - in P. balsamifera individuals - (mean 455.56 kb; SD: 227.547), although there was no significant difference (Figure B-2, Appendix).  There was no overlap between the candidate regions for adaptive introgression in P. balsamifera admixed individuals (9 regions) and those in P. trichocarpa admixed individuals (19 53  regions). Furthermore, the level of introgressed ancestry across the 19 chromosomes differ between admixed P. trichocarpa and admixed P. balsamifera individuals. In the former, the three  Table 3-1 Summary of the Populus balsamifera introgressed regions in admixed P. trichocarpa individuals (italics) and P. trichocarpa introgressed regions in admixed P. balsamifera individuals (bold). The size of the introgressed peaks is represented in kilobases (kb) and the number of genes (#) is shown. The height of the introgressed peaks is given in terms of the maximum units of standard deviations from the mean within each peak (‘Max b’ & ‘Max t’). Average Tajima’s Ds (D) noted were significantly different from the average in windows without introgressed regions. A measure of selection at the amino acid levels is given using alpha and is shown for those regions where a significant value was recovered.   Ch Genomic coordinates Size Height Selection   Start End kb #Genes Maxa b Maxa t Db b Db t Alpha b Alpha t Ch01 47,120,000 47,540,000 420 43 3.88        48,000,000 48,380,000 380 23 3.51   -0.72   Ch03 7,380,000 7,740,000 360 22  3.27  0.36    21,040,000 21,620,000 580 95 3.87    0.62 0.59 Ch05 140,000 380,000 240 43 3.64       4,060,000 4,640,000 580 93  3.73  0.39    4,980,000 5,440,000 460 50  3.75       11,580,000 11,820,000 240 17 3.28  -0.92    Ch06 3,540,000 3,700,000 160 22 3.09      Ch07 14,680,000 15,700,000 1,020 145 5.51   -0.33   Ch08 740,000 1,620,000 880 162  4.47 0.47    Ch09 1,380,000 1,760,000 380 22 3.53      Ch10 22,180,000 22,660,000 480 74 3.63   0.44   Ch11 1,940,000 2,120,000 180 25 3.24       17,580,000 17,780,000 200 16 3.68        17,800,000 18,600,000 800 97 4.11  -0.57    Ch12 1,380,000 1,680,000 300 43  3.25     Ch14 12,720,000 13,300,000 580 63 4.33        13,340,000 13,480,000 140 39 3.46      Ch15 0 580,000 580 87 3.72       13,480,000 13,920,000 440 71 3.48      Ch16 11,200,000 11,420,000 220 24  3.31      12,000,000 12,160,000 160 16  3.16      12,320,000 12,980,000 660 93  4.28       13,120,000 13,600,000 480 42  4.68     Ch17 3,080,000 3,900,000 820 98 4.03       9,720,000 10,420,000 700 42 5.17        12,720,000 13,380,000 660 85 4.39      a The height of the introgressed peaks in terms of units of standard deviations from the mean. ‘Max b’ and ‘Max t’ represent the highest value within the corresponding introgressed peak in P. balsamifera and P. trichocarpa respectively.  b Average Tajima’s D (D) significantly different from the average in windows without introgressed regions in pure P. balsamifera (b) and P. trichocarpa (t). 54    Figure 3-2 Proportion of admixed ancestry across 19 chromosomes showing candidate regions for adaptive introgression (sliding window analysis: size: 100-kb, step: 20-kb). (A) Proportion of P. balsamifera ancestry in admixed P. trichocarpa individuals. (B) Proportion of P. trichocarpa ancestry in admixed P. balsamifera individuals. Candidate regions for adaptive introgression – peaks above broken line - have ancestry higher than 3 standard deviations from the weighted mean across the whole genome based on SNP density per window (broken line). Chromosomes in gray did not show candidate regions for adaptive introgression. The locations of putative centromeres are shown as gray continuous lines (Pinosio et al. 2016).   chromosomes with the highest levels of P. balsamifera ancestry were chromosomes 17, 13 and 9. In the latter, the top three chromosomes with the highest levels of P. trichocarpa ancestry were 12, 5 and 16 (Figure B-3, Appendix).  Pearson’s tests revealed strong associations between the levels of P. balsamifera admixture in P. trichocarpa and the first principal component of variables associated with climate (temperature PC1 explained 60% of the variance; Pearson’s r = 0.51, p<0.00001; moisture PC1 explained 63% of the variance; Pearson’s r = 0.23, p<0.01); with increased levels of introgression at colder and drier environments (Figure 3-3). P. trichocarpa admixture in P. balsamifera also 55  increased in colder climates (Pearson’s r = 0.55, p<0.01) but there was no association with changes in moisture.    Figure 3-3 Relationship between levels of admixture and environmental variables. (Top) Variables associated with moisture (PC1. (Bottom) Variables associated with temperature). (Left) P. balsamifera (Pb) levels in admixed P. trichocarpa individuals. (Right) P. trichocarpa (Pt) levels in admixed P. balsamifera individuals. See Figure B-6, Appendix for loadings of the variables.  3.3.3 Subtelomeric enrichment of introgressed regions  Overall, candidate regions for adaptive introgression were found in a variety of chromosomal positions but there was a weak but very significant negative correlation between the levels of P. balsamifera introgressed ancestry and distance to the telomeres (Pearson r2 =-0.074, p-value < 10-15, Figure 3-4). There was no correlation between the levels of P. trichocarpa introgressed ancestry and distance to the telomeres in admixed P. balsamifera individuals (r2 = -0.002, p-value = 0.734, Figure B-4, Appendix).  56  The levels of introgression in predicted centromeric regions (Pinosio et al. 2016) were well below the significance threshold (i.e. 3SD from the whole genome average) and similar to the genomic average (P. trichocarpa ancestry in admixed P. balsamifera individuals: 0.1293; P. balsamifera ancestry in P. trichocarpa individuals: 0.0492; Figure 3-2).   Figure 3-4 Relationship between the levels of P. balsamifera introgressed ancestry and distance to the telomeres in admixed P. trichocarpa individuals (Pearson r2 =-0.074, p-value < 10-15).   3.3.4 Introgressed regions show enrichment for disease resistance genes  Enrichment analyses of biological terms revealed a different set of overrepresented GO and Pfam terms in the candidate regions for adaptive introgression in P. trichocarpa admixed individuals (14 GO and 31 Pfam terms) compared to those in P. balsamifera admixed individuals (14 GO and 27 Pfam terms). From the 58 enriched Pfam terms, two were associated with disease resistance genes: The Toll/interleukin-1 receptor (TIR, PF01582) was enriched in the P. balsamifera introgressed regions (10 genes), and the Leucine-rich repeat protein family (LRR_1, PF00560) was enriched in P. trichocarpa introgressed genes (6 genes) (Figure 3-5). Genes coding a TIR domain were located near telomeres on chromosomes 5 and 7. On chromosome 5, six TIR genes occurred in tandem. On chromosome 7, four TIR genes were interspersed with another type of disease resistance genes 57  coding NB-ARC domains (PF00931). Genes coding LRR_1 proteins were located on chromosomes 5 (1 gene), 8 (1 gene) and 16 (4 genes). On chromosome 16, the group of LRR_1 genes was interspersed with genes coding macrophage migration inhibitory factors (MIF, PF01187).  Only a few common KEGG (1 term) and micro RNA (10 terms) terms were enriched in both P. balsamifera and P. trichocarpa introgressed regions. The P. balsamifera introgressed region on chromosome 3 was enriched for genes that may play crucial roles biotic and abiotic stresses, such as those related to response to oxidative stress (GO:0006979) and peroxidase activity (GO:0004601).    Figure 3-5 Chromosome diagrams depicting introgressed regions (arrows) enriched for Pfam terms associated with R proteins. Purple: All genes encoding NBS-LRR domains across four chromosomes. Blue: P. balsamifera introgression into admixed P. trichocarpa individuals. Red: P. trichocarpa introgression into admixed P. balsamifera individuals.   58  3.3.5 Introgressed regions in pure individuals are under selection All the introgressed regions showed windows with Tajima’s D values either above or below 5% of the whole genome distribution, except for two introgressed regions on chromosome 5 and 16. Positive Tajima’s D values (i.e., excess of intermediate frequency alleles) significantly higher than the average from windows without introgressed peaks, which may result from balancing selection (Nielsen 2005), were found in three introgressed regions in pure P. trichocarpa, (two P. trichocarpa introgressed regions in admixed P. balsamifera and one P. balsamifera introgressed region in admixed P. trichocarpa) and in one region in pure P. balsamifera (a P. trichocarpa region in admixed P. balsamifera). Negative values significantly different to the average from genomic regions without introgression were found in two introgressed regions in pure P. trichocarpa (two P. balsamifera introgressed regions in admixed P. trichocarpa) and two regions in pure P. balsamifera (two P. balsamifera regions in admixed P. trichocarpa) (Table 3-1, Figure 3-6). Negative values of Tajima's D indicate an excess of low-frequency polymorphisms relative to expectation and may result from purifying selection. Two of these regions with a negative average of Tajima’s D (chromosomes 7 and 11) and the region with positive values (chromosome 10) were telomeric. Alpha, a measure of selection at the amino acid level, revealed signals of positive selection in the P. balsamifera introgressed region on chromosome 3 in about 60% of all amino acid substitutions in both P. trichocarpa and P. balsamifera pure individuals. 3.3.6 Are sex-determining regions protected from interspecific introgression? The sex-linked region did not show signals of introgression above the significance threshold (i.e. 3SD from the genomic average). Most of the SNPs significantly associated with sex mapped to chromosome 18 and 19 in version 3.0 (80%), where there were no introgressed regions (Figure B-5, 59  Appendix). Sex-determining regions on chromosomes 4 and 9 showed admixed ancestry levels below 10%. On chromosome 5 and chromosome 14, the regions mapped to sex-determination (5 kb on chromosome 5 and 3.5 kb on chromosome 14) were in close proximity to the introgressed regions but did not show admixture levels above the significant threshold (Figure B-5, Appendix).  3.4 Discussion  3.4.1 Direction and scale of adaptive introgression into P. trichocarpa in relation to environment My whole genome analysis revealed asymmetric patterns of introgression, with stronger signatures of P. balsamifera adaptive introgression into P. trichocarpa than the other way around. Adaptive introgression in admixed P. trichocarpa individuals was geographically limited to northwestern Canada and the Canadian Rockies, where introgression of P. balsamifera genes associated with adaptation to higher latitudes may allow admixed P. trichocarpa individuals to colonize colder environments in northern and eastern locations, as shown in the targeted local ancestry analysis (Chapter 2). This study revealed increased levels of introgression in both colder and drier environments, and a previous landscape genomic analysis identified 21 SNPs, located in candidate regions for adaptive introgression, that were strongly correlated with a number of geoclimate variables including latitude, mean annual temperature and mean coldest temperature (Geraldes et al. 2014). Furthermore, admixed individuals make up the majority (or entirety) of northern populations in the P. trichocarpa collection used in this study (Geraldes et al. in preparation).  The type of environments these species are currently occupying does not intuitively suggest that introgression from P. trichocarpa into P. balsamifera will be likely to be adaptive. In fact, 60  introgression from P. trichocarpa, a fast-growing tree adapted to mild and coastal climate, to P. balsamifera, a slower-growing stress-tolerant species occupying northern latitudes with extreme temperature ranges, might be maladaptive as it could decrease the stress tolerance of P. balsamifera (Richardson et al. 2014; Geraldes et al. 2014).  Figure 3-6 Average Tajima’s D within each introgressed region and in windows without introgressed blocks in pure P. trichocarpa and P. balsamifera. Red bars are P. trichocarpa introgressed regions in P. balsamifera and blue bars are P. balsamifera introgressed regions in P. trichocarpa that showed average Tajima’s D values significantly different from those in windows without introgressed regions. Gray bars show introgressed regions where the average Tajima’s D values were not significantly different from those in windows without introgressed regions.   The average width (i.e. size in base pairs) of the candidate regions for adaptive introgression coupled with the apparently different hybridization history of these species supports this idea. Admixed P. balsamifera individuals had high levels of P. trichocarpa admixture, implying that they are earlier generation backcrosses. Based on this hybridization history alone, the admixed P. balsamifera individuals, as earlier generation backcrosses, would be expected to display larger introgressed blocks compared to the admixed P. trichocarpa individuals, which are later generation 61  backcrosses (Buerkle & Lexer 2008). However, contrary to the generally larger blocks expected, the width of the candidate regions for adaptive introgression was similar in both species. Here I have uncovered a number of candidate regions related to adaptive introgression with strong signals of selection, including subtelomeric regions occupied by disease response genes (see below).  3.4.2 Implications for future climates Changing climate conditions are expected to have a far-reaching impact on forest species (IPCC et al. 2014; Sturrock et al. 2011), especially if warmer temperatures and increased precipitation increase the probability of tree diseases in northern locations. Southern P. trichocarpa populations may have been under intense selection pressures for disease resistance, as suggested by the correlation between latitude and leaf rust severity across P. trichocarpa populations from the Pacific Northwest (La Mantia et al. 2013). If changes in climate result in more favorable conditions for pathogen reproduction and survival at higher latitudes (Hicke et al. 2012; IPCC et al. 2014; Helfer 2014), pathogen aggressiveness may become increasingly important for P. balsamifera.  Here I show that P. trichocarpa introgressed genes in P. balsamifera were enriched for the leucine-rich repeat protein family (LRR_1, PF00560), which has been linked to innate immunity and sensing of pathogen-associated molecular patterns (Ng & Xavier 2011). Furthermore, these introgressed LRR_1 genes were interspersed with genes coding macrophage migration inhibitory factors (MIF, PF01187), which in mammals regulate adaptive and innate immunity (Ståldal et al. 2012). It has recently been suggested that MIF genes may have a parallel function in plants, regulating the stress response including innate immunity (Panstruga et al. 2015).  62  In addition, my results revealed that introgression from P. trichocarpa into P. balsamifera, likely to be more recent than introgression in the opposite direction, is more evident in cold environments contrary to my expectations. This suggests the possibility of climate change reversing the historical direction of introgression. If P. trichocarpa can indeed contribute beneficial disease resistance genes, adaptive introgression into P. balsamifera could play crucial roles under changing climatic conditions.  3.4.3 Disease resistance genes and structural properties may explain subtelomeric enrichment of introgressed regions  The sub-telomeric enrichment of introgressed regions in P. trichocarpa is an interesting finding of my study and could be explained by the type of gene families found in the subtelomeres and the unstable structural properties of these regions. In yeast, subtelomeric families are enriched for specific functional categories including response to stress and toxins, metabolism of a broad spectrum of compounds, as well as transporters (Brown et al. 2010). In plants, including maize, barley, and poplar, subtelomeres are often occupied by large clusters of plant resistance genes (Geffroy et al. 2000; Wei et al. 1999a; Duplessis et al. 2009). My study revealed one protein family associated with disease resistance (TIR, PF01582) overrepresented in P. balsamifera introgressed regions and, interestingly, all of the TIR introgressed genes were subtelomeric and arranged in clusters that showed signals of purifying selection (i.e. negative values of Tajima’s D lower than the neutral expectation). Taken together, these results show that subtelomeres are occupied by, and may be enriched for, gene families associated with local adaptation, where interspecific introgression may transfer important adaptive traits.   63  Subtelomeres, often the most variable region of eukaryotic genomes (Winzeler et al. 2003; Brown et al. 2010), are composed of various repeated elements and characterized by increased recombination, duplication, and mutation which may allow evolutionary adaptation and innovation (Rudd et al. 2007; Linardopoulou et al. 2005; Barton et al. 2008). In poplar, there is a weak negative correlation between distance to telomeres and levels of recombination (Slavov et al. 2012). A computational analysis in yeast revealed that subtelomeric families are evolving and expanding much faster than families that do not contain subtelomeric genes. Furthermore, non-subtelomeric genes that belong to the same functional categories showed lower variability compared with their subtelomeric counterparts (Brown et al. 2010). These results suggest that the high rates of sequence evolution and rapid gene turnover are inherent properties of subtelomeric regions rather than of the functional categories of genes. In Plasmodium falciparum, the subtelomeric location of var genes may favor the increased diversity in antigenicity, an elegant system to evade immune systems, due to higher levels of recombination in subtelomeric regions (Rubio et al. 1996). In humans, subtelomere dynamics including recent duplications between the subtelomeres of different chromosomes, have resulted in a striking variation of subtelomerically located OR genes and may contribute to the diversity in olfactory perception (Mefford & Trask 2002; Trask et al. 1998).  Overall, these studies show that subtelomeres harbor fast-evolving gene families and are hot spots of recombination and duplications. My analysis suggests adaptive introgression as an important potential corollary of the extensive sequence variation found in subtelomeric genes, and more fine-scale genomic studies of introgression are needed to evaluate the relative importance of the subtelomeric properties in shaping the architecture of adaptive introgression.   64  3.4.4 Possible protection of the sex-determining regions from interspecific gene flow Sex-determining regions have previously been shown to be protected from interspecific gene flow (Hu & Filatov 2016). My results are consistent with this, as shown here by the lack of significant signals of introgression in the poplar sex-determining region (SDR) (Geraldes et al. 2015). Previous studies in Populus have consistently mapped the gender determining locus to the proximal telomeric region of chromosome 19 (Gaudet et al. 2008; Yin et al. 2008; Geraldes et al. 2015), which is also in close proximity to the largest NBS disease resistance gene cluster in poplar (Kohler et al. 2008). Although subtelomeric regions and genes associated with adaptation could be more prone to be introgressed, my analysis suggests that sexually antagonistic mutations may be impeding introgression in the SDR of chromosome 19. In fact, it has been suggested that suppressed recombination associated with resistance genes may have co-evolved with sexual differentiation and allowed the emergence of an incipient sex chromosome in poplar (Tuskan et al. 2012; Caseys et al. 2015). Geraldes et al. (2015), the first study to fully characterize the SDR in P. trichocarpa, found no evidence of an incipient sex chromosome. Instead, the non-recombining region has remained remarkably small (13 genes over c. 100kb), although it does contain several putative resistance genes (Geraldes et al. 2015).  Geraldes et al. (2015) also found SNPs associated with sex in five additional chromosomes, likely due to a very poor genome assembly (in both v2.2 and v3.0) of the subtelomeric SDR of chromosome 19. Further refinement of the assembly is needed to make firm inferences about introgression patterns near the SDR in Populus.  Taken on balance, the results detailed here provide strong support for the idea that hybridization between species is an important source of adaptively significant variation. The 65  asymmetry of this effect between P. balsamifera and P. trichocarpa further indicates that the effects of introgression on adaptation will depend strongly on ecological contexts.    66  Chapter 4: Introgression from Populus balsamifera underlies adaptation and range boundaries in P. trichocarpa 4.1 Introduction Gene flow between species through hybridization can be a potent evolutionary force when recombination increases standing variation and creates opportunities for adaptive evolution (Hedrick 2013; Harrison & Larson 2014). Admixture can be seen as a collision of genomes, that nevertheless may be followed by the stable integration of genomic regions of one parental species into the genome of another (Buerkle & Lexer 2008). This process, called introgression, occurs through hybridization and repeated backcrossing (Rieseberg & Wendel 1993). Although introgression has the potential to detrimentally disrupt the recipient genomic background, it can also provide beneficial variants that result in accelerated adaptation and improved survival in new environments (Clarkson et al. 2014; Norris et al. 2015; Whitney et al. 2006; Whitney et al. 2015). Simulations and empirical evidence show that gene flow between species results in increased standing variation available for adaptive evolution (Barrett & Schluter 2008; Jordan 2016), which may play an important role in species’ ability to respond to a changing climate (Hamilton & Miller 2015). Standing variation is often more likely to result in adaptation and evolutionary rescue under rapidly changing conditions than de novo mutations (Orr & Unckless 2014) and this is particularly important for long-lived organisms such as trees (Petit & Hampe 2006; Savolainen & Pyhäjärvi 2007).  Populus trichocarpa (black cottonwood) is an ecologically and economically important forest tree species distributed throughout the western US and Canada, from northern California to southern Alaska, and adapted to relatively humid, moist, and mild conditions west of the Rocky 67  Mountains. Along this range, P. trichocarpa exhibits variation in several adaptive traits including phenology and disease susceptibility, which suggests that divergent selection plays an important role shaping the genetic and phenotypic distribution of this species (La Mantia et al. 2013; McKown et al. 2013). These traits exhibit high heritability and are strongly correlated with geoclimatic variables such as latitude, day length, and temperature (La Mantia et al. 2013; McKown et al. 2013).  In the interior and northern parts of its range, P. trichocarpa hybridizes freely with P. balsamifera where the distributions of the two species overlap (Suarez-Gonzalez et al. 2016; Geraldes et al. 2014). Populus balsamifera is a boreal species distributed from Alaska to Newfoundland, with high frost tolerance and able to tolerate a very large range in extreme temperatures (-62˚C to 44˚C) as well as to moderate annual precipitation (Richardson et al. 2014). These two sibling poplar species diverged in allopatry and despite their morphological similarity and recent divergence [~76 Ka (Levsen et al. 2012); but see (Ismail et al. 2012)], they are ecologically divergent and adapted to strongly contrasting environments (Richardson et al. 2014; Geraldes et al. 2014). Introgression from the northern and continental species P. balsamifera could transfer advantageous traits allowing admixed P. trichocarpa individuals to colonize colder environments in northern and interior locations. A targeted analysis demonstrated adaptive introgression from P. balsamifera into P. trichocarpa in a telomeric region of chromosome 15 (Chapter 2), and a whole genome analysis identified additional candidate regions for adaptive introgression (Chapter 3). However, the targeted study did not include phenotypic information from pure P. balsamifera and it is unknown if other introgressed regions, identified in the whole genome analysis, are associated with phenotype in admixed P. trichocarpa.   68  Here I implemented admixture mapping to explore if introgressed regions with unusually high levels of P. balsamifera ancestry are driving variation in locally adaptive traits. I used phenotypic analyses to detect if trait variation along a latitudinal gradient was associated with variation in geoclimatic variables in admixed and pure P. trichocarpa individuals. I also explore the specific effects of one introgressed region on trait variation in admixed individuals. Finally, I revisit a case of adaptive introgression in a telomeric region of chromosome 15, found in the targeted analysis (Chapter 2), and use phenotypic data from pure individuals to investigate the effects of this genomic region in P. trichocarpa and P. balsamifera background.  4.2 Methods 4.2.1 Samples I employed data from the whole genome local ancestry analysis (Suarez-Gonzalez et al. 2016) and from association studies in P. trichocarpa (McKown et al. 2013; La Mantia et al. 2013; McKown et al. 2014a) as well as phenotypic data generated specifically for this study (i.e. chlorophyll content index). The accessions used were from a collection of the British Columbia Ministry of Forests, Lands and Natural Resource Operations, outplanted in a common garden at the University of British Columbia (Xie et al. 2009). The local ancestry analysis and chlorophyll measurements also included P. balsamifera accessions from the Agriculture and Agri-Food Canada AgCanBaP collection (Soolanayakanahally et al. 2009; Suarez-Gonzalez et al. 2016). For admixture mapping, I used admixed individuals that showed introgression from P. balsamifera ancestry in the whole genome local ancestry analysis (Suarez-Gonzalez et al. 2016). I focused on accessions from northern and interior parts of the P. trichocarpa range to target locations where admixture has been detected (Geraldes et al. 2014; Suarez-Gonzalez et al. 2016). In addition, I used reference P. 69  trichocarpa and P. balsamifera individuals to detect SNPs with fixed differences. Pure P. trichocarpa individuals were used in the phenotypic analyses while data from pure P. balsamifera individuals was available for only one trait (chlorophyll content index) (Figure 4-1, Table A-1, Appendix). 4.2.2 Ancestries and phenotypes In the local ancestry analyses (Chapters 2 and 3)(Suarez-Gonzalez<i> et al.</i> 2016), SNPs were called in the whole genome of 211 individuals and each of the genotypes was sequenced at an expected coverage ranging from 15X to 30X using the Illumina HiSeq2000 platform (SRA: PRJNA276056). Each SNP was annotated using SNPeff (Cingolani et al. 2012) with version 3 of P. trichocarpa genome. Local ancestry was then estimated across the whole genome using RASPberry, a software that implements a reliable Hidden Markov model (HMM) for estimating ancestry segments in admixed populations (Wegmann et al. 2011). The ancestral configuration (P. balsamifera, mixed, P. trichocarpa) of each SNP was determined by only considering ancestries with probabilities >95%. In the present study, local ancestries were coded as 2 for sites with P. balsamifera ancestry, 1 for sites with mixed ancestry, and 0 for sites without P. balsamifera ancestry. The global ancestry levels were estimated by calculating the mean proportion of P. balsamifera ancestry for each individual. For phenotypic data, I used 59 traits encompassing phenological events, biomass accumulation, growth rates, disease susceptibility, as well as leaf, isotope and gas exchange-based ecophysiology from previous studies (McKown et al. 2013; La Mantia et al. 2013; McKown et al. 2014a). This dataset was collected throughout 2008 to 2012 from 461 P. trichocarpa accessions with 4 to 20 clonal replicates similar in age and condition, grown as stecklings under glasshouse conditions, and then out-planted in a common garden. In 2015, I measured chlorophyll absorbance 70  on leaves for the chlorophyll content index (CCI) using a CCM-200 plus SPAD chlorophyll meter (Opti-Sciences Inc., Hudson, NH, USA) in P. trichocarpa and P. balsamifera accessions.    Figure 4-1 Geographic distribution of admixed P. trichocarpa individuals (circles) used in admixture mapping. Additional phenotypic analyses also included pure P. trichocarpa individuals (red triangles). Pure P. balsamifera individuals (blue triangles) were included in the phenotypic analysis of one trait (chlorophyll content index). Ranges of P. trichocarpa and P. balsamifera are shown in red and blue, respectively (Little 1971). A square represents the location where P. trichocarpa homozygotes for the P. balsamifera haplotype in the introgressed region in chromosome 9 occur (Prince George). Diamonds represent the locations where heterozygous individuals for the introgressed haplotype in chromosome 9 occur.   4.2.3 Admixture mapping To explore the effects of introgression in the genetic architecture of adaptation in P. trichocarpa, I used a Bayesian method called BMIX (Shriner et al. 2011). BMIX empirically estimates 71  the testing burdens of admixture mapping by fitting an autoregressive model and estimating the effective number of tests based on autocorrelation. Since a block of ancestry from one parental population can be up to several megabases long, local ancestry estimates can be highly correlated in a genome. First, I estimated the number of effectively independent tests for each chromosome for each individual by fitting an autoregressive model to the local ancestries (Plummer et al. 2010). The spectral density was estimated at frequency zero and the order of the fitted autoregressive model was chosen by minimizing the Akaike information criterion. The effective number of tests were then summed for the chromosomes of each individual and averaged across individuals. Next, the phenotypes were regressed on local ancestry, adjusting for global ancestry using generalized linear models. Finally, the p-values from the regression models were converted into posterior probabilities. The threshold was the value at which the hypothesis favored by the posterior odds switches (i.e. 0.5) (Shriner et al. 2011). I focused on 19 regions with unusually high levels of P. balsamifera introgression identified by the whole genome local ancestry analysis (Chapter 3). Overall, 107 individuals with both local ancestry and phenotypic data were used in the admixture mapping analysis. Although this small sample size may generate false positives, I implemented downstream phenotypic analyses focusing on associations showing strong signals (i.e. posterior probabilities higher than 0.9), on SNPs fixed for different alleles in the parentals, and in a region that showed signals of adaptive introgression in the genomic and functional study (Chapter 2) (see below). 4.2.4 Phenotypic analysis of associations from admixture mapping  To explore the effects of P. balsamifera alleles on the phenotype of admixed P. trichocarpa, I selected SNPs that had both fixed differences in the pure species and displayed associations with 72  traits on the BMIX analysis. I also included all the fixed SNPs from the telomeric introgressed region on chromosome 15, a candidate region for adaptive introgression associated with chlorophyll levels (Chapter 2). Then, haplotypes in the admixed individuals were inferred with fastphase (Scheet & Stephens 2006) by creating the input files of fixed SNPs with FCGENE (Roshyara & Scholz 2014). To identify P. balsamifera and P. trichocarpa haplotypes, I performed neighbor-joining (NJ) analyses [1000 bootstrap replicates in MEGA (Tamura et al. 2007)] including admixed and pure individuals. Each of the P. trichocarpa individuals was classified into one of three genotypic categories: homozygotes for P. balsamifera haplotypes (bb), heterozygotes (bt) and homozygotes for P. trichocarpa haplotypes (tt), based on phased genomic sequences. Finally, phenotypic traits showing associations with the introgressed regions were compared among the three genotypic categories (bb, bt, and tt) in each of the haplotypes using ANOVAs with adjusted p-values with Bonferroni correction. For the analysis of variance on the chlorophyll content index of 2009, I included individuals that set bud after the summer solstice that year (prior to day 186 were removed), since following bud set, trees have a greater amount of chlorophyll on average compared to trees still within an active growing phase (McKown et al. 2016). For 2011 and 2015 data on bud set was not recorded.  4.2.5 Climate of tree origin in admixed and pure individuals To determine if trait variation was correlated with climate and admixture, I compiled 23 climate variables from ClimateNA (Wang et al. 2012) based on 1971–2000 and performed a principal component analysis (PCA) using the function prcomp in R. Eight of the variables were associated with moisture and 15 were associated with temperature (Table B-1, Appendix). The maximum length of day (DAY; h) was also calculated at each location as a proxy for photoperiodic 73  regime using the package geosphere in R v. 3.3.2 (R Development Core Team, http://www.r-project.org).  4.2.6 Enrichment analysis  To detect overrepresented biological terms in an introgressed haplotype associated with trait variation, I performed enrichment tests for various terms including Gene Ontology (GO) and Protein Family (Pfam) using Popgenie (Sjödin et al. 2009). The list of introgressed genes was compared with the list of all poplar genes (41,335) and the best-annotated orthologs in Arabidopsis thaliana were identified based on Popgenie annotation using Fisher´s exact test and the default p-value threshold (0.05). 4.3 Results 4.3.1 Introgressed regions from P. balsamifera are associated with trait variation in P. trichocarpa The admixture mapping analysis revealed 86,150 SNPs associated with 57 traits (across multiple years) with posterior distribution estimates above 0.5 (Table 4-1). In the 19 regions with unusually high levels of P. balsamifera introgression identified in the local ancestry analysis (Chapter 3), I detected 18,525 associations in 2,874 SNPs across seven chromosomes (5, 6, 9, 10, 14, 15 and 17). Introgressed regions on chromosome 5, 6, 15 and 17 showed associations with only one to four traits, while regions on chromosomes 9, 10 and 14 showed associations with 15 to 20 different traits (Table 4-2). Introgressed regions on chromosomes 9, 10 and 14 also displayed the strongest associations (i.e. posterior distributions above 0.9), comprising 3,429 SNPs and 20 trait associations (12 traits some measured in multiple years). In addition, the only introgressed region showing associations with disease resistance was located on chromosome 9 (Table 4-3).  74  A total of 143 SNPs located on chromosomes 9 (119 SNPs), 10 (18 SNPs) and 14 (6 SNPs) displayed both fixed differences in the pure species and associations with traits in BMIX. Neighbor-joining analyses, based on haplotypes from these fixed differences, revealed clear clusters of P. balsamifera and P. trichocarpa haplotypes and allowed me to classify admixed P. trichocarpa individuals as heterozygous or homozygous for P. balsamifera and P. trichocarpa haplotypes (Figure C-1, Appendix). For further phenotypic analyses, I focused on the introgressed ancestry block on chromosome 9 since this haplotype showed the strongest and highest number of associations. Table 4-1 Summary of the results from admixture mapping. The number of traits, SNPs and chromosomes are shown for all associations with posterior probabilities higher than 0.5 (all >0.5). Results are also displayed for association in introgressed regions [based on the whole genome local ancestry study (Chapter 3)] with posterior probabilities higher than 0.5 (introgressed >0.5) and 0.9 (introgressed >0.9).   Posterior probability category Trait category Dataset* all >0.5 introgressed >0.5 introgressed >0.9 Biomass 26 22 14 11 Disease 2 2 1 0 Ecophysiology 60 42 3 2 Phenology 20 19 13 7 Total 108 85 31 20 Number of chromosomes 19 19 7 3 SNPs 1,168,955  86,150 2,874 1,275  Associations 223,161  18,525  3,429   4.3.2 Admixture drives adaptively relevant trait variation across a latitudinal gradient  The traits showing the strongest associations with the introgressed regions were related to phenology and biomass. These traits showed strong correlations with maximum day length (DAY) and temperature (first principal component based on climate variables associated with temperature explaining 60% of the variance), which had significant interactions with the presence of admixture (i.e. significant differences between the slopes of pure and admixed individuals, p<0.05; Figure 4-2). 75  Admixed individuals from regions with longer DAY (higher latitudes) and colder environments had earlier phenology (i.e. bud set, leaf yellowing, and leaf drop), were smaller and had more damage by disease than those from southern regions, while in pure P. trichocarpa I did not detect a relationship between phenotypic traits and DAY or temperature. Pure P. trichocarpa individuals from northern regions had similar phenotypes to pure individuals from southern regions (Figures 4-2, 4-3). Also, the phenotype of pure P. trichocarpa individuals was similar to that in admixed individuals from lower latitudes (i.e. later phenology, greater size, less damage by disease) (Figure 4-2). The low statistical power in pure individuals, due to the small sample size at higher latitudes and colder environments, could explain the lack of association between traits and geoclimatic variables. Climate variables associated with moisture (first principal component explaining 63% of the variance) did not show significant interactions with admixture and traits. Furthermore, the whole genome levels of admixture were generally not correlated with trait variation (Pearson's correlation, p>0.05).  4.3.3 An introgressed haplotype on chromosome 9 restores parental phenotype in admixed P. trichocarpa   ANOVAs based on a suite of phenology, biomass, and disease resistance traits in admixed individuals with different haplotypes on chromosome 9 (Table 4-3) supported the results from BMIX (Tables 4-1 and 4-2) in most cases. All traits that showed significant differences among haplotypes, after Bonferroni correction, revealed the same trend: admixed individuals with two copies of the P. balsamifera haplotype in chromosome 9 were more similar to pure P. trichocarpa, from northern and southern populations, than to other admixed individuals without signals of introgression in this haplotype but with introgression in other parts of the genome. Admixed individuals homozygous for 76  the introgressed haplotype in chromosome 9 had later phenology, were bigger and had less damage by disease compared to admixed individuals homozygous for the P. trichocarpa haplotype in chromosome 9 (Figure 4-3).  Table 4-2 List of the P. balsamifera introgressed regions found in the whole genome local ancestry study (Chapter 3) and the number of traits associated with SNPs based on BMIX [Association (BMIX)’]. SNPs with fixed differences in the pure species and associated with traits (BMIX) [SNP (fixed- association)], and the traits associated with haplotypes based on ANOVAs [Association (ANOVA)] are also displayed. Max. balsa % refers to the height of the introgressed peaks in terms of the maximum percentage of P. balsamifera ancestry within the peak.  Introgressed regions start end Size (kb) Max balsa % SNP (fixed- association) Association (BMIX) Association (ANOVA) ch01a 47,120,000 47,540,000 420 20.92% 0 0  ch01b 48,000,000 48,380,000 380 19.43% 0 0  ch03 21,040,000 21,620,000 580 20.85% 0 0  ch05a 140,000 380,000 240 19.97% 0 1  ch05b 11,580,000 11,820,000 240 18.52% 0 0  ch06 3,540,000 3,700,000 160 17.78% 0 2  ch07 14,680,000 15,700,000 1,020 27.33% 0 0  ch09 1,380,000 1,760,000 380 19.52% 119 19 16 ch10 22,180,000 22,660,000 480 19.93% 18 21 2 ch11a 1,940,000 2,120,000 180 18.36% 0 0  ch11b 17,580,000 17,780,000 200 20.10% 0 0  ch11c 17,800,000 18,600,000 800 21.81% 0 0  ch14a 12,720,000 13,300,000 580 22.67% 6 15 5 ch14b 13,340,000 13,480,000 140 19.25% 0 0  ch15a 0 580,000 580 20.29% 0 1 3* ch15b 13,480,000 13,920,000 440 19.31% 0 0  ch17a 3,080,000 3,900,000 820 21.50% 0 1  ch17b 9,720,000 10,420,000 700 26.00% 0 0  ch17c 12,720,000 13,380,000 660 22.93% 0 0  *Haplotypes on chromosome 15 were based on SNPs that had fixed differences in the pure species. On chromosome 9, 10 and 14, haplotypes were based on SNPs that had both fixed differences and associations with traits in the BMIX analysis. ANOVAs were conducted for two additional traits that did not show significant associations in BMIX (summer chlorophyll content index in 2009 and 2011) but were significantly associated with SNPs on chromosome 15 in the targeted study on adaptive introgression (Chapter 2, Suarez-Gonzalez et al. 2016). 77     Table 4-3 List of traits that showed significant associations with introgressed haplotypes in both BMIX and ANOVA analyses. Traits with posterior probabilities higher than 0.9 in introgressed regions [based on the whole genome local ancestry study (Chapter 3)] are also shown. Detailed information about the traits can be found in Table C-1, Appendix.  Trait Chr Trait category Introgressed > 0.9 (BMIX) Activegrowthratecmday_2010 ch09 biomass  Heightcm_2010 ch09 biomass Yes Heightcm_2011 ch09 biomass Yes Heightgaincm_2009 ch09 biomass  Heightgaincm_2010 ch09 biomass Yes Volumecm3_2011 ch09 biomass  AUDPC_2010 ch09 disease  Budsetday_2009 ch09 phenology  Budsetday_2010 ch09 phenology Yes Growthperioddays_2010 ch09 phenology Yes Leafdropday_2009 ch09 phenology Yes Leaflifespandays_2010 ch09 phenology  Yellowing100_2010 ch09 phenology Yes Yellowing25_2010 ch09 phenology  Yellowing50_2010 ch09 phenology  Yellowing75_2010 ch09 phenology Yes Heightcm_2010 ch10 biomass Yes Heightcm_2011 ch10 biomass Yes Activegrowthratecmday_2010 ch14 biomass  Heightcm_2010 ch14 biomass Yes Heightcm_2011 ch14 biomass Yes Heightgaincm_2010 ch14 biomass Yes Yellowing100_2010 ch14 phenology  CCI_ju10 ch15 ecophysiology  Volumecm3: Bole volume calculated assuming a cone (McKown et al. 2013) AUDPC: Area under the disease progress curve (La Mantia et al. 2013) CCI_ju10: Chlorophyll concentration index June 10, 2015 78    Figure 4-2 Relationship between trait variation and geoclimate variables in admixed and pure P. trichocarpa. A: Correlation between trait variation and maximum length of day (DAY). B: Correlation between trait variation and traits associated with temperature (first principal component based on climate variables associated with temperature explaining 60% of the variance). Asterisks represent significant interactions between the slopes of admixed and pure P. trichocarpa. Detailed information about the traits can be found in Tables 4-3 and C-1, Appendix.79   Figure 4-3 Trait variation in admixed and pure P. trichocarpa across 16 traits associated with an introgressed haplotype on chromosome 9. bb (blue): homozygotes for the P. balsamifera haplotype, tb (purple): heterozygotes, tt (red): homozygotes for the P. trichocarpa haplotype. Pure P. trichocarpa, all homozygotes for the P. trichocarpa haplotype, from northern (pink) and southern (dark red) populations had similar phenotypes (p>0.05). Asterisks represent traits where bb was significantly different than tt. All 16 traits showed significant differences between tt and pure individuals, but there were no significant differences between bb and pure individuals, expect in Volumecm3_2011. Additional information about the ANOVAs can be found in Table C-2, Appendix. Detailed information about the traits can be found in Tables 4-3 and C-1, Appendix.   Phenotypes from heterozygous individuals were, for the most part, midpoints of those from homozygous individuals. This result suggests that the introgressed haplotype in chromosome 9 restores the parental P. trichocarpa phenotype.  P. balsamifera haplotypes on chromosome 9 were geographically limited to the interior and northwestern BC, with homozygotes for the P. balsamifera haplotypes occurring only in Prince George, and heterozygotes also in Terrace and north of Juneau (Figure 4-1). Prince George was the southernmost population, with the shortest DAY, from those where admixture has been detected.  The introgressed haplotype on chromosome 9 was enriched for genes coding for two types of protein families: PF07690, Major Facilitator Superfamily (Nitrate transporters NRT2: 80  Potri.009G008500 and Potri.009G008600) and PF00005, ABC transporter (ABC transporters: Potri.009G007800, Potri.009G008200) as well as for three miRNAs [ptc-miR473 (Cleavage), ptc-miR6448 (Translation), ptc-miR172 (Cleavage)].  4.3.4 Introgression in the telomeric region of chromosome 15 confirms signals of adaptive introgression I inferred haplotypes for the telomeric introgressed region on chromosome 15, already known as a strong candidate region for adaptive introgression (Suarez-Gonzalez et al. 2016), using 132 fixed SNPs. Introgressed P. balsamifera haplotypes showed strong associations with the chlorophyll content index across multiple years. The BMIX analysis showed associations in measurements from 2015, but ANOVAs based on haplotypes revealed significant differences among genotypes also in measurements from 2009 and 2011 (Figure 4-4). As in the targeted local ancestry analysis (Chapter 2), admixed individuals with P. balsamifera haplotypes in the telomeric region of chromosome 15 exhibited higher values of the chlorophyll content index compared to other admixed and pure individuals. The chlorophyll content index in pure P. balsamifera individuals was similar to that in pure P. trichocarpa.  4.4 Discussion 4.4.1 Admixture mapping connects phenotypic traits with genomic regions  The admixture mapping analysis in admixed P. trichocarpa individuals revealed numerous loci introgressed from P. balsamifera that underlie adaptively-relevant variation in phenology, biomass, disease resistance, and ecophysiology traits. These results give strong support that introgressive hybridization in late generation backcrosses is providing a reservoir of new genetic variation associated with adaptive characters in P. trichocarpa. Some of these traits are critical for 81  survival in northern environments and it is possible that a few are pleiotropic side effects of introgression.  In this study, seven of the 19 introgressed regions with unusually high levels of P. balsamifera admixture, uncovered in the local ancestry analysis (Chapter 3), were strongly associated with traits affecting phenology, growth, response to disease, and ecophysiology in P. trichocarpa background.   Figure 4-4 Variation in the chlorophyll content index in admixed (Ad) and pure P. trichocarpa (Pure Pt, dark red) and P. balsamifera (Pure Pb, dark blue) individuals. The chlorophyll content index was associated with an introgressed telomeric region on chromosome 15. The haplotype in the introgressed telomeric region on chromosome 15 is shown inside parentheses [tt (red): homozygotes for the P. trichocarpa haplotype, bt (purple): heterozygotes, bb(blue): homozygotes for the P. balsamifera haplotype]. Data from pure P. balsamifera is only available for estimates of chlorophyll content index from 2015. Shared letters above boxes indicate that the average chlorophyll content indices were not significantly different between those genotypes (p<0.05).  82  Two introgressed regions, in particular, one on chromosome 9 and another on chromosome 15, showed particularly strong and interesting associations with trait variation.  4.4.2 Introgression extends the species range of P. trichocarpa by driving clinal adaptation of phenology traits Numerous studies have shown that in trees from northern climates, bud set and growth cessation are initiated earlier compared to their southern counterparts because the former are adapted to the critical day length of northern regions (Rohde et al. 2011; Hall et al. 2007; Evans et al. 2016; Vitasse et al. 2014). Earlier reports on trait variation in these P. trichocarpa individuals confirmed these findings and revealed that most phenology events, including bud set, leaf yellowing, and leaf drop were strongly associated with geoclimatic variables such as DAY and temperature (McKown et al. 2013). These previous results illustrate the adaptive importance of phenology traits, and here I identified admixture as an important driver of this clinal adaptation.  P. trichocarpa occurs from California to Alaska, and the northern range extension of P. trichocarpa appears to be dependent, at least in a substantial part, on introgression of alleles from P. balsamifera, which is a boreal species distributed from Alaska to Newfoundland. The whole genome local ancestry analysis demonstrated a strong correlation between admixture and climate, showing increased levels of P. balsamifera introgression in colder and drier environments (Chapter 3). The phenotypic analyses support this finding and show that admixture, as well as introgression of certain genomic regions (e.g. haplotypes on chromosome 9) have strong pleiotropic effects on phenotypes that appear to play important roles in adaptation in northern populations of P. trichocarpa.  83  4.4.3 An introgressed haplotype on chromosome 9 restores parental phenotype at certain latitudes Although admixture with P. balsamifera clearly confers some adaptive traits on P. trichocarpa (i.e. early phenology in the north), there is a trade-off since admixture has a tendency to reduce the growth of the fast-growing P. trichocarpa and reduce disease resistance. Curiously, a particular P. balsamifera haplotype block on chromosome 9, when introgressed, has the ability to restore the parental P. trichocarpa phenotype (i.e. later phenology, higher growth and greater disease resistance). The introgressed “restorer block” in chromosome 9 was the most pleiotropic with the highest number of associations with biomass (4), phenology (5) and damage by disease (1) traits (in multiple years) and with the strongest signals (posterior probabilities > 0.9). Admixed individuals homozygous for introgressed P. balsamifera loci in chromosome 9 were more similar to pure P. trichocarpa individuals (from northern or southern regions) than to other admixed individuals heterozygous or homozygous for the P. trichocarpa haplotype in chromosome 9. Homozygotes for the P. balsamifera haplotype in chromosome 9 were found only in the southernmost location where admixture has been detected, and were taller (mean: 181.5cm, 2010), had later phenology (bud set day mean: 222.5 Julian day, 2010), and were more resistance to disease than other admixed individuals (mean height: 68.62 cm; bud set: 175 Julian day, 2010).  My results suggest that admixture is a strong driver for adaptation at high latitudes, where cold is combined with northern light regimes, but could be maladaptive in continental regions at lower latitudes where cold is combined with southern light regimes. In Prince George, the population with the shortest DAY from those where admixture has been detected, introgression of the haplotype on chromosome 9 restores the parental phenotype. Therefore, an allele from a 84  species with expected early dormancy is conferring delayed dormancy when in the alternative genetic background. Later phenology (i.e. delayed dormancy) in trees with these introgressed haplotypes could be an adaptive strategy as it maximizes carbon assimilation with late dormant bud formation and delayed leaf senescence, providing that it does not increase the risk of damage caused by early frosts on vegetative organs (Chuine 2010). The physiological mechanism of this restorer block is of potentially great interest. It is possible that certain introgressed alleles, via epistatic gene interactions, allow admixed trees to respond to changes in the environment by tracking a ‘phenological optimum’ more efficiently than other admixed individuals. Future studies including pure P. balsamifera individuals and common gardens in the latitude of origin could be used to further understand the role of this introgressed haplotype in clinal adaptation in P. trichocarpa. 4.4.4 Adaptive introgression increases plant disease resistance Admixed P. trichocarpa individuals with P. balsamifera haplotypes on chromosome 9 also showed less damage by disease than other admixed individuals, which could help explain their higher height and bole volume (in addition to their longer growing period and the faster active growth rate). Climate change, particularly at higher latitudes, can influence pathogen aggressiveness. Pathogens may thus become increasingly important (IPCC et al. 2014; Sturrock et al. 2011), especially for P. trichocarpa from northern populations. If adaptive introgression of P. balsamifera on chromosome 9 indeed increases survival under rust attacks, these admixed P. trichocarpa individuals could represent an important resource for forest management and breeding programs. The conservation of natural hybridization represents an underutilized option (Hamilton & 85  Miller 2015; Johnson et al. 2010) and my work contributes empirical evidence to consider when assessing the conservation value of introgression.  4.4.5 Candidate genes for adaptive introgression The introgressed region on chromosome 9 was enriched for genes from two protein families: Major Facilitator Superfamily (PF07690) and ABC Transporter (PF00005). Two orthologs of the nitrate transporter AtNRT2 (Potri.009G008500, Potri.009G008600), from the Major Facilitator Superfamily (PF07690), were associated with a number of phenological (bud set, leaf yellowing, leaf drop, growing period, height growth cessation) and biomass traits (height, height gain) here and in a previous association analysis (McKown et al. 2014b). One of the AtNRT2 orthologs (Potri.009G008600), which was also associated with damage by disease in a different association study (La Mantia et al. 2013), showed exceptionally strong correlations with geoclimate variables including latitude and temperature (Geraldes et al. 2014; Porth et al. 2015), and had SNPs that were Fst outliers in a landscape genomic analysis (Geraldes et al. 2014). These previous reports used the same P. trichocarpa populations but a different genotyping method and association mapping approach than those used in this study. Nitrate is essential for plant growth and plant development as a primary nutrient in the nitrogen assimilation pathway (Gojon et al. 2011). The nitrate transporter NRT2.1, with dual-nutrient transport/signaling functions, regulates nitrate uptake through systemic signals to the shoot in split-root systems (Gansel et al. 2001). Expression and functional analyses have shown that NRT2.1 is an environmental signal sensor controlling the development of the root in coordination with nutritional cues (Tsay et al. 2007; Ono et al. 2000). NRT2 genes are also critical responders to both abiotic and biotic stress. In Brassica, expression of NRT2.1 was adversely affected under 86  abiotic stress conditions and in Arabidopsis loss of function mutants showed reduced disease susceptibility to a bacterial pathogen (Pseudomonas syringae) (Camañes et al. 2012). Camañes et al. (2015) proposed that NRT2.1 influences disease response by favoring abiotic stress responses and down-regulating biotic stress repression (abscisic acid and jasmonic acid pathways). In Poplar, NRT2.1 genes are expressed more strongly in roots than in aerial tissues and are down-regulated in early spring (Sjödin et al. 2009). These genes could function in nitrogen regulation during seasonal remodeling of tree phenology related to the interphase between growth and dormancy (Forde 2000; Larisch et al. 2012; Footitt et al. 2013).  4.4.6 Introgressed alleles in a telomeric region of chromosome 15 create a transgressive phenotype in P. trichocarpa background This whole genome approach supports the findings from the targeted ancestry analysis showing associations between a telomeric region on chromosome 15 and the chlorophyll content index (Chapter 2). Here, phenotypic analyses revealed that P. balsamifera alleles in a P. trichocarpa background have an epistatic effect, with P. trichocarpa individuals homozygous for the introgressed haplotypes showing the highest chlorophyll content index. In the P. balsamifera background, these alleles seem to be acting in a different manner since the chlorophyll content index was lower compared to those in P. trichocarpa admixed individuals with the same alleles. In fact, the chlorophyll content index in pure P. balsamifera was similar to those in pure P. trichocarpa. This suggests that introgressed genes interact with the genetic background of the receiving species and result in a transgressive phenotype (Dittrich-Reed & Fitzpatrick 2012). Since phenology traits were not associated with this telomeric region, I ruled out bud set timing as a potential artifact explaining the greater chlorophyll content index in admixed P. trichocarpa individuals. If the 87  chlorophyll content index is associated with higher photosynthetic rates, faster growth, and higher carbon acquisition, as is the case in P. balsamifera (Soolanayakanahally et al. 2009), this phenotype could be of adaptive importance to counteract shorter growing seasons in P. trichocarpa populations at higher latitudes.  Overall, my study provides strong support that introgressive hybridization from P. balsamifera generates a reservoir of new genetic variation associated with adaptive characters that may allow improved survival in northern regions of the P. trichocarpa range.  88  Chapter 5: Conclusion 5.1 Summary In this thesis, I provided evidence for adaptive introgression in two Populus species, P. trichocarpa and P. balsamifera, by implementing local ancestry analyses together with functional, phenotypic and selection tests. These results contribute to our understanding of introgression as a source of evolutionary novelty and the role of admixture in clinal trait variation across a latitudinal gradient. To my knowledge, this is the first fine-scale study on natural hybrids of tree species with such a comprehensive view of the effects of admixture in adaptation. Although theoretical and empirical studies have shown that introgression is a widespread process that can provide novel genetic recombinants to promote adaptation to new environments (Arnold 2006), most studies on interspecific hybridization focus on the nature and strength of reproductive isolation. In addition, most reports on fine-scale patterns of introgression have mainly focused on taxa with short generation spans (Kim & Rieseberg 1999; Martin et al. 2006). Clarifying the magnitude and impact of introgressed genes contributing to functionally relevant variation in trees has great potential for forest ecology, management, and restoration efforts in the face of climate change (Lexer et al. 2004; Whitham et al. 2006). Based on a local ancestry analysis across the whole genome, I detected asymmetric patterns of introgression in two poplar tree species adapted to contrasting environments with stronger signals of introgression from P. balsamifera to P. trichocarpa than vice versa. Admixed P. trichocarpa individuals showed more candidate genomic regions for adaptive introgression compared with admixed P. balsamifera but also the largest introgressed peak. In addition, there was no overlap between the introgressed regions in P. trichocarpa compared to those in P. 89  balsamifera or the enriched GO and Pfam terms in the introgressed regions. These analyses also revealed overrepresentation of P. balsamifera introgression in subtelomeric regions and possible protection of the sex-determining regions from interspecific gene flow.  In admixed P. trichocarpa individuals, a number of candidate regions for adaptive introgression showed strong signals of selection and were enriched for genes that may play crucial roles for survival and adaptation. An admixture mapping analysis revealed numerous P. balsamifera introgressed loci underlying variation in phenology, biomass, disease resistance, and ecophysiology traits. These results provided strong support that introgressive hybridization in late generation backcrosses is providing a reservoir of new genetic variation associated with adaptive characters in P. trichocarpa. In particular, one introgressed region on chromosome 9 showed strong and interesting associations with trait variation, and a telomeric region on chromosome 15 showed associations with potential adaptive traits also based on a targeted approached which included functional tests. 5.2 Genome-wide patterns of introgression 5.2.1 Subtelomeric enrichment of adaptively introgressed regions  Genomic regions with unusually high levels of introgression were found in different chromosome positions but there was an enrichment of P. balsamifera introgressed ancestry in telomeric regions. The type of gene families found in the subtelomeres and the unstable structural properties of these regions could explain this interesting finding. In P. balsamifera introgressed regions, all of the TIR (PF01582) introgressed genes were subtelomeric and arranged in clusters that showed signals of purifying selection. This protein family is associated with disease resistance and was overrepresented in the candidate regions for adaptive introgression. These results, together 90  with previous studies, show that subtelomeres may be enriched for gene families associated with adaptation (Geffroy et al. 2000; Wei et al. 1999b; Duplessis et al. 2009; Brown et al. 2010), where interspecific introgression may transfer important adaptive traits.  Subtelomeres are also characterized by increased recombination, duplication, and mutation that may promote adaptive evolution (Rudd et al. 2007; Linardopoulou et al. 2005; Barton et al. 2008). In fact, subtelomeric families are evolving and expanding much faster than families that do not contain subtelomeric genes (Brown et al. 2010). Based on my analyses I put forward adaptive introgression as an important potential driver of the extensive sequence variation found in subtelomeric genes and further fine-scale genomic analyses are needed to study this process in other organisms.  5.2.2 Possible protection of the sex-determining regions from interspecific gene flow The lack of significant signals of introgression in the poplar sex-determining region (Geraldes et al. 2015) in my study is consistent with previous work showing that sex-determining regions are protected from interspecific gene flow (Hu & Filatov 2016). In Populus, the gender locus has always mapped to the proximal telomeric region of chromosome 19 (Gaudet et al. 2008; Yin et al. 2008; Geraldes et al. 2015), which is also in close proximity to the largest NBS disease resistance gene cluster in poplar (Kohler et al. 2008). Although subtelomeric regions and genes associated with adaptation could be more prone to be introgressed, my analysis suggests that sexually antagonistic mutations may be impeding introgression in the poplar sex-determining region.  5.3 Introgression into P. balsamifera: Implications for future climates Introgression from P. trichocarpa, a tree adapted to mild and coastal climate, might be maladaptive as it could decrease the stress tolerance of P. balsamifera in northern latitudes. The 91  average width (i.e. size in base pairs) of the candidate regions for adaptive introgression together with the apparently different hybridization history of these species supports this idea.  However, introgression from P. trichocarpa into P. balsamifera could play adaptive roles under a changing climate, especially if warmer temperatures and increased precipitation increase the probability of tree diseases in northern locations (IPCC et al. 2014; Sturrock et al. 2011). Under this scenario, P. trichocarpa, which may have been under intense selection pressures for disease resistance (La Mantia et al. 2013), could provide beneficial genetic variants previously tested by natural selection and become increasingly important for P. balsamifera. Here I show that P. trichocarpa introgressed genes in P. balsamifera were enriched for genes associated with innate immunity and could play key roles for the survival of P. balsamifera populations under a changing climate.  5.4 Introgression is a driver of adaptation in P. trichocarpa northern range My thesis shows that the northern range extension of P. trichocarpa, a species that occurs from California to Alaska, may be dependent on introgression of alleles from P. balsamifera. This idea was supported by the geographic location of adaptive introgression, which was limited to P. trichocarpa populations in northwestern Canada and the Canadian Rockies. In this region, introgression of P. balsamifera genes associated with adaptation to higher latitudes may allow admixed P. trichocarpa individuals to colonize colder environments. In fact, I found strong relationships between the levels of P. balsamifera admixture and climate variables associated with temperature and humidity, with increased levels of introgression in colder and drier environments. In addition, a previous landscape genomic analysis showed strong correlations between some of the candidate regions for adaptive introgression identified here and a number of geoclimate variables 92  including latitude, mean annual temperature and mean coldest temperature (Geraldes et al. 2014). Furthermore, admixed individuals make up the majority (or entirety) of northern populations in the P. trichocarpa collection used in this study (Geraldes et al. in preparation). 5.4.1 Introgression drives clinal adaptation of phenology traits Trees from northern climates are adapted to the critical day length of northern regions, where bud set and growth cessation are initiated earlier compared to southern counterparts (Rohde et al. 2011; Hall et al. 2007; Vitasse et al. 2014; McKown et al. 2013). Here I confirmed these findings and identified admixture as an important driver of clinal adaptation in important phenology traits. My results show that admixed P. trichocarpa individuals from regions with longer maximum day length (higher latitudes) and colder environments showed earlier phenology while in pure P. trichocarpa I did not detect a relationship between phenotypic traits and DAY or temperature. Admixed P. trichocarpa individuals from northern locations were also smaller and less resistant to disease. The smaller size in admixed trees could have resulted from their shorter growing period and the greater damage by disease in the southern common garden. However, northern P. trichocarpa admixed populations have been under lower selection pressures for disease resistance (La Mantia et al. 2013) since historical climate at higher latitudes has not been favorable for pathogen reproduction and survival (Hicke et al. 2012; Helfer 2014). My results suggest that admixture as well as introgression of certain alleles (e.g. haplotypes on chromosome 9 and 15) have strong pleiotropic effects and play an important role for adaptation in northern populations of P. trichocarpa.  93  5.4.2 An introgressed haplotype on chromosome 9 restores parental phenotype at certain latitudes Although admixture is a strong driver for adaptation at higher latitudes, it could be maladaptive at certain locations. In Prince George, the population with the shortest maximum day length of those where admixture has been detected, introgression of a haplotype on chromosome 9 restores the parental phenotype. This introgressed block on chromosome 9 was the most pleiotropic with the highest number of trait associations and with the strongest association signals. Individuals homozygous for introgressed loci on chromosome 9 had later phenology, were taller, and showed less damage by disease than other admixed individuals. Delayed dormancy in trees with these introgressed haplotypes could maximize carbon assimilation and be an adaptive strategy if late bud formation and leaf senescence do not increase the risk of damage by early frosts (Chuine 2010). Alternatively, introgressed alleles may allow admixed trees to track a ‘phenological optimum’ more efficiently than other admixed individuals in response to changes in the environment.  The phenotypic effects of the introgressed haplotype on chromosome 9 could have resulted from favorable gene combinations between the P. balsamifera introgressed loci and P. trichocarpa genomic background, or from the introduction of multiple linked mutations associated with adaptive traits and previously tested by natural selection on P. balsamifera background (Arnold 2004; Abbott et al. 2013; Barton 2001). In any case, these results suggest that adaptive variation from introgression at specific loci and geographic locations is playing key roles for adaptation in P. trichocarpa genomic background. 94  5.4.3 Introgressed alleles in a telomeric region of chromosome 15 create a transgressive phenotype in P. trichocarpa background  P. balsamifera alleles in a telomeric region of chromosome 15 have an additive effect in the P. trichocarpa background, with P. trichocarpa individuals homozygous for the introgressed haplotypes showing the highest chlorophyll content index. This finding was supported by both the whole genome and targeted analyses. In a P. balsamifera background, these alleles seem to be acting in a different manner since the chlorophyll content index was lower compared to that in P. trichocarpa admixed individuals with the same alleles. In fact, the chlorophyll content index in pure P. balsamifera was similar to that in pure P. trichocarpa. This suggests that introgressed genes interact with the P. trichocarpa background and result in a transgressive phenotype (Dittrich-Reed & Fitzpatrick 2012). If higher chlorophyll content index is associated with higher photosynthetic rates, faster growth, and higher carbon acquisition, as is the case in P. balsamifera (Soolanayakanahally et al. 2009), this phenotype could be of adaptive importance to counteract shorter growing seasons in P. trichocarpa populations from higher latitudes.  5.5 Future directions and applications of this research This study provides strong support that introgressive hybridization generates a reservoir of new genetic variation associated with adaptive characters that may allow improved survival in certain environments. To better understand the historical context of this adaptive introgression and asymmetric introgression patterns, future studies in this system should focus on exploring the demographic history of these two Populus species. In addition, fine-scale genomic studies of introgression are needed in other organisms to evaluate the relative importance of the subtelomeric properties in shaping the architecture of adaptive introgression.  95  5.5.1 Candidate genes in the introgressed region  This study also revealed numerous candidate regions and genes for adaptive introgression with strong signals of selection notably related to phenology, biomass, disease resistance and ecophysiology. Future functional studies could be implemented to test biochemical or physiological functions of P. balsamifera introgressed alleles compared with those from P. trichocarpa alleles. 5.5.1.1 PRR5 and COMT1 are strong candidate genes for adaptive introgression in the telomeric region on chromosome 15  The strongest candidate genes for local adaptation in the introgressed region on chromosome 15 were the PSEUDORESPONSE REGULATOR5 (PRR5) and CAFFEIC ACID 3-O-METHYLTRANSFERASE1 (COMT1). PRR5, an important component of the circadian clock mechanism, showed signals of balancing selection in P. balsamifera. Furthermore, introgressed PRR5 P. balsamifera variants have a nsSNP substitution located in a highly conserved region, which is essential for PRR5 transcription repressor activity (Nakamichi et al. 2010). This position in P. trichocarpa alleles, as in other PRR genes from other species, is usually occupied by a positively charged polar amino acid. In P. balsamifera PRR5 alleles this site is occupied by a non-polar amino acid.  It is possible that the P. balsamifera PRR5 allele represses target genes in a different manner compared to common P. trichocarpa alleles. In P. trichocarpa, I found that the levels of expression of the top 25 genes co-expressed with PRR5 are significantly lower in P. trichocarpa individuals homozygotes for P. balsamifera PRR5 alleles compared to either individuals that were heterozygous or homozygous for P. trichocarpa PRR5 alleles. This finding suggests that the introgressed protein variant is functionally distinct since it appears to differ in its transcriptional regulatory activity in the 96  clock-regulated gene network. Future functional studies could test the hypothesis that the PRR5 protein variants have different biochemical or physiological functions.  COMT1, which encodes a phenylpropanoid enzyme that could be involved in lignification or pathogen defense (Barakat et al. 2011), showed signals of purifying selection in both pure P. trichocarpa and P. balsamifera individuals. In COMT1, my analysis revealed evidence for functionally distinct P. balsamifera alleles based on gene expression differences, suggesting that P. balsamifera alleles have the potential to affect the phenotype of admixed individuals. One SNP in the promoter region, in tight linkage with a nsSNP that differentiates P. balsamifera from P. trichocarpa COMT1 alleles, could potentially be a cis-regulatory element causing the difference in expression. The introgressed COMT1 allele has an unusual substitution of a nonpolar for a polar amino acid that is nearly fixed and under selection in P. balsamifera. It is also possible that introgressed variants have lower enzyme efficacy and that an increase in gene expression is needed to overcome this change in enzyme activity.  Introgressed PRR5 and COMT1 alleles could depict the introduction of modular, or cassette-like variation into P. trichocarpa, where multiple linked introgressed alleles are associated with adaptive traits (Abbott et al. 2013). Future work will be required to test this hypothesis.  5.5.1.2 NRT2 is a strong candidate gene for adaptive introgression on chromosome 9 As in previous association analyses in these P. trichocarpa accessions (McKown et al. 2014b; La Mantia et al. 2013), orthologs of the nitrate transporter AtNRT2 were associated with a number of phenological, biomass, and disease susceptibility traits. These genes also showed exceptionally strong correlations with geoclimate variables (Geraldes et al. 2014; Porth et al. 2015) and had SNPs that were Fst outliers in a landscape genomics analysis (Geraldes et al. 2014). These genes could 97  function in nitrogen regulation during seasonal remodeling of tree phenology related to the interphase between growth and dormancy (Forde 2000; Larisch et al. 2012; Footitt et al. 2013) and future functional analysis could be used to test the role of PtNRT2 in tree phenology as well as functional differences between introgressed P. balsamifera and P. trichocarpa variants. 5.5.1.3 Candidate genes associated with oxidative stress in an introgressed region on chromosome 3 The introgressed region on chromosome 3 showed strong signatures of positive selection in the whole genome local ancestry analysis but was not associated with phenotype in the admixture mapping analysis. It is possible that the phenotypic trait being controlled by this introgressed block was not included in this study or that an interaction between genetic factors and the environment is at play. Future studies in reciprocal gardens in the north and glasshouse experiments will be necessary to test this idea. Since this region was enriched for genes associated with oxidative stress, experiments could test for resistance to marginal edaphic conditions. This is an interesting line of research since poplar trees are being put forward as an important resource for phytoremediation of contaminated sites (Castro-Rodriguez et al. 2016). 5.5.1.4 Candidate genes associated with disease resistance in an introgressed region on chromosome 7 Another introgressed region that showed signals of selection in the whole genome analysis was telomeric and located on chromosome 7. Although admixture mapping did not recover associations, this region included genes from a protein family associated with disease resistance. Since the disease susceptibility trait used in this study was calculated from natural infection of Melampsora x columbiana in a common garden (La Mantia et al. 2013), it is possible that the 98  admixed loci are associated with response to other types of fungal infection. A study of decay in P. trichocarpa from British Columbia revealed that two rot fungi, Polyporus delectans Peck and Pholiota destruens (Brand.) Quél., cause 92% of the loss in living trees from the middle Fraser region (Thomas & Podmore 1953). Future studies could test if the introgressed region on chromosome 7 is in fact associated with rust severity caused by common fungi species affecting admixed individuals in their native distribution.  If adaptive introgression from P. balsamifera increases survival under rust attacks, these admixed P. trichocarpa individuals could represent an important resource for forest management and breeding programs, in particular under a changing climate. 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The accession codes for haplotypes from three representative individuals are shown: one homozygote (bb) for P. balsamifera haplotypes (kim166), one heterozygote (bt; als14) and one homozygote (tt) for P. trichocarpa haplotypes (ame133). The NJ analyses were conducted in MEGA6. Since I did not have information about the local ancestry patterns for all the 146 genotypes (in RASPberry I only included 36 P. trichocarpa admixed individuals), I used the phased introgressed region of chromosome 15 from all P. trichocarpa genotypes (146) as well as from the reference panel of individuals and implemented a NJ tree analysis - 1000 bootstrap replicates in MEGA (Tamura et al. 2007). These NJ trees, based on either the entire 880-kb chromosome 15 region B (A) or a smaller 300-kb portion at the start of the B region (B), revealed a well-defined cluster (bootstrap values: 80 and 99 based on 880-kb and 300 kb regions respectively) where haplotypes from pure P. balsamifera individuals grouped with haplotypes from admixed P. trichocarpa individuals. Based on these trees I identified eight genotypes homozygous for P. balsamifera haplotypes (bb: both haplotypes of the individual were found inside the P. balsamifera cluster), 32 heterozygous genotypes (bt: only one haplotype was found inside the P. balsamifera cluster) and 104 genotypes homozygous for P. trichocarpa haplotypes (tt: none of the haplotypes were inside the P. balsamifera cluster). 122   Figure A-2. Proportion of P. balsamifera ancestry in admixed P. trichocarpa individuals analyzed in three sliding window sizes. P. balsamifera ancestry across (blue lines) chromosome 6, 12 and 15 is shown in three sliding window analyses (sizes: 100kb, 500kb, and 1Mb with steps: 20kb, 100kb, 200kb respectively). Grey broken lines represent SNP density per window.  123    Figure A-3. Neighbor-joining (NJ) tree of parental lineages based on the first 880 kb of chromosome 15. 25 P. trichocarpa accessions are shown in red and 25 P. balsamifera are shown in blue and purple. Branch lengths represent genetic distances using the p-distance method. For P. balsamifera, blue represents haplotypes from populations in the northwestern parts of the range and purple represents haplotypes from central populations. The NJ analysis was conducted in MEGA6.  124     Figure A-4. Linkage disequilibrium (LD) decay with distance in P. balsamifera, P. trichocarpa and admixed individuals based on single-nucleotide polymorphisms (SNPs) across three chromosomes (6, 12, 15 – dotted line) and three introgressed regions (region A on chromosome 6 – purple, region B on chromosome 15 – blue, region C on chromosome 15 – cyan). Bottom: LD decay with distance in a set of admixed P. trichocarpa individuals (see Supplementary M&M) based on SNPs across chromosomes (15 – dotted line) and two introgressed regions on chromosome 15 (region B– blue, region C– cyan).   Admixed P. trichocarpa 125   Figure A-5. Nucleotide diversity (π) levels across the first 1 MB of chromosome 15. π was assessed in sliding windows (size: 100kb, step: 20kb) in pure P. balsamifera (blue continuous line) and pure P. trichocarpa (red continuous line) individuals. In admixed P. trichocarpa individuals, the first 880kb of chromosome 15 showed signals of introgression from P. balsamifera. Nucleotide diversity values representing the 95% and 5% of the distribution are shown in blue and red broken lines for P. balsamifera and P. trichocarpa respectively.  Figure A-6. Partial protein alignment of different COMT1 homologs. All P. data were obtained from whole genome resequencing of the species indicated (Cronk et al., unpublished); other sequences are from Phytozome (http://www.phytozome.net). The alignment figure was created using CHROMA (http://www.llew.org.uk/chroma/). The star indicates the position of the Q287 variant in the P. balsamifera COMT1 sequence, a position occupied by a P287 in all other COMT sequences analyzed (indicted by red P in the consensus amino acid sequence). The arrows represent the conserved catalytic histidine (H) and glutamic acid (E) residues identified by Zubieta et al. (2002).  126   Figure A-7. Relative gene expression (based on 2–ΔCT) of COMT1 among P. trichocarpa individuals with different COMT1 alleles   Figure A-8. Box plot of the mean-centered FPKM values for the expression of the top 25 genes co-expressed with PRR5 in xylem. Genes had Pearson coexpression coefficients of > 0.66 with PRR5 across 389 samples. Data is from P. trichocarpa individuals homozygotes for P. balsamifera PRR5 alleles (WW), heterozygotes (WR) and homozygotes for P. trichocarpa PRR5 alleles (RR). ANOVA revealed a significantly lower expression in WW individuals compared to either WR or RR individuals (p<0.05). Top 25 genes coexpressed with PRR5 (PCC > 0.66): Potri.001G205800; Potri.002G178200; Potri.003G194000; Potri.004G177000; Potri.004G217600; Potri.006G004700; Potri.006G067700; Potri.006G143300; Potri.006G184800; Potri.006G279900; Potri.007G050100; Potri.008G004300; Potri.008G077100; Potri.008G206300; Potri.009G015000; Potri.009G137200; Potri.010G086700; Potri.011G043200; Potri.011G082600; Potri.012G003700; Potri.012G005900; Potri.012G082600; Potri.013G046300; Potri.018G107000; Potri.018G129700. -1012Relative Expression [Mean Centred Log2(FPKM)]PRR5_GenotypeCCCTTTRelative Expression of 25 PRR5-Coexpressed Genes in XylemRR   WR   WW 127    Figure A-9. LD plot of COMT1 in individuals from northern and central parts of P. trichocarpa’s range. Red diamonds represent pairs of SNPs with high levels of LD (D’=1, LOD ≥2). The green triangle represents nsSNP P287Q; the purple triangle represents site 240301 (P. trichocarpa version 3 - v3.0 genome) in the intergenic region upstream of the 5’UTR region. Asterisk (*) represent LD between these two sites (D’=1; r2=1). Red areas represent pairs of SNPs with high levels of LD (D’=1, LOD ≥2) and blue areas representing pairs of SNPs with LD comparisons with a low estimation confidence (LOD < 2). Dark triangles represent haplotype blocks based on Gabriel et al. (2002). 128   Figure A-10. Three-dimensional protein model of P. trichocarpa COMT1 based on template 1KYW. The homology model (in blue) was constructed using UCSF Chimera (http://www.cgl.ucsf.edu/chimera/) interface and Modeller based on template 1KYW (in beige) (Zubieta et al. 2002).   Table A-1. List of P. trichocarpa and P. balsamifera accessions used in this study. Data includes species (sp: P.b=P. balsamifera, P.t=P. trichocarpa), biogeographical data (lat=latitude, long=longitude), classification based on previous admixture and PCA analysis (Geraldes et al in preparation, code: admix.b=admixed P. balsamifera, admix.t=admixed P. trichocarpa, refbalsa=pure P. balsamifera, reftricho=pure P. trichocarpa). Blue, purple and yellow represents information from Chapter 2, 3 and 5 respectively. Targeted analysis refers to the local ancestry analysis based on chromosomes 6, 12 and 15. WG.analysis refers to the local ancestry analysis based on the whole genome. rasp: Included in Raspberry analysis. seq: Sequence analysis. RNA: RNAseq data. pheno: Phenotype data. b.aver and t.aver: average of P. balsamifera and P. trichocarpa ancestry across the whole genome respectively. rmv: accessions that did not show signals of introgression in Raspberry and were removed from downstream analyses. BMIX: admixture mapping analysis. NJ: Clusters based on the neighbour-joining analysis on chromosomes 09, 10, 14 and 15      Targeted.analysis WG.analysis Admixture.mapping ID sp lat long code rasp seq RNA pheno rasp b.aver t.aver rmv BMIX NJ9 NJ10 NJ14 NJ15 AP1004 P.b 55.24 114.36 admix.b Yes No No No Yes 30.25 53.38 Yes No NA NA NA NA AP1006 P.b 55.11 114.03 admix.b Yes No No No Yes 91.11 6.16 No No NA NA NA NA AP2298 P.b 59.11 122.46 admix.b Yes No No No Yes 46.06 35.39 Yes No NA NA NA NA AP2446 P.b 55.24 114.36 admix.b Yes No No No Yes 38.58 48.18 Yes No NA NA NA NA AP5230 P.b 55.24 114.36 admix.b Yes No No No Yes 43.25 42.03 Yes No NA NA NA NA AP5451 P.b 56.56 111.32 admix.b Yes No No No Yes 82.40 11.82 No No NA NA NA NA AP5452 P.b 55.09 113.15 admix.b Yes No No No Yes 87.59 8.21 No No NA NA NA NA AP5454 P.b 55.26 114.31 admix.b Yes No No No Yes 60.53 28.55 Yes No NA NA NA NA 129       Targeted.analysis WG.analysis Admixture.mapping ID sp lat long code rasp seq RNA pheno rasp b.aver t.aver rmv BMIX NJ9 NJ10 NJ14 NJ15 DEN13 P.b 63.39 -148.51 admix.b Yes Yes No No Yes 84.52 11.05 No No NA NA NA NA DEN4 P.b 63.39 -148.51 admix.b Yes Yes No No Yes 81.26 14.35 No No NA NA NA NA DESD22 P.b 59.45 -129.02 admix.b Yes No No No Yes 47.73 34.73 Yes No NA NA NA NA FTM5 P.b 56.56 -111.36 admix.b Yes Yes No No Yes 85.74 9.18 No No NA NA NA NA FTM6 P.b 56.56 -111.36 admix.b Yes Yes No No Yes 81.42 12.55 No No NA NA NA NA FTM8 P.b 56.56 -111.36 admix.b Yes Yes No No Yes 74.91 18.16 No No NA NA NA NA GPR1 P.b 54.75 -118.63 admix.b Yes Yes No No Yes 66.45 25.94 Yes No NA NA NA NA GPR10 P.b 54.75 -118.63 admix.b Yes No No No Yes 84.68 11.14 No No NA NA NA NA GPR11 P.b 54.75 -118.63 admix.b Yes Yes No No Yes 79.68 15.18 No No NA NA NA NA GPR12 P.b 54.75 -118.63 admix.b Yes Yes No No Yes 80.68 14.10 No No NA NA NA NA GPR13 P.b 54.75 -118.63 admix.b Yes Yes No No Yes 77.87 13.71 No No NA NA NA NA GPR14 P.b 54.75 -118.63 admix.b Yes No No No Yes 89.27 7.31 No No NA NA NA NA GPR2 P.b 54.75 -118.63 admix.b Yes Yes No No Yes 71.16 22.04 Yes No NA NA NA NA GPR3 P.b 54.75 -118.63 admix.b Yes Yes No No Yes 85.02 10.27 No No NA NA NA NA GPR4 P.b 54.75 -118.63 admix.b Yes No No No Yes 85.11 10.27 No No NA NA NA NA GPR5 P.b 54.75 -118.63 admix.b Yes Yes No No Yes 87.12 8.72 No No NA NA NA NA GPR6 P.b 54.75 -118.63 admix.b Yes No No No Yes 88.43 8.61 No No NA NA NA NA GPR7 P.b 54.75 -118.63 admix.b Yes Yes No No Yes 83.93 11.77 No No NA NA NA NA GPR8 P.b 54.75 -118.63 admix.b Yes No No No Yes 83.68 11.51 No No NA NA NA NA GPR9 P.b 54.75 -118.63 admix.b Yes No No No Yes 83.69 11.64 No No NA NA NA NA SRD9 P.b 56.27 -104.23 admix.b Yes Yes No No Yes 86.69 8.54 No No NA NA NA NA TNZA-4-3 P.b 58.3 -130.47 admix.b Yes Yes 2 Yes Yes 45.69 39.47 Yes No NA NA NA NA WHR11 P.b 60.7 -135.33 admix.b Yes Yes No No Yes 52.30 41.58 Yes No NA NA NA NA WOL6 P.b 57.58 -103.93 admix.b Yes Yes No No Yes 67.44 21.17 Yes No NA NA NA NA ALSC-1-4 P.t 59.62 -137.92 admix.t Yes Yes No Yes Yes 11.99 78.88 No Yes tt tt tt tb ALSC-1-5 P.t 59.62 -137.92 admix.t Yes Yes No Yes Yes 14.40 74.56 No Yes tb bb tb tb BELA-18-2 P.t 52.42 -126.17 admix.t No Yes No No Yes 0.00 100.00 Yes No tt tt tt tt BELA-18-5 P.t 52.42 -126.17 admix.t No Yes No No Yes 0.00 100.00 Yes No tt tt tt tt BELC-18-3 P.t 52.38 -126.6 admix.t No Yes No No Yes 0.05 99.90 No No NA NA NA NA BULA-11-4 P.t 55.25 -127.5 admix.t Yes Yes No Yes Yes 5.44 89.72 No Yes tt bb tb tb BULB-11-1 P.t 55.12 -127.35 admix.t No Yes No Yes Yes 3.14 93.87 No Yes tt tt tt tt BULF-11-2 P.t 54.55 -126.83 admix.t No Yes No Yes Yes 5.86 87.95 No Yes tb tt tb tt BULF-11-3 P.t 54.55 -126.83 admix.t No Yes No Yes Yes 5.09 88.67 No Yes tt tb tb tt BULF-11-4 P.t 54.55 -126.83 admix.t No Yes No Yes Yes 4.08 89.46 No Yes tt tt tt tt BULF-11-5 P.t 54.55 -126.83 admix.t No Yes No Yes Yes 6.21 87.56 No Yes tt tt tt tt BULG-11-2 P.t 54.45 -126.8 admix.t No Yes No Yes Yes 6.43 86.71 No Yes tt tt tt tb BULG-11-4 P.t 54.45 -126.8 admix.t No Yes No Yes Yes 7.63 84.82 No Yes tt tb bb tt BULG-11-5 P.t 54.45 -126.8 admix.t No Yes No Yes Yes 7.06 85.64 No Yes tt tt tt tt CDRE-10-1 P.t 55.02 -128.33 admix.t No Yes No Yes Yes 3.69 91.68 No Yes tt tt tt tt CDRE-10-3 P.t 55.02 -128.33 admix.t No Yes No Yes Yes 3.80 92.72 No Yes tt tt tt tt CHKC-19-4 P.t 51.77 -127.2 admix.t No Yes No No Yes 0.00 100.00 Yes No tt tt tt tt FKRB-6-1 P.t 56.73 -130.63 admix.t No Yes No Yes Yes 1.98 95.61 No Yes tt tt tt tt HALS-30-6 P.t 44.42 -123.33 admix.t No Yes No No Yes 0.00 100.00 Yes No tt tt tt tt HARB-26-1 P.t 50.62 -123.38 admix.t No NA NA NA Yes 0.16 99.83 No No NA NA NA NA HAZH-10-1 P.t 55.22 -127.67 admix.t No Yes No Yes Yes 4.43 91.09 No Yes tb tt tb tt HAZH-10-2 P.t 55.22 -127.67 admix.t No Yes 2 Yes Yes 5.08 89.91 No Yes tb tb tb tt HAZH-10-3 P.t 55.22 -127.67 admix.t No Yes No Yes Yes 4.84 89.80 No Yes tt tt tt tb HAZH-10-5 P.t 55.22 -127.67 admix.t No Yes No Yes Yes 4.96 90.31 No Yes tt tb tb tb HIXN-16-1 P.t 53.4 -122.63 admix.t Yes Yes 2 Yes Yes 10.70 80.13 No Yes bb tb bb tt HIXN-16-5 P.t 53.4 -122.63 admix.t Yes Yes No Yes Yes 9.17 79.75 No Yes tb tb bb tt HOMB-21-5 P.t 50.95 -124.9 admix.t No Yes No No Yes 0.00 100.00 Yes No tt tt tt tt HOMD-21-1 P.t 51.23 -124.95 admix.t No NA NA NA Yes 0.00 100.00 Yes No tt tt tt tt HOPG-27-5 P.t 49.12 -122.33 admix.t No NA NA NA Yes 0.00 100.00 Yes No tt tt tt tt IRVC-7-1 P.t 56.73 -129.73 admix.t No Yes No Yes Yes 3.98 93.19 No Yes tt tt tt tt IRVC-7-5 P.t 56.73 -129.73 admix.t No Yes No Yes Yes 3.15 93.28 No Yes tt tt tt tb IRVC-7-6 P.t 56.73 -129.73 admix.t No Yes No Yes Yes 3.51 93.30 No Yes tt tt tt tt IRVD-7-4 P.t 56.85 -129.62 admix.t Yes Yes No Yes Yes 3.54 92.25 No Yes tt tt tt tb ISKA-6-1 P.t 56.7 -131.15 admix.t No Yes No Yes Yes 1.41 95.59 No Yes tt tt tt tt ISKA-6-2 P.t 56.7 -131.15 admix.t No Yes No Yes Yes 1.54 97.17 No Yes tt tt tt tt ISKA-6-4 P.t 56.7 -131.15 admix.t No Yes No Yes Yes 1.92 95.72 No Yes tt tt tt tt ISKA-6-5 P.t 56.7 -131.15 admix.t No Yes No No Yes 3.91 93.48 No Yes tt tt tt tb ISKC-6-1 P.t 56.93 -130.33 admix.t No Yes No No Yes 4.05 91.76 No Yes tt tt tt tb ISKC-6-2 P.t 56.93 -130.33 admix.t No Yes No No Yes 4.41 91.42 No Yes tt tt bb tt ISKC-6-3 P.t 56.93 -130.33 admix.t No Yes No Yes Yes 5.71 90.19 No Yes tt tb bb tt ISKC-6-5 P.t 56.93 -130.33 admix.t No Yes No Yes Yes 4.88 89.52 No Yes tt tt tt tt KIMB-16-1 P.t 52.93 -121.17 admix.t Yes Yes No Yes Yes 8.10 82.49 No Yes tb tb tb tt KIMB-16-3 P.t 52.93 -121.17 admix.t Yes Yes 2 Yes Yes 11.87 76.62 No Yes tb tb bb tb KIMB-16-4 P.t 52.93 -121.17 admix.t No Yes No Yes Yes 7.11 83.74 No Yes tt tt tt tb KIMB-16-5 P.t 52.93 -121.17 admix.t Yes Yes No Yes Yes 9.75 79.55 No Yes bb tt tb tt KIMB-16-6 P.t 52.93 -121.17 admix.t Yes Yes No Yes Yes 10.98 79.59 No Yes bb bb tt bb KLNE-20-1 P.t 51.73 -125.57 admix.t No Yes No No Yes 0.15 98.93 No No NA NA NA NA KLNE-20-3 P.t 51.73 -125.57 admix.t No NA NA NA Yes 0.18 98.97 No No NA NA NA NA KLNE-20-4 P.t 51.73 -125.57 admix.t No NA NA NA Yes 0.50 99.02 No No NA NA NA NA KSPA-9-3 P.t 55.77 -128.53 admix.t No Yes No Yes Yes 3.49 93.82 No Yes tt tt tt tt KTMA-12-3 P.t 54.25 -128.52 admix.t No No No Yes Yes 0.21 99.33 No Yes tt tt tt tt KTMC-12-2 P.t 54.05 -128.68 admix.t No No No Yes Yes 0.10 99.43 No Yes tt tt tt tt KTSG-10-5 P.t 55.1 -127.92 admix.t No Yes No Yes Yes 3.89 93.30 No Yes tt tt tt tt LILB-26-1 P.t 50.62 -123.38 admix.t No NA NA NA Yes 0.48 99.25 No No NA NA NA NA LILC-26-1 P.t 50.62 -123.38 admix.t No NA NA NA Yes 0.49 98.84 No No NA NA NA NA MCGR-15-4 P.t 54.18 -122 admix.t No Yes No Yes Yes 8.67 84.21 No Yes tt tt tt tt MCGR-15-6 P.t 54.18 -122 admix.t No Yes No Yes Yes 8.49 83.62 No Yes tb tt bb tb MCGR-15-7 P.t 54.18 -122 admix.t No Yes No Yes Yes 8.19 83.78 No Yes tt tt tt tt MCGR-15-8 P.t 54.18 -122 admix.t Yes Yes No Yes Yes 7.91 83.33 No Yes tt bb tb bb 130       Targeted.analysis WG.analysis Admixture.mapping ID sp lat long code rasp seq RNA pheno rasp b.aver t.aver rmv BMIX NJ9 NJ10 NJ14 NJ15 NASC-8-2 P.t 55.05 -129.5 admix.t No Yes No Yes Yes 1.42 97.24 No Yes tt tt tt tt NASC-8-5 P.t 55.05 -129.5 admix.t No Yes No Yes Yes 1.86 96.55 No Yes tt tt tt tb NASD-8-2 P.t 55.05 -129.5 admix.t No Yes No No Yes 0.40 98.14 No Yes tt tt tt tt NASF-8-2 P.t 55.57 -128.78 admix.t No Yes No Yes Yes 1.22 97.16 No Yes tt tt tt tt NASH-8-1 P.t 55.72 -128.82 admix.t No Yes No Yes Yes 2.41 95.49 No Yes tt tt tt tt NASH-8-4 P.t 55.72 -128.82 admix.t No Yes No Yes Yes 1.72 95.82 No Yes tt tt tt tt NBON-29-1 P.t 45.58 -122 admix.t No Yes No No Yes 0.08 99.70 No No NA NA NA NA NHTA-27-3 P.t 49.97 -121.82 admix.t No NA NA NA Yes 0.62 98.17 No No NA NA NA NA NKND-3-2 P.t 58.93 -133.18 admix.t Yes Yes No Yes Yes 15.18 73.19 No Yes tt tt tt tt QAUS-16-1 P.t 52.72 -122.47 admix.t No Yes No Yes Yes 7.01 81.53 No Yes tt tt tt tb QAUS-16-3 P.t 52.72 -122.47 admix.t Yes Yes 2 Yes Yes 7.01 81.77 No Yes tb tt bb bb QAUS-16-4 P.t 52.72 -122.47 admix.t No Yes No Yes Yes 7.44 80.25 No Yes bb bb tt tt QAUS-16-7 P.t 52.72 -122.47 admix.t No Yes No Yes Yes 8.12 82.97 No Yes tb tb bb tb QBKR-16-3 P.t 52.95 -122.87 admix.t Yes Yes 2 Yes Yes 9.55 78.48 No Yes tt tt tt tb QBKR-16-4 P.t 52.95 -122.87 admix.t Yes Yes 2 Yes Yes 10.79 79.32 No Yes tt tb bb tt QBKR-16-5 P.t 52.95 -122.87 admix.t Yes Yes No Yes Yes 9.65 78.51 No Yes tb tb bb tb QCTN-16-1 P.t 53.03 -122.15 admix.t No Yes 2 Yes Yes 5.41 85.82 No Yes tt tb tb tb QCTN-16-3 P.t 53.03 -122.15 admix.t No Yes No Yes Yes 6.04 84.04 No Yes tt tb bb tt QFRS-16-1 P.t 53.07 -122.52 admix.t No Yes 2 Yes Yes 7.59 81.18 No Yes tt tt tt tb QFRS-16-2 P.t 53.07 -122.52 admix.t No Yes No Yes Yes 7.47 82.05 No Yes tb tb tb tt QFRS-16-3 P.t 53.07 -122.52 admix.t Yes Yes No Yes Yes 8.73 80.01 No Yes bb tb bb tb QFRS-16-4 P.t 53.07 -122.52 admix.t Yes Yes No Yes Yes 7.43 82.25 No Yes bb tb tb tb QFRS-16-5 P.t 53.07 -122.52 admix.t Yes Yes 2 Yes Yes 8.34 80.52 No Yes tb tb bb tt QLKE-16-1 P.t 52.97 -122.32 admix.t Yes Yes 2 Yes Yes 8.35 80.09 No Yes tb tb tb tb QLKE-16-2 P.t 52.97 -122.32 admix.t Yes Yes No Yes Yes 8.34 81.38 No Yes tt tt tt tt QLKE-16-3 P.t 52.97 -122.32 admix.t Yes Yes 2 Yes Yes 6.55 82.47 No Yes bb bb bb tt QLKE-16-4 P.t 52.97 -122.32 admix.t No Yes 2 Yes Yes 8.05 80.79 No Yes bb tb tb tt SHEL-15-1 P.t 54.03 -122.6 admix.t Yes Yes 2 Yes Yes 10.14 78.61 No Yes bb bb tb bb SHEL-15-2 P.t 54.03 -122.6 admix.t No Yes No Yes Yes 7.38 84.32 No Yes bb tb tt tb SHEL-15-3 P.t 54.03 -122.6 admix.t Yes Yes No Yes Yes 9.42 81.55 No Yes tt tt tt bb SHEL-15-4 P.t 54.03 -122.6 admix.t Yes Yes 2 Yes Yes 8.68 82.55 No Yes tb tt tb bb SHEL-15-6 P.t 54.03 -122.6 admix.t Yes Yes No Yes Yes 9.24 77.46 No Yes tt tb tb tt SKNC-10-1 P.t 54.77 -128.27 admix.t No Yes No Yes Yes 2.19 96.29 No Yes tt tt tt tt SKNC-10-2 P.t 54.77 -128.27 admix.t No Yes 2 Yes Yes 1.74 96.23 No Yes tt tt tt tt SKNL-10-1 P.t 54.45 -128.75 admix.t No Yes No Yes Yes 0.68 99.09 No Yes tt tt tt tt SKNM-10-3 P.t 54.4 -128.98 admix.t No Yes No Yes Yes 0.35 99.22 No Yes tt tt tt tt SKNM-10-6 P.t 54.4 -128.98 admix.t No Yes No Yes Yes 3.40 93.82 No Yes tt tt tt tt SKNN-10-3 P.t 54.4 -128.98 admix.t No Yes No Yes Yes 0.42 99.03 No Yes tt tt tt tb SKNP-10-11 P.t 54.4 -128.98 admix.t No Yes No Yes Yes 0.49 99.00 No Yes tt tt tt tt SKNP-10-9 P.t 54.4 -128.98 admix.t No Yes 4 Yes Yes 0.26 99.36 No Yes tt tt tt tt SKNQ-10-3 P.t 54.4 -128.98 admix.t No Yes No Yes Yes 0.77 98.58 No Yes tt tt tt tt SKNR-10-1 P.t 54.68 -128.35 admix.t No Yes No Yes Yes 0.25 99.38 No Yes tt tt tt tt SKWF-24-5 P.t 50.25 -123.93 admix.t No NA NA NA Yes 0.00 100.00 Yes No tt tt tt tt SLMC-28-2 P.t 50.28 -125.87 admix.t No NA NA NA Yes 0.10 99.74 No No NA NA NA NA SQMC-25-5 P.t 50.10 -123.37 admix.t No NA NA NA Yes 0.00 100.00 Yes No tt tt tt tt STEL-14-1 P.t 54.03 -124.92 admix.t Yes Yes No Yes Yes 8.36 84.21 No Yes tt tt tt tb STHA-21-5 P.t 50.82 -124.48 admix.t No Yes No No Yes 0.00 100.00 Yes No tt tt tt tt STKB-5-4 P.t 56.93 -131.78 admix.t No Yes No Yes Yes 3.07 93.28 No Yes tt tt tt bb STKD-5-1 P.t 57.33 -131.78 admix.t No Yes No Yes Yes 3.57 92.67 No Yes tt tt tt tt STKD-5-3 P.t 57.33 -131.78 admix.t No Yes No Yes Yes 3.65 92.35 No Yes tt tt tt tb STKE-5-3 P.t 57.52 -131.78 admix.t No Yes No Yes Yes 5.31 90.03 No Yes tt tt tt bb STKF-5-1 P.t 57.65 -131.57 admix.t Yes Yes No Yes Yes 5.34 90.82 No Yes tt tt tt tb STKG-4-4 P.t 57.95 -129.67 admix.t No Yes No Yes Yes 2.26 95.25 No Yes tt tt tt tt TAKA-3-3 P.t 58.6 -133.57 admix.t No Yes No Yes Yes 0.00 100.00 Yes No tt tt tt tt TAKB-3-3 P.t 58.7 -133.4 admix.t Yes Yes No Yes Yes 10.76 79.64 No Yes tt tt tt tt TAKB-3-4 P.t 58.7 -133.4 admix.t Yes Yes No Yes Yes 12.92 77.70 No Yes tt tt tt tb TAKB-3-5 P.t 58.7 -133.4 admix.t Yes Yes No Yes Yes 11.66 79.65 No Yes tt tb tb tb TATB-1-4 P.t 59.43 -137.83 admix.t Yes Yes No Yes Yes 12.12 79.41 No Yes tt tt tt tb TATB-1-7 P.t 59.43 -137.83 admix.t Yes Yes No Yes Yes 14.02 76.02 No Yes tt tb bb tb TLKH-11-1 P.t 54.67 -127.12 admix.t No Yes No Yes Yes 6.11 88.47 No Yes tt tt tt tt TRTB-7-5 P.t 56.57 -129.82 admix.t No Yes No Yes Yes 3.26 94.73 No Yes tt tt tt tb TRTB-7-6 P.t 56.57 -129.82 admix.t No Yes No Yes Yes 3.29 92.50 No Yes tt tt tt tt TRTB-7-7 P.t 56.57 -129.82 admix.t No Yes No Yes Yes 4.93 91.31 No Yes tt tt tt tt WFSH-13-6 P.t 54.6 -124.77 admix.t Yes Yes No Yes Yes 13.89 78.84 No Yes tt tt tt tt WHTE-28-3 P.t 50.13 -126.05 admix.t No NA NA NA Yes 0.13 99.69 No No NA NA NA NA WLOW-15-1 P.t 53.92 -122.28 admix.t No Yes No Yes Yes 7.02 84.12 No Yes tt tt tt tb WLOW-15-3 P.t 53.92 -122.28 admix.t No Yes 2 Yes Yes 6.53 85.74 No Yes tb tb tb tt WLOW-15-4 P.t 53.92 -122.28 admix.t No Yes No Yes Yes 7.50 83.46 No Yes tb tt tb tt WLOW-15-5 P.t 53.92 -122.28 admix.t Yes Yes No Yes Yes 4.85 86.39 No Yes tt bb tt tb ZYMJ-10-1 P.t 54.47 -127.93 admix.t No Yes No Yes Yes 1.36 97.72 No Yes tt tt tt tb DUN13 P.b 54.51 -98.35 refbalsa Yes Yes No No Yes 100.00 0.00 No No bb bb bb bb DUN14 P.b 54.51 -98.35 refbalsa Yes Yes No No Yes 100.00 0.00 No No bb bb bb bb DUN4 P.b 54.51 -98.35 refbalsa Yes Yes No No Yes 100.00 0.00 No No bb bb bb bb FBK11 P.b 64.9 -146.35 refbalsa Yes Yes No No Yes 100.00 0.00 No No bb bb bb bb FBK6 P.b 64.9 -146.35 refbalsa Yes Yes No No Yes 100.00 0.00 No No bb bb bb bb GIL1 P.b 56.35 -94.63 refbalsa Yes No No No Yes 100.00 0.00 No No bb bb bb bb GIL10 P.b 56.25 -94.36 refbalsa Yes No No No Yes 100.00 0.00 No No bb bb bb bb GYP10 P.b 51.77 -98.62 refbalsa Yes NA NA NA Yes 100.00 0.00 No No bb bb bb bb GYP13 P.b 51.77 -98.62 refbalsa Yes NA NA NA Yes 100.00 0.00 No No bb bb bb bb GYP9 P.b 51.77 -98.62 refbalsa Yes NA NA NA Yes 100.00 0.00 No No bb bb bb bb INU9 P.b 68.38 -133.77 refbalsa Yes No No No Yes 100.00 0.00 No No bb bb bb bb LAR12 P.b 54.45 -105.33 refbalsa Yes No No No Yes 100.00 0.00 No No bb bb bb bb MEL2 P.b 51.35 -102.62 refbalsa Yes No No No Yes 100.00 0.00 No No bb bb bb bb 131       Targeted.analysis WG.analysis Admixture.mapping ID sp lat long code rasp seq RNA pheno rasp b.aver t.aver rmv BMIX NJ9 NJ10 NJ14 NJ15 MIN1 P.b 50.32 -99.85 refbalsa Yes Yes No No Yes 100.00 0.00 No No bb bb bb bb NWL1 P.b 65.16 -126.44 refbalsa Yes No No No Yes 100.00 0.00 No No bb bb bb bb NWL10 P.b 65.16 -126.44 refbalsa Yes Yes No No Yes 100.00 0.00 No No bb bb bb bb NWL11 P.b 65.16 -126.44 refbalsa Yes No No No Yes 100.00 0.00 No No bb bb bb bb NWL112 P.b 65.16 -126.44 refbalsa Yes No No No Yes 100.00 0.00 No No bb bb bb bb NWL12 P.b 65.16 -126.44 refbalsa Yes Yes No No Yes 100.00 0.00 No No bb bb bb bb NWL7 P.b 65.16 -126.44 refbalsa Yes No No No Yes 100.00 0.00 No No bb bb bb bb NWL9 P.b 65.23 -126.67 refbalsa Yes No No No Yes 100.00 0.00 No No bb bb bb bb POR6 P.b 49.57 -98.18 refbalsa Yes Yes No No Yes 100.00 0.00 No No bb bb bb bb WAD1 P.b 52.37 -104.27 refbalsa Yes Yes No No Yes 100.00 0.00 No No bb bb bb bb WHR3 P.b 60.7 -135.33 refbalsa Yes Yes No No Yes 100.00 0.00 No No bb bb bb bb WHR4 P.b 60.7 -135.33 refbalsa Yes Yes No No Yes 100.00 0.00 No No bb bb bb bb ALAA-20-1 P.t 50.98 -126.12 reftricho Yes Yes No No Yes 0.00 100.00 No No tt tt tt tt AMER-13-1 P.t 54.63 -124.97 reftricho Yes Yes 14 No Yes 0.00 100.00 No No tt tt tt tt CHKD-19-4 P.t 51.77 -127.2 reftricho Yes Yes No No Yes 0.00 100.00 No No tt tt tt tt CHWJ-27-2 P.t 49.08 -121.72 reftricho Yes No No No Yes 0.00 100.00 No No tt tt tt tt CNYH-28-2 P.t 49.67 -125.07 reftricho Yes No No No Yes 0.00 100.00 No No tt tt tt tt DENB-17-2 P.t 52.83 -126.7 reftricho Yes No No No Yes 0.00 100.00 No No tt tt tt tt GLCB-26-3 P.t 50.1 -123 reftricho Yes No No No Yes 0.00 100.00 No No tt tt tt tt HOMC-21-1 P.t 51.23 -124.95 reftricho Yes NA NA NA Yes 0.00 100.00 No No tt tt tt tt KIMB-16-2 P.t 52.93 -121.17 reftricho Yes Yes 2 Yes Yes 0.00 100.00 No No tt tt tt tt KTMA-12-1 P.t 54.25 -128.52 reftricho Yes No 2 Yes Yes 0.00 100.00 No No tt tt tt tt KTMC-12-1 P.t 54.05 -128.68 reftricho Yes No No Yes Yes 0.00 100.00 No No tt tt tt tt MCHB-19-4 P.t 51.62 -126.58 reftricho Yes Yes No No Yes 0.00 100.00 No No tt tt tt tt MCMN-27-3 P.t 49.18 -122.58 reftricho Yes No No No Yes 0.00 100.00 No No tt tt tt tt NASH-8-5 P.t 55.72 -128.82 reftricho Yes Yes No Yes Yes 0.00 100.00 No No tt tt tt tt NECA-14-1 P.t 54.1 -124.43 reftricho Yes Yes No No Yes 0.00 100.00 No No tt tt tt tt NECB-14-6 P.t 53.95 -124.43 reftricho Yes Yes 3 No Yes 0.00 100.00 No No tt tt tt tt PHLC-22-2 P.t 50.68 -125.25 reftricho Yes Yes No No Yes 0.00 100.00 No No tt tt tt tt SKND-10-2 P.t 54.85 -128.33 reftricho Yes Yes No Yes Yes 0.00 100.00 No No tt tt tt tt SKNM-10-1 P.t 54.4 -128.98 reftricho Yes Yes No Yes Yes 0.00 100.00 No No tt tt tt tt SKNN-10-2 P.t 54.4 -128.98 reftricho Yes Yes 2 Yes Yes 0.00 100.00 No No tt tt tt tt SKNP-10-8 P.t 54.4 -128.98 reftricho Yes Yes 4 Yes Yes 0.00 100.00 No No tt tt tt tt SQMC-25-3 P.t 50.10 -123.37 reftricho Yes NA NA NA Yes 0.00 100.00 No No tt tt tt tt TLKH-11-5 P.t 54.67 -127.12 reftricho Yes Yes 2 Yes Yes 0.00 100.00 No No tt tt tt tt TNZA-4-1 P.t 58.3 -130.47 reftricho Yes Yes No No Yes 0.00 100.00 No No tt tt tt tt WELC-27-3 P.t 49.67 -121.42 reftricho Yes No No No Yes 0.00 100.00 No No tt tt tt tt  Table A-2. Parameters for the RASPberry model with the highest log likelihood value, based on a whole genome SNP data set in 25 pure P. balsamifera, 25 pure P. trichocarpa and 68 admixed individuals. *See Price et al. 2009 and Wegmann et al. 2011.  Parameter* Model defaultRate 5 //cM per Megabase timeSinceAdmixture 10 ancestralRate_tricho (Levsen)† 300 ancestralRate_balsamifera (Levsen)† 200 miscopyRate 0.05 mutation1 0.0079365 mutation2 0.0079365 miscopyMutation 0.01 recomputeWindowSize 2 collapsingDistance 3      132  Table A-3. Quantitative reverse transcription PCR (qTt-PCR) to test the levels of expression of COMT1 genes in different P. trichocarpa genotypes. Primers used in qRT-PCRPrimer Sequence CO1-rt-F GTTGATGCCATAATGCTGGCACAT CO1-rt-R CACGTACGTATTGAATGCACAGCACATC 18S-F  AATTGTTGGTCTTCAACGAGGAA 18S-R  AAAGGGCAGGGACGTAGTCAA *For qRT-PCR analyses, 5 individuals from each poplar genotypes were randomly selected: homozygotes for balsamifera COMT1 (QQ), homozygotes for trichocarpa COMT1 (PP) and heterozygotes (QP). RNA was isolated from leaf tissue harvested on August 2013 from 11:00 am to 1:00 pm, following the protocol of Kolosova et al. (2004) (1). RNA was quantified based on absorption at 260 nm and then reverse-transcribed into cDNA using the SuperScript First-Strand Synthesis system (Invitrogen). Primers for the target gene (COMT1: CO1-rt-F, CO1-rt-R) were designed using Primer-Blast (2) and primers from for the reference gene (18S) were obtained from (3) (18S-F, 18S-R). Using all cDNA samples and three technical replicates per sample, I performed quantitative PCR using SYBR® Select Master Mix with CFX Connect™ Real-Time PCR Detection System (Invitrogen). To analyze gene expression levels, the 2∆CT method (4) was used. I subtracted the Ct (threshold cycle) value of the target gene (COMT1) by the Ct value of the reference housekeeping gene (18S) to standardize for the amounts of RNA template. Here, the larger the ∆CT value, the lower the mRNA level. The Ct values were then compared between genotype groups using ANOVA and multiple comparison Tukey test. I also converted ΔCT values to a linear form using the term 2–ΔCT.  Table A-4. Candidate genes for local adaptation across P. trichocarpa’s range, on chromosome 6 and 15 based on FST outlier tests or association analysis with environmental variables and phenotypic traits (Geraldes et al. 2014; McKnown et al. 2014), and using a 34k SNP chip   Selected candidate* Chr Gene modela Start kbb Annotationc Fst testd Geo/Env correlatione Associa.f Intro¶      Fdist Bayescan    Yes 6 Potri.006G020600 1448.9 FAR1  *** ***    Yes 6 Potri.006G020700 1454.7 FHY3 *** *** Lat,Long   No 6 Potri.006G048500 3475.9 Glycosyltransferase *** *** Lat,MAT,MCMT,DD_0,DD_18   A No 6 Potri.006G049700 3580.3 AP2 ***       A No 6 Potri.006G053800 3923.9 Zinc finger ** ** MWMT   A Yes 15 Potri.015G000200 12.7 ABCB19 ***    B Yes 15 Potri.015G000500 26.5 LSU4  **    B Yes 15 Potri.015G002300 137.8 PRR5  *** *** Lat,MAT,MCMT,TD,DD_0,DD_18,EMT Phenology - Biomass- Ecophysiology B Yes 15 Potri.015G002600 162.0 TTG1 *** *** Lat,MAT,MCMT,TD,DD_0,DD_18,EMT Phenology - Biomass- Ecophysiology B Yes 15 Potri.015G003100 236.7 COMT1 ***  Lat  B Yes 15 Potri.015G004100 276.7 NAC062 **  Lat,MCMT,TD,DD_0,EMT Phenology - Ecophysiology B Yes 15 Potri.015G006500 406.8 Unknown  *** *** Lat,MCMT,TD,DD_0,EMT  B Yes 15 Potri.015G009100 597.6 Yippee *** *** Lat,MCMT,DD_0 Phenology B 133   Selected candidate* Chr Gene modela Start kbb Annotationc Fst testd Geo/Env correlatione Associa.f Intro¶ Yes 15 Potri.015G009300 611.5 Dof- zinc finger ** *** Lat,MAT,MCMT,TD,DD_0,DD_18,EMT Phenology - Ecophysiology B No 15 Potri.015G125500 13870.2 MADS box       Phenology - Biomass C           *Genes hypothesized to test positive for introgression based on signals of selection found with FST outlier tests or association analysis with environmental variables and phenotypic traits (Geraldes et al. 2014; McKnown et al. 2014) a Poplar gene models are annotated as Populus trichocarpa v3.0 genome b Start position (kb) on chromosome 15 as in Populus trichocarpa v3.0 genome c Annotated description of Arabidopsis thaliana homolog d Fst outlier tests; significance codes: 0 ‘***’ 0.001 ‘**’ (Geraldes et al. 2014) e Test of correlation with geographic and environmental variables (Geraldes et al. 2014). Lat: Latitude, MAT: mean annual temperature, MCMT: mean coldest month temperature, TD: continentality, DD_0: degree-days below 0°C, DD_18: degree-days below 18°C, EMT: extreme minimum temperature over the 30-year period  f McKnown et al. 2014 ¶Introgressed regions identified in this study, based on local ancestry analysis (RASPberry) in three chromosomes (6, 12, 15)  Table A-5. List of GO terms enriched in the introgressed regions from P. balsamifera into P. trichocarpa GO:0006396~RNA processing GO:0010218~response to far-red light GO:0016887~ATPase activity GO:0031974~membrane-enclosed lumen GO:0031981~nuclear lumen GO:0043228~non-membrane-bounded organelle GO:0043232~intracellular non-membrane-bounded organelle GO:0043233~organelle lumen GO:0048443~stamen development GO:0048466~androecium development GO:0070013~intracellular organelle lumen IPR003441:No apical meristem (NAM) protein IPR003593:ATPase, AAA+ type, core PIRSF028804:protein yippee-like SM00382:AAA 134  Table A-6. List of genes in introgressed region B on chromosome 15 that showed significantly different levels of expression in admixed (bb, bt) individuals compared to pure P. trichocarpa individuals       ANOVA Tukey tests   ANOVA Tukey tests Gene model v3 Description Leaf FPKM F-value p-value *Leaf Expression Xylem FPKM Fvalue pvalue *Xylem Expression Potri.015G001200 ET TRANSLATION PRODUCT-RELATED, Uncharacterized conserved protein 9.577835 30.65 1.51E-10 tt>bt>bb 22.81604 31.62 2.33E-10 tt>bt=bb Potri.015G001500   0.07304318 15.42 2.28E-06 bb>bt=tt 0.002600863 ns ns   Potri.015G002100  F-box domain 13.62576 29.91 2.29E-10 tt>bt>bb 7.238503 29.7 6.31E-10 tt>bt>bb Potri.015G002600 TTG1, TRANSPARENT TESTA GLABRA 1 18.45906 7.873 0.000768 bb=bt>tt 9.671041 50.61 4.74E-14 bb>bt>tt Potri.015G003100 COMT1 9.53016 72.54 <2e-16 bb>bt>tt 102.9831 72.48 <2e-16 bb>bt>tt Potri.015G003200   0.3963508 47.86 2.73E-14 bt>bb=tt 1.090179 ns ns   Potri.015G003800   0.1366683 14.23 5.39E-06 bt>tt 0.006370198 ns ns   Potri.015G004200 CCR4 CARBON CATABOLITE REPRESSOR PROTEIN 4 1.341835 7.833 0.000794 bt=bb>tt 0.327882 52.36 2.39E-14 bb>bt>tt Potri.015G005900   0.1446214 21.76 3.09E-08 bt>bb=tt 0.01141833 ns ns   Potri.015G006000   0.01184041 6.481 0.00249 bt>bb=tt 0.007379346 10.67 9.67E-05 bt>bb=tt Potri.015G007200   28.90136 ns ns   51.09009 14.89 4.60E-06 bb>bt=tt Potri.015G011300 alpha/beta hydrolase fold,Soluble epoxide hydrolase 6.392737 7.771 0.000837 bb>tt 2.703451 12.2 3.11E-05 bb=bt>tt Potri.015G012300   4.513397 ns ns   9.047215 19.31 2.50E-07 bb>bt=tt Potri.015G012700   13.73239 12.19 2.47E-05 bt=bb>tt 17.03197 ns ns   135  Table A-7. Gene expression levels in different genotypes of COMT1 based on qRT-PCR. QQ represents genotypes homozygotes for P. balsamifera haplotypes, QP represents heterozygotes and PP represents genotypes homozygotes for P. trichocarpa haplotypes  Sample COMT1_Genotype Ct_18S_reference gene Ct_COMT1 ΔCT (Cq_COMT1 - Cq_18S)  2–ΔCT QQ22 QQ 15.94 27.73 11.80 0.000280444 QQ28 QQ 16.31 28.26 11.95 0.000252031 QQ25 QQ 18.37 28.51 10.14 0.000884204 QQ30 QQ 17.49 28.17 10.68 0.000610946 QQ23 QQ 15.27 27.33 12.06 0.000234737 QP27 PQ 14.9 27.03 12.13 0.000223103 QP14 PQ 16.00 27.92 11.92 0.000258061 QP44 PQ 14.57 27.40 12.83 0.000137654 QP8 PQ 17.22 30.14 12.92 0.000128733 QP9 PQ 15.47 28.37 12.90 0.00013053 PP2 PP 13.88 28.28 14.40 4.64E-05 PP26 PP 14.15 28.47 14.32 4.89E-05 PP35 PP 13.59 26.89 13.30 9.89E-05 PP5 PP 13.28 28.17 14.89 3.29E-05 PP6 PP 13.69 27.53 13.84 6.84E-05                       136  Appendix B  Supplementary information for chapter 3  A.   B.  Figure B-11. Genome-wide ancestry analysis of Populus balsamifera and P. trichocarpa individuals based on 971K SNPs from 3691 genes in all 19 chromosomes using ancestry analysis (Geraldes et al. in preparation) and the whole genome using Raspberry. For local ancestry analyses in RASPberry, I selected 50 reference individuals (25 pure P. balsamifera and 25 pure P. trichocarpa) and 161 admixed individuals (129 P. trichocarpa individuals with P. balsamifera admixture, and 32 P. balsamifera individuals with P. trichocarpa admixture) from a collection of 435 P. trichocarpa and 448 P. balsamifera genotypes.   Figure B-12. Average width of P. balsamifera introgressed blocks in admixed P. trichocarpa individuals (blue) and P. trichocarpa introgressed blocks in admixed P. balsamifera individuals (red).   00.10.20.3Q-balsa RASPberry-balsa00.20.40.6Q-balsa RASPberry-balsa137    Figure B-13. Average P. balsamifera (A) and P. trichocarpa (B) introgressed ancestry in admixed P. trichocarpa individuals (A) and admixed P. balsamifera individuals (B) in 19 chromosomes.   Figure B-14 Correlation between the levels of P. trichocarpa introgressed ancestry and distance to the telomeres (Pearson r2 =-0.0024, p-value = 0.734).  138   Figure B-15. Proportion of introgressed ancestry across chromosomes that showed SNPs significantly associated with sex (gray lines) (Geraldes et al. 2016). Blue lines correspond to introgressed P. balsamifera ancestry into P. trichocarpa and red lines correspond to introgressed P. trichocarpa ancestry into P. balsamifera. Candidate regions for adaptive introgression – peaks above broken line - have introgressed ancestry higher than 3 standard deviations from the weighted mean across the whole genome based on SNP density per window (broken line).   Figure B-6. Loading of the temperature and moisture variables used in the PCA.   139   Table B-1. List of environmental variables used in a principal component analysis. Twenty-three climate variables were compiled from ClimateNA (Wang et al. 2012) based on 1971–2000. Type of variable Abbreviation Variable temperature MAT mean annual temperature (°C) temperature MWMT  mean warmest month temperature (°C) temperature MCMT  mean coldest month temperature (°C) temperature TD temperature difference between MWMT and MCMT or continentality (°C) temperature DD<0 degree-days below 0°C chilling degree-days temperature DD>5 degree-days above 5°C growing degree-days temperature DD<18 degree-days below 18°C heating degree-days temperature DD>18 degree-days above 18°C cooling degree-days temperature NFFD the number of frost-free days temperature FFP frost-free period temperature bFFP the day of the year on which FFP begins temperature eFFP the day of the year on which FFP ends temperature EMT extreme minimum temperature over 30 years temperature EXT extreme maximum temperature over 30 years temperature MAR mean annual solar radiation (MJ m-2 d-1) moisture MAP mean annual precipitation (mm) moisture MSP May to September precipitation (mm) moisture AHM  annual heat-moisture index (MAT+10)/(MAP/1000)) moisture SHM  summer heat-moisture index ((MWMT)/(MSP/1000)) moisture PAS precipitation as snow (mm) between August in previous year and July in current year moisture Eref Hargreaves reference evaporation (mm) moisture CMD Hargreaves climatic moisture deficit (mm) moisture RH mean annual relative humidity (%)                140  Appendix C  Supplementary information for chapter 4  Figure C-16. Haplotype blocks based on SNPs that had both fixed differences in the pure species and displayed associations with traits on the BMIX analysis. Filled circles depict haplotypes from pure individuals and open circles represent haplotypes from admixed individuals (red: P. trichocarpa, blue: P. balsamifera).  Table C-1. List of traits used in the admixture mapping analysis in P. trichocarpa. Trait code Trait details Trait category Source AB_density Abaxial density (# mm-2) ecophysiology McKown et al 2014 AB_pore_length Abaxial pore length (µm) ecophysiology McKown et al 2014 AB_SPI Abaxial SPI - stomatal pore index. ecophysiology McKown et al 2014 Activegrowthratecmday_2009 Active growth rate cm day biomass McKown et al 2013 Activegrowthratecmday_2010 Active growth rate cm day biomass McKown et al 2013 AD_density Adaxial density (# mm-2) ecophysiology McKown et al 2014 AD_pore_length Adaxial pore length (µm) ecophysiology McKown et al 2014 AD_SPI Adaxial SPI - stomatal pore index. ecophysiology McKown et al 2014 Ad_STM_distribution Adaxial stomata distribution ecophysiology McKown et al 2014 Ad_STM_presence Adaxial stomata presence ecophysiology McKown et al 2014 Ad_StomataNUM1 Adaxial stomata numbers ecophysiology McKown et al 2014 ADAB AD:AB density ratio-AD:AB pore ratio ecophysiology McKown et al 2014 ADAB_PL AD:AB density ratio-AD:AB pore ratio ecophysiology McKown et al 2014 Amax_2009 Maximum photosynthetic rate ecophysiology McKown et al 2013 Amax_2010 Maximum photosynthetic rate ecophysiology McKown et al 2013 Amax_mass_2009 Photosynthetic rate per unit dry mass ecophysiology McKown et al 2013 Amax_mass_2010 Photosynthetic rate per unit dry mass ecophysiology McKown et al 2013 AUDPC_2010 AUDPC area under the disease progress curve disease McKown et al 2014 AUDPC_2011 AUDPC area under the disease progress curve disease McKown et al 2014 Boledensitykgm_3_2012 Bole density kgm3 biomass McKown et al 2013 Bolemasskg_2012 Bole mass kg biomass McKown et al 2013 Branch_2009 Branch biomass McKown et al 2013 Budbreakday_2010 Bud break day phenology McKown et al 2013 Budbreakday_2011 Bud break day phenology McKown et al 2013 Budsetday_2008 Bud set day  phenology McKown et al 2013 Budsetday_2009 Bud set day  phenology McKown et al 2013 Budsetday_2010 Bud set day  phenology McKown et al 2013 C_N_2009 Carbon nitrogen ecophysiology McKown et al 2013 C_N_2010 Carbon nitrogen ecophysiology McKown et al 2013 Canopydurationdays_2009 Canopy duration days phenology McKown et al 2013 Canopydurationdays_2010 Canopy duration days phenology McKown et al 2013 CCI2015_ap29 Chlorophyll concentration index spring ecophysiology This study CCI2015_au7 Chlorophyll concentration index summer ecophysiology This study CCI2015_jl1 Chlorophyll concentration index summer ecophysiology This study CCI2015_jl15 Chlorophyll concentration index summer ecophysiology This study CCI2015_ju10 Chlorophyll concentration index summer ecophysiology This study CCI2015_ju16 Chlorophyll concentration index summer ecophysiology This study CCI2015_ju24 Chlorophyll concentration index summer ecophysiology This study 141  Trait code Trait details Trait category Source CCI2015_ma15 Chlorophyll concentration index spring ecophysiology This study CCI2015_ma22 Chlorophyll concentration index spring ecophysiology This study CCI2015_ma7 Chlorophyll concentration index spring ecophysiology This study Chlpost_budsetCCI_2009 Chlorophyll concentration index post bud set ecophysiology McKown et al 2013 Chlpost_budsetCCI_2011 Chlorophyll concentration index post bud set ecophysiology McKown et al 2013 ChlspringCCI_2009 Chlorophyll concentration index spring ecophysiology McKown et al 2013 ChlsummerCCI_2009 Chlorophyll concentration index summer ecophysiology McKown et al 2013 ChlsummerCCI_2011 Chlorophyll concentration index summer ecophysiology McKown et al 2013 d13Cwood_2012 Stable carbon isotope ratio wood ecophysiology McKown et al 2013 d15N_2009 Stable nitrogen isotope ratio ecophysiology McKown et al 2013 d15N_2010 Stable nitrogen isotope ratio ecophysiology McKown et al 2013 DENS Total density (# mm-2) ecophysiology McKown et al 2014 Dleaf_2009 Net discrimination leaf ecophysiology McKown et al 2013 Dleaf_2010 Net discrimination leaf ecophysiology McKown et al 2013 Growthperioddays_2009 Growth period days biomass McKown et al 2013 Growthperioddays_2010 Growth period days biomass McKown et al 2013 gsmol_2009 Stomatal conductance ecophysiology McKown et al 2013 gsmol_2010 Stomatal conductance ecophysiology McKown et al 2013 H_Dcm_cm_2009 Height diameter cm biomass McKown et al 2013 H_Dcm_cm_2010 Height diameter cm biomass McKown et al 2013 H_Dcm_cm_2011 Height diameter cm biomass McKown et al 2013 Heightcm_2008 Height cm biomass McKown et al 2013 Heightcm_2009 Height cm biomass McKown et al 2013 Heightcm_2010 Height cm biomass McKown et al 2013 Heightcm_2011 Height cm biomass McKown et al 2013 Heightgaincm_2009 Height gain cm biomass McKown et al 2013 Heightgaincm_2010 Height gain cm biomass McKown et al 2013 Heightgaincm_2011 Height gain cm biomass McKown et al 2013 Heightgrowthcessationday_2009 Height growth cessation day biomass McKown et al 2013 Leafdropday_2008 Leaf drop day phenology McKown et al 2013 Leafdropday_2009 Leaf drop day phenology McKown et al 2013 Leafdropday_2010 Leaf drop day phenology McKown et al 2013 Leafflushday_2010 Leaf flush day phenology McKown et al 2013 Leafflushday_2011 Leaf flush day phenology McKown et al 2013 Leafflushday_2012 Leaf flush day phenology McKown et al 2013 Leaflifespandays_2010 Leaf life span days phenology McKown et al 2013 Leafshapelength_width_2009 Leaf shape length width ecophysiology McKown et al 2013 Leavesperbud_2011 Leaves per bud ecophysiology McKown et al 2013 Leavesperbud_2012 Leaves per bud ecophysiology McKown et al 2013 LMApost_budset_2010 Leaf mass per unit area post budset ecophysiology McKown et al 2013 LMApost_budset_2011 Leaf mass per unit area post budset ecophysiology McKown et al 2013 LMAspring_2010 Leaf mass per unit area spring ecophysiology McKown et al 2013 LMAspring_2011 Leaf mass per unit area spring ecophysiology McKown et al 2013 LMAsummer_2009.1 Leaf mass per unit area summer ecophysiology McKown et al 2013 LMAsummer_2010.1 Leaf mass per unit area summer ecophysiology McKown et al 2013 LMAsummer_2011.1 Leaf mass per unit area summer ecophysiology McKown et al 2013 Logheightgrowthlogcmday_2009 Log height growth log cm day biomass McKown et al 2013 Logvolumegrowthlogcm3day_2009 Log volume growth log cm3 day biomass McKown et al 2013 Narea_2009 Nitrogen area ecophysiology McKown et al 2013 Narea_2010 Nitrogen area ecophysiology McKown et al 2013 Nmass_2009 Nitrogen mass ecophysiology McKown et al 2013 Nmass_2010 Nitrogen mass ecophysiology McKown et al 2013 NUE_2009 Photosynthetic nitrogen-use efficiency ecophysiology McKown et al 2013 NUE_2010 Photosynthetic nitrogen-use efficiency ecophysiology McKown et al 2013 Post_budsetperioddays_2009 Post bud set period days phenology McKown et al 2013 Post_budsetperioddays_2010 Post bud set period days phenology McKown et al 2013 SPI Stomatal pore index. ecophysiology McKown et al 2014 Tannins Tannins (µg mg DW-1) ecophysiology McKown et al 2014 Volumecm3_2009 Volume cm3 biomass McKown et al 2013 Volumecm3_2010 Volume cm3 biomass McKown et al 2013 Volumecm3_2011 Volume cm3 biomass McKown et al 2013 Volumegaincm3_2010 Volume gain cm3 biomass McKown et al 2013 Volumegaincm3_2011 Volume gain cm3 biomass McKown et al 2013 142  Trait code Trait details Trait category Source Wholetreemasskg_2012 Whole tree mass kg biomass McKown et al 2013 WUE_2009 Instantaneous water-use efficiency ecophysiology McKown et al 2013 WUE_2010 Instantaneous water-use efficiency ecophysiology McKown et al 2013 Yellowing100_2010 Yellowing 100 phenology McKown et al 2013 Yellowing25_2010 Yellowing 25 phenology McKown et al 2013 Yellowing50_2010 Yellowing 50 phenology McKown et al 2013 Yellowing75_2010 Yellowing 75 phenology McKown et al 2013  Table C-2. P-values of ANOVAs of 16 traits in pure and admixed P. trichocarpa. Comparisons Yellowing100_2010 Heightcm_2010 Heightgaincm_2009 Heightcm_2011 ch09_tb-Ch09_bb 0.4086 0.2839 0.6642 0.1928 Ch09_tt-Ch09_bb 0.0074 0.0148 0.0181 0.0142 Purenorth-Ch09_bb 0.8026 0.8933 0.7922 0.8791 Puresouth-Ch09_bb 0.8955 0.9978 1.0000 0.9847 Ch09_tt-ch09_tb 0.3503 0.7591 0.2562 0.9060 Purenorth-ch09_tb 0.0195 0.0193 0.0606 0.0088 Puresouth-ch09_tb 0.0323 0.1014 0.5749 0.0305 Purenorth-Ch09_tt 0.0000 0.0001 0.0000 0.0001 Puresouth-Ch09_tt 0.0000 0.0012 0.0079 0.0004 Puresouth-Purenorth 0.9993 0.9701 0.8193 0.9913  Growthperioddays_2010 Leaflifespandays_2010 Yellowing75_2010 Heightgaincm_2010 ch09_tb-Ch09_bb 0.2790 0.5959 0.4603 0.3796 Ch09_tt-Ch09_bb 0.0131 0.0253 0.0213 0.0320 Purenorth-Ch09_bb 1.0000 0.9803 0.7427 0.9560 Puresouth-Ch09_bb 0.9870 1.0000 0.9993 0.9990 Ch09_tt-ch09_tb 0.7376 0.3909 0.5527 0.8003 Purenorth-ch09_tb 0.2148 0.2024 0.0174 0.0626 Puresouth-ch09_tb 0.0577 0.5050 0.2489 0.1791 Purenorth-Ch09_tt 0.0060 0.0012 0.0000 0.0008 Puresouth-Ch09_tt 0.0004 0.0089 0.0029 0.0044 Puresouth-Purenorth 0.9913 0.9815 0.8375 0.9897  Budsetday_2010 Yellowing25_2010 AUDPC_2010 Yellowing50_2010 ch09_tb-Ch09_bb 0.2706 0.7051 0.3129 0.4867 Ch09_tt-Ch09_bb 0.0210 0.0351 0.0108 0.0208 Purenorth-Ch09_bb 0.9029 0.9443 1.0000 0.8652 Puresouth-Ch09_bb 0.9205 0.9251 0.9998 0.9815 Ch09_tt-ch09_tb 0.8616 0.3252 0.6139 0.5013 Purenorth-ch09_tb 0.0194 0.1870 0.3034 0.0434 Puresouth-ch09_tb 0.0188 0.9943 0.1608 0.8164 Purenorth-Ch09_tt 0.0002 0.0007 0.0101 0.0001 Puresouth-Ch09_tt 0.0001 0.2649 0.0014 0.0776 Puresouth-Purenorth 1.0000 0.4817 0.9998 0.5027  Volumecm3_2011 Activegrowthratecmday_2010 Leafdropday_2009 Budsetday_2009 ch09_tb-Ch09_bb 0.6619 0.5519 0.4029 0.8804 Ch09_tt-Ch09_bb 0.8374 0.0611 0.2255 0.2710 Purenorth-Ch09_bb 0.0063 0.9796 0.2144 0.6552 Puresouth-Ch09_bb 0.6837 1.0000 0.4192 0.6958 Ch09_tt-ch09_tb 0.9466 0.7359 1.0000 0.7409 Purenorth-ch09_tb 0.0000 0.1734 0.0003 0.0841 Puresouth-ch09_tb 0.0276 0.4331 0.0013 0.0912 Purenorth-Ch09_tt 0.0000 0.0039 0.0000 0.0010 Puresouth-Ch09_tt 0.0263 0.0227 0.0000 0.0009 Puresouth-Purenorth 0.1548 0.9851 0.9909 1.0000  

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