Science, Faculty of
DSpace
UBCV
Shalev, Tal James
Doctor of Philosophy - PhD
University of British Columbia
Western redcedar (WRC; Thuja plicata) is an ecologically, culturally, and economically important tree species. It is a key successional tree species and a major component of old-growth stands in its range, has been revered and used by First Nations of the Pacific Northwest of North America for millennia, and represents a $1.1B industry. WRC is unique among conifers in that it readily self-fertilizes (selfs) and does not appear to be affected by inbreeding depression for most fitness traits. Further, its tolerance of environmental stressors makes it a focal tree species for future range expansion due to climate change. Development of genomic resources and methods for conifers and other trees has become fundamental over the last decade for better understanding their biology and expediting tree improvement programs for growth and defense traits. As operational forestry shifts from old-growth to second-growth harvesting, using genomic data to better understand the population genetics, demography, and important growth and defense traits of WRC is essential. In this thesis I present novel genomic resources for WRC. These are then used to explore genetic diversity, mating system, and terpene chemistry traits associated with defense against ungulate browsing and fungal pathogens. I found that the genetic diversity of WRC is exceptionally low for a continuous tree species of its range size, likely due to range expansion from a single glacial refugium and its unique self-compatibility. Analysis of selfing lines revealed that WRC’s adaptability and responsiveness to selection may be due to balancing selection as a result of its demography. Further, not only does WRC show limited inbreeding depression for growth, terpene chemistry, or dendrochronological traits, but appropriate selection intensity can mitigate the effects of inbreeding for growth traits. I found that terpene synthase (TPS) genes in WRC occupy a unique clade from other conifers, and that many loci were associated with terpene chemistry traits in WRC, with substantial variation in effect sizes of loci between different traits. This work serves as a foundation for future genomic research, and provides fundamental data for improving breeding prospects for traits of interest in WRC.
GENOMICS OF WESTERN REDCEDAR (THUJA PLICATA) by Tal James Shalev BSc, The University of British Columbia, 2014 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Genome Science and Technology) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) September 2022 © Tal James Shalev, 2022 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Genomics of western redcedar (Thuja plicata) submitted by Tal James Shalev in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Genome Science and Technology Examining Committee: Dr. Joerg Bohlmann, Professor, Michael Smith Laboratories, UBC Supervisor Dr. Loren Rieseberg, Professor, Botany, UBC Supervisory Committee Member Dr. Amy Angert, Professor, Botany, UBC University Examiner Dr. Michael Russello, Professor, Biology, UBC-O University Examiner Additional Supervisory Committee Members: Dr. Michael Whitlock, Professor, Zoology, UBC Supervisory Committee Member Dr. Alvin Yanchuk, Team Lead Forest Genetics, BC Ministry of Forests Supervisory Committee Member iii Abstract Western redcedar (WRC; Thuja plicata) is an ecologically, culturally, and economically important tree species. It is a key successional tree species and a major component of old-growth stands in its range, has been revered and used by First Nations of the Pacific Northwest of North America for millennia, and represents a $1.1B industry. WRC is unique among conifers in that it readily self-fertilizes (selfs) and does not appear to be affected by inbreeding depression for most fitness traits. Further, its tolerance of environmental stressors makes it a focal tree species for future range expansion due to climate change. Development of genomic resources and methods for conifers and other trees has become fundamental over the last decade for better understanding their biology and expediting tree improvement programs for growth and defense traits. As operational forestry shifts from old-growth to second-growth harvesting, using genomic data to better understand the population genetics, demography, and important growth and defense traits of WRC is essential. In this thesis I present novel genomic resources for WRC. These are then used to explore genetic diversity, mating system, and terpene chemistry traits associated with defense against ungulate browsing and fungal pathogens. I found that the genetic diversity of WRC is exceptionally low for a continuous tree species of its range size, likely due to range expansion from a single glacial refugium and its unique self-compatibility. Analysis of selfing lines revealed that WRC’s adaptability and responsiveness to selection may be due to balancing selection as a result of its demography. Further, not only does WRC show limited inbreeding depression for growth, terpene chemistry, or dendrochronological traits, but appropriate selection intensity can mitigate the effects of inbreeding for growth traits. I found that terpene synthase iv (TPS) genes in WRC occupy a unique clade from other conifers, and that many loci were associated with terpene chemistry traits in WRC, with substantial variation in effect sizes of loci between different traits. This work serves as a foundation for future genomic research, and provides fundamental data for improving breeding prospects for traits of interest in WRC. v Lay Summary Western redcedar (WRC) is an important conifer tree species. Over the last decade, the application of genomics has become increasingly important in understanding the biology of conifers and improving approaches to breeding for important traits. I present new genomic data for WRC and use them to answer questions about the biology of this tree and inform future breeding approaches. I found that genetic diversity in WRC is low, likely due to being reduced to a small population during the last glaciation. Despite this, WRC is remarkably adaptable and responsive to natural and artificial selection; this is likely related to its unique ability among conifers to self-fertilize with little negative effect. I also found no apparent negative effect of multiple generations of self-fertilization on growth and chemistry traits associated with plant defense. Finally, I identified genomic areas associated with multiple traits of interest for the application of genomics to operational forestry. vi Preface Chapter 2: A version of Chapter 2 has been published as: Shalev, T.J., Yuen, M.M.S., Gesell, A., Yuen, A., Russell, J. H., & Bohlmann, J. An annotated transcriptome of highly inbred Thuja plicata (Cupressaceae) and its utility for gene discovery of terpenoid biosynthesis and conifer defense. Tree Genetics & Genomes 14, 35 (2018). https://doi.org/10.1007/s11295-018-1248-y. Tal Shalev conceived the chapter, performed all bioinformatics analyses, and wrote the manuscript. Macaire Yuen assisted in planning the bioinformatics analyses. Andreas Gesell performed the analytical chemistry. Agnes Yuen extracted RNA for sequencing. John Russell conceived the project and propagated the selfing lines. Joerg Bohlmann conceived and coordinated the project. All authors reviewed and revised the manuscript. Chapter 3: A version of Chapter 3 has been accepted for publication as: Shalev, T.J., Gamal El-Dien, O., Yuen, M.M.S., Shengqiang, S., Jackman, S.D., Warren, R.L., Coombe, L., van der Merwe, L., Stewart, A., Boston, L.B., Plott, C., Jenkins, J., He, G., Yan, J., Yan, M., Guo, J., Breinholt, J.W., Neves, L.G., Grimwood, J., Rieseberg, L.H., Schmutz, J., Birol, I., Kirst, M., Yanchuk, A.D., Ritland, C., Russell, J.H., & Bohlmann, J. The western redcedar genome reveals low genetic diversity in a self-compatible conifer. Genome Research (2022). https://doi.org/10.1101/gr.276358.121. Tal Shalev conceived the chapter, designed sample selection approach for selfing line sequencing, performed all data cleaning and filtration, developed and carried out population genetics and statistical analyses, and wrote the manuscript. vii Omnia Gamal El-Dien assisted in planning and carrying out population genetics and statistical analyses. Macaire Yuen performed bioinformatics analyses. Shu Shengqiang annotated the draft genome. Shaun Jackman assembled the western redcedar genome and provided Figures 3.1, 3.12 and 3.13 [these figures are also included, along with a description of the genome assembly, in Jackman (2019). They are reprinted here with permission]. René Warren and Lauren Coombe assisted in genome assembly. Lise van der Merwe contributed to study sampling design and provided plant materials. Ada Stewart, Lori Boston, Christopher Plott, Jerry Jenkins, Guifen He, Juying Yan, Mi Yan, and Jie Guo developed sequencing libraries and sequenced the genome. Jane Grimwood contributed to sequencing study design. Leandro Neves and Jesse Breinholt contributed to study genotyping design and carried out SNP genotyping. Jeremy Schmutz, Inanc Birol, Matias Kirst, and Alvin Yanchuk contributed to study design and interpretation of results. Carol Ritland contributed to study design and interpretation of results, managed DNA extraction for sequencing, and managed the project. John Russell conceived the project and was responsible for generation of most of the plant materials used. Joerg Bohlmann conceived and coordinated the project. All authors reviewed and revised the manuscript. Chapter 4: A version of chapter 4 has been submitted for publication with the following author list: Shalev, T.J., Gamal El-Dien, O., Yuen, M.M.S., van der Merwe, L., Kirst, M., Yanchuk, A.D., Ritland, C., Russell, J. H., & Bohlmann, J. Tal Shalev conceived the chapter, performed all data cleaning and filtration, designed sample selection approach for selfing line sequencing, developed and carried out all statistical analyses, and wrote the manuscript. Omnia Gamal El-viii Dien assisted in planning analyses and provided support in statistical analysis and interpretation. Mack Yuen provided bioinformatics support. Lise van der Merwe contributed to study sampling design and provided plant materials. Matias Kirst, and Alvin Yanchuk contributed to study design and interpretation of results. Carol Ritland contributed to study design and interpretation of results, managed DNA extraction for sequencing, and managed the project. John Russell conceived the project and was responsible for generation of most of the plant materials used. Joerg Bohlmann conceived and coordinated the project. All authors reviewed and revised the manuscript. ix Table of Contents Abstract ................................................................................................................................... iii Lay Summary ............................................................................................................................v Preface ..................................................................................................................................... vi Table of Contents .................................................................................................................... ix List of Tables ........................................................................................................................ xvii List of Figures....................................................................................................................... xxii List of Abbreviations ............................................................................................................ xxix Acknowledgements ............................................................................................................ xxxiii Dedication ............................................................................................................................ xxxv Western redcedar (Thuja plicata): Genomics of a self-compatible conifer ..........1 1.1 Background .......................................................................................................................1 1.2 Conifer genomics ..............................................................................................................2 1.3 Population genetics of western redcedar ............................................................................4 1.3.1 Genetic diversity .........................................................................................................4 1.3.2 Mating system of western redcedar .............................................................................5 1.4 Quantitative genetics of complex traits in western redcedar ...............................................7 1.4.1 Specialized chemistry traits of western redcedar .........................................................8 x 1.4.2 Genetic architecture of complex traits ....................................................................... 10 1.5 Thesis objectives .............................................................................................................. 12 An annotated transcriptome of highly inbred Thuja plicata (Cupressaceae) and its utility for gene discovery of terpenoid biosynthesis and conifer defense ......................... 15 2.1 Summary ......................................................................................................................... 15 2.2 Introduction ..................................................................................................................... 16 2.3 Materials and Methods .................................................................................................... 17 2.3.1 Plant materials .......................................................................................................... 17 2.3.2 Terpenoid extraction ................................................................................................. 18 2.3.3 GC-MS analysis ........................................................................................................ 18 2.3.4 RNA isolation and sequencing .................................................................................. 19 2.3.5 Transcriptome assembly ........................................................................................... 20 2.3.6 Redundancy reduction .............................................................................................. 21 2.3.7 Assembly validation ................................................................................................. 21 2.3.8 Transcriptome annotation ......................................................................................... 22 2.3.9 Analysis of orthologous protein groups ..................................................................... 23 2.3.10 TPS phylogeny ....................................................................................................... 23 2.3.11 Data availability ...................................................................................................... 24 xi 2.4 Results............................................................................................................................. 25 2.4.1 Major monoterpenes in foliage of the S5 lines........................................................... 25 2.4.2 Transcriptome assemblies and reference gene set ...................................................... 25 2.4.3 General annotation of the reduced-redundancy gene set ............................................ 26 2.4.4 Orthogroup annotation of the reduced-redundancy gene set ...................................... 27 2.4.5 Annotation and phylogeny of the WRC TPS gene family .......................................... 28 2.4.6 Terpene pathway candidate genes for thujone biosynthesis ....................................... 29 2.5 Discussion ....................................................................................................................... 30 2.5.1 A Cupressaceae transcriptome resource for conifer genomics ................................... 30 2.5.2 Quality of the WRC transcriptome assembly ............................................................. 31 2.5.3 WRC transcriptome annotation ................................................................................. 32 2.5.4 Phylogeny of WRC TPS ........................................................................................... 34 2.6 Conclusion ...................................................................................................................... 37 2.7 Tables .............................................................................................................................. 38 2.8 Figures ............................................................................................................................ 41 2.9 Datasets ........................................................................................................................... 49 The western redcedar genome reveals exceptionally low genetic diversity in a highly adaptable conifer.......................................................................................................... 51 xii 3.1 Summary ......................................................................................................................... 51 3.2 Introduction ..................................................................................................................... 52 3.3 Results............................................................................................................................. 55 3.3.1 The WRC genome assembly represents a highly complete conifer genome ............... 55 3.3.2 Population genomic analysis reveals extremely low levels of genetic diversity in WRC .......................................................................................................................................... 57 3.3.3 Persistent heterozygosity during complete selfing highlights genomic regions under selection ............................................................................................................................ 61 3.4 Discussion ....................................................................................................................... 64 3.4.1 The WRC genome .................................................................................................... 64 3.4.2 Genetic diversity in WRC ......................................................................................... 65 3.4.3 Selfing in WRC ........................................................................................................ 66 3.4.4 Implications for conservation, adaptation to climate change and breeding with genomic selection (GS) ..................................................................................................... 69 3.5 Methods and Materials .................................................................................................... 71 3.5.1 Plant materials .......................................................................................................... 71 3.5.2 Genome sequencing and assembly ............................................................................ 71 3.5.3 Genome annotation ................................................................................................... 73 3.5.4 SNP genotyping ........................................................................................................ 77 xiii 3.5.5 SNP filtering and annotation ..................................................................................... 79 3.5.6 Linkage disequilibrium ............................................................................................. 80 3.5.7 Population structure and genetic differentiation......................................................... 80 3.5.8 Nucleotide diversity .................................................................................................. 81 3.5.9 Effective population size (Ne) ................................................................................... 82 3.5.10 Genotype correction for SLs ................................................................................... 83 3.5.11 Change in heterozygosity and inbreeding coefficients over time ............................. 83 3.5.12 Isolating SNPs significantly deviating from expectations of drift ............................ 84 3.5.13 Data access ............................................................................................................. 85 3.6 Tables .............................................................................................................................. 86 3.7 Figures .......................................................................................................................... 130 3.8 Datasets ......................................................................................................................... 143 Genetic architecture of terpene chemistry and growth traits and the impact of inbreeding on these traits in western redcedar (Thuja plicata) ........................................... 145 4.1 Summary ....................................................................................................................... 145 4.2 Introduction ................................................................................................................... 147 4.3 Materials and Methods .................................................................................................. 150 4.3.1 Training population ................................................................................................ 150 xiv 4.3.2 Selfing lines ............................................................................................................ 150 4.3.3 DNA extraction ...................................................................................................... 151 4.3.4 SNP genotyping and filtering .................................................................................. 152 4.3.5 Phenotyping ............................................................................................................ 153 4.3.6 Estimation of narrow-sense heritability (h2) and genetic correlations ....................... 158 4.3.7 Adjusted phenotypes for BayesR ............................................................................ 159 4.3.8 Genetic architecture of chemistry and growth traits ................................................. 160 4.3.9 Inbreeding depression of growth and chemistry traits .............................................. 162 4.3.10 Data availability .................................................................................................... 166 4.4 Results........................................................................................................................... 166 4.4.1 SNP genotyping and filtration ................................................................................. 166 4.4.2 Growth and specialized chemistry traits in western redcedar ................................... 167 4.4.3 Heritabilities ........................................................................................................... 168 4.4.4 Genetic and phenotypic correlations ....................................................................... 169 4.4.5 Analysis of genetic architecture reveals genes involved in terpenoid biosynthesis and growth ............................................................................................................................. 170 4.4.6 Inbreeding has little impact on growth and chemistry traits even during extreme selfing ............................................................................................................................. 175 xv 4.5 Discussion ..................................................................................................................... 177 4.5.1 Genetic architecture varies even within similar traits ............................................... 177 4.5.2 WRC is not affected by inbreeding depression for chemistry and growth traits ....... 179 4.6 Tables ............................................................................................................................ 182 4.7 Figures .......................................................................................................................... 193 4.8 Datasets ......................................................................................................................... 201 Conclusion .......................................................................................................... 202 5.1 Selfing lines facilitate transcriptome and genome assembly ........................................... 202 5.2 Terpene biosynthesis in western redcedar ...................................................................... 204 5.2.1 Transcriptome mining uncovers novel Cupressaceae terpene synthases (TPS) and other genes of terpene biosynthesis and confirms the phylogenetic position of Cupressaceae and gymnosperm TPS ...................................................................................................... 204 5.2.2 Terpene biosynthesis is a complex trait ................................................................... 205 5.3 Genetic diversity and inbreeding in western redcedar .................................................... 206 5.3.1 High self-compatibility and balancing selection may drive adaptability and responsiveness to selection despite low genetic diversity in western redcedar .................. 207 5.3.2 No significant inbreeding depression for terpene chemistry and growth traits in WRC ........................................................................................................................................ 208 5.4 Conclusion .................................................................................................................... 209 xvi Bibliography .......................................................................................................................... 210 xvii List of Tables Table 2.1: Summary of output from Evigene pipeline. An additional 3305 sequences with ambiguous amino acids and sequences shorter than 100 amino acids long were removed from the Main set. Alt (High Identity): >=98% similarity to a sequence in Main; Alt (Lower Identity): <98% similarity to a sequence in Main, most likely a paralog; Main (w/ alt): Sequence with alternate in Alt; Main (no alt): Sequence with no alternates; Fragment Alternates: Fragmented sequences. ................................................................................................................................. 38 Table 2.2: Conditional Reciprocal Best BLAST (CRBB) results of the evigene combined assembly compared to the Trinity assembly. Higher % contigs with CRBB hits indicates better contiguity with the reference; lower mean reciprocal best hit (rbh) per reference indicates lower redundancy................................................................................................................................ 39 Table 2.3: BLAST results of the evigene combined assembly versus the Trinity assembly against the 1000 longest proteins in the P. glauca gene models, the P. taeda gene models and the A. thaliana representative gene set (e-value cut-off: 1e-05). ............................................ 40 Table 3.1: Overview of selfing lines (SLs) selected for the study. .......................................... 86 Table 3.2: Overview of sequencing data generated for the western redcedar draft genome. ................................................................................................................................................. 90 Table 3.3: Assembly metrics and statistics for each version of the WRC draft genome. ..... 91 Table 3.4: Metrics for each stage of the WRC draft genome assembly (v3). ........................ 92 xviii Table 3.5: A) Genome assembly completeness of seven currently available conifer genome assemblies as estimated by BUSCO v5.0.0 in genome mode (Embryophyta OrthoDB v10, Metaeuk). B) Predicted gene set completeness of seven sequenced conifer genomes as estimated by BUSCO v5.0.0 in protein mode (Embryophyta OrthoDB v10). ...................... 93 Table 3.6: Annotation metrics and statistics for the western redcedar draft genome. ......... 95 Table 3.7: Verification of the western redcedar (WRC) draft genome annotation. Of a set of 59 WRC transcripts from NCBI and 33 WRC terpene synthase transcripts, 48 and 15 were reliably identified (>90% BLASTN identity, best match in EXONERATE), respectively, in the WRC draft genome. .................................................................................................................. 96 Table 3.8: The range-wide population (n = 112) of western redcedar used in this study. The population was stratified into three subpopulations based on the analysis of O’Connell et al. (2008). .................................................................................................................................... 101 Table 3.9: SNP effects as predicted by Ensembl Variant Effect Predictor (McLaren et al. 2016) for 25,841 SNPs out of the 27,594 SNPs in our dataset. ............................................. 106 Table 3.10: Pairwise FST for all subpopulations. FST values are based on Weir and Cockerham’s estimator (Weir and Cockerham 1984). ........................................................ 107 Table 3.11: Annotation for single-copy ortholog genes with highest 1% of π estimates of nucleotide diversity (n = 47). ................................................................................................. 108 Table 3.12: Kruskal-Wallis analysis of variance in π and dXY estimates in single-copy orthologs across subpopulations. .......................................................................................... 115 xix Table 3.13: Mean (SD) observed heterozygosity (H) and inbreeding coefficient (F) in the range-wide population (RWP) (n = 112) and each generation of selfing lines (FS – S4: n = 28; S5: n = 11). ....................................................................................................................... 116 Table 3.14: A) Pairwise Sign test for variance of median observed (HO) and expected (HE) heterozygosities in each generation of selfing lines (SLs), testing whether median HO is significantly lower than median HE. B) One-tailed Wilcoxon signed rank test for variance of mean HO and HE in each generation of SLs, testing whether mean HO is significantly lower than mean HE. C) One-sample t-test for variance of mean observed (FO) and expected (FE) inbreeding coefficients in each generation of SLs. ........................................ 117 Table 3.15: Gene Ontology (GO) categories for genes containing outlier SNPs (n = 1,298). ............................................................................................................................................... 118 Table 3.16: Three genes associated with the gene ontology (GO) category "recognition of pollen" (GO:0048544) containing outlier SNPs. .................................................................. 123 Table 3.17: Geographic locations of origin of parent trees for selfing lines. ....................... 129 Table 4.1: Models used to predict breeding values (BVs) and estimate adjusted phenotypes for the genomic selection training population. For prediction of BVs, the inverse genomic realized relationship matrix (GRRM) was included as a random effect in the model. Model terms preceded with ‘idh’ used a block diagonal (heterogeneous) variance structure to split the variance for that term. ........................................................................................................................... 182 xx Table 4.2: Student’s t-test for differences in height and DBH for the three different test sites in the training population. Two-sample one-tailed t-tests were performed to assess whether height and DBH were significantly greater at the Powell River site when compared to the Jordan River and Port McNeill sites. .................................................................................. 184 Table 4.3: Narrow-sense heritability (h2) estimates from genomic realized relationships (G) and pedigree (A). ................................................................................................................... 185 Table 4.4: Genetic (lower triangle) and phenotypic (upper triangle) correlations between all study traits estimated using genomic realized relationships (A) and pedigree (B). ............ 186 Table 4.5: Trait-associated SNPs and effect sizes for each trait estimated using the approach of BayesR (Moser et al. 2015). Of the 36,262 SNPs used in the study, 23,913 were associated with traits. Of these, 10,178 were in genic regions and 3,933 were in intergenic regions. ................................................................................................................................... 188 Table 4.6: Number of SNPs in each putative linkage group derived from the Sequoiadendron giganteum (giant sequoia) draft genome (Scott et al. 2020). Percentage of total was scaled by the length of each putative giant sequoia chromosome. ............................. 189 Table 4.7: Estimates of inbreeding depression derived from mixed linear models for training population data (A) and OLS for selfing lines (B). ................................................ 190 Table 4.8: In four generations of complete selfing, deliberate selection for seedling height was the only significant predictor of height, despite an increase in the inbreeding coefficient F with each selfing generation (one-way ANOVA; OLS linear model). Moreover, there was a xxi significant interaction effect of F and type, as predicted height BVs of random lines declined while those of select lines increased (Figure 4.7C). ................................................................. 191 Table 4.9: Loci putatively under selection during selfing identified in Chapter 3 associated with terpene and growth traits. ............................................................................................ 192 xxii List of Figures Figure 2.1: Representative four-month-old WRC seedling (~ 20 – 25 cm tall) used for RNA isolation and sequencing. ........................................................................................................ 41 Figure 2.2: Pipeline for the de novo assembly and redundancy reduction of the WRC gene set, carried out for each inbred S5 line separately. ................................................................ 42 Figure 2.3: Monoterpene profiles of foliar samples for 12 different monoterpenes across the eight S5 lines. ........................................................................................................................... 43 Figure 2.4: Results of the BUSCO gene set completeness assessment. The reduced-redundancy gene set for WRC S5 Line 4 was found to be the most complete, with the lowest number of missing orthologs. .................................................................................................... 44 Figure 2.5: Top 20 most abundant InterPro domains and families identified in the WRC reduced-redundancy gene set. A) InterPro domain annotation was performed on the reduced-redundancy gene set obtained from the transcriptome assembly of a single plant of S5 Line 4. B) InterPro family annotation was performed on the reduced-redundancy gene set obtained from the transcriptome assembly of a single plant of S5 Line 4. ............................................................... 45 Figure 2.6: Maximum likelihood phylogeny of TPS. Phylogenetic analysis was performed with 241 TPS from across the plant kingdom, including 33 non-redundant full-length or near full-length WRC TPS. Classification of TPS sub-families are as described in Chen et al. xxiii (2011). TPS of gymnosperms cluster together in subfamily TPS-d and apart from the TPS of angiosperms, except for those putatively involved in primary metabolism. ................................ 46 Figure 2.7: Maximum likelihood phylogeny of gymnosperm TPS rooted to P. patens ent-kaurene synthase. Phylogenetic analysis was performed with 126 full-length TPS from gymnosperms, including 33 non-redundant full-length or near full-length WRC TPS. TPS in gymnosperms generally separate by high-level and general functions as indicated with the background colours showing clades of di-TPS of primary or secondary metabolism, ancestral sesqui-TPS, sesqui-TPS, mono-TPS, and hemi-TPS. Within these general functional clades, TPS of the Cupressaceae mostly cluster apart from TPS of the Pinaceae, highlighting the independent diversification and evolution of specific TPS functions in the Cupressaceae and the Pinaceae. Bootstrap support values over 80% are given. ........................................................................... 48 Figure 3.1: GenomeScope (Vurture et al. 2017) estimation of western redcedar (WRC) genome size. Genomescope estimated the size of the WRC genome at 9.8 Gbp. ..................... 130 Figure 3.2: Genetic structure is weak across the geographic range of WRC. A) Map of geographic origin for trees in the range-wide population (RWP) (n = 112). Subpopulations were defined a priori based on analysis outcomes of O’Connell et al. (2008). Trees were separated into three main subpopulations: Northern-Coastal (n = 77); Central (n = 26); and Southern-Interior (n = 9). B) STRUCTURE plot of the RWP for K = 2 and K = 3. Optimal K was determined by evaluating STRUCTURE results using the methods of Evanno et al. (2005) and Puechmaille (2016), and by the approach of fastStructure (Raj et al. 2014). Gene flow is present throughout all three subpopulations. C) Principal component analysis (PCA) of genetic distance xxiv between trees in the RWP. Latitudinal separation of trees from different s can be observed, although each principal component only explains a very small proportion of the variation between individuals, indicating that genetic differentiation is low. .......................................... 131 Figure 3.3: Within-scaffold linkage disequilibrium (LD) decays slowly in western redcedar. A) LD was assessed using SNPs with a minor allele frequency cutoff of 0.05 to reduce error associated with rare alleles (n = 22,751 SNPs). Decay was estimated using a non-linear model (red line); r2 decayed to baseline thresholds of 0.2 (purple dotted line) and 0.1 (pink dotted line) at 0.573 and 1.65 Mbp, respectively. B) Pairwise LD for all pairs of SNPs (n = 22,751). Each point on the plot represents the LD between two SNPs at a given distance from one another and relative position on the scaffold. Colour indicates LD range, with red indicating SNPs in strong LD. ......................................................................................................................................... 132 Figure 3.4: Discriminant Analysis of Principal Components (DAPC) assessment of optimal K for trees in the range-wide population (RWP) (n = 112). A) Selection of optimal K using the find.clusters approach. Optimal K is typically determined by selecting the K with lowest Bayesian Information Criterion (BIC) score. However, when the lowest BIC is assigned to K = 1, this is interpreted as an inability to resolve clusters (Miller et al. 2020). B) DAPC clustering using the first 10 principal components and K = 3. C) Composition plots of the reassignment of individual trees to clusters based on DAPC for K = 2-3. DAPC was not able to properly reassign trees from the RWP, placing all trees from the Central and Southern-Interior subpopulations together while showing complete mixing in the Northern-Coastal subpopulation. ................... 133 xxv Figure 3.5: Individual genetic distance as a factor of geographic distance in the range-wide population (n = 112). No significant association was observed between geographic distance and genetic distance when grouping trees based on geographic origin. Surprisingly, no significant association was observed between individual trees either (as shown in the figure), which would normally be expected in continuous populations expanding their range from one source. ........ 134 Figure 3.6: Average nucleotide diversity π and average nucleotide difference between subpopulations in the range-wide population (RWP). A) Overall distribution of average π of the RWP in SCOs. We detected 1,126 SCOs with a π of zero, with an average π of 0.00338. B) Overall distribution of average dXY between each pair of geographic subpopulations. No significant differences were observed between comparisons of different subpopulations. Inlays show all π estimates; main plots show π estimates with outliers in the top one percentile removed for clarity. The top 1% of π estimates account for 4% of the total estimated diversity, and the top 5% account for over 25% of the total. ...................................................................................... 135 Figure 3.7: Nucleotide diversity (π) in the range-wide population (n = 112). Average π in Single Copy Ortholog (SCO) genes across each putative linkage group (see Methods and Materials). The top 1% of SCOs with highest π, which account for 4% of total nucleotide diversity, are shown in red (n = 47; Table 3.11). ...................................................................... 136 Figure 3.8: Folded Site Frequency Spectrum (SFS) plots for A) All SNPs in the range-wide population (n = 27,594); B) All zero-fold degenerate SNPs (n = 4,091); and C) All four-fold degenerate SNPs (n = 1,815). .................................................................................................. 137 xxvi Figure 3.9: Stairway Plot (Liu and Fu 2020) on folded SFS of four-fold degenerate SNPs in the range-wide population. Ne begins to decline starting ca. 100 kya, dropping sharply several times, ca. 20 kya and 6 kya, consistent with population bottlenecks during the last glacial period. ............................................................................................................................................... 138 Figure 3.10: Change in heterozygosity (H) and inbreeding coefficients (F) over five successive generations of complete selfing in WRC. A) Observed vs. expected change in heterozygosity over five successive generations of complete selfing in n = 28 (FS – S4) and n = 11 (S5) different selfing lines (SLs), at n = 27,594 SNP loci, after manual error correction. Each line at each generation is represented by a single tree. Black points indicate boxplot outliers. Observed median heterozygosity declines faster than expected under theoretical expectations during complete selfing, despite many SNP loci remaining heterozygous across all generations. B) Inbreeding coefficients (F) for n = 28 samples (FS – S4) and n = 11 samples (S5). Black points indicate boxplot outliers. Under complete selfing, F is expected to increase by a factor of ½(1+F) in the previous generation (red diamonds). F increases at a slower rate than expected in our SLs. .................................................................................................................................. 139 Figure 3.11: Consequences of SNPs deviating from expectations of genetic drift. Comparison of the consequences of all SNPs with effects predicted by Variant Effect Predictor (VEP) (McLaren et al. 2016) (n = 25,841) and SNPs deviating from expectations of genetic drift with predicted effects (outlier SNPs; n = 1,073). “*” indicates a significant difference between the two sets of SNPs for proportion of SNPs for a certain consequence (Fisher’s Exact Test, p < 0.05). ...................................................................................................................................... 140 xxvii Figure 3.12: Depth of k-mer coverage profiles for multiple values of k estimated in ntCard (Mohamadi et al. 2017). Mode of depth of coverage c is labeled for each value of k. ............. 141 Figure 3.13: Flowchart of the assembly process of the WRC draft genome. ............................. 142 Figure 4.1: Density plots of the amounts of different foliar (A) and wood (B) monoterpenes in the training population. Density indicates the proportion of observations of a range of terpene concentrations. ............................................................................................................ 193 Figure 4.2: Distribution of height (A) and diameter at breast height (DBH; B) across the three progeny test sites in the training population. .............................................................. 194 Figure 4.3: Variance explained by each of three mixture distributions (σ2g×10-2 = large effect; σ2g×10-3 = small effect; σ2g×10-4 = polygenic effect) for each trait as determined by the approach of BayesR (Moser et al. 2015). .............................................................................. 195 Figure 4.4: Overlap of trait-associated SNPs for growth and chemistry traits in WRC. A) Overlap of SNPs with large effect between all assessed traits. Blue: Foliar terpene traits; Yellow: wood terpene traits; Red: growth traits; Purple: traits of different categories. B) Overlap of all trait-associated SNPs for foliar terpene traits. C) Overlap of all trait-associated SNPs for wood terpene traits. D) Overlap of all trait-associated SNPs for growth traits.................................... 197 Figure 4.5: Distribution of SNPs of three putative effect sizes as determined using BayesR (Moser et al. 2015), mapped across putative linkage groups based on the draft genome of Sequoiadendron giganteum (giant sequoia) (Scott et al. 2020). ............................................ 198 xxviii Figure 4.6: Distribution of the inbreeding coefficient F as determined by the approach of Yang et al. (2011) in the training population. ...................................................................... 199 Figure 4.7: Despite a noticeable increase in the inbreeding coefficient F in all selfing line trees with each successive selfing generation, the only significant predictor of height growth was whether a line was under deliberate artificial selection for height. A) Change in F during selfing. F increases with each selfing generation similarly within both random and select lines. B) Change in height BV during selfing. Height BVs increased with each selfing generation in select lines, while height BVs in random lines declined somewhat. Black line represents the regression of height BV on generation, indicating that change in height is not significant when line type is ignored. C) Interaction plot of predicted height BV and F. In the absence of selection, increasing values of F are negatively correlated with growth. However, under selection height BV increases despite an increase in F, indicating the effects of inbreeding are readily mitigated by moderate selective pressures in WRC. ................................................................................ 200 xxix List of Abbreviations ABLUP - Pedigree best linear unbiased predictor BC - British Columbia BLUP - Best linear unbiased predictor BV - Breeding value CCS - Circular consensus sequence CDS - Coding sequence CLRS - Cowichan Lake Research Station CPS - Copalyl diphosphate synthase CRBB - Conditional reciprocal best BLAST DAPC - Discriminant analysis of principal components DBH - Diameter at breast height DMAPP - Dimethylallyl diphosphate DW - Dry weight ES - Effect size FS - Full-sibling xxx FW - Fresh weight GBLUP - Genomic best linear unbiased predictor GBS - Genotyping-by-sequencing GC - Gas chromatography GEBV - Genomic estimated breeding value GO - Gene ontology GPP - Geranyl diphosphate GRRM - Genomic realized relationship matrix GS - Genomic selection GWAS - Genome-wide association study HPLC - High performance liquid chromatography HWE - Hardy-Weinberg equilibrium IBD - Identity by descent IPP - Isopentenyl diphosphate KS - Kaurene synthase LD - Linkage disequilibrium LG - Linkage group xxxi LRT - Likelihood ratio test LTR-RT - Long terminal repeat transposons MAC - minor allele count MAF - Minor allele frequency MFLNRORD - Ministry of Forests, Lands, Natural Resource Operations and Rural Development ML - Maximum likelihood MoF - Ministry of Forests MS - Mass spectrometry MTBE - Methyl tert-butyl ether MYA - Million years ago NGS - Next-generation sequencing ORF - Open reading frame PC - Principal component PCA - Principal component analysis QTL - Quantitative trait locus REML - Restricted effects maximum likelihood RFLP - Restricted fragment length polymorphism xxxii RIN - RNA integrity RRM - Realized relationship matrix RWP - Range-wide population SCO - Single copy ortholog SFS - Site frequency spectrum SL - Selfing line SMRT - Single molecule real time SNP - Single nucleotide polymorphism SNV - Single nucleotide variant TP - Training population TPS - Terpene synthase UPGMA - Unweighted pair group means analysis WRC - Western redcedar xxxiii Acknowledgements I would like to first and foremost acknowledge and thank my supervisor, Dr. Joerg Bohlmann, for the incredible show of support, faith, flexibility, and open-mindedness during the development of this thesis. You guided me to this point but let me choose my path, and for that I am deeply appreciative. My heartfelt thanks go out to Dr. John Russell, who got me started on my foray into the field of population and quantitative genetics and supported my learning in the early days of this project. You are greatly missed. I would like to thank Dr. Carol Ritland for always having an open door and time to share her knowledge and ideas, and Dr. Omnia Gamal El-Dien for her support, guidance, and assistance in developing this project into its completed form. My sincerest gratitude and respect go to my committee members, Dr. Loren Rieseberg, Dr. Michael Whitlock, and Dr. Alvin Yanchuk. Thank you for giving your time and expertise, asking hard questions, and keeping me on track during this project. Many thanks to Dr. Matias Kirst, for welcoming and accommodating me into his research group, and for his expert guidance through the pitfalls of this project. Thank you to the entire Bohlmann research group, past and present: Mack Yuen, Sharon Jancsik, Karen Reid, Melissa Mageroy, Angela Chiang, Jose Celedon, Judith Booth, Christine Chiu, Christopher Keeling, Christopher Roach, Justin Whitehill, Hannah Henderson, Andreas Gesell, xxxiv Agnes Yuen, Suzanna Ivamoto, Sandra Irmisch, Jenny Jo, Fred Sunstrum, Lina Madilao, Kristina Kshatriya, Hannah Liu, Lars Kruse, and Dylan Perera. This work was made possible due to funding from NSERC, Genome BC, and Genome Canada. Finally, I would like to thank my parents, for supporting me and believing in me, always. xxxv Dedication This thesis is dedicated to my loving wife, Enav, who has supported me every step of the way; my son, Aidan, to whom I have oft-times been both teacher and student, and who has inspired me to become a better father, mentor, and person; and my daughter, Zoe, for whom I strive to create a better future, and a better world. 1 Western redcedar (Thuja plicata): Genomics of a self-compatible conifer 1.1 Background Gymnosperms are an ancient group of plants with fossil records dating over 300 million years ago (MYA) (Stewart 1983). Diverging from a common ancestor with the angiosperm lineage between 250 and 265 MYA, they are characterized by exposed ovules and seeds, which are not enclosed within an ovary or fruit, respectively (Christenhusz et al. 2011). Extant gymnosperms are comprised of the Cycadophyta (cycads), Gnetophyta (gnetophytes), and Coniferophyta (conifers), as well as the separate species Ginkgo biloba. Conifers are by far the largest group of gymnosperms with approximately 615 known species (Christenhusz et al. 2011; Farjon 2018). Western redcedar (Thuja plicata; WRC) is a conifer species of the family Cupressaceae. WRC are large conifers, growing up to 60 metres tall and 2.5 metres in diameter when mature. Leaves are scale-like and grow in four overlapping rows of opposite pairs, with leaves in one of the pairs appearing folded. Cones are small (~ 1 cm in length and diameter) and scaled, maturing in late summer. In its native range, WRC is contiguously distributed along the Pacific coast of North America from northern California to southeastern Alaska as well as into the interior of British Columbia (BC) and Washington State (Eyre 1980). In the context of Universal Adaptive Strategy Theory, WRC can best be described as a stress-tolerator, which grows well over a wide variety of conditions but rarely dominates stands (Grime 1977; Antos et al. 2016). WRC is typically at a competitive advantage in sites with wet and/or low-productivity soil when 2 compared to other conifer species with which it coexists, such as western hemlock (Tsuga heterophylla) and coastal Douglas-fir (Pseudotsuga menziesii) (Sajedi et al. 2012; Kranabetter et al. 2013). WRC is deeply ingrained in coastal First Nations culture, and its products have been used for millennia for a variety of practical and spiritual purposes such as construction of houses and boats, carving totems and weaving baskets (Hebda and Mathewes 1984). WRC remains highly prized for its durable and lightweight wood, which is widely used for house siding, interior paneling, decking and outdoor furniture (Gonzalez 2004). WRC is one of only five extant Thuja species and is estimated to have diverged from its North American sister species T. occidentalis around 26 MYA (Li and Xiang 2005). Phylogenetic analyses suggest WRC is most closely related to the Korean arborvitae (T. koraiensis), likely due to a reticulate evolutionary history of the genus (Li and Xiang 2005; Peng and Wang 2008). Current climate models predict that the range of WRC will increase substantially over the next century, particularly in the interior of British Columbia (BC) (Gray and Hamann 2013). Thus, it is currently an important species for genomic analysis and expediting traditional breeding cycles via genomic selection (Grattapaglia 2017). 1.2 Conifer genomics Conifers are known for their large genomes, in which repetitive sequences, transposable elements, long introns, gene duplications, pseudogenes, and gene fragments are ubiquitous (Birol 3 et al. 2013; Nystedt et al. 2013; De La Torre et al. 2014; Zimin et al. 2014, 2017; Warren et al. 2015; Stevens et al. 2016; Neale et al. 2017). Until recently, conifer genomics research has largely focused on trees in the Pinaceae, the largest conifer family. Several of them, such as white spruce (Picea glauca), Norway spruce (Picea abies), loblolly pine (Pinus taeda), sugar pine (Pinus lambertiana) and Douglas-fir have been recent targets for genome sequencing. Long terminal repeat transposons (LTR-RT) have been found to account for approximately 58% of the genome in both Norway spruce and loblolly pine, and for nearly 67% of the genome in sugar pine, which helps explain the size of these genomes (Nystedt et al. 2013; Neale et al. 2014; Wegrzyn et al. 2014; Stevens et al. 2016). Moreover, the accumulation of non-functional pseudogenes and gene-like fragments complicates assembly and analysis of these large genomes (Kovach et al. 2010; Warren et al. 2015), and many of the currently available conifer genome assemblies remain highly fragmented, with N50 numbers ranging from 0.822 kbp in Norway spruce to 2.92 Mbp in sugar pine (Nystedt et al. 2013; Crepeau et al. 2017). Only recently, the use of specialized re-scaffolding approaches have allowed for conifer genome assemblies to reach near-chromosome level of resolution, although at the cost of decreased accuracy (Scott et al. 2020; Neale et al. 2022; Niu et al. 2022). Cupressaceae genomes differ from those in the Pinaceae in a number of key ways. First, the karyotype is different (n = 11 in Cupressaceae; n = 12 in Pinaceae). This is likely due to rearrangements of orthologous linkage groups which occurred during the evolutionary split between the two families, thus rather significantly affecting the overall synteny and structure of these genomes (De Miguel et al. 2015). Second, overall genome size is smaller (9 – 20 Gbp in 4 Cupressaceae; 18 – 31 Gbp in Pinaceae) (Hizume et al. 2001; De La Torre et al. 2014; Stevens et al. 2016). And finally, while most conifer genomes are diploid, there have been a number of whole-genome duplication events within the Cupressaceae leading to some notable exceptions (e.g. the hexaploid Sequoia sempervirens [coast redwood] or many polyploid Juniperus species) (Hizume et al. 2001; Farhat et al. 2019; Neale et al. 2022). WRC is a diploid with n = 11 chromosomes and has a genome size estimated at 12.5 Gbp (Hizume et al. 2001). 1.3 Population genetics of western redcedar 1.3.1 Genetic diversity Genetic diversity refers to any measure of the variability in the genetic material of a population; it is this variation upon which natural selection acts, driving evolution (Fisher 1930). Genetic diversity can have important consequences for conservation and fitness; in the case of economically important crops or trees such as WRC, diversity can be critical in providing the variation from which key economic traits can be selected for (Ledig 1988; Young et al. 2000; Leimu et al. 2006). Studies using isozyme and restricted fragment length polymorphism (RFLP) molecular markers have previously suggested low genetic diversity in WRC relative to other conifers (Copes 1981; Glaubitz et al. 2000). O’Connell et al. (2008) assessed the most likely pattern of WRC range expansion following the recession of the Cordilleran and Laurentide ice sheets at the end of the most recent glacial period using eight microsatellite markers. Using unweighted pair group means analysis 5 (UPGMA), populations of WRC from across the species’ range were clustered into three geographic groups, designated south-interior (coastal United States from California to Washington and interior of Washington, Idaho and BC), central (southern coast of BC and southeast Vancouver Island), and northern-coastal (central and northern coast of BC, central and north Vancouver Island, Haida Gwaii and southern Alaska). Populations in the south of WRC’s current distribution had the greatest genetic variation, while genetic diversity decreased with increasing latitude; moreover, evidence for historical bottlenecks was found for the southern-interior group, but not for the other geographic groups, consistent with independent routes of range expansion north and inland from a single glacial refugium south of the glacial maximum. The authors estimated genetic variation overall to be higher than in previous studies; however, microsatellite variation is generally higher than variation in other markers due to high mutation rates at these loci (Goldstein and Schlötterer 1999; Zimmerman et al. 2020). 1.3.2 Mating system of western redcedar WRC is unique among conifers in that it engages in a mixed mating system of outcrossing and self-fertilization (selfing). Indeed, the average outcrossing rate of WRC has been estimated to be approximately 70% in natural populations (O’Connell et al. 2001, 2004); in contrast, most conifers have outcrossing rates higher than 80% and some, such as Douglas-fir, have outcrossing rates as high as 88% (O’Connell 2003). High rates of outcrossing in conifers are generally attributed to strong inbreeding depression in fitness traits due to homozygous lethal or sublethal 6 alleles during embryonic and early life stages, as well as increased mortality and reduced growth later (Sorensen 1982, 1999; Bishir and Namkoong 1987; Savolainen et al. 1992; Williams et al. 2003; Williams 2008). WRC exhibits a number of traits that appear to increase the likelihood of self-fertilization. Seed and pollen cones are typically found evenly distributed on trees, and cones are receptive to pollen for a period of 10 to 20 days, during which pollen shed occurs continuously, leading to an overall elevated degree of self-pollination when compared to other conifers (Colangeli and Owens 1990). Self-pollinated ovules are only slightly more likely to undergo embryo abortion, and seed potential (number of ovules per cone) does not differ significantly between self-pollinated and outcrossed WRC (Owens et al. 1990). In a foundational study, Russell and Ferguson (2008) successfully selfed multiple lines of WRC for five generations in a complete selfing model, an exceptional feat which has not been replicated in any other conifer. They found no significant inbreeding depression for seed weight or vigor and less than 20% (10/60) of the lines generated went extinct during the process. In all surviving lines, growth was normal and there was no evidence of deleterious effects of lethal or sublethal homozygous alleles. Inbreeding depression for growth traits as well appears to be minimal. It was estimated that up to 10% of merchantable volume may be lost by age 9, and only 8% may be lost at over 60 years of growth due to inbreeding in seed orchards and breeding populations (Russell et al. 2003; Wang and Russell 2006). This suggests that with appropriate selection intensity, selfing and subsequent outcrossing could potentially be used as a tool to improve efficiency of selection via 7 access to additional additive genetic variation, similar to approaches in crop species such as maize (Russell and Ferguson 2008; Carena et al. 2010). In the absence of strong inbreeding depression, there are potential benefits to increased selfing. Importantly, selfing can confer reproductive assurance, allowing self-compatible populations to expand their range even after extreme bottleneck scenarios (Baker 1955; Lande and Schemske 1985; Pannell et al. 2015). Given the likelihood of a significant population bottleneck during the last glacial period, and the subsequent pattern of range expansion, selfing likely played an important role in defining the current range and widespread distribution of WRC (O’Connell et al. 2008). 1.4 Quantitative genetics of complex traits in western redcedar Harvesting of WRC in British Columbia is shifting from native old-growth forests to planted second-growth forests. However, second-growth WRC can be more susceptible to pathogens such as heartwood rot, which is caused by a succession of different fungal species (Debell et al. 1999; Gonzalez 2004). WRC seedlings are also susceptible to damage by deer browsing (Vourc’h et al. 2002a; Stroh et al. 2008). Breeding of WRC with high durability and resistance to disease and herbivores is a primary goal of the BC tree improvement program, which benefits from knowledge of the genomics, molecular biology, and biochemistry of the natural defense mechanisms of this species (Russell and Yanchuk 2012). 8 1.4.1 Specialized chemistry traits of western redcedar Terpenes, or terpenoids, are a class of compounds derived from the five-carbon building blocks isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP). Terpenes constitute a large group of metabolites in plants, with over 40,000 structures involved in both primary, or ‘general’, and secondary, or ‘specialized’, metabolism (Bohlmann and Keeling 2008). Many terpenes have been found to be associated with the defense of plants against pests and pathogens (Tholl 2015). In WRC, monoterpenes including thujone, which are biosynthesized and accumulate in glandular cavities of the foliage, are involved in deterrence of deer browsing (Vourc’h et al. 2002a; Foster et al. 2013; Gesell et al. 2015). Thujone biosynthesis proceeds via the universal monoterpene precursor geranyl diphosphate (GPP), which is converted to the monoterpene (+)-sabinene by (+)-sabinene synthase (Foster et al. 2013), followed by hydroxylation to (+)-trans-sabinol by the cytochrome P450 (+)-sabinene hydroxylase (Gesell et al. 2015). The two proposed remaining steps are oxidation of the (+)-trans-sabinol intermediate to (+)-sabinone, which is subsequently reduced to both the major (α-thujone) and minor (β-thujone) products; these reactions may be catalysed by a putative alcohol dehydrogenase and aldo/keto reductase, respectively (Karp and Croteau 1982; Gesell et al. 2015). WRC heartwood rot may involve a succession of over 30 different species of fungi, with certain species of non-decay fungi facilitating the infiltration of decay fungi by detoxifying defense metabolites in the sapwood and heartwood (Van der Kamp 1975, 1986). In stems of WRC, terpenoids of the tropolone class, the most abundant of which are β- and γ-thujaplicin (Daniels and Russell 2007), are toxic to a variety of fungal pathogens and are associated with 9 protection of living trees against heartwood rot (Rennerfelt 1948; Rudman 1963; Debell et al. 1999; Sturrock et al. 2017). Lignans, which are derived from the phenylpropanoid pathway, may also play a role in the post-harvest protection against fungal decay and in turn the durability of WRC wood products (Morris and Stirling 2012; Stirling and Morris 2016). While levels of terpenoids in the wood of WRC decrease after trees have been harvested, levels of lignans such as plicatic acid, plicatin and thujaplicatin are more stable in post-harvest wood products (Chedgy et al. 2009; Morris and Stirling 2012; Stirling et al. 2017). Research on the effects of these metabolites on WRC durability is complicated by the fact that their accumulation is associated with heartwood development, a process that typically does not occur before trees are at least 20 years old (Russell and Daniel 2010). The mechanisms by which lignans contribute to heartwood decay resistance are not known. Lignans have recently been proposed to contribute to decay resistance via metal chelation and antioxidant scavenging (Schultz and Nicholas 2000; Stirling and Morris 2016). Plicatic acid has also been found to be associated with lower equilibrium moisture content in the heartwood, which may contribute to increased decay resistance in wood in service (Stirling and Morris 2006; Stirling et al. 2017). Much remains to be learned about biosynthesis of specialized metabolites such as lignans and tropolones in WRC that accumulate in heartwood, their mode of action in WRC interaction with fungi, and the genetic architecture (that is, the underlying genetic basis of a trait and the genetic properties which lead to variation in a trait) and molecular mechanisms controlling these chemical traits (Debell et al. 1999; Morris and Stirling 2012; Stirling and Morris 2016; Stirling et al. 2017). 10 1.4.2 Genetic architecture of complex traits Complex traits are traits that are controlled by a large network of genes and their interactions with environmental factors (Goddard et al. 2016). It has long been known that quantitative traits, such as height, are controlled in such a way, and it is becoming increasingly likely that most traits are complex and quantitative to some degree (Yang et al. 2010; Boyle et al. 2017). Genome wide association studies (GWAS) using single nucleotide polymorphism (SNP) markers have been used to attempt to find the causal loci for important traits of interest, such as human disease, or growth, yield, and pest and pathogen resistance in crops (Visscher et al. 2017; Liu and Yan 2019; Uffelmann et al. 2021). GWAS generally involves linear or logistical regression of individual SNP markers set as fixed effects against a measured phenotype to test for association of markers (and thus genomic loci) with a trait of interest. However, the number of SNPs (p) is typically greater than the number of individuals (n) in a dataset, such that using SNPs as fixed effects means that no unique solution can be drawn from the regression. Since the overall variance explained by SNPs must be less than the total genetic variance in the model, it becomes impossible to accurately predict the effect sizes of individual SNPs. Indeed, GWAS has been effective in finding major genes for a large number of important traits; however, even studies with very large population sizes lack power to identify loci with very small effect sizes, an issue termed the ‘missing heritability’ (Yang et al. 2010). Genomic selection (GS) has emerged as an important tool in the application of genomic approaches to livestock, crop, and tree breeding (Meuwissen et al. 2001; Goddard and Hayes 2007; Grattapaglia 2017). GS estimates the effects of all markers spread as densely as possible 11 across the genome; thus, the uncertainty surrounding quantitative trait locus (QTL) identification and effect estimation for such traits can be accounted for, as well as capturing loci with very small effect size (Meuwissen et al. 2001). The core tenets underlying GS are the establishment of a “training” population for which genotypic and phenotypic data are available, estimation of allele effects at all loci using specialized models, and application of these data to predict genomic estimated breeding values (GEBVs) in a “target” population for selection (Lorenz et al. 2011). In this approach, we are under the expectation 𝐸𝐸(𝐛𝐛|𝑦𝑦, 𝐱𝐱) = ∫ 𝑝𝑝(𝐛𝐛|𝑦𝑦, 𝐱𝐱)𝐛𝐛d𝐛𝐛, where 𝑝𝑝(𝐛𝐛|𝑦𝑦, 𝐱𝐱) is the probability of a phenotype 𝑦𝑦 given a vector of individual genotypes 𝐱𝐱 and SNP effects 𝐛𝐛 (Meuwissen et al. 2001). The most common approach to GS, Genomic Best Linear Unbiased Predictors (GBLUP), assumes a normally and independently distributed prior for SNP effects with a mean of 0 and equal variance for all SNPs, essentially corresponding to pseudo-infinitesimal model in which all SNPs have an equivalent and small effect on every trait. However, newer Bayesian GS approaches such as BayesR (Erbe et al. 2012) allow for a flexible prior which can approximate multiple different possible distributions for 𝐛𝐛, and may provide an alternative to GWAS and a potential solution to the ‘missing heritability’ problem in structured breeding populations (Moser et al. 2015; Goddard et al. 2016). This approach has been reliably applied to find QTL associated with milk production and quality in livestock (Goddard et al. 2016; MacLeod et al. 2016; Kemper et al. 2018), human disease (Moser et al. 2015; Zeng et al. 2018; Lloyd-Jones et al. 2019), and crop disease resistance (Pasam et al. 2017). 12 1.5 Thesis objectives WRC is an ecologically, culturally and economically important tree species, yet, very little work has been done describing its genomics and applying genomic approaches to better understanding the history, population genetics, mating system, and key traits of this tree. The objectives of this thesis are to: 1) Develop genomic resources for WRC; 2) Use these resources to: a. Discover genes of terpenoid biosynthesis and explore the evolution of terpene synthase (TPS) genes in gymnosperms; b. Answer questions on the demography, genetic diversity, and mating system of WRC; and c. Analyse and describe the genetic architecture and inbreeding depression of complex growth and chemistry traits in WRC In Chapter 2, I describe the assembly and annotation of a reference transcriptome for WRC assembled from a single fifth-generation completely selfed seedling. The transcriptome is used in gene discovery of multiple unique TPS genes in WRC and the phylogeny of TPS in plants is revisited in light of this new information. The main questions of Chapter 2 are: 1) Can the use of highly homozygous selfed trees facilitate sequencing and assembly of a high-quality, reduced-redundancy gene set for WRC? 13 2) Do WRC TPS cluster with other conifer TPS, and do they share the same conserved motifs? In Chapter 3, I present the draft genome assembly, annotation and a novel panel of SNP genotypes for GS training and target populations as well as for a range-wide population of WRC and an array of selfing lines. I use this data to assess genetic diversity and demography across the range of WRC, examine heterozygosity and inbreeding coefficients during selfing, and identify loci which may be under selection during up to five generations of complete selfing. The main questions of Chapter 3 are: 1) How does the WRC genome compare to the genome of other conifers? 2) Is there evidence of genetic differentiation in a natural population of WRC across the geographic range? 3) Do effective population size and demography support the hypothesis of a single glacial refugium origin for WRC? 4) Is there evidence for purifying selection during selfing, and which loci may be under selection during selfing? Lastly, in Chapter 4 I use SNP genotyping data for a GS training population of over 1,500 trees to identify putatively causal loci, their effect sizes, and distribution across a putative genomic linkage map for height, diameter, three foliar terpene traits and four heartwood terpene traits. I 14 also determine whether there is apparent inbreeding for growth, terpene chemistry, and dendrochronological traits in WRC, and identify predictors for growth and terpene chemistry traits during selfing in WRC. The main questions of Chapter 4 are: 1) Which environmental variables have a significant effect on WRC growth and terpene chemistry traits of interest? 2) Which loci are associated with these traits, which have the largest effect sizes, and how are they putatively distributed across the WRC genome? 3) Is there evidence for inbreeding depression for terpene chemistry, growth, and dendrochronological traits in WRC, and can selection for height growth overcome inbreeding depression during selfing? 15 An annotated transcriptome of highly inbred Thuja plicata (Cupressaceae) and its utility for gene discovery of terpenoid biosynthesis and conifer defense 2.1 Summary Western redcedar (Thuja plicata; Cupressaceae; WRC) is an ecologically and economically important conifer species of the Pacific Northwest. Regeneration of WRC forests is affected by ungulate browsing, which removes current growth and hampers development of young trees. Monoterpenes make WRC foliage less palatable and can deter browsing. Genomic resources are required to advance knowledge of terpene accumulation and breeding of WRC for herbivore resistance. Unlike most conifers, WRC readily selfs to produce genotypes of reduced heterozygosity. We used seedlings of eight different fifth-generation selfed lines for monoterpene analysis and transcriptome sequencing. Trinity, Velvet/Oases, TransABySS and SOAPdenovoTrans were used to generate independent transcriptome assemblies for each line. Sequence redundancy was reduced using the EvidentialGene pipeline. The best assembly, as determined by metrics of completeness, contiguity and accuracy, was used to produce a WRC reference gene set of 28,279 sequences, of which 77% were annotated with significant BLASTp hits and 89% with significant InterProScan hits. An orthology-based approach was used to annotate gene families. Manually curated annotation identified 33 putative full-length terpene synthases (TPS). A maximum-likelihood phylogeny revealed that WRC TPS cluster apart from those of Pinaceae within the gymnosperm TPS-d clade. Use of selfed lines enabled the 16 development and annotation of a reduced-redundancy gene set for a gymnosperm of the Cupressaceae family. This gene set serves as a foundation for future functional characterization of WRC TPS and other defense genes, and as a resource for the annotation of protein coding sequences in the WRC genome. 2.2 Introduction Western redcedar (Thuja plicata; WRC) is a conifer of the Cupressaceae family native to the Pacific Northwest of North America where it plays an important role in coastal forest ecology. WRC has been used by First Nations for millennia for a multitude of practical and spiritual purposes (Hebda and Mathewes 1984). In the forest industry, WRC is highly prized for its durable and lightweight wood, which makes it an ideal material for weather exposed building applications such as siding and roofing, decks, and outdoor furniture (Gonzalez 2004). In Canada, timber harvesting of WRC is shifting from old-growth forests to planted second-growth forests. However, plantations of WRC seedlings are susceptible to damage by deer browsing, and second-growth WRC can be more susceptible to pathogens such as cedar leaf blight and fungal heartwood rot. The WRC breeding program of the Province of British Columbia is currently focussing on heartwood durability and resistance to disease and herbivores (Russell and Yanchuk 2012). Monoterpenes such as thujones, which are biosynthesized and accumulate in glandular cavities of the foliage, are involved in deterrence of deer browsing (Vourc’h et al. 2002a; Foster et al. 2013; Gesell et al. 2015). In addition, terpenoids of the 17 tropolone class are associated with protection of living trees against heartwood rot (Debell et al. 1999), and lignans also play a role in the post-harvest durability of WRC wood products (Morris and Stirling 2012). Knowledge of the biochemistry, molecular biology and genomics of the natural defense mechanisms of WRC will benefit the WRC breeding program and the management of this valuable resource. Here, we describe a reduced-redundancy transcriptome developed from four-month old, highly inbred WRC lines (Russell and Ferguson 2008) and its annotation. The quality of the transcriptome assembly and annotation was assessed for discovery of terpene synthase (TPS) genes. 2.3 Materials and Methods 2.3.1 Plant materials WRC lines were inbred by controlled self-pollination for five generations (S5) to produce eight different lines used in this study as described in (Russell and Ferguson 2008). The eight lines are identified as 1022-113 (here named Line 1), 1645-455 (Line 2), 2121-111 (Line 3), 2165-444 (Line 4), 2323-211 (Line 5), 2923-121 (Line 6), 2944-565 (Line 7), and 646-454 (Line 8). Seedlings were grown in the greenhouse at the Cowichan Lake Research Station of the British Columbia Ministry of Forests, Lands, Natural Resource Operations and Rural Development. 18 2.3.2 Terpenoid extraction Foliage and shoot tips of approximately 1.5 cm length were collected from three biological replicates of each of the eight S5 lines for terpene extraction. Samples of approximately 10 mg fresh weight (FW) were collected into glass vials containing 1.5 ml methyl tert-butyl ether (MTBE) with isobutyl benzene and dichlorodehydroabietic acid as internal standards. After overnight incubation, samples were centrifuged for 15 min at 1,000 x g. One milliliter of each extract was transferred into a new glass vial for monoterpene analysis by gas chromatography-mass spectrometry (GC-MS). The extracted tissue was dried and dry weights (DW) determined. 2.3.3 GC-MS analysis Monoterpenes were analysed by GC-MS on Agilent 6890A/5975C or 7890A/7000A systems. Injections were done in pulsed splitless mode with inlet temperature at 250°C. Helium was used as a carrier gas. Metabolites were separated on a DB-WAX column (J&W 122-7032; 30 m x 250 µm, 0.25-µm film thickness) as previously described (Gesell et al. 2015) and identified by comparison with authentic standards or by matching references in the National Institute of Standards and Technology (NIST) data collection incorporated into the W9N08L database (Wiley). For relative quantification, peak areas were integrated and normalized to internal standards. 19 2.3.4 RNA isolation and sequencing For each of the eight S5 lines, an individual four-month old seedling was removed from soil and washed with water (Figure 2.1). To reduce potential microbial contamination (Elbeltagy et al. 2000), plants were rinsed for 30 seconds with 70% ethanol, soaked for 5 minutes in 2% (v/v) sodium hypochlorite, and rinsed twice with Millipore water. Plants were dried with paper towels and frozen in liquid nitrogen. The entire plants were ground into a fine powder using a Retsch Mixer Mill (MM301) and RNA extracted according to a modified protocol of the Ambion PureLink Plant RNA Reagent (Thermo Fisher, Waltham, MA, USA). Specifically, 1 ml of PureLink Plant RNA Reagent and 2 mg of polyvinylpolypyrrolidone was added to 100 mg of plant powder. An additional 800 µl chloroform wash was used with an extended centrifugation time of 15 minutes. RNA pellets were dissolved in 50 µl RNAase-free water. RNA quality and quantity was assessed using a NanoDrop ND-1000 spectrophotometer (ThermoFisher Scientific, Wilmington, DE, USA) and by electrophoresis in 1% agarose. Further quality and quantity controls were performed on an Agilent 2100 Bioanalyzer (Agilent Technologies, Waldbronn, Germany) using Agilent 6000 Nano Chips (Cat 5067-1511). RNA (4 to 10 µg per sample) with RNA Integrity Numbers (RIN) of 7.0 or higher was sent to the McGill University & Genome Quebec Innovation Centre (http://www.gqinnovationcenter.com), where eight rRNA depleted, stranded paired-end libraries were produced and multiplexed on one lane for each of the two separate runs for RNA-Seq on the Illumina HiSeq 2000 (100bp PE) platform. 20 2.3.5 Transcriptome assembly Sequence reads from two separate sequencing runs were combined for transcriptome assembly. Reads were trimmed for quality using Trimmomatic (Bolger et al. 2014). The first 12 bases of each read were removed and bases at the 3’ end with a quality score lower than 3 were removed. Reads shorter than 70 bp after trimming were discarded. Trimmed reads were merged using BBMerge (https://sourceforge.net/projects/bbmap/; parameters: qtrim=t, trimq=0, minlength=50, verystrict=t). Sequence reads were assembled separately for each of the eight S5 lines. Initially, four different de novo transcriptome assemblers were used and resulting assemblies compared for each line. Specifically, we produced assemblies using Trinity v2.2.0 (Haas et al. 2013), Velvet v1.2.10/Oases v0.2.8 (Zerbino and Birney 2008; Schulz et al. 2012), SOAPdenovoTrans v1.03 (Xie et al. 2014) and TransABySS v1.5.3 (Robertson et al. 2010). Trinity was used to assemble reads for a single k-mer length (k = 25) and the other three assemblers were used to assemble reads for 12 different k-mer lengths (k = 21, 23, 25, 27, 29, 37, 45, 53, 61, 69, 77, 85). Additional options used for each assembler were as follows: Trinity: -SS_lib_type RF; Velvet/Oases: -read_trkg yes, -cov_cutoff 3, -ins_length 200, -min_trans_lgth 200; SOAPdenovoTrans: max_rd_len=100, rd_len_cutof=100, avg_ins=200, reverse_seq=0, asm_flags=3, map_len=32; TRansABySS: --length 200; unmerged and merged reads were used for sequence content, original trimmed reads were used for paired-end information. With these different parameters, initially 37 different assemblies were generated for each S5 line for the purpose of comparing assembly qualities. 21 2.3.6 Redundancy reduction Sequence reads for each S5 line were mapped back separately to all assemblies for that line using Transrate v1.0.1 (Smith-Unna et al. 2016). Contigs were discarded if read pairs did not align, were not in the correct orientation, if read pairs were not on the same contig, or read pairs overlapped on either end of the contig. All contigs from all assemblers were then pooled into a single file. The EvidentialGene tr2aacds pipeline was employed for redundancy reduction and open reading frame (ORF) prediction (Gilbert 2013; Nakasugi et al. 2014; Orsini et al. 2016). The resulting eight (one for each S5 line) reduced-redundancy protein sequence sets were used for all further analyses. In addition, sets of sequences that were highly similar (>98%) to sequences in the sets of reduced-redundancy sequences were retained separately. The reduced-redundancy sequence sets were further refined by removing peptides either shorter than 100 amino acids or containing ambiguous amino acids using an in-house script. A pipeline for the development of the gene sets is shown in Figure 2.2 including the software used in the EvidentialGene pipeline at each step. 2.3.7 Assembly validation The reduced-redundancy gene sets for each of the eight S5 lines were compared for completeness, contiguity and accuracy. Completeness was assessed using Benchmarking Single-Copy Universal Orthologs (BUSCOs) as implemented in BUSCO v2.0, using the plantae lineage data set (Simão et al. 2015). Assembly contiguity and accuracy were evaluated by Conditional Reciprocal Best Blast (CRBB) as implemented in Transrate against (i) the Arabidopsis thaliana 22 representative gene set from TAIR (Lamesch et al. 2012) and (ii) the white spruce (Picea glauca) gene models (Rigault et al. 2011) from PLAZA Gymnosperms v1.0 (Proost et al. 2015) and by comparing average bitscore and total number of BLASTp hits against the 1000 longest proteins from these data sets (standalone BLAST+ 2.2.31; e-value cut-off: 1e-05) (Altschul et al. 1990; Camacho et al. 2009). The RNA-Seq assembly and derived reduced-redundancy gene set obtained from one individual plant of the S5 Line 4, which had the highest overall assembly quality, was used as the final WRC transcriptome gene set described and annotated in this paper. 2.3.8 Transcriptome annotation Annotations of predicted protein coding sequences were produced using BLASTp searches in standalone BLAST+ against a set of databases, which were searched sequentially: The entire set of predicted protein coding WRC sequences was searched first against all Acrogymnospermae protein sequences in the NCBI nr database, using an e-value cut-off of 1e-20 and a query coverage per subject cut-off of 20%. Only a single best reciprocal BLAST hit was retained for each query sequence. The process was repeated for all non-annotated sequences against all Viridiplantae sequences in Swissprot, all Viridiplantae sequences in UniRef90, Plant RefSeq, all Viridiplantae sequences in the NCBI nr database, and finally all of nr, in that order. InterProScan v5.19-58.0 was used to identify different protein families and domains within the gene set (Jones et al. 2014). The above annotations were used to identify putative full-length TPS (with a minimal length of 400 amino acids), putative aldo-keto reductases and putative alcohol 23 dehydrogenases. Putative full-length TPS sequences were validated against a database of known TPS sequences and functions (Zerbe et al. 2013; Warren et al. 2015). 2.3.9 Analysis of orthologous protein groups Protein sequence sets from fifteen different plant species were downloaded from PLAZA: Amborella trichopoda; Arabidopsis thaliana; Cycas micholitzii; Ginkgo biloba; Gnetum montanum; Oryza sativa japonica; Physcomitrella patens; Picea abies; Picea glauca; Picea sitchensis; Pinus sylvestris; Populus trichocarpa; Taxus baccata; Vitis vinifera; and Zea mays. All sets were individually clustered to 90% similarity using CD-HIT (Fu et al. 2012). These sets, together with the WRC sequence set, were used in OrthoFinder v1.1.8 (Emms and Kelly 2015) to determine gene family membership for WRC protein-coding sequences. PFAM domain information was obtained for each species and used to characterize the different orthogroups. The results were validated by assessing clustering of WRC TPS with previously known TPS and observing the top PFAM hits for the orthogroups containing them. 2.3.10 TPS phylogeny A representative set of 241 full-length TPS (Dataset 2.1) from a variety of plant species including angiosperms (108 sequences across six species), gymnosperms (126 sequences across 28 species, including seven from the Cupressaceae species Callitropsis nootkatensis, Chaemacyparis formosensis, Chaemacyparis obtusa, and Taiwania cryptomerioides), the lycophyte Selaginella moellendorffii (six sequences), the moss P. patens (one sequence) and all 24 putative full-length TPS from the WRC gene set (33 sequences) was used to construct a maximum likelihood (ML) phylogeny. Sequences were aligned by local pairwise alignment in MAFFT 2.721 (Katoh and Standley 2013). RAxML version 8.2.8 (Stamatakis 2014) was used implementing a gamma model of rate heterogeneity and the JTT amino acid substitution matrix with rapid bootstrapping. Bootstrapping analysis determined that bootstrap convergence was reached after 200 iterations. The best tree as determined by 20 fast and slow ML searches was then plotted using iTOLv3 (Letunic and Bork 2016). A phylogenetic tree of the gymnosperm TPS sequences (126 sequences; Dataset 2.2) from the aforementioned alignments was generated separately using the same methods and was rooted to ent-kaurene synthase from P. patens in iTOL (Letunic and Bork 2016). 2.3.11 Data availability The sequence data supporting this work can be found at the NCBI BioProject Database under BioProject ID PRJNA399722. Supplemental Dataset files have been uploaded to the following GitHub repository: https://github.com/tshalev/WRC-transcriptome-paper. 25 2.4 Results 2.4.1 Major monoterpenes in foliage of the S5 lines Prior to transcriptome sequencing we assessed the relative amounts of different monoterpenes in seedlings of the eight different S5 lines. The purpose of this analysis was to ensure that monoterpenes involved in the deterrence of ungulate browsing were present in the lines used for RNA extraction and sequencing. The monoterpenes α-thujone, β-thujone, (+)-sabinene, β-myrcene and α-pinene were detected in foliar extracts across the eight different S5 lines. Results of relative quantitative analysis are shown in Figure 2.3. 2.4.2 Transcriptome assemblies and reference gene set To develop a transcriptome-based reference gene set for WRC, we first developed a series of transcriptome assemblies for each of the eight S5 lines. By using individual plants and S5 inbred lines, heterozygosity of the starting material was reduced. To maximize gene discovery, we initially used four different de novo assemblers and a variety of assembly conditions. The resulting assemblies were processed through the EvidentialGene pipeline (Figure 2.2) to produce a non-redundant gene set for each line. Details of the EvidentialGene method and comparison between performances of the different assemblers, which motivated their application in this work, have been previously described (Gilbert 2013; Nakasugi et al. 2014; Orsini et al. 2016). Results of the transcriptome assemblies for each of the eight S5 lines are summarized in Table 2.1. The transcriptome assembly for Line 4 was selected based on completeness, contiguity and 26 accuracy, as measured by BUSCO, CRBB and BLAST searches, respectively, to annotate a reference gene set. The gene set obtained for Line 4 was 88.8% complete based on a BUSCO search against the OrthoDB plantae lineage (http://busco.ezlab.org/v1/), with a sequence match duplication rate of only 6% (Figure 2.4). The gene set identified in the transcriptome of Line 4 was also the least redundant as measured by CRBB against the P. glauca gene models and the A. thaliana representative gene set (Table 2.2) and had a high average bitscore when BLAST searches were performed against these two data sets (Table 2.3). In summary, the initial 1,413,399 transcripts identified with the four different assemblers from 33,824,978 sequence reads for Line 4 were reduced to 28,279 putative protein coding gene sequences in the final reduced-redundancy gene set (Dataset 2.3). An additional 40,691 putative protein coding sequences were retained in the alternate gene set, which may represent isoforms or paralogs (Dataset 2.4). 2.4.3 General annotation of the reduced-redundancy gene set Annotations of the reduced-redundancy gene set of Line 4 were obtained by sequential BLASTp searches against several plant sequence databases together with an InterProScan search to identify conserved protein domains and Gene Ontology (GO) IDs. Of the 28,279 sequences in the reduced-redundancy gene set 21,730 (77%) had significant BLASTp homology to database proteins and 25,053 (89%) had InterProScan hits; of these InterProScan hits, 17,063 had corresponding GO terms. In total, the combined BLASTp and InterProScan matches annotated 25,387 sequences (90%) of the WRC Line 4 reduced-redundancy gene set (Dataset 2.5). The 27 WRC gene set covers 2,976 identified domains across 2,904 InterPro protein families, the largest of which being the family of cytochrome P450s with 565 sequences. Distributions of the top 20 InterPro domains and families are shown in Figure 2.5. 2.4.4 Orthogroup annotation of the reduced-redundancy gene set We assigned WRC sequences of the reduced-redundancy gene set of Line 4 to protein groups, or gene families, based on their orthology with protein sequences in other plants species. For this purpose, we generated a multi-species set of 498,235 protein sequences from public databases of a variety of plant species, of which 391,179 were assigned to 19,660 orthogroups using OrthoFinder (Dataset 2.6). Of the 28,279 WRC sequences in the reduced-redundancy gene set 25,562 sequences (90.4%) were assigned to 9,335 different orthogroups, of which nine orthogroups appeared to be specific to WRC (Dataset 2.7). The orthogroup with the most WRC sequences (240 members) consisted of cytochrome P450s, with more WRC sequences in this group than any of the other species. The largest WRC-specific orthogroup contained 11 proteins with no PFAM matches; these sequences also had no BLASTp and InterProScan matches (Datasets 2.5 and 2.8). A summary of all orthogroups and the top five PFAM IDs associated with each one can be found in Dataset 2.8. TPS sequences were distributed among four different orthogroups, all of which contained exclusively TPS. The largest of these groups had 478 members, 35 of which were from WRC. 50 WRC sequences in total were found in these four groups. 28 2.4.5 Annotation and phylogeny of the WRC TPS gene family In the general annotation of the reduced-redundancy gene set of Line 4, we identified 50 sequences with significant BLASTp homology to TPS and matching InterPro domain information. Manual inspection of these sequences identified 33 as putative full-length or near full-length TPS of at least 400 aa (Dataset 2.9). These same 33 sequences were also found in the orthogroup analysis. Previously established phylogenies of the plant TPS family placed gymnosperm TPS in three different subfamilies, TPS-c, TPS-d and TPS-e/f (Bohlmann and Keeling 2008; Chen et al. 2011; Warren et al. 2015). TPS-c contains copalyl diphosphate synthases (CPS), bifunctional CPS/kaurene synthases (CPS/KS) and other diterpene synthases. TPS-e/f contains kaurene synthases (KS), and other apparently ancestral mono-, sesqui- and diterpene synthases. The vast majority of known gymnosperm TPS fall into the TPS-d subfamily, which appears to be exclusive to gymnosperms. The 33 full-length/near full-length TPS identified in the WRC reduced-redundancy gene set fell into the subfamilies TPS-d and TPS-e/f (Figure 2.6). Within the TPS-d subfamily, WRC sequences and TPS from other species of the Cupressaceae plant family cluster together separately from clusters of TPS of the Pinaceae. The latter plant family has previously been a focus of analysis of gymnosperm TPS (Warren et al. 2015), while the Cupressaceae have only sporadically been covered. With the comprehensive set of 33 full-length/near full-length WRC TPS identified and annotated in the reduced-redundancy gene set of an individual S5 plant, we constructed a maximum likelihood phylogeny to specifically analyse patterns of TPS diversification in the gymnosperms (Figure 2.7). This analysis highlighted clusters of 29 Cupressaceae TPS that separated from clusters of Pinaceae TPS. Many of the WRC TPS belong to larger classes of gymnosperm TPS that contain members previously functionally characterized as monoterpene synthases and sesquiterpene synthases (Warren et al. 2015); they also include TPS that cluster with known diterpene synthases of primary and secondary metabolism (Warren et al. 2015). 2.4.6 Terpene pathway candidate genes for thujone biosynthesis Using a smaller transcriptome data set, genes for two steps of thujone biosynthesis and the biochemical functions of the encoded proteins, a TPS and a cytochrome P450, were previously reported (Foster et al. 2013; Gesell et al. 2015). The complete biosynthesis of thujones has been proposed to require another two reactions catalysed by an alcohol dehydrogenase and an aldo-keto reductases (Foster et al. 2013; Gesell et al. 2015), for which the corresponding WRC genes remain to be discovered. In the general annotation of the reduced-redundancy gene set of Line 4, we identified 99 sequences with significant BLASTp homology to alcohol dehydrogenases and matching InterPro domain information, and 87 matches for aldo-keto reductases. These were further reduced to 90 and 75 sequences, respectively, by removing sequences shorter than 300 aa and sequences with incomplete ORFs. 30 2.5 Discussion 2.5.1 A Cupressaceae transcriptome resource for conifer genomics Published conifer genomes are currently available for a few members of the Pinaceae family (Birol et al. 2013; De La Torre et al. 2014; Neale et al. 2014; Wegrzyn et al. 2014; Warren et al. 2015; Stevens et al. 2016; Zimin et al. 2017), and no genome assembly has been published for a member of the Cupressaceae family. In general, conifer genomes are extremely large, rich in repeat elements, and all published genome assemblies are still highly fragmented. Therefore, high-quality transcriptome resources, in particular those that cover full-length sequences and have been filtered for redundancy, remain essential for gene discovery and annotation of the protein coding gene space in conifers and gymnosperms in general. As a resource for functional genomics in the Cupressaceae and in advance of sequencing the WRC genome, we developed a reduced-redundancy transcriptome for young WRC plants. WRC does not display the high levels of heterozygosity and inbreeding depression that is characteristic of many other conifers, and this species can readily be inbred with short generation times (Russell et al. 2003; O’Connell and Ritland 2005). Use of heterozygous sequence data can increase redundancy in de novo sequence assembly by interfering with de Bruijn graph construction of the assembler; thus, by using deep RNA-Seq coverage with individual plants from WRC lines that have been selfed for five generations (Russell and Ferguson 2008), and by removing sequence duplicates, we obtained transcriptomes of low redundancy compared to what is expected from sequencing RNA from highly heterozygous plant material (Zharkikh et al. 2008; Miller et al. 2010; Velasco et al. 2010; Duan et al. 2012). The transcriptome of one S5 31 line, Line 4, was selected to produce and annotate a representative core gene set of 28,279 putatively protein coding sequences. Given the rigorous filtering for duplicates, and given that the four-month old seedlings do not represent gene expression that may be specific for mature tree traits, this core gene set may be an underestimation of the total protein coding gene space of the WRC genome. Since discovery of genes involved with resistance to disease and deterrence of herbivores is a primary goal to support WRC tree improvement, we confirmed that the plants used for transcriptome sequencing contained signature monoterpenes of the WRC defense system, specifically α-thujone and its biosynthetic precursor sabinene (Foster et al. 2013; Gesell et al. 2015). The two previously characterized genes of the thujone biosynthetic pathway, namely the TPS (+)-sabinene synthase (Foster et al. 2013) and the cytochrome P450 (+)-sabinene 3-hydroxylase (Gesell et al. 2015) were present in the core gene set described here, supporting the quality of this resource for defense gene discovery. In addition to a set of 33 different full-length TPS and a large family of cytochrome P450 genes, the reference gene set also includes full-length candidate alcohol dehydrogenases and candidate aldo-keto reductases for the two proposed remaining steps in the conversion of (+)-trans-sabin-3-ol to α-thujone (Gesell et al. 2015). 2.5.2 Quality of the WRC transcriptome assembly Completeness, contiguity and accuracy of the WRC transcriptome were assessed using BUSCO, CRBB and BLASTp searches against the 1,000 longest A. thaliana and P. glauca gene sets. 32 Assembly completeness was high among the non-redundant sequence sets of all S5 lines, ranging from 86.4% to 88.8%, and duplication levels were low, ranging from 5.3% to 7%. The low percentage of missing and fragmented BUSCOs affirms the quality of the assembly. This level of completeness is in line with and even surpasses that of similar studies using the BUSCO completeness metric for de novo transcriptome assembly of other plants, while keeping redundancy low (Visser et al. 2015; Baillie et al. 2017; Blande et al. 2017). In tests of contiguity and accuracy by CRBB and BLAST coverage to the A. thaliana and P. glauca gene sets, the gene sets for the different S5 lines all scored similarly. Thus, selection of a line for the WRC transcriptome-based reference gene set was based on completeness and redundancy. The transcriptomes of all lines had a high proportion of significant BLAST hits with the 1,000 longest proteins for A. thaliana and P. glauca. Large sequences are often the most difficult to assemble and are susceptible to assembly artefacts and missing data; thus, a large number of high-scoring matches to these sequences serves as good indicator of accuracy of assembly (Martin and Wang 2011; Gordon et al. 2015). 2.5.3 WRC transcriptome annotation For 77% of the WRC sequences we obtained annotations based on similarity searches in public databases. All of the remaining 23% contained ORFs and therefore potentially also code for proteins. Using strict cut-off criteria, we largely eliminated BLAST matches with uninformative top hits such as “unknown protein” or “hypothetical protein”. Any sequences with uninformative names in the final annotation were those that did not significantly match with any other sequence 33 in any of the databases searched. In the annotation, we prioritized sequence matches with conifers. The largest protein family in the WRC data set were cytochrome P450s, which is in agreement with large cytochrome P450 families in other plant species and their diverse functions in plant metabolism (Schuler and Werck-Reichhart 2003; Nelson 2009). The full-length TPS identified in the WRC gene set resulted mostly from the assemblies generated by Velvet/Oases, while other assemblers generated more of the sequences that were filtered for being too short or fragmented. This supports the notion that different assemblers perform better on certain sequences and gene families than others (Nakasugi et al. 2014). Identifying orthologous groups of proteins between related species helps to cluster genes of related structure or function, thus allowing for annotation of poorly annotated genomes and gene sets via homology with more well-established ones (Tatusov et al. 1997). We used the PLAZA Gymnosperms set of sequences together with several angiosperm sequence sets for comparisons with the WRC reference gene set. The largest orthogroup overall was composed mostly of PPRs, which are characterized by degenerate tandem repeats of 35 aa motifs. Because of their repetitive and diverse nature, it is possible that they were clustered into the largest group by sheer volume of numbers (Geddy and Brown 2007). PPR-containing proteins are purported to have a wide diversity of functions (Lurin et al. 2004; Kotera et al. 2005; Yagi et al. 2013), and little functional redundancy within the family (Lurin et al. 2004). The group with the most WRC sequences was comprised of cytochrome P450s. Interestingly, there were far more WRC sequences in this group than any other species in the orthogroup analysis, implying that WRC may have an especially large number of cytochrome P450 coding sequences. 34 BLASTp and InterProScan searches identified 50 non-redundant TPS, 33 of which were full-length or near full-length. The orthogroup annotation identified the same exact 50 sequences in groups characterized by proteins containing TPS protein domains, lending support to our results obtained with different annotation methods. WRC TPS were split across three orthogroups. The largest group contained sequences matching mono-TPS and sesqui-TPS, while the second largest group contained sequences matching di-TPS, ent-kaurene synthases and copalyl diphosphate synthases. The separation of these groups is in concurrence with the phylogenetic results described below. The third group contained only partial sequences lacking certain conserved domains. A comparison of putative ADH sequences by BLASTp homology to both (-)-isopiperitenol/(-)-carveol dehydrogenase from Mentha piperita (Ringer et al. 2005) and a short-chain dehydrogenase/reductase from Zingiber zerumbet (Okamoto et al. 2011), two known ADH associated with terpene biosynthesis, found sequences with 27% and 29% sequence homology, respectively, although this may be due to the relatively distant evolutionary divergence between the species. 2.5.4 Phylogeny of WRC TPS Terpenes are a major and diverse chemical and physical defense system of conifers of the Cupressaceae and Pinaceae families (Keeling and Bohlmann 2006a, 2006b; Zulak and Bohlmann 2010). The diversity of chemical structures of these specialized metabolites is generated by families of TPS (Bohlmann et al. 1998; Chen et al. 2011). The majority of conifer TPS fall into 35 the gymnosperm-specific TPS-d subfamily of the plant TPS gene family, and fewer conifer TPS cluster with the TPS-c and TPS-e/f subfamilies (Martin et al. 2004; Chen et al. 2011; Keeling et al. 2011; Warren et al. 2015). The vast majority of the conifer and gymnosperm TPS with known biochemical functions and well-documented associations with TPS-subfamilies are from members of the Pinaceae family, and these are mostly from species of spruce (Picea spp.), pine (Pinus spp.), and fir (Abies spp.) (Keeling et al. 2011; Warren et al. 2015). A recent attempt to place TPS from Platycladus orientalis, a Cupressaceae, into the phyologeny of the plant TPS gene family led to a proposed separation of the TPS-d subfamily into two groups, designated TPS-d and TPS-conifer (Hu et al. 2016). However, this separation appeared to be flawed as it was based on a set of sequences that included a large number of fragmented and in many cases very short partial sequences, as opposed to full-length sequences, which typically leads to misalignments and improper clustering of TPS sequences as we found when we attempted to reproduce the results reported in Hu et al. (2016). The present TPS phylogenies (Figures 2.6 and 2.7), with the inclusion of 33 new full-length or near full-length WRC TPS sequences, do not support the segregation of the gymnosperm TPS as suggested by Hu et al. (2016); rather, the high quality and large number of WRC full-length TPS support the previously established gymnosperm TPS-d clade (Bohlmann et al. 1998; Keeling and Bohlmann 2006b; Chen et al. 2011; Keeling et al. 2011; Warren et al. 2015). Overall, sequences in the TPS-d group appear to cluster primarily by association with plant species and families (Figures 2.6 and 2.7). Across the gymnosperm TPS-d subfamily, we found WRC TPS associated with clusters of mono-TPS, sesqui-TPS and di-TPS of secondary 36 (i.e. specialized) metabolism (Figure 2.7). Our analysis of WRC TPS-d members associated with di-TPS of secondary metabolism (clade shown in red in Figure 2.7), revealed a separation by presence or absence of conserved sequence motifs. WRC TPS of this group all lack the DDXXD motif common to most other di-TPS of conifer diterpene secondary metabolism, except for one T. plicata sequence (TpTPS10, Figure 2.7) and a pimara-8(14),15-diene synthase from Taiwania cryptomerioides. The latter two contain the DDXXD motif, but lack both the conserved DXDD and KRX6W motifs. These two motifs (DXDD and KRX6W) are also missing in the taxadiene synthases of taxol biosynthesis in Taxus species. WRC TpTPS31 clustered together with taxadiene synthases, which was surprising since no TPS from any other gymnosperm clusters closely with these particular di-TPS. However, the sequence of TpTPS31, being shorter than that of taxadiene synthases, lacks the DDXXD motif, and it remains to be tested whether this sequence encodes a functional enzyme. TPS in the ancestral gymnosperm sesqui-TPS group (clade shown in blue in Figure 2.7), which was originally grouped together with the di-TPS of secondary metabolism (Martin et al. 2004; Keeling et al. 2011), contain an apparently ancestral ~200 amino acid domain, differentiating them from the other sesqui-TPS (clade shown in yellow in Figure 2.7). The group of ancestral gymnosperm sesqui-TPS contains members from the Pinaceae family, as well as from Ginkgo biloba and C. taitungensis, but none of the WRC TPS clustered with this group. Outside of the TPS-d subfamily, WRC TPS clustered with KS sequences, which fall within the TPS-e/f subfamily (Figures 2.6 and 2.7). We did not observe WRC TPS sequences in the TPS-c subfamily, which contains monofunctional ent-CPS of gymnospersm and angiosperm gibberellin biosynthesis (Chen et al. 2011; Hall et al. 2013), 37 suggesting that this gene was expressed at very low levels in the tissue used for the transcriptome sequencing. Overall, the discovery of a large family of full-length TPS in the reduced-redundancy WRC gene set and its annotation by phylogeny with other gymnosperm TPS (Figure 2.7) serves as a well-defined resource for the future functional characterization of this important conifer gene family in a Cupressaceae. 2.6 Conclusion The use of highly inbred lines of WRC 5th-generation selfed lines allowed for the development and annotation of a comprehensive and low-redundancy transcriptome-based gene set for a gymnosperm member of the Cupressaceae family. The high quality of this WRC reference gene set was validated by comparisons with gene sets from a large number of other plant species and by orthogroup and other database annotations. The WRC reference gene set was further validated by the discovery and detailed annotation of a set of 50 TPS, of which 33 were full-length or near full-length, providing the first comprehensive coverage of TPS from the Cupressaceae in an updated phylogeny of the gymnosperm TPS family of conifer defense. This WRC and Cupressaceae reference gene set will serve as a foundation for future functional characterization of WRC TPS and other defense genes, and will be a key resource for the annotation of protein coding sequences in the WRC genome. 38 2.7 Tables Table 2.1: Summary of output from Evigene pipeline. An additional 3305 sequences with ambiguous amino acids and sequences shorter than 100 amino acids long were removed from the Main set. Alt (High Identity): >=98% similarity to a sequence in Main; Alt (Lower Identity): <98% similarity to a sequence in Main, most likely a paralog; Main (w/ alt): Sequence with alternate in Alt; Main (no alt): Sequence with no alternates; Fragment Alternates: Fragmented sequences. Class Retain (%) Drop (%) Retain Drop Alt (High Identity) 15.8 26.6 36 809 62 044 Alt (Lower Identity) 4.2 1.6 10 054 4020 Main (w/ alt) 9.9 3.8 23 286 2119 Main (no alt) 3.5 13 8480 30 429 Fragment Alternates 0 8.6 0 47 692 Total 34 66 78 269 153 233 39 Table 2.2: Conditional Reciprocal Best BLAST (CRBB) results of the evigene combined assembly compared to the Trinity assembly. Higher % contigs with CRBB hits indicates better contiguity with the reference; lower mean reciprocal best hit (rbh) per reference indicates lower redundancy. Query T. plicata – Trinity Assembly (n = 38 431) T. plicata – Evigene Assembly (n = 28 461) Reference P. glauca gene models (n = 28 909) % contigs with CRBB 58.2 56.8 % reference with CRBB 40.0 37.3 Mean rbh per reference 0.69 0.56 % reference with at least 85% coverage 30.5 29.9 % reference with at least 95% coverage 23.4 23.0 Reference A. thaliana Representative Gene Set (n = 27 416) % contigs with CRBB 48.2 49.3 % reference with CRBB 35.9 34.4 Mean rbh per reference 0.68 0.51 % reference with at least 85% coverage 21.1 21.5 % reference with at least 95% coverage 13.3 13.6 40 Table 2.3: BLAST results of the evigene combined assembly versus the Trinity assembly against the 1000 longest proteins in the P. glauca gene models, the P. taeda gene models and the A. thaliana representative gene set (e-value cut-off: 1e-05). Query T. plicata – Trinity Assembly (n = 38 431) T. plicata – Evigene Assembly (n = 28 461) Reference P. glauca gene models – 1000 longest proteins # of significant hits 1140 1150 Mean bitscore 702.42 703.41 Reference A. thaliana representative gene set – 1000 longest proteins # of significant hits 1265 1295 Mean bitscore 785.22 798.34 41 2.8 Figures Figure 2.1: Representative four-month-old WRC seedling (~ 20 – 25 cm tall) used for RNA isolation and sequencing. 42 Figure 2.2: Pipeline for the de novo assembly and redundancy reduction of the WRC gene set, carried out for each inbred S5 line separately. 43 Figure 2.3: Monoterpene profiles of foliar samples for 12 different monoterpenes across the eight S5 lines. 44 Figure 2.4: Results of the BUSCO gene set completeness assessment. The reduced-redundancy gene set for WRC S5 Line 4 was found to be the most complete, with the lowest number of missing orthologs. 45 Figure 2.5: Top 20 most abundant InterPro domains and families identified in the WRC reduced-redundancy gene set. A) InterPro domain annotation was performed on the reduced-redundancy gene set obtained from the transcriptome assembly of a single plant of S5 Line 4. B) InterPro family annotation was performed on the reduced-redundancy gene set obtained from the transcriptome assembly of a single plant of S5 Line 4. 46 Figure 2.6: Maximum likelihood phylogeny of TPS. Phylogenetic analysis was performed with 241 TPS from across the plant kingdom, including 33 non-redundant full-length or near full-length WRC TPS. Classification of TPS sub-families are as described in Chen et al. (2011). TPS of gymnosperms cluster together in subfamily TPS-d and apart from the TPS of angiosperms, except for those putatively involved in primary metabolism. 47 48 Figure 2.7: Maximum likelihood phylogeny of gymnosperm TPS rooted to P. patens ent-kaurene synthase. Phylogenetic analysis was performed with 126 full-length TPS from gymnosperms, including 33 non-redundant full-length or near full-length WRC TPS. TPS in gymnosperms generally separate by high-level and general functions as indicated with the background colours showing clades of di-TPS of primary or secondary metabolism, ancestral sesqui-TPS, sesqui-TPS, mono-TPS, and hemi-TPS. Within these general functional clades, TPS of the Cupressaceae mostly cluster apart from TPS of the Pinaceae, highlighting the independent diversification and evolution of specific TPS functions in the Cupressaceae and the Pinaceae. Bootstrap support values over 80% are given. 49 2.9 Datasets Dataset 2.1: Sequences of 241 plant terpene synthase (TPS) used in construction of a maximum-likelihood phylogeny of plant TPS. Dataset 2.2: Sequences of 126 gymnosperm and a single P. patens TPS used in construction of a maximum-likelihood phylogeny of gymnosperm TPS. Dataset 2.3: Sequence data for the core WRC gene set. Gene set containing the 28,279 core, reduced-redundancy protein sequences for predicted ORFs as produced by the EvidentialGene pipeline. Dataset 2.4: Sequence data for the alternate WRC gene set. Gene set containing 40,691 additional putative protein-coding sequences, which may be potential gene isoforms or paralogs. Dataset 2.5: Summary of significant BLASTp and InterProScan hits for the main reduced-redundancy gene set of Line 4. BLAST columns are as described in the BLAST Command Line Applications User Manual (https://www.ncbi.nlm.nih.gov/books/NBK279690/). The pipeline for BLASTing and filtering hits is described in the Methods section. GO names are separated by Biological Process (P), Molecular Function (F) and Cellular Component (C). The InterPro ID column lists all InterPro domains found for the queried sequence. Top PFAM hit describes the hit with the highest score against the PFAM database for each sequence, using an e-value cut-off of 1e-5. Dataset 2.6: Statistical summary of orthogroup analysis for all sequences assigned to orthogroups. Of the 498,235 protein coding sequences from 16 different plant species submitted 50 for orthogroup analysis, 391,179 were successfully assigned to 19,660 orthogroups. The majority of orthogroups (11,616) had an average of less than one gene per species; the largest orthogroup (3201 genes) had an average of 151–200 genes per species. A large number of orthogroups (5614) had members from only two species; however, a similarly large number (3835) had members from all 16 species. Dataset 2.7: Statistical summary of orthogroup analysis results for each species. The species with the lowest number of genes assigned to orthogroups was P. patens, with only 57.5% of sequences assigned; the highest was P. glauca with 92.4%. 90.4% of our WRC gene set was successfully assigned to orthogroups; 0.1% of WRC sequences were in species-specific orthogroups. Dataset 2.8: Summary of orthogroup composition and function. The number of orthogroup members from each species, together with the total number of genes in each orthogroup and the top five PFAM hits for each orthogroup. The largest orthogroup, with 3201 genes consisted mainly of pentatricopetide-repeat containing protein-coding genes, a large protein family in plants with little functional redundancy (Lurin et al. 2004). Dataset 2.9: Sequence data for 33 putative full-length TPS genes from the WRC gene set. Putative TPS were identified using BLASTp, InterProScan and orthogroup analysis, and after removal of partial ORFs and proteins less than 400 aa long were reduced to a set of 33 putative full-length TPS. 51 The western redcedar genome reveals exceptionally low genetic diversity in a highly adaptable conifer 3.1 Summary We assembled the 9.8 Gbp genome of western redcedar (WRC, Thuja plicata), an ecologically and economically important conifer species of the Cupressaceae. The genome assembly, derived from a uniquely inbred tree produced through five generations of self-fertilization (selfing), was determined to be 86% complete by BUSCO analysis – one of the most complete genome assemblies for a conifer. Population genomic analysis revealed WRC to be one of the most genetically depauperate wild plant species, with an effective population size of approximately 400 and no significant genetic differentiation across its geographic range. Nucleotide diversity, π, is low for a continuous tree species, with many loci exhibiting zero diversity, and the ratio of π at zero- to four-fold degenerate sites is relatively high (~ 0.34), suggestive of weak purifying selection. Using an array of genetic lines derived from up to five generations of selfing, we explored the relationship between genetic diversity and mating system. While overall heterozygosity was found to decline faster than expected during selfing, heterozygosity persisted at more than 1,000 loci, suggestive of associative overdominance. Non-reference alleles at such loci often harbor deleterious mutations and are rare in natural populations, implying that balanced polymorphisms are maintained by linkage to dominant beneficial alleles. This may account for how WRC remains responsive to natural and artificial selection, despite extremely low genetic diversity. 52 3.2 Introduction Gymnosperms are an ancient group of plants, with fossil records dating over 300 million years ago (MYA) (Stewart 1983). Conifers are by far the largest group of gymnosperms with approximately 615 known species (Christenhusz et al. 2011; Farjon 2018). The Pinaceae form the largest conifer family, and genomes of numerous members of the Pinaceae, such as white spruce (Picea glauca), Norway spruce (Picea abies), loblolly pine (Pinus taeda), sugar pine (Pinus lambertiana), and Douglas-fir (Pseudotsuga menziesii), have been sequenced (Birol et al. 2013; Nystedt et al. 2013; De La Torre et al. 2014; Zimin et al. 2014, 2017; Warren et al. 2015; Stevens et al. 2016; Neale et al. 2017). Such efforts revealed the notoriously complex nature of their immense genomes, which are rife with repetitive sequences, transposable elements, long introns, gene duplications, pseudogenes, and gene fragments. However, little genomic research has been completed on conifers of other families. In particular, the Cupressaceae, such as cypresses, junipers, and redwoods, are thought to have undergone a whole genome duplication unique from the Pinaceae (Li et al. 2015). There is also evidence for substantial rearrangements of orthologous linkage groups during the evolutionary history of the two families, that resulted in differences in karyotype (n = 11 in Cupressaceae; n = 12 in Pinaceae) (De Miguel et al. 2015), genome size (9 – 20 Gbp in Cupressaceae; 18 – 31 Gbp in Pinaceae) (Hizume et al. 2001; De La Torre et al. 2014; Stevens et al. 2016), and likely other genomic differences. However, the genomes of only two Cupressaceae species, giant sequoia (Sequoiadendron giganteum) and the hexaploid coast redwood (Sequoia sempervirens), have been published (Scott et al. 2020; Neale et al. 2022). 53 Western redcedar (WRC, Thuja plicata) is an ecologically, economically, and culturally important species in the Cupressaceae. Endemic to the Pacific Northwest of North America and ranging from Northern California to Southern Alaska, WRC is a stress-tolerant, slow-growing tree prized for its durable, lightweight and rot-resistant wood (Grime 1977). WRC is one of only five extant Thuja species and is estimated to have diverged from its North American sister species Thuja occidentalis around 26 MYA (Li and Xiang 2005). Genetic studies have indicated low diversity in WRC (Copes 1981; Glaubitz et al. 2000; O’Connell et al. 2008). Microsatellite data suggest that all WRC originated from an isolated refugium near the southern end of its current distribution and radiated north and inland following the last glacial period (O’Connell et al. 2008). Current climate models predict that its range will increase over the next century, particularly in the interior of British Columbia (BC) (Gray and Hamann 2013), thus making it a priority for genome analysis and expediting of traditional breeding cycles via genomic selection (GS). Uniquely among conifers, WRC employs a mixed mating system of outcrossing and self-fertilization (selfing), with a mean outcrossing rate of around 70% (El-Kassaby et al. 1994; O’Connell et al. 2001, 2004), and appears to suffer very little inbreeding depression for fitness growth traits (Wang and Russell 2006; Russell and Ferguson 2008). Mating systems in plants, particularly rates of selfing (s) and its complement, outcrossing (1 –s), are of interest to evolutionary biologists due to their implications for genetic diversity and fitness and have been investigated extensively over the past century (Stebbins 1957; Lande and Schemske 1985; Barrett and Eckert 1990; Barrett et al. 2003; Wright et al. 2013). Though inbreeding depression 54 resulting from selfing can lead to negative fitness impacts, a benefit of selfing may include reproductive assurance (Fisher 1941; Baker 1955), which, in the absence of strong inbreeding depression, can allow self-compatible populations to expand their geographic range faster than obligate outcrossers (Lande and Schemske 1985). Research on inbreeding in plants has mostly focused on mating strategies in angiosperms (Barrett and Eckert 1990; Jarne and Charlesworth 1993; Vogler and Kalisz 2001; Barrett et al. 2003; Kalisz et al. 2004; Wright et al. 2013). Characterization of mixed mating systems in conifers has been limited, largely by their long generation times and generally high self-incompatibility (Sorensen 1982; Bishir and Namkoong 1987; Remington and O’Malley 2000; Williams et al. 2003; Williams 2008). The exceptional ability of WRC to maintain such a mating system has allowed for successful selfing for up to five generations in experimental trials, making WRC a potential model for the study of inbreeding in conifers and more broadly in gymnosperms (Russell and Ferguson 2008). Here we introduce the first genome sequence for WRC and present unique features of the WRC genome in the context of its evolutionary history. We show that the mating system of WRC, likely driven by range expansion from a small founder population since the last glaciation, has resulted in exceptionally low genetic diversity and the purging of highly deleterious alleles. We further explore the effects of extreme selfing on heterozygosity and selective pressures in multiple selfing lines (SLs) and find evidence of widespread balancing selection across the genome, likely in part due to associative overdominance. 55 3.3 Results 3.3.1 The WRC genome assembly represents a highly complete conifer genome The assembly of conifer genomes remains challenging partly due to their large size (Nystedt et al. 2013; Warren et al. 2015; Stevens et al. 2016; Zimin et al. 2017) and high heterozygosity (Prunier et al. 2016). Given WRC’s unique selfing abilities, we were able to facilitate assembly of a WRC reference genome using a fifth-generation SL tree (2323-211-S5; Table 3.1) expected to be > 98% homozygous. The S5 reference genome assembly was generated from a combination of short fragment paired-end reads, large fragment mate-pair reads, and linked-reads from large molecules, using 13 libraries and 28 lanes of Illumina sequencing, with a sequencing read length of 2×151 bp (Table 3.2). Overall genome depth of coverage was estimated to be 77×. The WRC genome was previously estimated to be 12.5 Gbp in size across 11 chromosomes (Ohri and Khoshoo 1986; Hizume et al. 2001). GenomeScope (Vurture et al. 2017) estimated the genome size at 9.8 Gbp (Figure 3.1). We calculated approximately one single nucleotide variant (SNV) every 4.6 kbp, for an estimated genome-wide heterozygosity of 0.000216 – an exceptionally low estimate, highlighting the value of SLs for genome sequencing and assembly. We assembled 7.95 Gbp of the estimated 9.8 Gbp genome to produce a draft assembly with an N50 of 2.31 Mbp, the largest scaffold being 16.3 Mbp. This assembly comprises 67,895 scaffolds > 1 kbp (Table 3.3; Table 3.4). Benchmarking Universal Single Copy Ortholog 56 (BUSCO) analysis (Simão et al. 2015) determined the genome assembly to be 86% complete in the gene space. This is one of the highest completeness estimates for a conifer genome (Table 3.5A). Genome annotation using evidence from Iso-Seq full-length cDNAs resulted in the identification of 39,659 gene models supported by the alignment of unique primary transcripts (Dataset 3.1), and an additional 26,150 alternative transcripts. A total of 25,984 gene models had Pfam protein family annotation, 31,537 had transcriptome support over their full length (100%), and 19,506 had peptide homology coverage support 90% or greater (Table 3.6; Dataset 3.2). Intron length ranged from 20 bp to 148.3 kbp, which is consistent with estimates from other conifers, with maximum lengths ranging from 68 kbp (Norway spruce) (Nystedt et al. 2013) up to 579 kbp (sugar pine) (Stevens et al. 2016). Scott et al. (2020) reported a maximum intron length of 1.4 Mb in the highly contiguous giant sequoia genome assembly. Repeat elements comprised 60% of the WRC genome, which is low compared to other conifers. Repeats comprised 79% of the sugar pine (Stevens et al. 2016) and giant sequoia genomes (Scott et al. 2020). Single copy orthologs (SCOs) were detected by orthogroup comparison to the giant sequoia gene set, yielding 11,937 SCOs (Dataset 3.3). BUSCO analysis found the predicted gene set to be 90.5% complete, much higher than any other conifer gene set to date (Table 3.5B). We further validated the completeness of the genome assembly and annotation using a panel of 59 full-length WRC sequences from GenBank, of which 48 were reliably identified in the genome annotation. We also searched for a set of 33 WRC terpene synthase (TPS) transcripts, of which we reliably (>90% identity) identified 15 57 (Shalev et al. 2018) (Table 3.7; Dataset 3.4). This confirms the completeness of the gene space and quality of the draft genome annotation, while suggesting that BUSCO core genes may somewhat overestimate gene space completeness when considering family or species-specific genes. 3.3.2 Population genomic analysis reveals extremely low levels of genetic diversity in WRC We estimated nucleotide diversity, short-range linkage disequilibrium (LD), population structure, genetic differentiation, and effective population size (Ne) in n =112 unrelated trees from across the geographic range of WRC (range-wide population; RWP) (Table 3.8). Trees were grouped into three subpopulations: Northern-Coastal (n = 77), Central (n = 26), and Southern-Interior (n = 9) (Figure 3.2A). Using a panel of single nucleotide polymorphisms (SNPs) that were genotyped via targeted sequence capture approach, we identified 2,454,925 variant and invariant sites, which were filtered separately and resulted in final sets of 27,594 SNPs (Dataset 3.5) and 2,186,998 invariant sites. Total mean SNP depth was 36.6×. We annotated 25,841 SNPs across 3,117 genomic scaffolds using the Ensembl Variant Effect Predictor (VEP) (McLaren et al. 2016) (Dataset 3.6). We detected 18,445 SNPs within 6,039 genes, 3,730 of which were SCOs. Intergenic loci made up 28.6% of all annotated SNPs (7,396), 1,844 of which are in regions 0.5 to 2 kbp up- or downstream of coding regions. Within coding regions, 51.6% (4,569) were missense variants while 44.9% (3,978) were synonymous variants (Table 3.9). 58 Linkage disequilibrium Decay of linkage disequilibrium (LD), the non-random association of alleles at different loci in a population, can inform on how likely different loci are to be assorted together during recombination. We assessed short-range LD as represented by the squared correlation coefficient r2 in the RWP, at a minor allele frequency (MAF) threshold of 0.05 to avoid bias due to rare alleles (n = 22,751 SNPs). The mean of all pairwise r2 estimates was 0.277 with a median of 0.122. The half-decay value (the distance in which r2 decays to half of the 90th percentile value) was 0.0984 Mbp. LD decayed to an r2 of 0.2 at 0.573 Mbp, and an r2 of 0.1 at 1.65 Mbp (Figure 3.3A). Further, high LD (r2 > 0.8) appears to exist for SNPs millions of bp apart (Figure 3.3B). These estimates for LD decay are several orders of magnitude greater than those found in other conifers, as well as many other tree species, where LD has been reported to decay rapidly within tens to a few thousand bp (Krutovsky and Neale 2005; Heuertz et al. 2006; Pyhäjärvi et al. 2011; Pavy et al. 2012; Fahrenkrog et al. 2017). Population structure and genetic differentiation To assess genetic structure, we used a filtered set of n = 5,114 SNPs (Dataset 3.7; see Methods and Materials). We analysed STRUCTURE (Pritchard et al. 2000) results using two post-hoc cluster identification methods. The ΔK method of Evanno et al. (2005) identified an optimal K of two; this approach may return a K of two more often than expected when genetic structure is weak (Janes et al. 2017). The approach of Puechmaille (2016), which can help resolve K when 59 subsampling is uneven, identified an optimal K of two or three. Analysis of fastStructure (Raj et al. 2014) results using cross-validation suggested that optimal K may lie between one and five. These results suggest genetic structure is exceptionally weak in our RWP. Indeed, there is apparent gene flow between trees in all three subpopulations across all three STRUCTURE clusters (Figure 3.2B). We also applied non-parametric approaches of Discriminant Analysis of Principal Components (DAPC) and Principal Component Analysis (PCA). Cross-validation for DAPC with a priori cluster definitions optimally retained just 10 PCs and two discriminant functions capturing 15.7% of the conserved variance; however, de novo k-means clustering failed to resolve any clusters, identifying an optimal K of one (Figure 3.4). PCA revealed a latitudinal gradient of differentiation along the first principal component (PC), with some separation of the Southern-Interior subpopulation along the second PC (Figure 3.2C), mostly for trees originating from California and Oregon. However, the first PC only explains 3.00% of the variance in the data, and the second explains 1.50%. These results are consistent with DAPC and suggest that gene flow has been prevalent across the range of WRC. No significant differentiation was found between trees from different subpopulations based on a hierarchical FST test (FST = 0.0258, p = 0.952) (Table 3.10), and no significant isolation by distance was found by our Mantel test for subpopulations (r = 0.0204, p = 0.665) and individuals (r = 0.0354, p = 0.285) (Figure 3.5). 60 Nucleotide diversity in WRC We estimated nucleotide diversity π (Nei and Li 1979) in the RWP and absolute nucleotide divergence dXY between subpopulations. Average π (SD) across 10,632 SCOs was 0.00339 (0.00848) (Figure 3.6A; Figure 3.7; Table 3.11); 1,126 genes had a π of zero. Across 10 kb windows, average π was 0.00374 (0.0189), indicating diversity is higher in noncoding regions. Average dXY was not significantly different between any pair of subpopulations nor was it significantly different from π in SCOs (p > 0.05, Kruskal-Wallis rank sum test) (Figure 3.6B; Table 3.12). To assess the efficacy of purifying selection, we estimated π0/π4, the ratio of π in 0-fold to 4-fold degenerate sites. We found a π0 of 0.00215 (0.0108) and a π4 of 0.00624 (0.0154), yielding a π0/π4 of 0.344. The site frequency spectrum (SFS) for 4-fold SNPs appeared to decay slower than the SFS for 0-fold and for all SNPs, supporting evidence of a recent bottleneck and indicating that there may be stronger positive selection at these sites (Figure 3.8). Effective population size (Ne) Ne can be defined as the idealized population size expected to experience the same rate of loss of genetic diversity as the population under observation (Wright 1931). We estimated Ne using the LD method of NeEstimator (Do et al. 2014). SNPs were mapped to the giant sequoia genome (Scott et al. 2020) (Dataset 3.8) and SNPs estimated to be at least 1.7 MB apart were isolated for 61 the analysis, resulting in a set of n = 509 SNPs (Dataset 3.9). Ne was estimated to be 411.6 (JackKnife 95% CI: 316.9, 577.9). We further explored demography using Stairway Plot 2 (Liu and Fu 2020). We observe a decline in Ne from ~ 40,000 beginning ~100 kya, accelerating from ~20 kya down to under 400 by present day, consistent with one or more bottleneck events during the recent glacial maximum (Figure 3.9). Our estimates of Ne are extremely low for a continuous tree population; for example, species in Picea (Chen et al. 2010), Pinus (Brown et al. 2004), and Populus (Fahrenkrog et al. 2017) have estimated Ne in the range of 104 – 105. 3.3.3 Persistent heterozygosity during complete selfing highlights genomic regions under selection To examine the effect of complete selfing on heterozygosity and selection in WRC, we selected 189 trees from the 15 FS families, forming 41 SLs for SNP genotyping. The process of SNP calling is error-prone, and despite filtering for multiple quality criteria, errors are likely to remain in any SNP data set. Using SLs, we were able to correct for erroneous genotyping calls and impute missing genotypes for SLs up to S4 (n = 28) or S5 (n = 11), retaining n = 151 trees (Dataset 3.10). We used all filtered SNPs for these analyses (n = 27,594 SNPs). Under selfing in diploids, heterozygosity is expected to decline by 50% in each generation purely through genetic drift. Mean heterozygosity declined slower than expected (Figure 3.10A; Table 3.13; Table 3.14A), while median observed heterozygosity was 62 significantly lower than expected beginning in generation S3 (pairwise Sign test; Table 3.14A). Mean heterozygosity at FS was 0.301, while in the RWP it was 0.233 (Table 3.13). In comparison, Chen et al. (2013) found mean heterozygosities of 0.33 and 0.36 in lodgepole pine (Pinus contorta) and white spruce, respectively. The inbreeding coefficient F is the probability of any two alleles being identical by descent (IBD) and is a measure of reduction in heterozygosity due to inbreeding. We estimated F for each sample in the SLs and the RWP using the approach of Yang et al. (2011) (FUNI), filtering for LD and MAC (n = 8,049 SNPs). Mean F increased from -0.0587 at FS to 0.668 at S5 – significantly less than expected (p = 0, one-sample t-test), indicating that the observed reduction in heterozygosity cannot entirely be attributed to inbreeding (Figure 3.10B; Table 3.13; Table 3.14B). Mean F in the RWP was 0.177, further emphasizing the degree of inbreeding in wild populations (Table 3.13). Following the expectation of a 50% decline in heterozygosity per generation under complete selfing and assuming a model of only genetic drift, we anticipated that 25% of SLs would become fixed for one allele at any given locus and 25% for the other in each generation. Thus, by generation S4, 46.875% of SLs should fix for each allele, and 6.25% should remain heterozygous. We identified 1,298 SNPs that deviated from expected proportions of fixation using a Holm p-value correction threshold of 10-5 (hereafter: outlier SNPs) (Dataset 3.11). Of these, 229 fixed for the reference allele and 4 fixed for the alternate allele more often than would be expected under drift alone; meanwhile, all outlier SNPs had a higher proportion of heterozygous alleles by S4 than expected under drift. Outlier SNPs were present on all putative 63 LGs in the genome, and mean depth was similar to the mean depth of the total SNP set (33.4× vs 36.6×, respectively). VEP predicted effects for 1,073 outlier SNPs, 408 (38.0%) of which were in coding regions (Dataset 3.12). Gene Ontology (GO) annotation was available for 136 genes containing outlier SNPs; six GO categories were over-represented in outliers when compared to the entire SNP set (p < 0.05, Fisher’s Exact Test) (Table 3.15). When comparing SNP effect categories, we found stop gained (p = 6.61×10-3), missense variants (p = 1.58×10-5), and intergenic variants (7.08×10-9) to be over-represented in outlier SNPs, while synonymous variants (p = 0.0304) and 5’ (p = 1.17×10-7) and 3’ UTR variants (p = 2.57×10-7) were under-represented (Figure 3.11). Outlier SNPs causing the most severe consequences (stop gained or lost, splice acceptor or donor changed, start gained or lost) either remained heterozygous or fixed for the reference allele at these loci in nearly every line, thus preventing these changes, a pattern observed in Populus trichocarpa as well (Evans et al. 2014) (Dataset 3.11). In the RWP, the minor allele for these SNPs was always the alternate allele, i.e., the allele inducing the severe change. This pattern suggests that balanced polymorphisms may be maintained by selection favouring linked dominant alleles, i.e., associative overdominance (Bierne et al. 2000). 64 3.4 Discussion Genome analysis of WRC, a self-compatible conifer, revealed low genetic diversity, high levels of LD, and low Ne across its geographic range. WRC emerges as a genetically depauperate wild plant species, providing insight into how selfing may have facilitated its expansion across its current geographic range, but at the expense of genetic variation. 3.4.1 The WRC genome Sequence assembly of large and repetitive conifer genomes is becoming more feasible, with new technologies such as Single Molecule Real Time (SMRT) long-read sequencing or linked-reads (Zimin et al. 2017). WRC is one of only two conifers outside of the Pinaceae whose genome sequence has been published. Recently, Scott et al. (2020) reported the first genome sequence in Cupressaceae, giant sequoia, with a near-chromosome-scale assembly of 8.125 Gbp of the estimated 9 Gbp genome using a combination of Oxford Nanopore long reads and Illumina short reads together with a Dovetail HiRise Chicago and Hi-C statistical scaffolding and assembly approach. Though future chromosome-level assembly would be of value to improve contiguity, BUSCO completeness scores (86.0% and 84.3% for WRC and giant sequoia, respectively) and the very high completeness of the annotated gene set suggest that the WRC assembly is currently of very high quality for the gene space. Previous studies using flow cytometry estimated WRC’s genome size at 12.0 – 12.5 Gbp (Ohri and Khoshoo 1986; Hizume et al. 2001). The WRC genome assembled at 7.95 Gbp. This discrepancy may be partially explained by filtering of k-65 mers with very high depth of coverage in GenomeScope to remove organelle-derived reads, which may also remove other heterochromatic sequences such as centromeres and telomeres; however, a recent study in maize (Zea mays) found that selfing over several generations can reduce genome size by up to 7.9% (Roessler et al. 2019), which may suggest that we are observing genome loss in WRC as well given the genome assembly source. Flow cytometry to assess genome loss during selfing would be a valuable future endeavour. 3.4.2 Genetic diversity in WRC We estimated π to be 0.0034 in SCOs and 0.0037 across all sequenced space. These estimates are lower than many other plant species using comparable methods, for example, Norway spruce (π = 0.0049 – 0.0063) (Wang et al. 2020), weedy broomcorn millet (π = 0.14; Panicum miliaceum) (Li et al. 2021), and most Populus species (π = 0.0041 – 0.011) (Liu et al. 2022). We found lower π estimates only in highly cultivated plants, such as soybean (π = 0.0015; Glycine spp.) (Bayer et al. 2022), or rare, isolated species, such as Populus qiongdaoensis (π = 0.0014), which is restricted to a single small island and has an estimated Ne of ~500 (Liu et al. 2022). Additionally, our probe selection strategy for genotyping targeted regions of high variability due to the very low levels of polymorphism in initial sequencing runs. This may have led to inflated estimates of π, suggesting that genome-wide diversity may be even lower. The high observed π0/π4 ratio (0.35) may suggest weak purifying selection in WRC (Chen et al. 2017); it could also be indicative of demography, as π0 returns to equilibrium quicker 66 than π4 following a bottleneck event (Brandvain and Wright 2016; Chen et al. 2019). The low Ne (~ 400) and general lack of population structure, genetic differentiation, or nucleotide divergence between geographic subpopulations despite a relatively wide geographic range and continuous population suggest that much of the variation in WRC was likely eliminated due to bottlenecks following the last glacial period, a pattern confirmed by our Stairway Plot results and affirming the conclusions of previous studies (Copes 1981; Glaubitz et al. 2000; O’Connell et al. 2008). Mating system likely plays a role as well in WRC’s low diversity. It has been argued that selfing species should generally have a lower nucleotide diversity due to a reduction of the effective recombination rate (Buckler IV and Thornsberry 2002). Thus, the exceptionally slow rate of LD decay observed in our RWP is further evidence of a recent population bottleneck or long-term effects of inbreeding (Golding and Strobeck 1980; Zhang et al. 2004; Slatkin 2008). In future studies, more extensive sampling, in particular for the Southern-Interior region, could help in gaining more accurate estimates of genetic differentiation across populations of WRC. 3.4.3 Selfing in WRC Heterozygosity declined faster than expected under complete selfing. Despite starting at an F of 0, our FS generation had a low mean heterozygosity (0.296), and with each successive generation, F increased slower than expected, indicating that IBD does not fully explain the reduction in heterozygosity in WRC (Wright 1922; Slate et al. 2004). The lack of strong fitness costs associated with selfing in WRC (Wang and Russell 2006; Russell and Ferguson 2008) 67 suggests that most strongly deleterious alleles have been purged from the genome, presumably due to past population bottlenecks and inbreeding. Nonetheless, even weak purifying selection could explain the faster than expected decline in heterozygosity. Of greater interest, however, is that the majority of loci deviating from expectations of drift during selfing remained heterozygous, suggestive of balancing selection or associative overdominance at these loci, with high levels of LD promoting genetic hitch-hiking near loci under selection to remain heterozygous. Over-representation of missense variants in outlier loci, and the overall rarity of missense mutations in natural populations, offer further support for associative overdominance as an explanation both for the retention of heterozygosity at these loci. This is congruent with relatively high π0/π4 in the RWP, suggesting strong positive selection to maintain current allele frequencies. No outlier loci remained heterozygous in all lines, which suggests that these loci do not harbour strongly deleterious or lethal mutations. Further, all three genotypes exist for many of these loci in the RWP. Excess heterozygosity can also occur from genotyping error due to the presence of paralogs. To address this source of error, we employed stringent filters for maximum mean depth, allele balance, excess heterozygosity, and deviations from HWE. Further, the low estimated π in the RWP as well as similar mean depths (~36×) for outlier SNPs and the total SNP set suggest paralog content is minimal. Higher than expected heterozygosity was observed during selfing in eucalypts (Eucalyptus grandis) (Hedrick et al. 2016) and maize (Roessler et al. 2019). However, the average heterozygosity in these species is notably much higher (~ 0.65 in each for S1, compared with 0.16 at S1 for WRC). It is also possible that our genotyping strategy, 68 in which probes were designed to capture highly variable sites, may have influenced heterozygosity estimates. Future analyses using whole-genome sequencing or comprehensive genotyping-by-sequencing (GBS) for comparison may be of value. Several GO categories were over-represented in outlier SNPs; of particular interest is GO:0048544, pollen recognition, which involves the recognition and rejection of self-pollen. Three genes putatively involved in pollen recognition were under selection to remain heterozygous during selfing (Table 3.16). Russell and Ferguson (Russell and Ferguson 2008) found that 43% of trees at S4 displayed protogyny (receptivity of female strobili occurring before pollen shedding) compared to just 6.7% at FS, suggesting that selective pressures to reduce selfing exist in WRC. However, WRC is known to be highly receptive to self pollen (Ritland et al. 2020), suggesting that these genes could be involved in maintaining WRC’s ability to successfully and viably self. Functional analysis of these pollen recognition genes could give valuable insight into the mechanisms of selfing and the lack of inbreeding depression in WRC. We recognize that use of SLs of single seed descent makes differentiating between patterns of selection and genetic drift difficult, as genetic drift is stronger when there are fewer individuals in a population. The use of multiple cloned seedlings for each SL in future studies could help improve our analysis, with the potential to find more SNPs under selection. 69 3.4.4 Implications for conservation, adaptation to climate change and breeding with genomic selection (GS) Current breeding of WRC focuses on traits such as growth and herbivore and disease resistance; thus, low genetic diversity may have considerable ecological and potential economic consequences. When low genetic diversity is observed in plant or animal populations, conservation strategies may become necessary to maintain existing genetic variation and reduce the risk of extreme inbreeding depression, especially when census population size in the wild is small. Although ours and previous results (O’Connell et al. 2008) indicate its range was likely reduced to a single refugium during the last glaciation, WRC has since greatly expanded throughout the Pacific Northwest. We found genetic isolation by distance to be small, consistent with the low observed variation. Yet, successful selection of genetically superior families for these traits has been possible. Provenance trials have revealed significant local adaptation among natural populations of WRC (Cherry 1995), and WRC can be found in a variety of different climates, moisture levels, elevations, and light availabilities (Grime 1977; Antos et al. 2016). Resistance to cedar leaf blight, a foliar fungal pathogen, has been observed to be an adaptation to native climate, with trees from wetter climates showing greater resistance than those from drier climates, regardless of geographical distance (Russell et al. 2007). These observations suggest sufficient genetic variation exists within and between natural populations upon which selection can act. Furthermore, WRC is well known for its high phenotypic plasticity (El-Kassaby 1999), possibly due to epigenetic variation (Zhang et al. 2013), although the fraction of plasticity that is adaptive remains unknown. Our observation of balanced polymorphisms, due in part to 70 associative overdominance, offers a potential explanation for WRC’s reported adaptability and response to selection. Together with self-compatibility, which is known to facilitate range expansion (Baker 1955), WRC may be less threatened by climate change and other anthropogenic pressures than might be expected based on its low genetic diversity. WRC's apparent adaptability and potential for range expansion make it an important forest tree in a time of changing climate and environments (Gray and Hamann 2013). Low genetic diversity and unique mating system need to be considered as WRC breeding adopts strategies of GS that largely rely on controlling relatedness in the population (Ritland et al. 2020). Recombination rate is another important consideration, as GS relies on the presence of LD between SNPs and causal regions for traits, in addition to relatedness between individuals (Meuwissen et al. 2001). WRC's high LD may be an advantage for finding linked SNPs, but may also increase the risk of unintentional selection for correlated traits not under selection. This could be mitigated by whole genome sequencing across breeding populations, similar to GS approaches in livestock breeding (Raymond et al. 2018; Georges et al. 2019). WRC is a fascinating example of adaptation in a long-lived conifer, despite very low levels of genetic variation. As our understanding of the genome improves, we will be able to improve prospects for survival and maintenance of this tree as an ecologically and economically significant species and better understand and test how selfing behaviour evolves and can be advantageous in wild plant populations. 71 3.5 Methods and Materials 3.5.1 Plant materials The WRC RWP represented n = 112 individuals originating from across the geographic range growing at the Cowichan Lake Research Station (CLRS) at Mesachie Lake, BC, Canada. Trees were separated into three geographic subpopulations, Northern-Coastal, Central, and Southern-Interior, based on UPGMA clustering of genetic distances (O’Connell et al. 2008). SLs were produced over 12 years (1995 – 2007) using an accelerated breeding approach (Russell and Ferguson 2008) at CLRS. Briefly, 15 pairs (30 individuals) of unrelated parents from across coastal BC and Vancouver Island (Table 3.17) were crossed to create 15 FS families and ensure an initial inbreeding coefficient of F = 0. Each FS line was then selfed for up to five generations (S1 – S5) with one generation every two years, facilitated by GA3 hormone treatment. A single S5 seedling of SL 23 (2323-211-S5) was used for genome sequencing. We selected 189 individuals from the 15 FS selfing families for genotyping for the SL analysis (Table S1). 3.5.2 Genome sequencing and assembly Foliar tissue was used for DNA extraction for genome sequencing and genotyping. For genome sequencing, purified nuclear genomic DNA was extracted at BioS&T (http://www.biost.com/, Montreal, Canada) (Birol et al. 2013) and sequenced at the Joint Genome Institute (JGI; Berkeley, USA). For SNP genotyping, DNA was isolated from lyophilized tissue with a modified protocol of Xin and Chen (Xin and Chen 2012). DNA extraction protocol 72 modifications included: the addition of 10% polyvinylpyrrolidone to the CTAB extraction buffer, all centrifugation steps were performed for 10-12 min at 10,000 rpm, the amount of Qiagen MagAttract solution was increased to 7µl, one-minute wait time for binding on the magnetic block was added, and 115µl TE buffer was used for DNA dilution. The concentration and quality of DNA was verified with a Nanodrop 2000c (Fisher Scientific, Toronto, ON, Canada), Quantiflor (Promega Corporate, Madison, WI, USA) and 0.8% agarose gel to cross check for the quantity and quality of the DNA. Genome sequencing was executed using three types of libraries: short fragment paired-end, large fragment mate-pair, and linked-reads from large molecules using 10× Genomics Chromium. Illumina paired-end adapters were trimmed using Trimadap v0.1r11 (https://github.com/lh3/trimadap). Mate-pair sequencing adapters were removed using NxTrim v0.4.3 (O’Connell et al. 2015). For 10× Genomics libraries, adapters were trimmed using Long Ranger Basic v2.1.6 (https://github.com/10XGenomics/longranger). Depth of k-mer coverage profiles were computed for multiple values of k using ntCard v1.0.1 (Mohamadi et al. 2017) (Figure 3.12). The largest value of k providing a k-mer coverage of at least 15 was selected, based on an estimated coverage of > 99.9%, yielding k = 128 (Lander and Waterman 1988). We analysed and visualized k-mer profiles using GenomeScope v1.0.0 (Vurture et al. 2017). Paired-end reads were assembled using ABySS v2.1.4 (parameters: k=128; kc=3) and scaffolded using the mate-pair reads with ABySS-Scaffold (Jackman et al. 2017) (Figure 3.13). Linked-reads were aligned and misassemblies were identified and corrected with Tigmint v1.1.2 (Jackman et al. 2018). The assembly was scaffolded using the linked-reads with ARCS v1.0.5 (Yeo et al. 73 2018) (-c 2; -m 4-20000) and ABySS-Scaffold (-n 5-7; -s 5000-20000). Molecule size of the linked read libraries was estimated using ChromeQC v1.0.4 (https://bcgsc.github.io/chromeqc). Assembled scaffolds were screened against bacterial proteins, organelle (chloroplast and mitochondria) sequences, and adapters on submission to NCBI and contaminants were removed. We estimated completeness of the WRC genome assembly and other conifer genome assemblies using BUSCO v5.0.0 in genome mode, on OrthoDB Embryophyta v10 (Simão et al. 2015; Waterhouse et al. 2018; Kriventseva et al. 2019), which determines the proportion and completeness of single-copy genes from the Embryophtya database (1,614 models) present in the genome. 3.5.3 Genome annotation RNA extraction Young and mature foliage, xylem and bark samples from a variety of samples (NCBI Umbrella BioProject NCBI PRJNA704616) were ground to a fine powder and total RNA extracted using Purelink ®Plant RNAReagent (Life Technologies, Carlsbad, CA, USA). RNA concentration was determined using a NanoDrop 1000 (Thermo Scientific, Waltham, MA, USA). RNA integrity (RIN) was assessed using Bioanalyzer 2100 RNA Nano chip assays (Agilent, Santa Clara, CA, USA). The minimum RIN was 8. 74 Transcriptome sequencing and gene space annotation For PacBio Iso-Seq, full-length cDNAs were synthesized from total RNA using template-switching technology with SMARTer PCR cDNA Synthesis kit (Clontech, CA, USA). First-strand cDNA was amplified with PrimeSTAR GL DNA Polymerase (Clontech) using template-switching oligos to produce double-stranded cDNA, which was purified with AMPure PB beads and either non-size selected or selected for 2-10 kb cutoff by BluePippin (Sage Science, MA, USA). Amplified cDNA was end-repaired and ligated with blunt end PacBio sequencing adaptors using SMRTbell Template Prep Kit 1.0 (Pacific Biosciences, CA, USA), exonuclease treated to remove unligated products, and purified using AMPure PB beads. PacBio Sequencing primer was annealed to the SMRTbell template library and sequencing polymerase was bound using Sequel II Binding kit 1.0. SMRTbell template libraries were sequenced on a Pacific Biosciences' Sequel II sequencer using v4 sequencing primer, 8M v1 SMRT cells, and v1.0 sequencing chemistry with 1×1440 (24-hour) sequencing run times. For Illumina RNA-seq libraries, sample prep was performed on the PerkinElmer Sciclone NGS robotic liquid handling system using Illumina TruSeq Stranded mRNA HT sample prep kit utilizing poly-A mRNA selection (https://support.illumina.com/sequencing/sequencing_kits/truseq-stranded-mrna.html). Libraries were quantified using KAPA Biosystem’s next-generation sequencing library qPCR kit and run on a Roche LightCycler 480 real-time PCR instrument. Libraries were multiplexed and the pool of libraries was prepared for sequencing on the Illumina NovaSeq 6000 sequencing platform using NovaSeq XP v1 reagent kits, S4 flow cell, following a 2×150 indexed run recipe. 75 Gene space annotation was carried out using transcript assemblies from 1) 1.4B 2×150 and 50M 2×100 stranded paired-end Illumina RNA-seq reads using PERTRAN (Shengqiang et al. 2013); 2) 18M PacBio Iso-Seq Circular Consensus Sequences (CCS) which were corrected and collapsed by genome guided correction pipeline to obtain 530K putative full-length transcripts; and 3) previous RNA-seq assemblies (Shalev et al. 2018). PASA v2.0.2 (Haas et al. 2003) was used to assemble 375,524 transcripts from these three sources. Briefly, PERTRAN executes genome-guided transcriptome short-read (Illumina) assembly via GSNAP (Wu and Nacu 2010) and builds splice alignment graphs after alignment validation, realignment and correction; Iso-Seq CCS correction and clustering aligns CCS reads to the genome with GMAP (Wu and Nacu 2010) while performing intron correction for small indels in splice junctions, if present, and clusters alignments when all introns are the same or have at least 95% overlap for single exon. Gene loci were determined by transcript assembly alignments and EXONERATE v2.4.0 (Slater and Birney 2005) alignments of proteins from Arabidopsis thaliana, Glycine max, Populus trichocarpa, Oryza sativa, Vitis vinifera, Aquilegia coerulea, Solanum lycopersicum, Amborella trichopoda, Physcomitrella patens, Selaginella moellendorffii, Sphagnum magellanicum, UniProt Pinales and Cupressales, and Swiss-Prot proteomes to the repeat-soft-masked WRC genome using RepeatMasker v4.0.8 (Smit et al. 2015) with up to 2 kbp extension on both ends unless extending into another locus on the same strand. For EXONERATE alignments, an intron size cut-off of 90 kb was used. The repeat library consisted of de novo repeats identified by RepeatModeler v1.0.11 (Smit and Hubley 2015) from the WRC genome and repeats in RepBase. Gene models were predicted by homology-based predictors, 76 FGENESH+ v3.1.1 (Salamov and Solovyev 2000), FGENESH_EST v2.6, and EXONERATE and PASA assembled ORFs (in-house homology constrained ORF finder). The best-scored predictions for each locus were selected using multiple positive factors, including transcriptome, EST and protein support, and one negative factor; overlap with repeats. The selected gene predictions were improved by PASA, adding untranslated regions (UTRs), splicing correction, and alternative transcripts. PASA-improved gene models were subjected to protein homology analysis to the proteomes mentioned above to obtain Cscore (BLASTP score ratio to mutual best hit BLASTP score) and protein coverage (highest percentage of protein aligned to the best homologs). PASA-improved transcripts were selected based on the Cscore, protein coverage, transcriptome and EST coverage, and the overlap of coding sequence (CDS) with repeats. Transcripts were selected with Cscore ≥ 0.5 and protein coverage ≥ 0.5, or if they had transcriptome or EST coverage, but their CDS overlap with repeats was less than 20%. For gene models with CDS overlap with repeats of more than 20%, Cscore was required to be at least 0.9 and homology coverage at least 70% to be selected. Selected gene models were subjected to Pfam analysis. Gene models > 30% of the protein in Pfam TE domains were removed, along with weak gene models (HMMER e-value < 10-5). Incomplete gene models, low homology support without fully transcriptome supported gene models and short single exons (< 300 bp CDS) without protein domains nor strong expression gene models were manually removed. All software was run using default parameters. We estimated completeness of the WRC primary transcript gene set and other conifer gene sets using BUSCO v5.0.0 in protein mode on OrthoDB Embryophyta v10. We also 77 assessed coverage of 59 complete WRC sequences found on NCBI and 33 WRC terpene synthase sequences (Shalev et al. 2018) . Sequences were searched against the genome using BLAST+ v2.10.0 (Altschul et al. 1990; Camacho et al. 2009), and presence or absence analysed using EXONERATE. SCOs were identified using OrthoFinder v2.5.4 (Emms and Kelly 2019), isolating genes identified in one copy in the WRC gene set when compared against the giant sequoia gene set (Scott et al. 2020). 3.5.4 SNP genotyping Targeted sequencing-based genotyping was done by Capture-Seq methodology at Rapid Genomics (Gainesville FL, USA). A set of 57,630 probes was designed for initial marker discovery, from which a panel of 20,858 probes was selected for genotyping. A set of transcriptomes (Shalev et al. 2018) (PRJNA704616) was aligned to the reference genome to identify SNPs. Candidate probes (120 nt) were initially designed in silico and were selected by removing candidates with poor base composition for hybridization (GC content <0.2 and >0.6, high G content >0.2 and long homopolymers >7), followed by removing probes aligning to more than one position on the reference genome (≥90% identity and length). A final set of 57,630 probes was selected, representing 14,517 scaffolds (average 3.9 probes/scaffold), with 37,294 targeting at least one SNP and 19,706 mapping to intergenic regions not containing pre-identified SNPs. 78 A set of 128 individuals were selected to validate the probe panel and associated polymorphisms. Genomic DNA (0.5 µg) was fragmented (mean size 300 bp), followed by repair of ends, phosphorylation, adenylation, ligation of Illumina compatible adapters containing 8bp indexes and 5’ T-overhang, and 10 cycles PCR amplification with universal primers to produce sequencing-ready libraries. Libraries were quantified using PicoGreen. Libraries from 16 samples were pooled, hybridized to the 120 nt RNA probes following Agilent’s SureSelect Target Enrichment System (Agilent Technologies) and sequenced on an Illumina HiSeq X machine with paired-end 150 bp cycle for an average sequencing depth per sample of 15×. Sequence data were aligned to the reference genome with BWA-MEM v0.7.17 (Li 2013) and sets of four samples were combined to increase sequencing depth for identifying markers. Putative SNPs were identified using FreeBayes v1.2.0 (Garrison and Marth 2012) in 150bp on either side of the 57,630 probes and filtered probes that had more than 17 SNPs per 420 bp target region (150 bp + 120 bp + 150 bp) to avoid over-capture. The sequencing depth of the probes was used to select the final set of 20,858 probes, removing probes on both sides of the distribution (low and high sequencing depth), for Capture-Seq on the remainder of the samples. SNP discovery was carried out for a large group of 4,832 trees, which included our RWP, SLs, and training and target populations for genomic selection; the latter two were not included in analyses for this study. 79 3.5.5 SNP filtering and annotation Variant sites were filtered using VCFtools v0.1.17 (Danecek et al. 2011) with the following flags: --max-missing 0.95; --mac 3; --minQ 30; --min-meanDP 15; --max-meanDP 60. SNPs with an allele balance > 0.25 and < 0.75 or < 0.01 were retained to eliminate incorrectly called heterozygotes using vcffilter in vcflib v1.0.1 (Garrison 2016). A stringent maximum mean depth of 60× was used to reduce the likelihood of erroneous calls due to sequence paralogy. SNPs were also filtered for excess heterozygosity and deviations from HWE in the RWP at a p-value cutoff of 0.001. This extremely stringent cut-off was used to account for expected low outcrossing rate and to further eliminate potential paralogs. Invariant sites were filtered using the following flags: --max-missing 0.95; --min-meanDP 15; --max-meanDP 60. Variant effect prediction was carried out using the Ensembl Variant Effect Predictor r103; one effect per SNP was selected, and for compound effects, only the most severe consequence was retained (McLaren et al. 2016). Relationships between trees were estimated by generating a genomic realized relationship matrix for all individuals using the ‘A.mat’ function of rrBLUP v4.6.1 in R (Endelman 2011; R Core Team 2021). For the RWP, five trees with a relatedness coefficient > 0.2 were excluded from analyses. For the SLs, nine individuals whose relationships did not match the a priori pedigree were removed from analysis (Table S1). 80 3.5.6 Linkage disequilibrium Pairwise LD (r2) was estimated in the RWP using PLINK v1.9 (Chang et al. 2015). LD was calculated for all scaffolds containing at least two SNPs (--r2; --ld-window-r2 0; --ld-window-kb 999999; --ld-window 999999; --maf 0.05). Under drift-recombination equilibrium, our expectation of LD decay over distance will be: 𝐸𝐸�𝑟𝑟2� = 1(1 + 𝐶𝐶) Where C is the product of the population recombination parameter ρ = 4Ner and the distance in bp, Ne is the effective population size and r is the recombination rate per bp (Sved 1971). Adjusting for population size n and a low level of mutation, decay of LD was estimated as a factor of n, and C (Hill and Weir 1988; Remington et al. 2001; Marroni et al. 2011; Fahrenkrog et al. 2017). 𝐸𝐸�𝑟𝑟2� = � 10 + 𝐶𝐶(2 + 𝐶𝐶)(11 + 𝐶𝐶)� �1 + (3 + 𝐶𝐶)(12 + 12𝐶𝐶 + 𝐶𝐶2)𝑛𝑛(2 + 𝐶𝐶)(11 + 𝐶𝐶) � C was estimated by non-linear regression, using the nls function in R. 3.5.7 Population structure and genetic differentiation SNPs were further filtered to include only SNPs with an r2 threshold of 0.1 using a 1.65 Mb window, following the outcome of our LD analysis, a MAC threshold of 3 was used to remove singletons, and only SCO and intergenic SNPs were retained (n = 5,114 SNPs). Population 81 structure of the RWP was estimated using STRUCTURE v2.3.4 (Pritchard et al. 2000; Falush et al. 2007) and a DAPC using the adegenet package (v2.1.1) in R (Jombart 2008; Jombart et al. 2010; Jombart and Ahmed 2011). A hierarchical FST test as implemented in the hierfstat package (v0.04-22) in R (Goudet 2005) was used to assess genetic differentiation between subpopulations in the RWP and a Mantel test was executed using mantel.randtest for 9,999 permutations in ade4 v1.7-15 in R (Dray and Dufour 2007) to assess isolation by distance. PCA was performed on the genotype matrix of each subpopulation to visualize genetic distance between individuals using ade4. STRUCTURE software was run using 10,000 MCMC repetitions with 10,000 repetitions of burn-in and 10 iterations of each K. Results were analysed using methods of Evanno et al. (2005) and Puechmaille (2016) of cluster and admixture estimation and selection; fastStructure v1.0 (Raj et al. 2014) was also used with 10-fold cross-validation. For DAPC, find.clusters was used to select the optimal number of clusters based on Bayesian Information Criterion (BIC), and 10-fold cross-validation with 1,000 replicates was performed with xvaldapc to select the number of PCs and discriminant functions to retain. 3.5.8 Nucleotide diversity To avoid downward bias due to missing data, variant and invariant sites were used to estimate π and dXY for n = 10,632 SCO genes and 10 kb windows in the RWP using pixy v1.2.6.beta1 (Korunes and Samuk 2021). Zero- and four-fold degenerate sites were identified using the 82 NewAnnotateRef.py script (Williamson et al. 2014), and π0/π4 was calculated over all SCO genes, standardized by the number of sites at each locus to account for differing numbers of 0- and 4-fold degenerate sites. 3.5.9 Effective population size (Ne) We estimated Ne using the LD model estimation method under random mating as implemented in NeEstimator v2.1 (Do et al. 2014). This method uses background LD shared among samples to estimate Ne; thus, SNPs with as little LD as possible are required (Waples and Do 2008; Gilbert and Whitlock 2015). Due to the high LD observed in WRC, we first generated putative linkage groups (LGs) for the WRC genome by aligning all genomic scaffolds containing SNPs to the giant sequoia genome (Scott et al. 2020) using BLAST+. Scaffolds were assigned to their most likely LG based on bitscore. We then used the nucmer command from MUMmer v4 (Marçais et al. 2018) to determine the most likely alignment region for each scaffold in each LG (Dataset S8). We retained SNPs estimated to be at least 1.7 Mbp. A MAF threshold of 0.05 was established to eliminate bias that may be introduced by rare alleles, and a 95% nonparametric JackKnife confidence interval was taken for the estimated value, as recommended by Waples and Do (2008) and Gilbert and Whitlock (2015). Stairway Plot 2 (Liu and Fu 2020) was used on the folded SFS from 4-fold degenerate positions to further assess Ne changes over time. We used the following parameters: nseq = 224; 83 L = 115,800; pct_training = 0.67; nrand = 55, 111, 167, 222; ninput = 200; mu = 7e-9; year_per_generation = 50. 3.5.10 Genotype correction for SLs Genotype correction in continuous SLs used two criteria: Individuals with homozygous calls in at least two consecutive generations were considered to be homozygous for that allele in all subsequent generations; and individuals with heterozygous calls in at least two consecutive generations were considered to be heterozygous for all preceding generations, up to and including the FS generation. SNPs that could not be corrected following these criteria were removed. We manually corrected genotypes for SLs which had been completely genotyped from either S1 – S4 or S1 – S5 (Table S1). For SLs in which only the S3 generation had not been sequenced, we imputed the genotypes for the S3 generation at each locus for SNPs where no other genotype was possible, and marked the rest as missing. 3.5.11 Change in heterozygosity and inbreeding coefficients over time Corrected genotypes were used to calculate the observed and expected changes in heterozygosity and inbreeding coefficients over time in the SLs, and in the RWP. Observed heterozygosity was calculated at each SNP locus for each generation across all SLs using the adegenet package in R. Expected heterozygosities in the S1 – S5 generations were calculated for each SNP locus as half the observed heterozygosity of the previous generation. We calculated inbreeding coefficients in 84 PLINK using the --ibc flag to obtain a measure for inbreeding based on the correlation between uniting gametes (FUNI). This metric is defined by Yang et al. (2011) for each ith SNP and each jth individual as 𝐹𝐹UNI = 𝑥𝑥𝑖𝑖𝑖𝑖2 − (1 + 2𝑝𝑝𝑖𝑖)𝑥𝑥𝑖𝑖𝑖𝑖 + 2𝑝𝑝𝑖𝑖22𝑝𝑝𝑖𝑖(1 − 𝑝𝑝𝑖𝑖) Where x is the number of copies of the reference allele and p is the population-wide allele frequency at that locus. Calculations of FUNI do not consider LD; thus, we used SNPs filtered for LD and MAC as described above. In a diploid population, F should increase as 𝐹𝐹𝑡𝑡+1 = 12𝑁𝑁𝑒𝑒 +�1 − 12𝑁𝑁𝑒𝑒� 𝐹𝐹𝑡𝑡 each generation; thus, under complete selfing we expect F to increase by a factor of 𝐹𝐹𝑡𝑡+1 = 12 (1 + 𝐹𝐹𝑡𝑡). F was calculated using corrected genotypes in SLs and uncorrected genotypes in the RWP. 3.5.12 Isolating SNPs significantly deviating from expectations of drift To identify loci that diverge from patterns expected under genetic drift, we evaluated all SNPs for which the FS generation was heterozygous and then observed whether the SNP went to fixation or not by the S4 generation in our 28 complete, corrected SLs. The S5 generation was excluded from this analysis due to small sample size. For statistical analysis, each SL was considered an independent replicate. SLs were categorized at each generation as 'fixed for reference allele', 'fixed for alternate allele', or 'not fixed'. The observed number of SLs in each 85 category was tabulated for the S4 generation. The expected number of SLs in each category was calculated following the expectation of a 50% reduction in heterozygosity in each generation, resulting in an expectation of 6.25% of the SLs being heterozygous, 46.875% being homozygous for the reference allele, and 46.875% being homozygous for the alternate allele in the S4 generation. A χ2 test was performed for SNPs with genotyping data present in at least 10 SLs to test for significant differentiation from this expectation, and a false discovery rate of 10-5 was used for Holm correction to correct for multiple hypothesis testing across all SNPs. Variant effects were predicted for significant SNPs, and a Fisher's Exact Test was used to determine the presence of over or under-representation of significant SNPs and over-representation of GO categories in the significant SNPs. 3.5.13 Data access The genome sequence reads, assembly and annotation, and transcriptomes used in annotation generated in this study have been submitted to the NCBI BioProject database (https://www.ncbi.nlm.nih.gov/bioproject/) under accession number PRJNA704616. The SNP data generated in this study has been submitted to the Zenodo data repository under DOI 10.5281/zenodo.6562381, and is available in the Supplemental Datasets. Code and raw data used for generating data files and figures, as well as copies of the genome annotation and Supplemental Dataset files have been uploaded to the following GitHub repository: https://github.com/tshalev/WRC-genome-paper. 86 3.6 Tables Table 3.1: Overview of selfing lines (SLs) selected for the study. Selfing Line FS S1 S2 S3 S4 S5 *** 1 1_1 111 111-2 111-22 111-221 111-221-S5 1 1_2 122 122-1 122-12 122-121 1 1_4 145 145-5 145-54 145-545 145-545-S5 *** 6 6_1 611 611-1 611-11 611-111 6 6_2 623 623-2 623-21 623-211 *** 6 6_4 646 646-4 646-45 646-454 646-454-S5 7 7_1 712 712-1 712-11 712-111 *** 7 7_2 721 721-2 721-21 721-211 *** 7 7_4 745 745-4 745-44 745-444 *** 8* 8_2 821 821-1 821-11 821-111 *** 8 8_4 845 845-4 845-44 845-444 8 8_5 854 854-5 854-54 854-544 10 10_1 1011 1011-1 1011-12 1011-121 87 *** 10 10_2 1022 1022-1 1022-11 1022-113 1022-113-S5 *** 10 10_5 1056 1056-4 1056-45 1056-454 13 13_2 1323 1323-2 1323-21 1323-211 *** 13 13_3 1332 1332-1 1332-11 1332-111 *** 13 13_4 1345 1345-5 1345-54 1345-544 *** 16 16_1 1611 1611-2 1611-21 1611-211 16 16_2 1621 1621-2 1621-22 1621-221 *** 16 16_5 1654 1654-4 1654-45 1654-454 1654-454-S5 *** 17 17_2 1721 1721-1 1721-11 1721-111 1721-111-S5 17 17_4 1744 1744-5 1744-54 1744-544 *** 17 17_5 1755 1755-5 1755-54 1755-545 1755-545-S5 19 19_1 1912 1912-1 1912-13 1912-131 *** 19 19_2 1922 1922-1 1922-11 1922-111 *** 19 19_5 1955 1955-5 1955-54 1955-544 1955-544-S5 *** 20 20_1 2013 2013-1 2013-13 2013-131 *** 20 20_4 2045 2045-5 2045-54 2045-544 88 21 21_1 2111 2111-1 2111-11 2111-111 *** 21 21_2 2121 2121-1 2121-11 2121-111 2121-111-S5 *** 21 21_6 2165 2165-4 2165-44 2165-444 2165-444-S5 *** 23 23_2 2323 2323-2 2323-21 2323-211 2323-211-S5** *** 23 23_4 2344 2344-4 2344-46 2344-464 *** 26 26_1 2612 2612-1 2612-12 2612-121 *** 26 26_4 2645 2645-4 2645-46 2645-464 27 27_1 2712 2712-2 2712-21 2712-211 *** 27 27_2 2721 2721-1 2721-12 2721-121 27 27_6 2765 2765-5 2765-55 2765-554 *** 29 29_2 2923 2923-1 2923-12 2923-121 2923-121-S5 *** 29 29_4 2944 2944-5 2944-56 2944-565 2944-565-S5 89 Sequenced; Correctly Labeled Manually imputed Sequenced; Mislabeled Not Sequenced; Dead/Missing Not Sequenced; not imputed The first digit is the line identifier. Each subsequent digit identifies the seedling that was chose to continue that SL. Every generation adds an additional digit to assign a unique identifier to each individual up until the S4 generation. S5 is indicated by adding S5 to the end of the identifier. * Entire FS line was incorrectly labeled and is unrelated to the other lines starting with "8". ** S5 tree that was used for genome sequencing and assembly. *** Lines used in heterozygosity and selection under selfing analysis (n = 28). 90 Table 3.2: Overview of sequencing data generated for the western redcedar draft genome. Library type Fragment Lanes Sequence Depth Paired end 431 bp 3 437 Gbp 35 433 bp 3 412 Gbp 33 430 bp 2 257 Gbp 20.6 419 bp 2 252 Gbp 20.2 Total 10 1,358 Gbp 108.6 Mate pair 2.7 kbp 2 239 Gbp 19.1 5.4 kbp 2 283 Gbp 22.6 9.0 kbp 2 283 Gbp 22.6 10.9 kbp 2 282 Gbp 22.6 15.5 kbp 2 259 Gbp 20.7 Total 10 1,346 Gbp 107.7 Linked-reads 35 kbp 2 236 Gbp 18.9 35 kbp 2 241 Gbp 19.3 20 kbp 2 241 Gbp 19.3 22 kbp 2 241 Gbp 19.3 Total 8 959 Gbp 76.7 The sequencing read length is 2 x 151 bp. Depth of coverage is calculated using raw sequencing data before trimming adapters and an estimated genome size of 12.8 Gbp. 91 Table 3.3: Assembly metrics and statistics for each version of the WRC draft genome. Assembly version N50 (Mbp) NG50 (Mbp) Largest scaffold (Mbp) Size (Gbp) L50 (bp) LG50 (bp) Scaffolds > 1kbp redcedar-v1 1.45 1.07 9.79 7.95 1,642 2,463 94,166 redcedar-v2 2.23 1.63 15.3 7.95 1,067 1,605 90,083 redcedar-v3 2.31 1.71 16.3 7.95 1,035 1,551 67,895 92 Table 3.4: Metrics for each stage of the WRC draft genome assembly (v3). Stage N50 (Mbp) NG50 (Mbp) Largest scaffold (Mbp) Size (Gbp) L50 (bp) LG50 (bp) Scaffolds Unitigs 0.00698 0.00420 0.0119 7.52 294,835 524,000 1,811,335 Contigs (PE) 0.0178 0.0129 166 7.97 128,893 196,169 882,457 Scaffolds (MP) 0.377 0.280 2.77 7.95 6,320 9,479 95,754 Tigmint 0.375 0.278 2.77 7.95 6,355 9,531 96,557 ARCS 2.04 1.50 16.3 7.95 1,176 1,761 68,083 Tigmint + ARCS 2.31 1.71 16.3 7.95 1,035 1,551 67,895 NG50 is calculated using a genome size of 10 Gbp, rounded up from the 9.8 Gbp estimated by GenomeScope. Number of scaffolds and draft genome size includes sequences that are 1 kbp or larger. "Scaffolds (MP)" shows the assembly contiguity after scaffolding with Illumina mate-pair sequencing. "Tigmint + ARCS" shows the assembly contiguity after correcting misassemblies using Tigmint and then scaffolding with ARCS. "ARCS" scaffolds the assembly without first correcting misassemblies using Tigmint. Comparing these two rows shows the effects of Tigmint on improving contiguity. 93 Table 3.5: A) Genome assembly completeness of seven currently available conifer genome assemblies as estimated by BUSCO v5.0.0 in genome mode (Embryophyta OrthoDB v10, Metaeuk). B) Predicted gene set completeness of seven sequenced conifer genomes as estimated by BUSCO v5.0.0 in protein mode (Embryophyta OrthoDB v10). A Taxon Thuja plicata Sequoiadendron giganteum (Scott et al. 2020) Picea glauca (Warren et al. 2015) Picea abies (Nystedt et al. 2013) Pinus lambertiana (Stevens et al. 2016) Pinus taeda (Zimin et al. 2014) Pseudotsuga menziesii (Neale et al. 2017) Western redcedar Giant sequoia White spruce Norway spruce Sugar pine Loblolly pine Douglas-fir Family Cupressaceae Cupressaceae Pinaceae Pinaceae Pinaceae Pinaceae Pinaceae Complete BUSCOs (C) 1388 1361 802 564 995 803 1196 Complete and single-copy BUSCOs (S) 1293 1272 678 468 881 664 1067 Complete and duplicated BUSCOs (D) 95 89 124 96 114 139 129 Fragmented BUSCOs (F) 128 116 387 433 321 454 242 Missing BUSCOs (M) 98 137 425 617 298 357 176 Total BUSCO groups searched 1614 1614 1614 1614 1614 1614 1614 Percentage complete 86.0% 84.3% 49.7% 34.9% 61.5% 49.7% 74.1% 94 B Taxon Thuja plicata Sequoiadendron giganteum Picea glauca Picea abies Pinus lambertiana Pinus taeda Pseudotsuga menziesii Western redcedar Giant sequoia White spruce Norway spruce Sugar pine Loblolly pine Douglas-fir Family Cupressaceae Cupressaceae Pinaceae Pinaceae Pinaceae Pinaceae Pinaceae Complete BUSCOs (C) 1461 806 291 454 1183 673 1106 Complete and single-copy BUSCOs (S) 1367 747 141 381 1090 579 1014 Complete and duplicated BUSCOs (D) 94 59 150 73 93 94 92 Fragmented BUSCOs (F) 69 260 172 441 121 313 190 Missing BUSCOs (M) 84 548 1151 719 310 628 318 Total BUSCO groups searched 1614 1614 1614 1614 1614 1614 1614 Percentage complete 90.5% 49.9% 18.0% 28.1% 73.3% 41.7% 68.5% 95 Table 3.6: Annotation metrics and statistics for the western redcedar draft genome. Primary transcripts (loci) 39,659 Alternative transcripts (loci) 26,150 Total transcripts (loci) 65,809 For primary transcripts: Average number of exons per transcript 4.1 Median exon length (bp) 188 Median intron length (bp) 235 Minimum intron length (bp) 20 Maximum intron length (bp) 148,235 Gene model support (value is number of gene models): Any EST support 33,377 EST support over 100% of their lengths 31,537 EST support over 95% of their lengths 32,083 EST support over 90% of their lengths 32,352 EST support over 75% of their lengths 32,732 EST support over 50% of their lengths 32,983 Peptide homology coverage of 100% 1,388 Peptide homology coverage of over 95% 13,980 Peptide homology coverage of over 90% 19,506 Peptide homology coverage of over 75% 27,030 Peptide homology coverage of over 50% 31,979 Pfam annotation 25,894 Panther annotation 30,847 KOG annotation 12,416 KEGG Orthology annotation 7,088 E.C. number annotation 11,362 96 Table 3.7: Verification of the western redcedar (WRC) draft genome annotation. Of a set of 59 WRC transcripts from NCBI and 33 WRC terpene synthase transcripts, 48 and 15 were reliably identified (>90% BLASTN identity, best match in EXONERATE), respectively, in the WRC draft genome. Gene Genomic scaffold Sequence source 1 gi|1001017507|gb|KU701356.1| Thuja plicata putative LOV domain-containing protein mRNA, complete cds 29379333 NCBI 2 gi|1241840804|gb|KY214317.1| Thuja plicata putative glycoside hydrolase family 2 protein mRNA, complete cds 29378743 NCBI 3 gi|1242943498|gb|KY233467.1| Thuja plicata putative MNS3 mRNA, complete cds 29381436 NCBI 4 gi|1242943576|gb|KY233506.1| Thuja plicata putative sphingomyelin synthetase family protein mRNA, complete cds 29378174 NCBI 5 gi|1242943687|gb|KY233562.1| Thuja plicata putative SPPA1 mRNA, complete cds 29378649 NCBI 6 gi|1242943771|gb|KY249831.1| Thuja plicata putative ABA hypersensitive 1 mRNA, complete cds 29380247 NCBI 7 gi|1242943809|gb|KY249738.1| Thuja plicata putative ABCG7 mRNA, complete cds 29378727 NCBI 8 gi|1242943845|gb|KY249850.1| Thuja plicata putative ATRRP44A mRNA, complete cds 29382381 NCBI 9 gi|1242944211|gb|KY296273.1| Thuja plicata cyclophilin-like peptidyl-prolyl cis-trans isomerase family protein mRNA, complete cds 29377565 NCBI 10 gi|1242944351|gb|KY296166.1| Thuja plicata putative OWL1 mRNA, complete cds 29377407 NCBI 11 gi|1248694559|gb|KY314818.1| Thuja plicata ATTRM11 mRNA, complete cds 29380894 NCBI 12 gi|1248694675|gb|KY314876.1| Thuja plicata FG-GAP repeat-containing protein mRNA, complete cds 29377050 NCBI 97 13 gi|1248694816|gb|KY314948.1| Thuja plicata INO80 mRNA, complete cds 29379961 NCBI 14 gi|1248694885|gb|KY314984.1| Thuja plicata ATCCH1 fatty acid oxygenation upregulated 2 mRNA, complete cds 29377034 NCBI 15 gi|1248694997|gb|KY367630.1| Thuja plicata cytochrome P450 mRNA, complete cds 29382440 NCBI 16 gi|1248695181|gb|KY367685.1| Thuja plicata FGGY family of carbohydrate kinase mRNA, complete cds 29378653 NCBI 17 gi|1248695219|gb|KY367704.1| Thuja plicata importin alpha isoform 9 mRNA, complete cds 29381189 NCBI 18 gi|1248695293|gb|KY367741.1| Thuja plicata HCF145 mRNA, complete cds 29377609 NCBI 19 gi|1248695329|gb|KY367759.1| Thuja plicata AP4B mRNA, complete cds 29377919 NCBI 20 gi|1248695367|gb|KY367796.1| Thuja plicata no exine fromation 1 mRNA, complete cds 29379245 NCBI 21 gi|1248695439|gb|KY367851.1| Thuja plicata Spc97 / Spc98 family mRNA, complete cds 29379997 NCBI 22 gi|1248695589|gb|KY367926.1| Thuja plicata saccharopine dehydrogenase mRNA, complete cds 29381726 NCBI 23 gi|1248695663|gb|KY367963.1| Thuja plicata ATDUR3 mRNA, complete cds 29377833 NCBI 24 gi|1248695843|gb|KY368053.1| Thuja plicata Zn-dependent exopeptidases superfamily protein mRNA, complete cds 29382329 NCBI 25 gi|1248695921|gb|KY368092.1| Thuja plicata ATDPE1 mRNA, complete cds 29377587 NCBI 26 gi|1786237212|gb|MK817116.1| Thuja plicata DXS1 mRNA, complete cds 29379753 NCBI 27 gi|1786237229|gb|MK817117.1| Thuja plicata DXS2B mRNA, complete cds 29381756 NCBI 28 gi|1786237253|gb|MK817118.1| Thuja plicata DXS2A mRNA, complete cds 29378340 NCBI 29 gi|1786237278|gb|MK817119.1| Thuja plicata DXS3 mRNA, complete cds 29377322 NCBI 98 30 gi|1890528724|gb|MT468207.1| Thuja plicata diterpene synthase (diTPS1) mRNA, complete cds 29379660 NCBI 31 gi|1890528727|gb|MT468208.1| Thuja plicata diterpene synthase (diTPS2) mRNA, complete cds 29381898 NCBI 32 gi|1890528729|gb|MT468209.1| Thuja plicata diterpene synthase (diTPS3) mRNA, complete cds 29382319 NCBI 33 gi|510793902|gb|KC767281.1| Thuja plicata sabinene synthase mRNA, complete cds 29377789 NCBI 34 gi|635148812|gb|KJ195050.1| Thuja plicata phototropin (PHOT1) mRNA, complete cds 29378985 NCBI 35 gi|635148826|gb|KJ195057.1| Thuja plicata phototropin (PHOT2) mRNA, complete cds 29378009 NCBI 36 gi|6694696|gb|AF210063.1| Thuja plicata dirigent-like protein (psd_Tp1) mRNA, complete cds 29378666 NCBI 37 gi|6694698|gb|AF210064.1| Thuja plicata dirigent protein (psd_Tp2) mRNA, complete cds_Tp4) mRNA, complete cds 29377524 NCBI 38 gi|6694702|gb|AF210066.1| Thuja plicata dirigent-like protein (psd_Tp4) mRNA, complete cds 29380546 NCBI 39 gi|6694706|gb|AF210068.1| Thuja plicata dirigent-like protein (psd_Tp6) mRNA, complete cds 29377169 NCBI 40 gi|6694708|gb|AF210069.1| Thuja plicata dirigent protein (psd_Tp7) mRNA, complete cds 29207864 NCBI 41 gi|7542587|gb|AF242506.1| Thuja plicata clone 4 pinoresinol-lariciresinol reductase mRNA, complete cds 29377837 NCBI 42 gi|7578912|gb|AF242500.1| Thuja plicata phenylcoumaran benzylic ether reductase homolog Tp1 mRNA, complete cds 29381391 NCBI 43 gi|821372382|gb|KP004988.1| Thuja plicata (+)-sabinene-3-hydroxylase mRNA, complete cds 29379924 NCBI 44 gi|821372384|gb|KP015848.1| Thuja plicata cytochrome P450 CYP76AA20 mRNA, complete cds 29381501 NCBI 45 gi|821372386|gb|KP015849.1| Thuja plicata cytochrome P450 CYP76AA21 mRNA, complete cds 29380148 NCBI 46 gi|821372394|gb|KP015853.1| Thuja plicata cytochrome P450 CYP76AA25 mRNA, complete cds 29377693 NCBI 99 47 gi|821372396|gb|KP015854.1| Thuja plicata cytochrome P450 CYP76AA26 mRNA, complete cds 29377042 NCBI 48 gi|821372398|gb|KP015855.1| Thuja plicata cytochrome P450 CYP76Z2 mRNA, complete cds 29377249 NCBI 1 TpTPS2_evgvelvLoc955t9 29194648 Putative WRC TPS transcripts 2 TpTPS3_evgvelvLoc2319t10 29382319 Putative WRC TPS transcripts 3 TpTPS10_evgvelvLoc9436t1 29381898 Putative WRC TPS transcripts 4 TpTPS13_evgvelvLoc5170t3 29378510 Putative WRC TPS transcripts 5 TpTPS21_evgvelvLoc4915t2 29379564 Putative WRC TPS transcripts 6 TpTPS24_evgvelvLoc16400d36129t1 29381804 Putative WRC TPS transcripts 7 TpTPS25_evgvelvLoc2138t7 29382319 Putative WRC TPS transcripts 8 TpTPS26_evgvelvLoc4951t5 29380592 Putative WRC TPS transcripts 9 TpTPS27_evgvelvLoc1076t3 29377789 Putative WRC TPS transcripts 10 TpTPS28_evgvelvLoc842d111924t1 29379713 Putative WRC TPS transcripts 100 11 TpTPS29_evgvelvLoc7235d121309t1 29380776 Putative WRC TPS transcripts 12 TpTPS30_evgvelvLoc16257d162311t1 29381146 Putative WRC TPS transcripts 13 TpTPS31_evgR1187289 29380228 Putative WRC TPS transcripts 14 TpTPS32_evgR741119 29379604 Putative WRC TPS transcripts 15 TpTPS33_evgS303450 29380592 Putative WRC TPS transcripts 101 Table 3.8: The range-wide population (n = 112) of western redcedar used in this study. The population was stratified into three subpopulations based on the analysis of O’Connell et al. (2008). Tree ID Subpopulation Location Latitude (° N) Longitude (° W) Elevation (m) 2 Central Jordan River 48.477 -123.929 50 85 Central Powell Lake 50.233 -124.22 15 148 Central Tzoonie River 49.867 -123.44 425 149 Central Tzoonie River 49.867 -123.44 365 150 Central Tzoonie River 49.867 -123.733 450 164 Central Powell Lake 50.30 -124.37 15 165 Central Lois Lake 49.817 -124.19 250 171 Central Lewis Lake 49.933 -124.22 470 173 Central Labour Day Lake 49.133 -124.27 975 175 Central Francis Lake 48.933 -124.42 503 176 Central San Mateo Bay 48.95 -124.59 90 181 Central Cameron Lake 49.32 -124.48 15 191 Central Jervis Inlet 50.117 -123.783 98 202 Central Red Tusk 49.767 -123.24 240 203 Central Sechelt Creek 49.676 -123.535 400 204 Central Bear Creek 49.8 -123.32 580 263 Central Narrows Inlet 49.083 -123.733 120 264 Central Narrows Inlet 49.083 -123.44 210 351 Central Second Lake 49.1 -124.13 300 392 Central Hardy Island 49.75 -124.18 37 394 Central Lois Lake 49.8 -124.17 262 426 Central Jordan River 48.52 -124.17 730 430 Central Errington 49.267 -124.05 152 435 Central Chemainus 48.933 -123.75 115 102 438 Central Bamberton 48.617 -123.33 168 497 Central Caycuse Lake 48.8 -124.36 230 1 Northern-Coastal Keogh River 50.617 -127.19 61 5 Northern-Coastal Mahatta Creek 50.4 -127.783 122 10 Northern-Coastal Mahatta Creek 50.317 -127.39 396 15 Northern-Coastal Mahatta Creek 50.4 -127.783 155 19 Northern-Coastal Benson Lake 50.4 -127.26 637 20 Northern-Coastal Snowsaddle Mountain 50.25 -127.33 305 40 Northern-Coastal Keogh River 50.633 -127.23 91 44 Northern-Coastal Waukwaas Creek 50.55 -127.18 183 47 Northern-Coastal Waukwaas Creek 50.583 -127.21 30 49 Northern-Coastal Misty Lake 50.633 -127.283 91 58 Northern-Coastal MacJack River 50.617 -128.09 60 87 Northern-Coastal Louise Island 52.92 -131.85 600 88 Northern-Coastal Skedans Islands 52.95 -131.57 395 94 Northern-Coastal Talunkwan Island 52.829 -131.752 335 95 Northern-Coastal Tasu 52.817 -132.04 275 97 Northern-Coastal Tasu 52.817 -132.04 565 113 Northern-Coastal Gogit Passage 52.633 -131.29 245 116 Northern-Coastal Sedgwick Bay 52.6 -131.34 305 120 Northern-Coastal Tanu Passage 52.767 -131.44 365 121 Northern-Coastal Tanu Island 52.767 -131.41 305 126 Northern-Coastal Ramsay Island 52.564 -131.379 150 128 Northern-Coastal Huxley Island 52.432 -131.268 185 133 Northern-Coastal Huston Inlet 52.267 -131.17 185 137 Northern-Coastal Tangil Peninsula 52.791 -131.705 305 138 Northern-Coastal Talunkwan Island 52.829 -131.692 215 144 Northern-Coastal Stewardson Inlet 49.417 -126.18 410 103 156 Northern-Coastal Waring Creek 49.95 -126.08 350 158 Northern-Coastal Cougar Creek 49.717 -126.25 355 220 Northern-Coastal Roselle Lake 50.517 -127 120 221 Northern-Coastal Nimpkish Lake 50.483 -126.59 75 230 Northern-Coastal Long Lake 51.3 -127.02 317 231 Northern-Coastal Long Lake 51.233 -127.14 268 232 Northern-Coastal Naysash Inlet 51.3 -127.2 110 235 Northern-Coastal Amback Creek 51.717 -127.02 189 241 Northern-Coastal Rivers Inlet 51.60 -127.58 300 243 Northern-Coastal Hardy Inlet 51.717 -127.38 159 253 Northern-Coastal Kwatna 52.033 -127.25 985 261 Northern-Coastal Link Lake 52.433 -127.42 950 266 Northern-Coastal Slim Creek 52.87 -132.08 75 281 Northern-Coastal Gwaii Haanas 52.45 -131.45 45 285 Northern-Coastal Misty Lake 50.616 -127.24 90 301 Northern-Coastal Waukwaas River 50.6 -127.23 100 306 Northern-Coastal Denad Creek 50.55 -128 25 322 Northern-Coastal Koprino River 50.55 -127.5 280 325 Northern-Coastal Hathaway Creek 50.55 -127.51 95 329 Northern-Coastal Victoria Lake 50.43 -127.40 123 331 Northern-Coastal Victoria Lake 50.317 -127.22 200 334 Northern-Coastal Snowsaddle Mountain 50.25 -127.2 213 336 Northern-Coastal Victoria Lake 50.35 -127.22 122 340 Northern-Coastal Utlah Creek 50.3 -127.26 183 343 Northern-Coastal Victoria Lake 50.05 -127.417 457 344 Northern-Coastal Victoria Lake 50.05 -127.417 457 347 Northern-Coastal Utlah Creek 50.283 -127.25 244 366 Northern-Coastal Henderson Lake 49.1 -125.02 300 104 367 Northern-Coastal Henderson Lake 49.1 -125.017 200 368 Northern-Coastal Henderson Lake 49.10 -125.03 280 378 Northern-Coastal Tranquil Inlet 49.2 -125.4 3 381 Northern-Coastal Sydney Inlet 49.483 -126.283 65 386 Northern-Coastal Cypre River 49.367 -125.867 180 402 Northern-Coastal Tahsish River 50.15 -127.13 15 404 Northern-Coastal Chamiss Bay 50.08 -127.35 350 415 Northern-Coastal Lull Creek 50.7 -126.02 183 421 Northern-Coastal Wood Bay 50.317 -125.2 200 444 Northern-Coastal Anutz Lake 50.3 -126.55 80 446 Northern-Coastal Kinman Creek 50.35 -126.54 250 454 Northern-Coastal Mt. Bate 49.85 -126.51 1000 461 Northern-Coastal Artlish Caves 50.133 -127.083 100 465 Northern-Coastal Union Island 50.029 -127.277 50 466 Northern-Coastal Hohoae Island 50.048 -127.207 50 490 Northern-Coastal Mitla Creek 49.5 -125.58 220 493 Northern-Coastal Fortune Creek 49.183 -125.44 365 501 Northern-Coastal Snug Bay 49.033 -125.01 60 503 Northern-Coastal Nahmint Lake 49.183 -125.05 150 505 Northern-Coastal Bigtree Creek 50.267 -125.43 150 508 Northern-Coastal Brasseau Bay 50.4 -125.59 45 779 Northern-Coastal Mt. Washington 49.733 -125.14 950 784 Northern-Coastal Mt. Washington 49.7 -125.05 110 214 Southern-Interior Burke Mountain 49.317 -122.44 440 411 Southern-Interior Debeck Creek 49.483 -122.36 275 527 Southern-Interior Tumtum Lake 51.75 -119.09 960 528 Southern-Interior Tumtum Lake 51.083 -119.09 910 531 Southern-Interior East Barrier Lake 50.85 -119.44 610 105 566 Southern-Interior Price Creek 40.05 -124.15 50 648 Southern-Interior Rector Ridge 45.783 -123.4 246 662 Southern-Interior Simmons Loop 45.367 -123.45 246 1081 Southern-Interior Alsea River 44.45 -123.59 49 106 Table 3.9: SNP effects as predicted by Ensembl Variant Effect Predictor (McLaren et al. 2016) for 25,841 SNPs out of the 27,594 SNPs in our dataset. SNP effect # of SNPs % of total Region IMPACT Splice acceptor variant 14 0.0507 Non-coding High Splice donor variant 9 0.0326 Non-coding High Stop gained 120 0.435 Coding High Stop lost 104 0.377 Coding High Start lost 9 0.0326 Coding High Missense variant 4569 16.6 Coding Moderate Splice region variant 311 1.13 Non-coding Low Stop retained variant 74 0.268 Coding Low Synonymous variant 3978 14.4 Coding Low 5' UTR variant 2953 10.7 Non-coding Modifier 3' UTR variant 3423 12.4 Non-coding Modifier Intron variant 2881 10.4 Non-coding Modifier Upstream gene variant 982 3.56 Intergenic Modifier Downstream gene variant 862 3.12 Intergenic Modifier Intergenic variant 5552 20.1 Intergenic Modifier Unknown 1753 6.35 - - Total 27594 Effects are listed from top to bottom in order of severity from most to least severe. IMPACTs are defined by Ensembl as follows: High: The variant is assumed to have high (disruptive) impact in the protein, probably causing protein truncation, loss of function or triggering nonsense mediated decay. Moderate: A non-disruptive variant that might change protein effectiveness. Low: A variant that is assumed to be mostly harmless or unlikely to change protein behaviour. Modifier: Usually non-coding variants or variants affecting non-coding genes, where predictions are difficult or there is no evidence of impact. 107 Table 3.10: Pairwise FST for all subpopulations. FST values are based on Weir and Cockerham’s estimator (Weir and Cockerham 1984). Subpopulation Northern-Coastal Central Southern-Interior Northern-Coastal - - - Central 0.0256 - - Southern-Interior 0.0322 0.00984 - 108 Table 3.11: Annotation for single-copy ortholog genes with highest 1% of π estimates of nucleotide diversity (n = 47). Gene Annotation source Annotation access code Annotation Thupl.29353611s0001 KOG KOG3355 Mitochondrial sulfhydryl oxidase involved in the biogenesis of cytosolic Fe/S proteins Thupl.29353611s0001 EC 1.8.3.2 Thiol oxidase Thupl.29353611s0001 PFAM PF04777 Erv1 / Alr family Thupl.29353611s0001 PANTHER PTHR12645 ALR/ERV Thupl.29353611s0001 KEGGORTH K17783 Thupl.29353611s0001 GO GO:0016972 thiol oxidase activity Thupl.29353611s0001 GO GO:0055114 oxidation-reduction process Thupl.29376125s0001 PFAM PF01535 PPR repeat Thupl.29376125s0001 PANTHER PTHR24015:SF1076 Thupl.29376125s0001 PANTHER PTHR24015 FAMILY NOT NAMED Thupl.29376125s0001 PFAM PF13812 Pentatricopeptide repeat domain Thupl.29376125s0001 PFAM PF13041 PPR repeat family Thupl.29377148s0010 PANTHER PTHR21660 THIOESTERASE SUPERFAMILY MEMBER-RELATED Thupl.29377148s0010 PANTHER PTHR21660:SF8 Thupl.29377148s0010 KOG KOG3328 HGG motif-containing thioesterase Thupl.29377148s0010 EC 3.1.2.20 Acyl-CoA hydrolase Thupl.29377148s0010 KEGGORTH K17362 Thupl.29377148s0010 PFAM PF03061 Thioesterase superfamily Thupl.29377170s0054 GO GO:0015078 hydrogen ion transmembrane transporter activity Thupl.29377170s0054 PANTHER PTHR33445 Thupl.29377170s0054 GO GO:0015986 ATP synthesis coupled proton transport Thupl.29377170s0054 PFAM PF00430 ATP synthase B/B' CF(0) Thupl.29377170s0054 KEGGORTH K02109 F-type H+-transporting ATPase subunit b [EC:3.6.3.14] Thupl.29377170s0054 GO GO:0045263 proton-transporting ATP synthase complex, coupling factor F(o) Thupl.29377194s0003 PANTHER PTHR36382 Thupl.29377260s0007 GO GO:0003676 nucleic acid binding Thupl.29377260s0007 PANTHER PTHR10501 U1 SMALL NUCLEAR RIBONUCLEOPROTEIN A/U2 SMALL NUCLEAR RIBONUCLEOPROTEIN B Thupl.29377260s0007 PANTHER PTHR10501:SF19 109 Thupl.29377260s0007 PFAM PF00076 RNA recognition motif. (a.k.a. RRM, RBD, or RNP domain) Thupl.29377480s0001 PANTHER PTHR31903:SF6 Thupl.29377480s0001 PANTHER PTHR31903 FAMILY NOT NAMED Thupl.29377529s0012 PANTHER PTHR14043:SF2 CASP Thupl.29377529s0012 GO GO:0006891 intra-Golgi vesicle-mediated transport Thupl.29377529s0012 KOG KOG0963 Transcription factor/CCAAT displacement protein CDP1 Thupl.29377529s0012 KEGGORTH K09313 homeobox protein cut-like Thupl.29377529s0012 PFAM PF08172 CASP C terminal Thupl.29377529s0012 GO GO:0030173 integral to Golgi membrane Thupl.29377529s0012 PANTHER PTHR14043 CCAAT DISPLACEMENT PROTEIN-RELATED Thupl.29377537s0021 GO GO:0016592 mediator complex Thupl.29377537s0021 PANTHER PTHR13114 TRANSCRIPTIONAL CO-ACTIVATOR CRSP77-RELATED Thupl.29377537s0021 PFAM PF10156 Subunit 17 of Mediator complex Thupl.29377537s0021 GO GO:0006357 regulation of transcription from RNA polymerase II promoter Thupl.29377537s0021 GO GO:0001104 RNA polymerase II transcription cofactor activity Thupl.29377537s0021 KEGGORTH K15133 Thupl.29377559s0001 PANTHER PTHR33702:SF5 Thupl.29377559s0001 PANTHER PTHR33702 Thupl.29377622s0002 PFAM PF00067 Cytochrome P450 Thupl.29377622s0002 PANTHER PTHR24298 FAMILY NOT NAMED Thupl.29377622s0002 KOG KOG0156 Cytochrome P450 CYP2 subfamily Thupl.29377622s0002 GO GO:0016705 oxidoreductase activity, acting on paired donors, with incorporation or reduction of molecular oxygen Thupl.29377622s0002 PANTHER PTHR24298:SF196 Thupl.29377622s0002 GO GO:0055114 oxidation-reduction process Thupl.29377622s0002 GO GO:0005506 iron ion binding Thupl.29377622s0002 GO GO:0020037 heme binding Thupl.29377723s0001 GO GO:0005515 protein binding Thupl.29377723s0001 PANTHER PTHR27000 Thupl.29377723s0001 PFAM PF08263 Leucine rich repeat N-terminal domain Thupl.29377723s0001 PFAM PF00560 Leucine Rich Repeat Thupl.29377723s0001 KOG KOG0472 Leucine-rich repeat protein 110 Thupl.29377723s0001 PFAM PF13855 Leucine rich repeat Thupl.29377723s0001 EC 2.7.11.1 Non-specific serine/threonine protein kinase Thupl.29377763s0003 KOG KOG0995 Centromere-associated protein HEC1 Thupl.29377763s0003 PANTHER PTHR10643 KINETOCHORE PROTEIN NDC80 Thupl.29377763s0003 PFAM PF03801 HEC/Ndc80p family Thupl.29377763s0003 KEGGORTH K11547 kinetochore protein NDC80 Thupl.29377821s0008 PANTHER PTHR34960 Thupl.29378039s0003 KEGGORTH K15032 Thupl.29378039s0003 GO GO:0003690 double-stranded DNA binding Thupl.29378039s0003 GO GO:0006355 regulation of transcription, DNA-dependent Thupl.29378039s0003 PANTHER PTHR13068 CGI-12 PROTEIN-RELATED Thupl.29378039s0003 PANTHER PTHR13068:SF78 Thupl.29378039s0003 PFAM PF02536 mTERF Thupl.29378171s0004 PANTHER PTHR31956:SF2 Thupl.29378171s0004 PANTHER PTHR31956 FAMILY NOT NAMED Thupl.29378171s0004 GO GO:0016788 hydrolase activity, acting on ester bonds Thupl.29378171s0004 SIGNALP SignalP-TM Thupl.29378171s0004 PFAM PF04185 Phosphoesterase family Thupl.29378171s0004 KEGGORTH K01114 phospholipase C [EC:3.1.4.3] Thupl.29378212s0007 PANTHER PTHR11205 RIBOSOMAL PROTEIN S7 Thupl.29378212s0007 KEGGORTH K02992 small subunit ribosomal protein S7 Thupl.29378212s0007 PANTHER PTHR11205:SF23 Thupl.29378212s0007 GO GO:0006412 translation Thupl.29378212s0007 PFAM PF00177 Ribosomal protein S7p/S5e Thupl.29378215s0002 PANTHER PTHR21576:SF11 SUBFAMILY NOT NAMED Thupl.29378215s0002 PANTHER PTHR21576 UNCHARACTERIZED NODULIN-LIKE PROTEIN Thupl.29378215s0002 PFAM PF06813 Nodulin-like Thupl.29378351s0005 PFAM PF04561 RNA polymerase Rpb2, domain 2 Thupl.29378351s0005 EC 2.7.7.6 DNA-directed RNA polymerase Thupl.29378351s0005 GO GO:0032549 ribonucleoside binding Thupl.29378351s0005 GO GO:0003677 DNA binding Thupl.29378351s0005 GO GO:0006351 transcription, DNA-dependent Thupl.29378351s0005 PANTHER PTHR20856 DNA-DIRECTED RNA POLYMERASE I SUBUNIT 2 Thupl.29378351s0005 GO GO:0003899 DNA-directed RNA polymerase activity Thupl.29378351s0005 PANTHER PTHR20856:SF21 Thupl.29378511s0008 PFAM PF00076 RNA recognition motif. (a.k.a. RRM, RBD, or RNP domain) 111 Thupl.29378511s0008 PANTHER PTHR44678:SF2 Thupl.29378511s0008 KOG KOG4205 RNA-binding protein musashi/mRNA cleavage and polyadenylation factor I complex, subunit HRP1 Thupl.29378511s0008 PANTHER PTHR44678 Thupl.29378511s0008 GO GO:0003676 nucleic acid binding Thupl.29378604s0007 PFAM PF14542 GCN5-related N-acetyl-transferase Thupl.29378604s0007 PANTHER PTHR31435:SF1 Thupl.29378604s0007 PANTHER PTHR31435 FAMILY NOT NAMED Thupl.29378642s0001 KOG KOG0048 Transcription factor, Myb superfamily Thupl.29378642s0001 PANTHER PTHR10641 MYB-LIKE DNA-BINDING PROTEIN MYB Thupl.29378642s0001 KEGGORTH K09422 myb proto-oncogene protein, plant Thupl.29378642s0001 PFAM PF00249 Myb-like DNA-binding domain Thupl.29378642s0001 PANTHER PTHR10641:SF741 Thupl.29378866s0005 PFAM PF00564 PB1 domain Thupl.29378866s0005 GO GO:0005515 protein binding Thupl.29378866s0005 PANTHER PTHR45179 Thupl.29379270s0015 SIGNALP SignalP-noTM Thupl.29379270s0015 PANTHER PTHR31151:SF0 SUBFAMILY NOT NAMED Thupl.29379270s0015 PFAM PF07944 Putative glycosyl hydrolase of unknown function (DUF1680) Thupl.29379270s0015 GO GO:0046373 L-arabinose metabolic process Thupl.29379270s0015 PFAM PF05270 Alpha-L-arabinofuranosidase B (ABFB) Thupl.29379270s0015 PANTHER PTHR31151 FAMILY NOT NAMED Thupl.29379270s0015 KEGGORTH K09955 hypothetical protein Thupl.29379270s0015 GO GO:0046556 alpha-N-arabinofuranosidase activity Thupl.29379270s0015 EC 3.2.1.185 Non-reducing end beta-L-arabinofuranosidase Thupl.29379302s0001 PFAM PF00400 WD domain, G-beta repeat Thupl.29379302s0001 GO GO:0005515 protein binding Thupl.29379302s0001 PANTHER PTHR44566 Thupl.29379302s0001 KOG KOG0265 U5 snRNP-specific protein-like factor and related proteins Thupl.29379350s0002 KOG KOG0747 Putative NAD+-dependent epimerases Thupl.29379350s0002 EC 2.5.1.6 Methionine adenosyltransferase Thupl.29379350s0002 PFAM PF04321 RmlD substrate binding domain Thupl.29379350s0002 PANTHER PTHR43242 Thupl.29379356s0013 PFAM PF11069 Protein of unknown function (DUF2870) Thupl.29379356s0013 PANTHER PTHR13238:SF0 SUBFAMILY NOT NAMED Thupl.29379356s0013 PANTHER PTHR13238 PROTEIN C21ORF59 112 Thupl.29379568s0009 PANTHER PTHR13282:SF7 Thupl.29379568s0009 PANTHER PTHR13282 UNCHARACTERIZED Thupl.29379760s0002 PFAM PF14629 Origin recognition complex (ORC) subunit 4 C-terminus Thupl.29379760s0002 PANTHER PTHR12087 ORIGIN RECOGNITION COMPLEX SUBUNIT 4 Thupl.29379760s0002 KEGGORTH K02606 origin recognition complex subunit 4 Thupl.29379760s0002 PFAM PF13191 AAA ATPase domain Thupl.29379760s0002 KOG KOG2228 Origin recognition complex, subunit 4 Thupl.29379768s0005 PANTHER PTHR11538:SF54 Thupl.29379768s0005 PFAM PF10354 Domain of unknown function (DUF2431) Thupl.29379768s0005 PANTHER PTHR11538 PHENYLALANYL-TRNA SYNTHETASE Thupl.29379768s0005 KEGGORTH K19307 Thupl.29379768s0005 KOG KOG4174 Uncharacterized conserved protein Thupl.29380174s0008 KOG KOG2255 Peptidyl-tRNA hydrolase Thupl.29380174s0008 PANTHER PTHR17224 PEPTIDYL-TRNA HYDROLASE Thupl.29380174s0008 PFAM PF01195 Peptidyl-tRNA hydrolase Thupl.29380174s0008 SIGNALP SignalP-noTM Thupl.29380174s0008 GO GO:0004045 aminoacyl-tRNA hydrolase activity Thupl.29380174s0008 KEGGORTH K01056 peptidyl-tRNA hydrolase, PTH1 family [EC:3.1.1.29] Thupl.29380174s0008 PANTHER PTHR17224:SF4 Thupl.29380174s0008 EC 3.1.1.29 Aminoacyl-tRNA hydrolase Thupl.29380345s0016 PANTHER PTHR17408:SF0 SUBFAMILY NOT NAMED Thupl.29380345s0016 PFAM PF15247 Histone RNA hairpin-binding protein RNA-binding domain Thupl.29380345s0016 PANTHER PTHR17408 HISTONE RNA HAIRPIN-BINDING PROTEIN Thupl.29380345s0016 GO GO:0003729 mRNA binding Thupl.29380345s0016 GO GO:0003723 RNA binding Thupl.29380622s0001 PFAM PF12907 Zinc-binding Thupl.29380622s0001 PANTHER PTHR33788 Thupl.29380622s0001 PFAM PF04419 4F5 protein family Thupl.29380622s0001 PANTHER PTHR33788:SF7 Thupl.29380839s0001 PANTHER PTHR34132 Thupl.29380839s0001 SIGNALP SignalP-TM Thupl.29380839s0001 PANTHER PTHR34132:SF1 Thupl.29381147s0004 PANTHER PTHR11122 APOSPORY-ASSOCIATED PROTEIN C-RELATED 113 Thupl.29381147s0004 KEGGORTH K01792 glucose-6-phosphate 1-epimerase [EC:5.1.3.15] Thupl.29381147s0004 PFAM PF01263 Aldose 1-epimerase Thupl.29381147s0004 PANTHER PTHR11122:SF22 Thupl.29381147s0004 KOG KOG1594 Uncharacterized enzymes related to aldose 1-epimerase Thupl.29381147s0004 EC 5.1.3.15 Glucose-6-phosphate 1-epimerase Thupl.29381147s0004 GO GO:0016853 isomerase activity Thupl.29381147s0004 GO GO:0005975 carbohydrate metabolic process Thupl.29381153s0002 PANTHER PTHR33384 Thupl.29381313s0001 PFAM PF15697 Thupl.29381388s0004 GO GO:0051287 NAD binding Thupl.29381388s0004 PANTHER PTHR42938:SF14 Thupl.29381388s0004 KOG KOG0068 D-3-phosphoglycerate dehydrogenase, D-isomer-specific 2-hydroxy acid dehydrogenase superfamily Thupl.29381388s0004 PANTHER PTHR42938 Thupl.29381388s0004 EC 1.1.1.95 Phosphoglycerate dehydrogenase Thupl.29381388s0004 GO GO:0055114 oxidation-reduction process Thupl.29381388s0004 PFAM PF01842 ACT domain Thupl.29381388s0004 GO GO:0008152 metabolic process Thupl.29381388s0004 GO GO:0016616 oxidoreductase activity, acting on the CH-OH group of donors, NAD or NADP as acceptor Thupl.29381388s0004 PFAM PF02826 D-isomer specific 2-hydroxyacid dehydrogenase, NAD binding domain Thupl.29381388s0004 PFAM PF00389 D-isomer specific 2-hydroxyacid dehydrogenase, catalytic domain Thupl.29381533s0004 KEGGORTH K19363 Thupl.29381533s0004 PFAM PF10601 LITAF-like zinc ribbon domain Thupl.29381533s0004 PANTHER PTHR23292 LIPOPOLYSACCHARIDE-INDUCED TRANSCRIPTION FACTOR REGULATING TUMOR NECROSIS FACTOR ALPHA Thupl.29381572s0009 PANTHER PTHR34572 Thupl.29381572s0009 PANTHER PTHR34572:SF1 Thupl.29381913s0008 PFAM PF00010 Helix-loop-helix DNA-binding domain Thupl.29381913s0008 PANTHER PTHR16223:SF69 Thupl.29381913s0008 GO GO:0046983 protein dimerization activity Thupl.29381913s0008 PANTHER PTHR16223 FAMILY NOT NAMED Thupl.29381992s0007 PANTHER PTHR37191 Thupl.29382008s0012 PANTHER PTHR36398 114 Thupl.29382050s0004 PANTHER PTHR11122:SF12 Thupl.29382050s0004 KEGGORTH K01792 glucose-6-phosphate 1-epimerase [EC:5.1.3.15] Thupl.29382050s0004 GO GO:0005975 carbohydrate metabolic process Thupl.29382050s0004 PANTHER PTHR11122 APOSPORY-ASSOCIATED PROTEIN C-RELATED Thupl.29382050s0004 GO GO:0016853 isomerase activity Thupl.29382050s0004 EC 5.1.3.15 Glucose-6-phosphate 1-epimerase Thupl.29382050s0004 PFAM PF01263 Aldose 1-epimerase Thupl.29382050s0004 KOG KOG1594 Uncharacterized enzymes related to aldose 1-epimerase Thupl.29382252s0002 PFAM PF13837 Myb/SANT-like DNA-binding domain Thupl.29382252s0002 PANTHER PTHR31307 FAMILY NOT NAMED Thupl.29382252s0002 PANTHER PTHR31307:SF7 Thupl.29382282s0007 PANTHER PTHR13620:SF19 Thupl.29382282s0007 PANTHER PTHR13620 3-5 EXONUCLEASE Thupl.29382337s0001 PANTHER PTHR14154 UPF0041 BRAIN PROTEIN 44-RELATED Thupl.29382337s0001 PFAM PF03650 Uncharacterized protein family (UPF0041) Thupl.29382337s0001 KOG KOG1589 Uncharacterized conserved protein Thupl.29382337s0001 GO GO:0005743 mitochondrial inner membrane Thupl.29382337s0001 PANTHER PTHR14154:SF2 BRAIN PROTEIN 44/YHR162W(YEAST) Thupl.29382337s0001 GO GO:0006850 mitochondrial pyruvate transport 115 Table 3.12: Kruskal-Wallis analysis of variance in π and dXY estimates in single-copy orthologs across subpopulations. Subpopulation Average π (SD) Kruskal-Wallis χ2 p-value Northern-Coastal 0.00336 (0.00741) 2.50 0.287 Central 0.00331 (0.00851) Southern-Interior 0.00340 (0.00954) Subpop. comparison Mean dXY (SD) Kruskal-Wallis χ2 p-value Northern-Coastal/Central 0.00343 (0.00884) 0.765 0.682 Northern-Coastal/Southern-Interior 0.00344 (0.00849) Central/Southern-Interior 0.00349 (0.00893) 116 Table 3.13: Mean (SD) observed heterozygosity (H) and inbreeding coefficient (F) in the range-wide population (RWP) (n = 112) and each generation of selfing lines (FS – S4: n = 28; S5: n = 11). H F RWP 0.233 (0.133) 0.177 (0.143) FS 0.301 (0.206) -0.0587 (0.0273) S1 0.170 (0.155) 0.313 (0.0511) S2 0.108 (0.137) 0.481 (0.0602) S3 0.0659 (0.126) 0.581 (0.0687) S4 0.0505 (0.112) 0.647 (0.0629) S5 0.0322 (0.0950) 0.668 (0.0664) 117 Table 3.14: A) Pairwise Sign test for variance of median observed (HO) and expected (HE) heterozygosities in each generation of selfing lines (SLs), testing whether median HO is significantly lower than median HE. B) One-tailed Wilcoxon signed rank test for variance of mean HO and HE in each generation of SLs, testing whether mean HO is significantly lower than mean HE. C) One-sample t-test for variance of mean observed (FO) and expected (FE) inbreeding coefficients in each generation of SLs. A Generation HO (median) HE (median) S p-value FS 0.286 - - - S1 0.143 0.143 12710 1 S2 0.0741 0.0714 13498 1 S3 0.0357 0.0357 11100 3.04×10-70 S4 0 0.0179 11703 7.58×10-31 S5 0 0.00893 4544 0 B Generation HO (mean) HE (mean) V p-value FS 0.301 - - - S1 0.170 0.150 151282585 1 S2 0.108 0.0752 186421489 1 S3 0.0659 0.0376 166922880 1 S4 0.0505 0.0188 189216297 1 S5 0.0322 0.00940 105227680 1 C Generation FO (mean) FE (mean) t 95% CI p-value FS -0.0587 - - - - S1 0.313 0.471 -16.4 0.293, 0.332 1.46×10-15 S2 0.481 0.735 -22.3 0.458, 0.504 0 S3 0.581 0.868 -22.1 0.554, 0.608 0 S4 0.647 0.934 -24.1 0.623, 0.672 0 S5 0.668 0.967 -15.0 0.623, 0.712 3.60×10-8 118 Table 3.15: Gene Ontology (GO) categories for genes containing outlier SNPs (n = 1,298). GO category GO ID # of genes with GO category Frequency (out of 147 genes) * GO:0055114 oxidation-reduction process 24 0.163265 GO:0005524 ATP binding 21 0.142857 GO:0005515 protein binding 20 0.136054 GO:0004672 protein kinase activity 15 0.102041 GO:0006468 protein phosphorylation 15 0.102041 * GO:0020037 heme binding 10 0.068027 * GO:0005506 iron ion binding 9 0.061224 * GO:0016705 oxidoreductase activity, acting on paired donors, with incorporation or reduction of molecular oxygen 8 0.054422 GO:0003676 nucleic acid binding 7 0.047619 GO:0016020 membrane 7 0.047619 GO:0016021 integral to membrane 6 0.040816 GO:0016491 oxidoreductase activity 6 0.040816 * GO:0043531 ADP binding 6 0.040816 GO:0006355 regulation of transcription, DNA-dependent 5 0.034014 GO:0006508 proteolysis 5 0.034014 GO:0008152 metabolic process 5 0.034014 GO:0005525 GTP binding 4 0.027211 GO:0005737 cytoplasm 4 0.027211 GO:0006412 translation 4 0.027211 GO:0000287 magnesium ion binding 3 0.020408 GO:0003677 DNA binding 3 0.020408 GO:0003735 structural constituent of ribosome 3 0.020408 GO:0003824 catalytic activity 3 0.020408 GO:0005634 nucleus 3 0.020408 GO:0005840 ribosome 3 0.020408 GO:0006810 transport 3 0.020408 GO:0007165 signal transduction 3 0.020408 GO:0016887 ATPase activity 3 0.020408 * GO:0048544 recognition of pollen 3 0.020408 GO:0003690 double-stranded DNA binding 2 0.013605 GO:0003899 DNA-directed RNA polymerase activity 2 0.013605 GO:0004190 aspartic-type endopeptidase activity 2 0.013605 GO:0004601 peroxidase activity 2 0.013605 119 GO:0005622 intracellular 2 0.013605 GO:0005975 carbohydrate metabolic process 2 0.013605 GO:0006457 protein folding 2 0.013605 GO:0006979 response to oxidative stress 2 0.013605 GO:0008270 zinc ion binding 2 0.013605 GO:0016616 oxidoreductase activity, acting on the CH-OH group of donors, NAD or NADP as acceptor 2 0.013605 GO:0016627 oxidoreductase activity, acting on the CH-CH group of donors 2 0.013605 GO:0016757 transferase activity, transferring glycosyl groups 2 0.013605 GO:0016787 hydrolase activity 2 0.013605 GO:0042626 ATPase activity, coupled to transmembrane movement of substances 2 0.013605 GO:0046872 metal ion binding 2 0.013605 GO:0050660 flavin adenine dinucleotide binding 2 0.013605 GO:0051082 unfolded protein binding 2 0.013605 GO:0055085 transmembrane transport 2 0.013605 GO:0000049 tRNA binding 1 0.006803 GO:0000148 1,3-beta-D-glucan synthase complex 1 0.006803 GO:0000166 nucleotide binding 1 0.006803 GO:0001104 RNA polymerase II transcription cofactor activity 1 0.006803 GO:0002098 tRNA wobble uridine modification 1 0.006803 GO:0003700 sequence-specific DNA binding transcription factor activity 1 0.006803 GO:0003723 RNA binding 1 0.006803 GO:0003743 translation initiation factor activity 1 0.006803 GO:0003777 microtubule motor activity 1 0.006803 GO:0003779 actin binding 1 0.006803 GO:0003843 1,3-beta-D-glucan synthase activity 1 0.006803 GO:0003854 3-beta-hydroxy-delta5-steroid dehydrogenase activity 1 0.006803 GO:0003872 6-phosphofructokinase activity 1 0.006803 GO:0003885 D-arabinono-1,4-lactone oxidase activity 1 0.006803 GO:0003924 GTPase activity 1 0.006803 GO:0003993 acid phosphatase activity 1 0.006803 GO:0004012 phospholipid-translocating ATPase activity 1 0.006803 GO:0004185 serine-type carboxypeptidase activity 1 0.006803 GO:0004252 serine-type endopeptidase activity 1 0.006803 GO:0004375 glycine dehydrogenase (decarboxylating) activity 1 0.006803 GO:0004386 helicase activity 1 0.006803 GO:0004452 isopentenyl-diphosphate delta-isomerase activity 1 0.006803 120 GO:0004518 nuclease activity 1 0.006803 GO:0004553 hydrolase activity, hydrolyzing O-glycosyl compounds 1 0.006803 GO:0004559 alpha-mannosidase activity 1 0.006803 GO:0004722 protein serine/threonine phosphatase activity 1 0.006803 GO:0004749 ribose phosphate diphosphokinase activity 1 0.006803 GO:0004812 aminoacyl-tRNA ligase activity 1 0.006803 GO:0004828 serine-tRNA ligase activity 1 0.006803 GO:0004843 cysteine-type deubiquitinase activity 1 0.006803 GO:0005215 transporter activity 1 0.006803 GO:0005743 mitochondrial inner membrane 1 0.006803 GO:0005783 endoplasmic reticulum 1 0.006803 GO:0005852 eukaryotic translation initiation factor 3 complex 1 0.006803 GO:0006013 mannose metabolic process 1 0.006803 GO:0006075 (1->3)-beta-D-glucan biosynthetic process 1 0.006803 GO:0006096 glycolysis 1 0.006803 GO:0006281 DNA repair 1 0.006803 GO:0006351 transcription, DNA-dependent 1 0.006803 GO:0006357 regulation of transcription from RNA polymerase II promoter 1 0.006803 GO:0006364 rRNA processing 1 0.006803 GO:0006383 transcription from RNA polymerase III promoter 1 0.006803 GO:0006397 mRNA processing 1 0.006803 GO:0006418 tRNA aminoacylation for protein translation 1 0.006803 GO:0006434 seryl-tRNA aminoacylation 1 0.006803 GO:0006470 protein dephosphorylation 1 0.006803 GO:0006486 protein glycosylation 1 0.006803 GO:0006546 glycine catabolic process 1 0.006803 GO:0006694 steroid biosynthetic process 1 0.006803 GO:0006784 heme a biosynthetic process 1 0.006803 GO:0006801 superoxide metabolic process 1 0.006803 GO:0006850 mitochondrial pyruvate transport 1 0.006803 GO:0006950 response to stress 1 0.006803 GO:0007010 cytoskeleton organization 1 0.006803 GO:0007018 microtubule-based movement 1 0.006803 GO:0007023 post-chaperonin tubulin folding pathway 1 0.006803 GO:0008017 microtubule binding 1 0.006803 GO:0008168 methyltransferase activity 1 0.006803 GO:0008171 O-methyltransferase activity 1 0.006803 GO:0008236 serine-type peptidase activity 1 0.006803 121 GO:0008299 isoprenoid biosynthetic process 1 0.006803 GO:0008375 acetylglucosaminyltransferase activity 1 0.006803 GO:0008641 small protein activating enzyme activity 1 0.006803 GO:0008734 L-aspartate oxidase activity 1 0.006803 GO:0009055 electron carrier activity 1 0.006803 GO:0009165 nucleotide biosynthetic process 1 0.006803 GO:0009435 NAD biosynthetic process 1 0.006803 GO:0010333 terpene synthase activity 1 0.006803 GO:0015035 protein disulfide oxidoreductase activity 1 0.006803 GO:0015095 magnesium ion transmembrane transporter activity 1 0.006803 GO:0015631 tubulin binding 1 0.006803 GO:0015693 magnesium ion transport 1 0.006803 GO:0015914 phospholipid transport 1 0.006803 GO:0016226 iron-sulfur cluster assembly 1 0.006803 GO:0016579 protein deubiquitination 1 0.006803 GO:0016592 mediator complex 1 0.006803 GO:0016614 oxidoreductase activity, acting on CH-OH group of donors 1 0.006803 GO:0016788 hydrolase activity, acting on ester bonds 1 0.006803 GO:0016829 lyase activity 1 0.006803 GO:0016853 isomerase activity 1 0.006803 GO:0016972 thiol oxidase activity 1 0.006803 GO:0017038 protein import 1 0.006803 GO:0018024 histone-lysine N-methyltransferase activity 1 0.006803 GO:0019538 protein metabolic process 1 0.006803 GO:0030001 metal ion transport 1 0.006803 GO:0030247 polysaccharide binding 1 0.006803 GO:0031072 heat shock protein binding 1 0.006803 GO:0032040 small-subunit processome 1 0.006803 GO:0032549 ribonucleoside binding 1 0.006803 GO:0034227 tRNA thio-modification 1 0.006803 GO:0034968 histone lysine methylation 1 0.006803 GO:0042256 mature ribosome assembly 1 0.006803 GO:0043022 ribosome binding 1 0.006803 GO:0045454 cell redox homeostasis 1 0.006803 GO:0046983 protein dimerization activity 1 0.006803 GO:0048193 Golgi vesicle transport 1 0.006803 GO:0051015 actin filament binding 1 0.006803 GO:0051017 actin filament bundle assembly 1 0.006803 GO:0051287 NAD binding 1 0.006803 122 GO:0051536 iron-sulfur cluster binding 1 0.006803 * Overrepresented in outlier SNPS when compared to the full SNP set (n = 27,594) (p < 0.05, Fisher's Exact Test) 123 Table 3.16: Three genes associated with the gene ontology (GO) category "recognition of pollen" (GO:0048544) containing outlier SNPs. GENE - Thupl.29378133s0001 SNP Gene Consequence cDNA position CDS position Protein position Amino acids Codons Annotation access code Annotation source Annotation 29378133_44550 Thupl.29378133s0001 synonymous_variant 2075 1236 412 R agG/agA PTHR27002:SF42 PANTHER 29378133_44550 Thupl.29378133s0001 synonymous_variant 2075 1236 412 R agG/agA PF07714 PFAM Protein tyrosine kinase 29378133_44550 Thupl.29378133s0001 synonymous_variant 2075 1236 412 R agG/agA PTHR27002 PANTHER 29378133_44550 Thupl.29378133s0001 synonymous_variant 2075 1236 412 R agG/agA PF00954 PFAM S-locus glycoprotein family 29378133_44550 Thupl.29378133s0001 synonymous_variant 2075 1236 412 R agG/agA GO:0004672 GO protein kinase activity 29378133_44550 Thupl.29378133s0001 synonymous_variant 2075 1236 412 R agG/agA 2.7.11.1 EC Non-specific serine/threonine protein kinase. 29378133_44550 Thupl.29378133s0001 synonymous_variant 2075 1236 412 R agG/agA KOG1187 KOG Serine/threonine protein kinase 29378133_44550 Thupl.29378133s0001 synonymous_variant 2075 1236 412 R agG/agA GO:0006468 GO protein phosphorylation 29378133_44550 Thupl.29378133s0001 synonymous_variant 2075 1236 412 R agG/agA GO:0048544 GO recognition of pollen 29378133_44550 Thupl.29378133s0001 synonymous_variant 2075 1236 412 R agG/agA PF01453 PFAM D-mannose binding lectin 29378133_44565 Thupl.29378133s0001 missense_variant 2090 1251 417 E/D gaG/gaC PTHR27002:SF42 PANTHER 29378133_44565 Thupl.29378133s0001 missense_variant 2090 1251 417 E/D gaG/gaC PF07714 PFAM Protein tyrosine kinase 29378133_44565 Thupl.29378133s0001 missense_variant 2090 1251 417 E/D gaG/gaC PTHR27002 PANTHER 124 29378133_44565 Thupl.29378133s0001 missense_variant 2090 1251 417 E/D gaG/gaC PF00954 PFAM S-locus glycoprotein family 29378133_44565 Thupl.29378133s0001 missense_variant 2090 1251 417 E/D gaG/gaC GO:0004672 GO protein kinase activity 29378133_44565 Thupl.29378133s0001 missense_variant 2090 1251 417 E/D gaG/gaC 2.7.11.1 EC Non-specific serine/threonine protein kinase. 29378133_44565 Thupl.29378133s0001 missense_variant 2090 1251 417 E/D gaG/gaC KOG1187 KOG Serine/threonine protein kinase 29378133_44565 Thupl.29378133s0001 missense_variant 2090 1251 417 E/D gaG/gaC GO:0006468 GO protein phosphorylation 29378133_44565 Thupl.29378133s0001 missense_variant 2090 1251 417 E/D gaG/gaC GO:0048544 GO recognition of pollen 29378133_44565 Thupl.29378133s0001 missense_variant 2090 1251 417 E/D gaG/gaC PF01453 PFAM D-mannose binding lectin 29378133_44567 Thupl.29378133s0001 missense_variant 2092 1253 418 C/Y tGt/tAt PTHR27002:SF42 PANTHER 29378133_44567 Thupl.29378133s0001 missense_variant 2092 1253 418 C/Y tGt/tAt PF07714 PFAM Protein tyrosine kinase 29378133_44567 Thupl.29378133s0001 missense_variant 2092 1253 418 C/Y tGt/tAt PTHR27002 PANTHER 29378133_44567 Thupl.29378133s0001 missense_variant 2092 1253 418 C/Y tGt/tAt PF00954 PFAM S-locus glycoprotein family 29378133_44567 Thupl.29378133s0001 missense_variant 2092 1253 418 C/Y tGt/tAt GO:0004672 GO protein kinase activity 29378133_44567 Thupl.29378133s0001 missense_variant 2092 1253 418 C/Y tGt/tAt 2.7.11.1 EC Non-specific serine/threonine protein kinase. 29378133_44567 Thupl.29378133s0001 missense_variant 2092 1253 418 C/Y tGt/tAt KOG1187 KOG Serine/threonine protein kinase 29378133_44567 Thupl.29378133s0001 missense_variant 2092 1253 418 C/Y tGt/tAt GO:0006468 GO protein phosphorylation 29378133_44567 Thupl.29378133s0001 missense_variant 2092 1253 418 C/Y tGt/tAt GO:0048544 GO recognition of pollen 29378133_44567 Thupl.29378133s0001 missense_variant 2092 1253 418 C/Y tGt/tAt PF01453 PFAM D-mannose binding lectin 125 29378133_44649 Thupl.29378133s0001 synonymous_variant 2174 1335 445 T acT/acC PTHR27002:SF42 PANTHER 29378133_44649 Thupl.29378133s0001 synonymous_variant 2174 1335 445 T acT/acC PF07714 PFAM Protein tyrosine kinase 29378133_44649 Thupl.29378133s0001 synonymous_variant 2174 1335 445 T acT/acC PTHR27002 PANTHER 29378133_44649 Thupl.29378133s0001 synonymous_variant 2174 1335 445 T acT/acC PF00954 PFAM S-locus glycoprotein family 29378133_44649 Thupl.29378133s0001 synonymous_variant 2174 1335 445 T acT/acC GO:0004672 GO protein kinase activity 29378133_44649 Thupl.29378133s0001 synonymous_variant 2174 1335 445 T acT/acC 2.7.11.1 EC Non-specific serine/threonine protein kinase. 29378133_44649 Thupl.29378133s0001 synonymous_variant 2174 1335 445 T acT/acC KOG1187 KOG Serine/threonine protein kinase 29378133_44649 Thupl.29378133s0001 synonymous_variant 2174 1335 445 T acT/acC GO:0006468 GO protein phosphorylation 29378133_44649 Thupl.29378133s0001 synonymous_variant 2174 1335 445 T acT/acC GO:0048544 GO recognition of pollen 29378133_44649 Thupl.29378133s0001 synonymous_variant 2174 1335 445 T acT/acC PF01453 PFAM D-mannose binding lectin GENE - Thupl.29378176s0002 SNP Gene Consequence cDNA position CDS position Protein position Amino acids Codons Annotation access code Annotation source Annotation 29378176_1711897 Thupl.29378176s0002 missense_variant 1228 1087 363 H/D Cat/Gat PTHR27002:SF343 PANTHER 29378176_1711897 Thupl.29378176s0002 missense_variant 1228 1087 363 H/D Cat/Gat GO:0005524 GO ATP binding 29378176_1711897 Thupl.29378176s0002 missense_variant 1228 1087 363 H/D Cat/Gat SignalP-noTM SIGNALP 29378176_1711897 Thupl.29378176s0002 missense_variant 1228 1087 363 H/D Cat/Gat PF01453 PFAM D-mannose binding lectin 29378176_1711897 Thupl.29378176s0002 missense_variant 1228 1087 363 H/D Cat/Gat PTHR27002 PANTHER 126 29378176_1711897 Thupl.29378176s0002 missense_variant 1228 1087 363 H/D Cat/Gat GO:0006468 GO protein phosphorylation 29378176_1711897 Thupl.29378176s0002 missense_variant 1228 1087 363 H/D Cat/Gat PF08276 PFAM PAN-like domain 29378176_1711897 Thupl.29378176s0002 missense_variant 1228 1087 363 H/D Cat/Gat PF00954 PFAM S-locus glycoprotein family 29378176_1711897 Thupl.29378176s0002 missense_variant 1228 1087 363 H/D Cat/Gat PF00069 PFAM Protein kinase domain 29378176_1711897 Thupl.29378176s0002 missense_variant 1228 1087 363 H/D Cat/Gat GO:0048544 GO recognition of pollen 29378176_1711897 Thupl.29378176s0002 missense_variant 1228 1087 363 H/D Cat/Gat KOG1187 KOG Serine/threonine protein kinase 29378176_1711897 Thupl.29378176s0002 missense_variant 1228 1087 363 H/D Cat/Gat GO:0004672 GO protein kinase activity 29378176_1711897 Thupl.29378176s0002 missense_variant 1228 1087 363 H/D Cat/Gat 2.7.11.1 EC Non-specific serine/threonine protein kinase. 29378176_1711928 Thupl.29378176s0002 missense_variant 1259 1118 373 S/N aGt/aAt PTHR27002:SF343 PANTHER 29378176_1711928 Thupl.29378176s0002 missense_variant 1259 1118 373 S/N aGt/aAt GO:0005524 GO ATP binding 29378176_1711928 Thupl.29378176s0002 missense_variant 1259 1118 373 S/N aGt/aAt SignalP-noTM SIGNALP 29378176_1711928 Thupl.29378176s0002 missense_variant 1259 1118 373 S/N aGt/aAt PF01453 PFAM D-mannose binding lectin 29378176_1711928 Thupl.29378176s0002 missense_variant 1259 1118 373 S/N aGt/aAt PTHR27002 PANTHER 29378176_1711928 Thupl.29378176s0002 missense_variant 1259 1118 373 S/N aGt/aAt GO:0006468 GO protein phosphorylation 29378176_1711928 Thupl.29378176s0002 missense_variant 1259 1118 373 S/N aGt/aAt PF08276 PFAM PAN-like domain 29378176_1711928 Thupl.29378176s0002 missense_variant 1259 1118 373 S/N aGt/aAt PF00954 PFAM S-locus glycoprotein family 29378176_1711928 Thupl.29378176s0002 missense_variant 1259 1118 373 S/N aGt/aAt PF00069 PFAM Protein kinase domain 29378176_1711928 Thupl.29378176s0002 missense_variant 1259 1118 373 S/N aGt/aAt GO:0048544 GO recognition of pollen 127 29378176_1711928 Thupl.29378176s0002 missense_variant 1259 1118 373 S/N aGt/aAt KOG1187 KOG Serine/threonine protein kinase 29378176_1711928 Thupl.29378176s0002 missense_variant 1259 1118 373 S/N aGt/aAt GO:0004672 GO protein kinase activity 29378176_1711928 Thupl.29378176s0002 missense_variant 1259 1118 373 S/N aGt/aAt 2.7.11.1 EC Non-specific serine/threonine protein kinase. 29378176_1711929 Thupl.29378176s0002 missense_variant 1260 1119 373 S/R agT/agG PTHR27002:SF343 PANTHER 29378176_1711929 Thupl.29378176s0002 missense_variant 1260 1119 373 S/R agT/agG GO:0005524 GO ATP binding 29378176_1711929 Thupl.29378176s0002 missense_variant 1260 1119 373 S/R agT/agG SignalP-noTM SIGNALP 29378176_1711929 Thupl.29378176s0002 missense_variant 1260 1119 373 S/R agT/agG PF01453 PFAM D-mannose binding lectin 29378176_1711929 Thupl.29378176s0002 missense_variant 1260 1119 373 S/R agT/agG PTHR27002 PANTHER 29378176_1711929 Thupl.29378176s0002 missense_variant 1260 1119 373 S/R agT/agG GO:0006468 GO protein phosphorylation 29378176_1711929 Thupl.29378176s0002 missense_variant 1260 1119 373 S/R agT/agG PF08276 PFAM PAN-like domain 29378176_1711929 Thupl.29378176s0002 missense_variant 1260 1119 373 S/R agT/agG PF00954 PFAM S-locus glycoprotein family 29378176_1711929 Thupl.29378176s0002 missense_variant 1260 1119 373 S/R agT/agG PF00069 PFAM Protein kinase domain 29378176_1711929 Thupl.29378176s0002 missense_variant 1260 1119 373 S/R agT/agG GO:0048544 GO recognition of pollen 29378176_1711929 Thupl.29378176s0002 missense_variant 1260 1119 373 S/R agT/agG KOG1187 KOG Serine/threonine protein kinase 29378176_1711929 Thupl.29378176s0002 missense_variant 1260 1119 373 S/R agT/agG GO:0004672 GO protein kinase activity 29378176_1711929 Thupl.29378176s0002 missense_variant 1260 1119 373 S/R agT/agG 2.7.11.1 EC Non-specific serine/threonine protein kinase. GENE - Thupl.29381500s0009 128 SNP Gene Consequence cDNA position CDS position Protein position Amino acids Codons Annotation access code Annotation source Annotation 29381500_2652205 Thupl.29381500s0009 synonymous_variant 2321 2196 732 A gcG/gcC PF01453 PFAM D-mannose binding lectin 29381500_2652205 Thupl.29381500s0009 synonymous_variant 2321 2196 732 A gcG/gcC PF08276 PFAM PAN-like domain 29381500_2652205 Thupl.29381500s0009 synonymous_variant 2321 2196 732 A gcG/gcC KOG1187 KOG Serine/threonine protein kinase 29381500_2652205 Thupl.29381500s0009 synonymous_variant 2321 2196 732 A gcG/gcC PF00954 PFAM S-locus glycoprotein family 29381500_2652205 Thupl.29381500s0009 synonymous_variant 2321 2196 732 A gcG/gcC GO:0048544 GO recognition of pollen 29381500_2652205 Thupl.29381500s0009 synonymous_variant 2321 2196 732 A gcG/gcC PF00069 PFAM Protein kinase domain 29381500_2652205 Thupl.29381500s0009 synonymous_variant 2321 2196 732 A gcG/gcC PTHR27002 PANTHER 29381500_2652205 Thupl.29381500s0009 synonymous_variant 2321 2196 732 A gcG/gcC GO:0006468 GO protein phosphorylation 29381500_2652205 Thupl.29381500s0009 synonymous_variant 2321 2196 732 A gcG/gcC GO:0005524 GO ATP binding 29381500_2652205 Thupl.29381500s0009 synonymous_variant 2321 2196 732 A gcG/gcC 2.7.11.1 EC Non-specific serine/threonine protein kinase. 29381500_2652205 Thupl.29381500s0009 synonymous_variant 2321 2196 732 A gcG/gcC GO:0004672 GO protein kinase activity 29381500_2652205 Thupl.29381500s0009 synonymous_variant 2321 2196 732 A gcG/gcC PTHR27002:SF331 PANTHER 129 Table 3.17: Geographic locations of origin of parent trees for selfing lines. Parent Selfing Line Location Latitude (° N) Longitude (° W) Elevation (m) 491 1 Bacchante Bay 49.45 -126.10 305 13 1 O'Connell Lake 50.35 -127.70 122 150 6 Tzoonie River 49.87 -123.73 450 49 6 Misty Lake 50.63 -127.28 91 213 7 Norrish Creek 49.22 -122.15 400 76 7 Hathaway Creek 50.58 -127.82 105 142 8 Bedwell Sound 49.37 -125.75 490 289 8 Misty Lake 50.63 -127.30 60 263 10 Narrows Inlet 49.08 -123.73 120 306 10 Denad Creek 50.55 -128.00 25 341 13 Utlah Creek 50.28 -127.43 183 323 13 Koprino River 50.55 -127.85 280 367 16 Henderson Lake 49.10 -125.02 200 381 16 Sydney Inlet 49.48 -126.28 65 386 17 Cypre River 49.37 -125.87 180 253 17 Labouchere Channel 52.03 -127.25 152 403 19 Eelstow Passage 50.10 -127.17 60 191 19 Jervis Inlet 50.12 -123.78 98 424 20 Chatham Channel 50.52 -126.25 75 318 20 Ronning Creek 50.57 -128.27 105 435 21 Chemainus 48.93 -123.75 115 15 21 Mahatta Creek 50.40 -127.78 155 461 23 Artlish Caves 50.13 -127.08 100 5 23 Mahatta Creek 50.40 -127.78 122 344 26 Victoria Lake 50.05 -127.42 457 313 26 Brink Lake 50.68 -128.27 213 343 27 Victoria Lake 50.05 -127.42 457 319 27 Ronning Creek 50.57 -128.27 115 333 29 Marble River 50.25 -127.33 244 312 29 Brink Lake 50.70 -128.27 122 130 3.7 Figures Figure 3.1: GenomeScope (Vurture et al. 2017) estimation of western redcedar (WRC) genome size. Genomescope estimated the size of the WRC genome at 9.8 Gbp. 131 Figure 3.2: Genetic structure is weak across the geographic range of WRC. A) Map of geographic origin for trees in the range-wide population (RWP) (n = 112). Subpopulations were defined a priori based on analysis outcomes of O’Connell et al. (2008). Trees were separated into three main subpopulations: Northern-Coastal (n = 77); Central (n = 26); and Southern-Interior (n = 9). B) STRUCTURE plot of the RWP for K = 2 and K = 3. Optimal K was determined by evaluating STRUCTURE results using the methods of Evanno et al. (2005) and Puechmaille (2016), and by the approach of fastStructure (Raj et al. 2014). Gene flow is present throughout all three subpopulations. C) Principal component analysis (PCA) of genetic distance between trees in the RWP. Latitudinal separation of trees from different s can be observed, although each principal component only explains a very small proportion of the variation between individuals, indicating that genetic differentiation is low.132 Figure 3.3: Within-scaffold linkage disequilibrium (LD) decays slowly in western redcedar. A) LD was assessed using SNPs with a minor allele frequency cutoff of 0.05 to reduce error associated with rare alleles (n = 22,751 SNPs). Decay was estimated using a non-linear model (red line); r2 decayed to baseline thresholds of 0.2 (purple dotted line) and 0.1 (pink dotted line) at 0.573 and 1.65 Mbp, respectively. B) Pairwise LD for all pairs of SNPs (n = 22,751). Each point on the plot represents the LD between two SNPs at a given distance from one another and relative position on the scaffold. Colour indicates LD range, with red indicating SNPs in strong LD. 133 Figure 3.4: Discriminant Analysis of Principal Components (DAPC) assessment of optimal K for trees in the range-wide population (RWP) (n = 112). A) Selection of optimal K using the find.clusters approach. Optimal K is typically determined by selecting the K with lowest Bayesian Information Criterion (BIC) score. However, when the lowest BIC is assigned to K = 1, this is interpreted as an inability to resolve clusters (Miller et al. 2020). B) DAPC clustering using the first 10 principal components and K = 3. C) Composition plots of the reassignment of individual trees to clusters based on DAPC for K = 2-3. DAPC was not able to properly reassign trees from the RWP, placing all trees from the Central and Southern-Interior subpopulations together while showing complete mixing in the Northern-Coastal subpopulation. 134 Figure 3.5: Individual genetic distance as a factor of geographic distance in the range-wide population (n = 112). No significant association was observed between geographic distance and genetic distance when grouping trees based on geographic origin. Surprisingly, no significant association was observed between individual trees either (as shown in the figure), which would normally be expected in continuous populations expanding their range from one source. 135 Figure 3.6: Average nucleotide diversity π and average nucleotide difference between subpopulations in the range-wide population (RWP). A) Overall distribution of average π of the RWP in SCOs. We detected 1,126 SCOs with a π of zero, with an average π of 0.00338. B) Overall distribution of average dXY between each pair of geographic subpopulations. No significant differences were observed between comparisons of different subpopulations. Inlays show all π estimates; main plots show π estimates with outliers in the top one percentile removed for clarity. The top 1% of π estimates account for 4% of the total estimated diversity, and the top 5% account for over 25% of the total. 136 Figure 3.7: Nucleotide diversity (π) in the range-wide population (n = 112). Average π in Single Copy Ortholog (SCO) genes across each putative linkage group (see Methods and Materials). The top 1% of SCOs with highest π, which account for 4% of total nucleotide diversity, are shown in red (n = 47; Table 3.11). 137 Figure 3.8: Folded Site Frequency Spectrum (SFS) plots for A) All SNPs in the range-wide population (n = 27,594); B) All zero-fold degenerate SNPs (n = 4,091); and C) All four-fold degenerate SNPs (n = 1,815). 138 Figure 3.9: Stairway Plot (Liu and Fu 2020) on folded SFS of four-fold degenerate SNPs in the range-wide population. Ne begins to decline starting ca. 100 kya, dropping sharply several times, ca. 20 kya and 6 kya, consistent with population bottlenecks during the last glacial period. 139 Figure 3.10: Change in heterozygosity (H) and inbreeding coefficients (F) over five successive generations of complete selfing in WRC. A) Observed vs. expected change in heterozygosity over five successive generations of complete selfing in n = 28 (FS – S4) and n = 11 (S5) different selfing lines (SLs), at n = 27,594 SNP loci, after manual error correction. Each line at each generation is represented by a single tree. Black points indicate boxplot outliers. Observed median heterozygosity declines faster than expected under theoretical expectations during complete selfing, despite many SNP loci remaining heterozygous across all generations. B) Inbreeding coefficients (F) for n = 28 samples (FS – S4) and n = 11 samples (S5). Black points indicate boxplot outliers. Under complete selfing, F is expected to increase by a factor of ½(1+F) in the previous generation (red diamonds). F increases at a slower rate than expected in our SLs. 140 Figure 3.11: Consequences of SNPs deviating from expectations of genetic drift. Comparison of the consequences of all SNPs with effects predicted by Variant Effect Predictor (VEP) (McLaren et al. 2016) (n = 25,841) and SNPs deviating from expectations of genetic drift with predicted effects (outlier SNPs; n = 1,073). “*” indicates a significant difference between the two sets of SNPs for proportion of SNPs for a certain consequence (Fisher’s Exact Test, p < 0.05).141 Figure 3.12: Depth of k-mer coverage profiles for multiple values of k estimated in ntCard (Mohamadi et al. 2017). Mode of depth of coverage c is labeled for each value of k. 142 Figure 3.13: Flowchart of the assembly process of the WRC draft genome. 143 3.8 Datasets Dataset 3.1: Primary transcripts in FASTA format. Dataset 3.2: Predicted gene functions for annotated genes. Dataset 3.3: List of western redcedar (WRC) single-copy ortholog (SCO) genes. Dataset 3.4: Sequences used for validation of the genome annotation in FASTA format. Dataset 3.5: SNP genotypes for the range-wide population (RWP) in VCF format (n = 27,594 SNPs). Dataset 3.6: VEP SNP annotation for all annotated SNPS. Dataset 3.7: Unlinked (r2 = 0.1 at 1654 kbp) SCO intergenic SNP set of the RWP for population genetics in VCF format (n = 5,114 SNPs). Dataset 3.8: WRC SNPs successfully mapped to putative linkage groups (LGs) on the Sequoiadendron giganteum genome (n = 26,140 SNPs). Dataset 3.9: Unlinked (based on putative LGs) SNP set of the RWP used for Ne estimation (n = 509 SNPs). Dataset 3.10: Corrected SNP genotypes for manually corrected and imputed selfing lines in PLINK binary format. Dataset 3.11: Statistical analysis and outcomes for SNP fate based on expectations of genetic drift for all SNPs after four generations of selfing. 144 Dataset 3.12: Annotation of outlier SNPs deviating from expectations of genetic drift during selfing. 145 Genetic architecture of terpene chemistry and growth traits and the impact of inbreeding on these traits in western redcedar 4.1 Summary Western redcedar (WRC; Thuja plicata) is a conifer of the Pacific Northwest of North America prized for its durable and rot-resistant wood. WRC has naturally low outcrossing rates and readily self-fertilizes in nature. Challenges faced in WRC breeding and propagation involve selecting trees for accelerated growth while also ensuring enhanced heartwood rot resistance and resistance to ungulate browsing, as well as mitigating potential effects of inbreeding depression. Terpenes, a large and diverse class of specialized metabolites, confer both rot and browse resistance in the wood and foliage of WRC, respectively. Using a Bayesian modeling approach, we isolated single nucleotide polymorphism (SNP) markers estimated to be associated with three different foliar terpene traits and four different heartwood terpene traits, as well as two growth traits. We found that all traits were complex, being associated with between 1,700 and 3,600 SNPs linked with putatively causal loci, with significant polygenic components. Growth traits tended to have a larger polygenic component while terpene traits had larger major gene components; SNPs with small or polygenic effect were spread across the genome, while larger effect SNPs tended to be localized to specific linkage groups. To determine whether there was inbreeding depression for terpene chemistry or growth traits, we used mixed linear models for a genomic selection training population to estimate the effect of the inbreeding coefficient F on 146 foliar terpenes, heartwood terpenes, and several growth and dendrochronological traits. We found no significant inbreeding depression for any assessed trait. We further assessed inbreeding depression for growth and foliar terpene traits across four generations of complete selfing and found that not only was inbreeding depression not significant, but that selection for height growth was the only significant predictor for growth during selfing, suggesting that selfing during operational breeding can be mitigated by increased selection intensity. 147 4.2 Introduction Western redcedar (Thuja plicata; WRC) is an ecologically, economically and culturally important conifer of the Cupressaceae family. WRC is endemic to North America’s Pacific Northwest, and is held in high regard by coastal First Nations and Native Americans, for whom WRC has served for millennia as a keystone societal resource. WRC wood has been used in the construction of buildings, watercrafts, totem poles, and ceremonial masks, among other important practical and spiritual uses (Hebda and Mathewes 1984). It has also found extensive modern use in exposed building applications due to its durable, lightweight and naturally rot-resistant wood (Gonzalez 2004). Though it currently ranges from Northern California to Southern Alaska, WRC is thought to have expanded its range since the last glaciation from a single refugium south of its current distribution (O’Connell et al. 2008). This expansion may have been facilitated by WRC’s unique mating system among conifers: WRC readily propagates via self-fertilization, and appears to be largely unaffected by inbreeding depression for fitness traits (Russell et al. 2003; Wang and Russell 2006; Russell and Ferguson 2008). Despite low genetic diversity and small effective population size in natural populations (Chapter 3), WRC is responsive to natural and artificial selection, and thrives in a wide range of environmental and climatic conditions (Grime 1977; Antos et al. 2016), likely due to strong genome-wide balancing selection (Chapter 3). WRC timber has, in the past, been largely harvested from primary old growth forests in Canada. To improve sustainability of harvesting practices, there has been momentum to adopt 148 planting and harvesting of second growth WRC. However, WRC seedlings grown in plantations are vulnerable to heavy ungulate browsing, greatly reducing survival; furthermore, second growth WRC have been found to be susceptible to a host of fungal pathogens, such as those responsible for cedar leaf blight and heartwood rot (Gonzalez 2004). Thus, Canada’s WRC tree improvement program is currently focused on breeding trees for improved growth, heartwood durability and resistance to disease and herbivores (Russell and Yanchuk 2012). Terpenes, or terpenoids, are a class of compounds derived from the five-carbon building blocks isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP). Terpenes constitute a large group of metabolites in plants, with over 40,000 structures involved in both general and specialized metabolism (Bohlmann and Keeling 2008). Monoterpenes (terpenes containing ten carbon atoms) deposited in oil glands in WRC foliage deter browsing of ungulates (Vourc’h et al. 2002a, 2002b). Similarly, monoterpenoid tropolones called thujaplicins present in the heartwood of WRC have been implicated in the tree’s natural defense against fungal pathogens (Barton and Macdonald 1971; Van der Kamp 1986). The first two steps in the biosynthetic pathway of α-thujone, the most abundant foliar monoterpene, have been identified, but the genes involved in the final steps of the pathway have yet to be identified (Foster et al. 2013; Gesell et al. 2015). Compared with foliar monoterpenes, much less is known about the pathways of thujaplicin biosynthesis. Only the pathway of β-thujaplicin has been studied in depth (Liu and Yamauchi 2006; Ko et al. 2015; Tu et al. 2016; Zhang et al. 2019). It is thought to be synthesized through a terpinolene intermediate following conversion from GPP, based on studies in cell cultures of white cedar (Cupressus lusitanica) (Fujita et al. 2000, 2014). However, 149 the genes involved in the majority of the pathway, including the formation of the seven-membered tropolone ring, remain unknown. Recently, large-scale genotyping of WRC has been carried out for the implementation of genomic selection for growth and chemistry traits in WRC, with the goal of expediting breeding cycles for these traits (Gamal El-Dien et al., accepted). However, relatively little is known about the genetic architecture (that is, the underlying genetic basis of a trait and the genetic properties which lead to variation in a trait) and molecular mechanisms controlling these traits in WRC. It is also unknown which loci may have an impact on inbreeding depression, or lack thereof, in growth or chemistry traits in WRC. Given WRC’s unique mating system and low genetic diversity, a better understanding of the areas of the genome controlling traits of interest would be beneficial in optimizing the process of genomic selection, and for learning and understanding more about this important tree species. Here, we use recently developed genomic resources to define the genetic architecture of complex growth and terpene chemistry traits in WRC. We then assess inbreeding depression for these traits using an array of WRC selfing lines. 150 4.3 Materials and Methods 4.3.1 Training population The training population (TP) for genomic selection was comprised of 1520 progeny trees from 26 half-sib families in a polycross mating design growing at three different British Columbia Ministry of Forests (BCMoF) progeny test sites in BC, Canada. A total of 520, 520, and 480 trees were sampled from sites containing large progeny trials at Jordan River, Powell River, and Port McNeill, respectively (Gamal El-Dien et al., accepted). Trees were sampled in an incomplete block design, with trees from the same family at different sites considered as ‘replicates’, and each ‘block’ containing a single ‘replicate’ from each family. On average, 20 trees per polycross family were selected from each site. For further details, see Gamal El-Dien et al. (accepted). 4.3.2 Selfing lines Breeding and propagation of selfing lines (SLs) was carried out between 1995 and 2007 at the Cowichan Lake Research Station (CLRS) at Lake Mesachie, British Columbia (BC), Canada. Fifteen pairs (30 individuals) of unrelated grafted parent trees taken originally from various locations across Coastal BC and Vancouver Island (Chapter 3) and growing at CLRS were crossed to generate 15 full-sib (FS) families. This was done to ensure that all lines would initialize with an inbreeding coefficient of F = 0. A total population of 60 seedlings was generated for selfing in the accelerated breeding cycle as described by Russell and Ferguson 151 (2008). Briefly, in the first year, seed was germinated in the greenhouse, grown for five months before re-potting and induction of flowering with gibberellic acid (GA3), and then moved outside for chilling. In the second year, trees were moved back into the greenhouse and were completely encased in pollination bags to allow for selfing, and cones were then collected to begin the process again, allowing for one generation every two years. Seedlings from each FS family were planted in three replicates in a greenhouse. Shoot heights were measured after four months, and the two seedlings with the top growth per family were selected based on standard normal deviates; this selection continued at each generation to form the ‘selected’ lines. A further two seedlings per FS family were selected at random at each generation; these formed the ‘random’ lines. Three trees from each line were chosen at each generation (S1 – S5) and brought through the entire selfing process to mitigate the likelihood of line extinction. Ten out of the 60 lines went extinct during the process. Foliar tissue was collected for DNA extraction and foliar chemistry analysis from FS – S4 trees between four and 15 years of age growing in outdoor plots at CLRS, and one-year-old S5 seedlings growing in a greenhouse at CLRS, for a total of n = 191 trees in n = 41 SLs. 4.3.3 DNA extraction DNA was isolated as described in Chapter 3. Briefly, a modified protocol of Xin and Chen (2012) was followed, but with the addition of 10% polyvinylpyrrolidone to the CTAB extraction buffer, all centrifugation steps were performed for 10-12 min at 10,000 rpm, the amount of 152 Qiagen MagAttract solution was increased to 7µl, one-minute wait time for binding on the magnetic block was added, and 115µl TE buffer was used for DNA dilution. The concentration and quality of DNA was verified with a Nanodrop 2000c (Fisher Scientific, Toronto, ON, Canada), Quantiflor (Promega Corporate, Madison, WI, USA) and 0.8% agarose gel to cross check for the quantity and quality of the DNA. 4.3.4 SNP genotyping and filtering Targeted sequencing-based genotyping was done by Capture-Seq methodology at Rapid Genomics, as described in Chapter 3. Briefly, 20,858 probes were selected from genomic regions likely to contain polymorphisms for genotyping, based on the annotation of the current draft WRC genome (Chapter 3). A set of transcriptomes (Shalev et al. 2018) (PRJNA704616) was aligned to the reference genome to identify SNPs. SNPs were then called from both genic and intergenic regions for a total of 4,832 trees, comprising the TP and SL, as well as the target population and parent trees for genomic selection (Chapter 3). We filtered SNPs using vcftools v0.1.17 (Danecek et al. 2011) with the following flags: --max-missing 0.95; --minQ 30; --min-meanDP 15; --max-meanDP 60; --maf 0.01. SNPs with an allele balance (the ratio of reference allele reads to overall reads in heterozygous individuals) greater than 0.25 and lower than 0.75, or lower than 0.01 or greater than 0.99, were retained using vcffilter in vcflib (Garrison 2016) to eliminate incorrectly called heterozygotes. Samples with more than 40% missing data were removed. A stringent maximum mean depth of 60× was 153 chosen to reduce the likelihood of erroneous calls due to sequence paralogy. SNPs were then filtered for excess heterozygosity at a p-value cut-off of 0.001. For SLs, further genotype correction was carried out as described in Chapter 3, using SNPs filtered for all above criteria except minor allele frequency (MAF). Briefly, 20 lines with available SNP data from multiple consecutive generations were corrected if a heterozygous call in a later generation followed consecutive homozygous calls in previous generations, or if a homozygous call for one genotype followed consecutive homozygous calls for the other genotype. SNPs that could not be corrected following these criteria were removed. For seven SLs in which only the S3 generation had not been sequenced, we imputed the genotypes for the S3 generation at each locus for SNPs where no other genotype was possible, and marked the rest as missing. SNPs were converted to genotype dosage matrix using the --012 flag in vcftools. A genomic realized relationship matrix (GRRM) was generated for both the TP and the selfing lines using the ‘A.mat’ function from rrBLUP v4.6.1 (Endelman 2011) in R v4.1.2 (R Core Team 2021). SNPs were annotated using the Ensembl Variant Effect Predictor r103 (McLaren et al. 2016) and the draft genome annotation (Chapter 3). 4.3.5 Phenotyping In our analysis of WRC traits, we focused on the most abundant foliar monoterpenes (α-thujone, β-thujone and sabinene), wood thujaplicins (α-, β-, γ-thujaplicin and β-thujaplicinol), and height 154 and diameter at breast height at 15 years of age (Dataset 4.1). For the SLs, we focused on foliar monoterpenes and height at 4 months of age. Foliar chemistry Foliar chemistry was analysed from foliar tissue sampled from TP trees at 15 years of age and from SL trees between one and 15 years of age at Glynn Road Analytical Chemistry Lab (Victoria, BC), as described in Gamal El-Dien et al. (accepted). Briefly, sample extraction and analysis followed the method of Kimball et al. (2005) with modifications. Frozen samples were ground using liquid nitrogen and a mortar and pestle. Approximately 250 mg of wet weight sample was extracted into 4 ml of methanol containing pentadecane as an internal standard. An additional subsample was measured for moisture factor to provide a 70°C oven dry weight measurement. Samples were extracted for 48 hours and analysed on a Clarus 580 Gas Chromatography (GC) unit (PerkinElmer, MA, USA) with a 30 m ZB-5MSi GC column using Flame Ionization Detection (FID). Peaks were identified by comparing retention times to reference standards, and mass spectrometry (MS) was used for further confirmation when necessary. 155 Wood chemistry For wood chemistry, increment cores were sampled from TP trees at 18 years of age in the fall of 2017. Cores were taken at breast height (1.3 m), and were oriented to intercept the pith of the stem to ensure all heartwood material was captured in the sample, while capturing the entire diameter of the tree. One-half (one radius) of each core was retained for dendrochronological analysis. The other half was ground for five to ten seconds in an electric grinder. The grinder was swept after each sample and cleaned with methanol after every tenth sample. Ground samples were conditioned at 40°C for 48 hrs before extraction to ensure consistency between samples, as the small sample weights did not allow for individual moisture contents. Extraction and analysis of wood metabolites was carried out at FPInnovations (Vancouver, BC) using the approach of (Daniels and Russell 2007) with some modifications, as described by Gamal El-Dien et al. (accepted). Briefly, gradient elution from 10% acetonitrile to 65% acetonitrile in 44 min at a flow rate of 1.4 ml/min was used for all analyses. Samples were extracted in 1-2 ml of ethanol using crotonic acid para-bromophenacyl as an internal standard. Samples were sonicated in an ultrasonic bath for two hrs and 0.5 ml of sample extract was placed in a filter vial for analysis by reverse phase liquid chromatography using an Agilent 1260 HPLC (Agilent Technologies, CA, USA). Reference standards were used for identification of terpene compounds. 156 Growth Height and Diameter at Breast Height (DBH, ~ 1.3 m) were measured for trees in the TP at approximately 15 years of age in 2014. Height was measured for 4-month-old seedling of all SLs up to S4 between 1997 and 2005. Dendrochronology For dendrochronology, each core was scanned into the program Coordinate Recorder (Cybis Elektronik & Data AB, Sweden). Each image was assessed to (1) identify ring boundaries and detect false rings (e.g. intra-annual bands of latewood cells); and (2) assign calendar years to each ring, assuming the last ring with complete latewood was 2016 for all samples. A constant threshold was applied to automatically detect the earlywood-latewood boundary within each ring. Three time-series of data representing total ring, earlywood and latewood widths were recorded for each tree. The calendar year of the pith or inner ring was recorded. Number of years from this year to sampling date was designated as “pith age”. The majority of cores intercepted the pith and included the bark. Ring counts represented age of the trees (at coring height) and ring widths were summed to represent growth over their lifespan. For cores that included arcing rings adjacent to the pith, we calculated correction factors for the missed portion of the radius and number of rings (Duncan 1989): 157 Equation 4.1: 𝐌𝐌𝐌𝐌𝐌𝐌𝐌𝐌𝐌𝐌𝐌𝐌 𝐩𝐩𝐩𝐩𝐩𝐩𝐩𝐩𝐌𝐌𝐩𝐩𝐩𝐩 𝐩𝐩𝐨𝐨 𝐩𝐩𝐭𝐭𝐌𝐌 𝐩𝐩𝐫𝐫𝐌𝐌𝐌𝐌𝐫𝐫𝐌𝐌 = 𝑳𝑳𝑳𝑳𝑳𝑳𝑳𝑳𝑳𝑳𝑳𝑳𝟐𝟐𝟖𝟖×𝑯𝑯𝑳𝑳𝑯𝑯𝑳𝑳𝑳𝑳𝑳𝑳 + (𝐇𝐇𝐌𝐌𝐌𝐌𝐇𝐇𝐭𝐭𝐩𝐩 + 𝟐𝟐) where the length and height (mm) of the arcing latewood of the inner ring were measured with digital calipers; and Equation 4.2: 𝐍𝐍𝐫𝐫𝐍𝐍𝐛𝐛𝐌𝐌𝐩𝐩 𝐩𝐩𝐨𝐨 𝐍𝐍𝐌𝐌𝐌𝐌𝐌𝐌𝐌𝐌𝐌𝐌 𝐩𝐩𝐌𝐌𝐩𝐩𝐇𝐇𝐌𝐌 = 𝐌𝐌𝐌𝐌𝐌𝐌𝐌𝐌𝐌𝐌𝐌𝐌 𝐩𝐩𝐩𝐩𝐩𝐩𝐩𝐩𝐌𝐌𝐩𝐩𝐩𝐩 𝐩𝐩𝐨𝐨 𝐩𝐩𝐭𝐭𝐌𝐌 𝐩𝐩𝐫𝐫𝐌𝐌𝐌𝐌𝐫𝐫𝐌𝐌𝐀𝐀𝐀𝐀𝐌𝐌𝐩𝐩𝐫𝐫𝐇𝐇𝐌𝐌 𝐩𝐩𝐌𝐌𝐩𝐩𝐇𝐇 𝐰𝐰𝐌𝐌𝐌𝐌𝐩𝐩𝐭𝐭 where the width of the three rings closest to the pith were averaged. Using these corrections, the radius from the pith to the latewood of the 2016 ring was calculated by summing the measured ring widths and adding the estimated missed portion of the radius. Total number of rings was calculated as the number of measured rings plus the estimated number of missing rings. For cores missing the bark, there is no standard method to estimate the number of potentially missing sapwood rings, so no correction was applied to address this potential source of error. Their ring counts and summed ring widths provide minimum estimates of age and growth. The number of sapwood rings per sample was counted and ring widths summed. Number of heartwood rings was calculated as total number of rings minus number of sapwood rings. Heartwood width was calculated as total radius minus sapwood width. 158 4.3.6 Estimation of narrow-sense heritability (h2) and genetic correlations We estimated narrow sense heritability (h2) for our traits in the TP from SNP data using univariate mixed linear models (Genomic Best Linear Unbiased Predictors; GBLUP) and from SNP-corrected pedigree data (ABLUP) (Gamal El-Dien et al., accepted) in ASReml-R v4.1.0.149 (Butler et al. 2017): Equation 4.3: 𝒚𝒚 = 𝑿𝑿𝑿𝑿 + 𝒁𝒁𝒁𝒁 + 𝜺𝜺 where 𝑦𝑦 is an n-dimensional vector of phenotypes, 𝛽𝛽 is a p-dimensional vector of fixed effects, 𝑿𝑿 is an n × p design matrix relating phenotypes to fixed effects, 𝑎𝑎 is an n-dimensional vector of random effects including the inverse GRRM from SNP data or the realized relationship matrix (RRM) from pedigree, 𝒁𝒁 is an n × n identity matrix relating phenotypes to unique individual effects, and 𝜀𝜀 is an n-dimensional vector of residual errors with variance I𝜎𝜎𝑒𝑒2. Mixed linear models were used to account for incomplete block study design and make inference on genetic parameters for the entire WRC population from families in the TP. Restricted effects maximum likelihood (REML) ratio test was implemented using the asremlPlus v4.2-18 package in R (https://github.com/briencj/asremlPlus) to test and compare included random effects. The models used for each trait can be found in Table 4.1. For all models, a heterogeneous variance structure (dsum option in ASReml-R) was used to split the residual variance by site. We calculated h2 as: 159 Equation 4.4: 𝑳𝑳𝟐𝟐 = 𝝈𝝈𝑳𝑳𝟐𝟐𝝈𝝈𝑳𝑳𝟐𝟐+𝝈𝝈𝑳𝑳𝟐𝟐 where 𝜎𝜎𝑔𝑔2 is the additive genetic variance from the model and 𝜎𝜎𝑒𝑒2 is the residual variance. In cases where a heterogeneous residual variance structure was used, 𝜎𝜎𝑒𝑒2 was calculated by taking the mean of the residual variance components. Standard errors were calculated using the ‘delta’ approach of Venables and Ripley (2000) as implemented in the ‘vpredict’ function in ASReml-R. Genetic and phenotypic correlations were estimated using bivariate mixed linear models for each pair of traits in ASReml-R. An unstructured variance model was used for the inverse RRM. Genetic correlations were calculated from both genomic and pedigree data as 𝑟𝑟𝑔𝑔 = 𝑐𝑐𝑐𝑐𝑐𝑐𝑔𝑔√𝑉𝑉𝑔𝑔1𝑉𝑉𝑔𝑔2 where 𝑉𝑉𝑔𝑔1 and 𝑉𝑉𝑔𝑔2 are the additive genetic variances of each trait in the model and 𝑐𝑐𝑐𝑐𝑐𝑐𝑔𝑔 is the additive genetic covariance of these traits. Phenotypic correlations were calculated as 𝑟𝑟𝑝𝑝 = 𝑐𝑐𝑐𝑐𝑐𝑐𝑔𝑔+𝑐𝑐𝑐𝑐𝑐𝑐𝑒𝑒√(𝑉𝑉𝑔𝑔1+𝑉𝑉𝑒𝑒1)(𝑉𝑉𝑔𝑔2𝑉𝑉𝑒𝑒2) where 𝑉𝑉𝑒𝑒1 and 𝑉𝑉𝑒𝑒2 are the residual variances of each trait in the model and 𝑐𝑐𝑐𝑐𝑐𝑐𝑒𝑒 is the residual covariance of these traits (Isik et al. 2015). 4.3.7 Adjusted phenotypes for BayesR To simplify our model for use in BayesR, we used an adjusted phenotype for each trait in the TP to train the Bayesian model. This adjusted phenotype was obtained using ASReml-R by extracting the residual of the model in Equation 4.3, as described above (Dataset 4.2). In this 160 case, the random effect for additive genetic component was excluded from the model. Fixed effects were tested for significance using a Wald test. 4.3.8 Genetic architecture of chemistry and growth traits BayesR (Erbe et al. 2012; Moser et al. 2015) was used to identify SNPs linked with putatively causal loci (hereafter, trait-associated SNPs) and estimate genetic architecture of our traits of interest in the TP. BayesR allows us to estimate the variance explained by all SNPs and thus the effects of individual SNPs by fitting all SNPs in the model simultaneously (Moser et al. 2015). We implemented BayesR assuming the default prior mixture of four normal distributions with N(0, 0 × 𝜎𝜎𝑔𝑔2) (no additive genetic effect), N(0, 10-4 × 𝜎𝜎𝑔𝑔2) (polygenic effect), N(0, 10-3 × 𝜎𝜎𝑔𝑔2) (small effect) , and N(0, 10-2 × 𝜎𝜎𝑔𝑔2) (large effect), respectively, where 𝜎𝜎𝑔𝑔2 is the additive genetic variance. Five independent chains were run for each trait to ensure model convergence and to obtain averaged posterior estimates, using the following settings in BayesR: -numit 300000; -burnin 100000; -thin 10; -permute. Convergence of the model was inspected using trace plots for h2 and each posterior distribution, and the Gelman-Rubin convergence diagnostic (Gelman and Rubin 1992; Brooks and Gelman 1998). SNP effect size (ES) was calculated using the equation Equation 4.5: 𝑬𝑬𝑬𝑬 = 𝟐𝟐𝑿𝑿𝟐𝟐𝒇𝒇(𝟏𝟏 − 𝒇𝒇 ) 161 Where 𝛽𝛽 is the regression effect of the model and 𝑓𝑓 is the frequency of the minor allele (Park et al. 2010). ES and mixture distributions were averaged across all five chains and SNPs were assigned to each mixture distribution based on estimated number of SNPs for each distribution from the model and SNP ES. After ranking and extracting all SNPs with some effect for each trait, we then generated set plots using UpSetR v1.4.0 in R (Conway et al. 2017) to isolate and further annotate and explore SNPs which overlapped between different traits. In particular, we looked at 1) shared SNPs between traits with significant genetic correlations; 2) SNPs unique to any single category of traits (foliar chemistry, wood chemistry, or growth); and 3) SNPs shared between trait categories. To estimate localization of SNPs across the genome, we used putative linkage groups (LGs) generated previously for the WRC genome (Chapter 3) by aligning all genomic scaffolds containing SNPs to the Sequoiadendron giganteum (giant sequoia) draft genome (Scott et al. 2020) using BLAST+ v2.10.0 (Altschul et al. 1990; Camacho et al. 2009). Briefly, scaffolds were assigned to their most likely LG based on bitscore. We then used the nucmer command from MUMmer v4 (Marçais et al. 2018) to determine the most likely alignment region for each scaffold in each LG. 162 4.3.9 Inbreeding depression of growth and chemistry traits Inbreeding depression in the training population Another goal of our study was to determine whether WRC shows evidence of inbreeding depression for growth and chemistry traits. We first calculated inbreeding coefficients in PLINK v1.9 (Chang et al. 2015) using the --ibc flag to obtain a measure for inbreeding based on the correlation between uniting gametes (FUNI). This metric, designated Fhat3 in PLINK, is defined by Yang et al. (2011) for each 𝑖𝑖th SNP and each 𝑖𝑖th individual as Equation 4.6: 𝑭𝑭𝐔𝐔𝐍𝐍𝐔𝐔 = 𝒙𝒙𝑯𝑯𝒊𝒊𝟐𝟐 −(𝟏𝟏+𝟐𝟐𝒑𝒑𝑯𝑯)𝒙𝒙𝑯𝑯𝒊𝒊+𝟐𝟐𝒑𝒑𝑯𝑯𝟐𝟐𝟐𝟐𝒑𝒑𝑯𝑯(𝟏𝟏−𝒑𝒑𝑯𝑯) where 𝑥𝑥 is the number of copies of the reference allele and 𝑝𝑝 is the population-wide allele frequency at that locus. SNPs were filtered for linkage disequilibrium (LD) using PLINK (flags: --indep-pairwise 1654 kb 1 0.1) based on previous estimates of LD in WRC (Chapter 3), prior to estimation of inbreeding coefficients in the TP and SLs. To examine whether there was significant inbreeding depression for growth traits and chemistry traits we used mixed linear models for our TP, following the same approach used to obtain adjusted phenotypes, and added FUNI as a covariate in the model (Table 4.1). For this analysis, we examined total foliar monoterpenes, total wood thujaplicins, height, DBH, heartwood radius, sapwood radius, and the ratio of sapwood to heartwood. A Wald test was then used to test for significance of FUNI. 163 Analysis of growth and chemistry traits during selfing Using SLs, we further aimed to explore the effect of selection for height during extreme inbreeding, and identify which SNPs associated with growth and chemistry traits identified in this study may be under selection during selfing. Due to limitations in data availability, we only had access to BVs for height in our SLs. BVs for height were predicted for each generation in each line as described in Russell and Ferguson (2008). Briefly, BVs were predicted by ABLUP using a mixed linear model (Equation 4.3), where 𝑦𝑦 is an n-dimensional vector of raw phenotypes, 𝑿𝑿 is an n × p design matrix for fixed effects, 𝛽𝛽 is an n-dimensional vector of fixed effects, 𝒁𝒁 is an n x p design matrix for random effects, 𝑎𝑎 is an n-dimensional vector of random effects including the inverse relationship matrix from pedigree, and 𝜀𝜀 is an n-dimensional vector of residuals. Specifically, the following model was used: Equation 4.7: 𝒚𝒚𝑯𝑯𝒊𝒊𝒊𝒊𝒊𝒊(𝒎𝒎)𝑳𝑳 = 𝒖𝒖 + 𝑳𝑳𝑯𝑯 + 𝑳𝑳𝑯𝑯�𝒓𝒓𝒊𝒊� + 𝑳𝑳𝒊𝒊 + 𝑳𝑳𝑯𝑯𝑳𝑳𝒊𝒊 + 𝒇𝒇𝒊𝒊 + 𝑳𝑳𝑯𝑯𝒇𝒇𝒊𝒊 + 𝒇𝒇𝒊𝒊(𝒊𝒊𝒎𝒎) + 𝑳𝑳𝑯𝑯𝒇𝒇𝒊𝒊(𝒊𝒊𝒎𝒎) +𝑳𝑳𝑯𝑯𝒊𝒊𝒊𝒊𝒊𝒊(𝒎𝒎)𝑳𝑳 where 𝑢𝑢 is the population mean; 𝑔𝑔𝑖𝑖, 𝑔𝑔𝑖𝑖�𝑟𝑟𝑖𝑖�, 𝑡𝑡𝑘𝑘 , and 𝑔𝑔𝑖𝑖𝑡𝑡𝑘𝑘 are the fixed effects of the 𝑖𝑖th selfed generation, the 𝑖𝑖th replication within the 𝑖𝑖th selfed generation, the 𝑘𝑘th line type (random or selected), and the interaction between the 𝑖𝑖th selfed generation and 𝑘𝑘th line type, respectively; 𝑓𝑓𝑙𝑙, 164 𝑔𝑔𝑖𝑖𝑓𝑓𝑙𝑙, 𝑓𝑓𝑙𝑙(𝑙𝑙𝑚𝑚), and 𝑔𝑔𝑖𝑖𝑓𝑓𝑙𝑙(𝑙𝑙𝑚𝑚) are the random effects of the 𝑙𝑙th family, the interaction between 𝑖𝑖th selfed generation and 𝑙𝑙th family, the 𝑚𝑚th line within the 𝑙𝑙th family, and the interaction between 𝑖𝑖th selfed generation and 𝑚𝑚th line within 𝑙𝑙th family, respectively; and 𝑒𝑒𝑖𝑖𝑖𝑖𝑘𝑘𝑙𝑙(𝑚𝑚)𝑛𝑛 is the random error associated with trees within replication. BVs for foliar monoterpenes were calculated using a mixed linear model (GBLUP) in ASReml-R: Equation 4.8: 𝒚𝒚𝑯𝑯𝒊𝒊𝒊𝒊 = 𝒖𝒖 + 𝑳𝑳𝑯𝑯 + 𝒊𝒊𝒊𝒊 + 𝒁𝒁𝒊𝒊 + 𝑳𝑳𝑯𝑯𝒊𝒊𝒊𝒊 where 𝑢𝑢 is the population mean; 𝑔𝑔𝑖𝑖 and 𝑙𝑙𝑖𝑖 are the fixed effects of the 𝑖𝑖th selfed generation and the 𝑖𝑖th growing location at CLRS, respectively; 𝑎𝑎𝑘𝑘 is the additive genetic effect of the 𝑘𝑘th tree as estimated from the inverse GRRM from SNP data, and 𝑒𝑒𝑖𝑖𝑖𝑖𝑘𝑘 is the random error associated with trees within each generation and location. Inbreeding depression in selfing lines We assessed significance of F for height BVs and total foliar monoterpene BVs (Dataset 4.3) in the SLs using ordinary least squares (OLS) in the following linear model: Equation 4.9: 𝒚𝒚𝑯𝑯 = 𝒖𝒖 + 𝑭𝑭𝑯𝑯 + 𝑳𝑳𝑯𝑯 165 where 𝑢𝑢 is the population mean, 𝐹𝐹𝑖𝑖 is the fixed effect of the 𝑖𝑖th tree’s inbreeding coefficient, and 𝑒𝑒𝑖𝑖 is the random error associated with inbreeding coefficients. Assumptions of independent and identical distribution of error terms were tested in R using residual plots and normality histograms. Effect of selection for height on height BVs and inbreeding depression We used OLS to estimate the effect of selection on height BVs in the following linear model: Equation 4.10: 𝒚𝒚𝑯𝑯𝒊𝒊 = 𝒖𝒖 + 𝑭𝑭𝑯𝑯 + 𝑳𝑳𝒊𝒊 + 𝑭𝑭𝑯𝑯𝑳𝑳𝒊𝒊 + 𝑳𝑳𝑯𝑯𝒊𝒊 where 𝑢𝑢 is the population mean; 𝐹𝐹𝑖𝑖, 𝑡𝑡𝑖𝑖 , 𝐹𝐹𝑖𝑖𝑡𝑡𝑖𝑖, and are the fixed effects of the 𝑖𝑖th tree’s inbreeding coefficient, the 𝑖𝑖th line type (random or selected), the interaction between the 𝑖𝑖th inbreeding coefficient and the 𝑖𝑖th line type, respectively; and 𝑒𝑒𝑖𝑖𝑖𝑖 is the random error associated with these fixed effects. Growth and chemistry associated loci deviating from expectations of genetic drift during selfing We examined whether loci associated with growth or specialized chemistry traits in WRC may be under selective pressures during selfing. Using a set of 1,298 outlier SNPs found to be deviating from expectations of genetic drift during selfing in a previous study (Chapter 3), we 166 assessed whether there was overlap between these SNPs and SNPs found in this study to be associated with each trait. 4.3.10 Data availability The SNP data generated in this study has been submitted to the Zenodo data repository under DOI 10.5281/zenodo.6562381 Code and raw data used for generating data files and figures and Supplemental Dataset files have been uploaded to the following GitHub repository: https://github.com/tshalev/WRC-traits-inbreeding-paper. 4.4 Results 4.4.1 SNP genotyping and filtration Using a targeted genotyping approach, we captured 51,638 polymorphic loci. Filtering across all trees yielded 36,262 SNPs (Dataset 4.4). Among SNPs in annotated regions, 9,855 were in intergenic regions and 24,418 were in genic regions. Fourteen TP trees from families external to the recorded pedigree as identified by the corrected pedigree (Gamal El-Dien et al., accepted) and two trees with 40% or more missing data were excluded, resulting in a final n = 1,504. We corrected erroneous SNP calls for the SLs using the approach of Chapter 3, resulting in continuous SLs to S4 (n = 28) or S5 (n = 11) with a total of n = 151 trees with genotyping data. 167 4.4.2 Growth and specialized chemistry traits in western redcedar Foliar terpenes The foliar terpenes assessed in this study are all part of the same biosynthetic pathway, in which sabinene is produced from the universal monoterpene precursor, geranyl diphosphate (GPP), and culminates in the major and minor products α- and β-thujone, respectively (Karp and Croteau 1982; Foster et al. 2013; Gesell et al. 2015). Thus, we expected to see significant genetic and phenotypic correlations between these traits. Foliar chemistry data were obtained for n = 1,499 trees. The most abundant foliar monoterpene by far in our TP was α-thujone, with a mean (SD) concentration of 23,249 (7,538) µg/g DW, followed by sabinene with a mean concentration of 5,718 (2,314) and finally β-thujone with a mean of 4,732 (1,679). There was substantial variation in foliar terpene concentration between trees, with α-thujone concentrations ranging from 6,256 to 57,408, sabinene from 559 to 22,903, and β-thujone from 1,468 to 13,445 µg/g DW (Figure 4.1A). Sampling site had a significant effect on all foliar terpenes (p < 0.01, Wald test). Wood thujaplicins Wood chemistry data were obtained for n = 1,492 trees. Of the four thujaplicins analysed in this study, α- and γ-thujaplicin were the most abundant, with mean (SD) concentrations of 396.5 168 (275.6) and 361.1 (340.8) µg/g conditioned wood (CW), respectively, followed by β-thujaplicin with 210.2 (225.6) and β-thujaplicinol with 67.5 (66.8) µg/g CW. The distribution of these phenotypes was right-skewed, with more trees exhibiting lower than average concentrations (Figure 4.1B). For all wood thujaplicins, sampling site had a significant effect (p < 0.001, Wald test). Pith age had a significant effect on α- and γ-thujaplicin and β-thujaplicinol (p < 10-5, Wald test). Sapwood to heartwood ratio had a significant effect on α-thujaplicin and β-thujaplicinol (p < 0.005, Wald test). Growth Both height and DBH were significantly greater overall at the Powell River site, with a mean height (SD) of 1,084.5 (156.8) cm and DBH of 143.0 (32.7) mm compared to the means of Jordan River [761.6 (102.0) cm for height and 110.0 (24.5) mm for DBH (p = 0, one-tailed t-test)] and Port McNeill [683.1 (119.2) cm for height and 98.6 (27.7) mm for DBH (p = 0, one-tailed t-test)] (Figure 4.2; Table 4.2). Thus, site had a very significant effect on both height and DBH at 15 years (p = 0, Wald test). 4.4.3 Heritabilities We recorded moderate genomic h2 for α-thujone and sabinene (0.27 each), with a lower h2 for β-thujone (0.16) (Table 4.3). Pedigree based estimates of h2 for α-thujone and sabinene were similar but slightly higher (0.35 and 0.39, respectively), while the estimate for β-thujone was 169 lower (0.14). For wood thujaplicins, β-thujaplicin had the highest genomic h2 (0.30), although the pedigree-based estimate was lower (0.21). α- and γ-thujaplicin and β-thujaplicinol all had moderate to low genomic h2 (0.26, 0.19, and 0.13, respectively), with similar pedigree-based results. Height and DBH both had low genomic h2 (0.14 and 0.10, respectively). In all cases, genomic h2 had lower standard error than pedigree estimates. 4.4.4 Genetic and phenotypic correlations As the foliar terpenes in this study are in the same biosynthetic pathway, we hypothesized that genetic and phenotypic correlations between them would be strong. Genetic and phenotypic correlations were similar and high between α- and β-thujone (0.65 and 0.60, respectively) (Table 4.4). Genetic correlations were moderate between both α-thujone and sabinene (0.35) and β-thujone and sabinene (0.30); however, the phenotypic correlation between α-thujone and sabinene was high (0.66). Due to the structural similarity of the wood thujaplicins, we expected to observe genetic and phenotypic correlations between them. We recorded moderate to high genetic and phenotypic correlations between the different wood thujaplicins (Table 4.4). Overall, β-thujaplicin had moderately low genetic correlations with γ-thujaplicin and β-thujaplicinol (0.22 and 0.20, respectively), but a moderately high genetic correlation with α-thujaplicin (0.46). Interestingly, γ-thujaplicin and β-thujaplicinol had the highest genetic correlation of all traits studied, and a very high phenotypic correlation as well (0.76 each). We observed the greatest 170 discrepancy between genomic and pedigree-based estimates for wood thujaplicins, with some estimates differing by as much as 0.15 between methods. Height and DBH were strongly genetically and phenotypically correlated (0.75 and 0.85, respectively). Between groups of traits, the largest significant genetic correlation was observed between β-thujone and DBH (0.39). There were very small or no significant genetic correlations between wood thujaplicins and other traits, with the exception of sabinene and α-thujaplicin (0.31). There was a moderate but significant negative genetic correlation between β-thujaplicinol and α-thujone (-0.20); there were also negative phenotypic correlations between almost all wood thujaplicins and growth traits (Table 4.4). Standard errors of genomic estimates were much lower than pedigree estimates, which is expected. 4.4.5 Analysis of genetic architecture reveals genes involved in terpenoid biosynthesis and growth Using BayesR, we identified SNPs linked with trait-associated SNPs for each of the traits in this study (Table 4.5; Dataset 4.5). There was a strong negative rank correlation (-0.93) between the number of trait-associated SNPs and total variance explained by large effect SNPs (10-2 × 𝜎𝜎𝑔𝑔2), suggesting that traits with a greater degree of control by major genes have less loci overall associated with them. There was a strong correlation (0.99) between number of trait-associated SNPs and number of genes associated with each trait; indeed, only 28% of annotated trait-171 associated SNPs were in intergenic regions, indicating that an increase in number of trait-associated SNPs also entails an increase in the number of genes controlling a trait. Height and DBH were expected to be mainly polygenic, given their highly quantitative nature (Yang et al. 2010; Boyle et al. 2017). We found that this was largely true, with 29% and 37% of the variance in height and DBH, respectively, explained by SNPs in genes with a polygenic effect (Figure 4.3). Monoterpene traits displayed a range of genetic architectures. Most had a larger major gene component, with γ-thujaplicin and β-thujaplicinol having a large amount of variance explained by SNPs with large effect (67% and 62%, respectively). Sabinene, on the other hand, had the largest component of small effect SNPs (45%), with only 23% of variance explained by large effect SNPs. SNPs and genes associated with chemistry and growth traits Using our knowledge of the genetic architecture of and genetic correlations between our traits of interest, we further sought to identify the SNPs that are associated with each trait, and shared between traits with significant genetic correlations (Figure 4.4A). We used the current WRC genome annotation (Chapter 3) to identify which of these SNPs were in genic regions (Table 4.5). In particular, to better understand and identify candidates for the final steps of thujone biosynthesis, we isolated genes associated with α- and β-thujone and sabinene, as well as genes with SNPs shared between α- and β-thujone, but not sabinene, which occurs earlier in the 172 biosynthetic pathway, and finally genes shared by all three traits (Figure 4.4B). Thujone biosynthesis proceeds via the universal monoterpene precursor geranyl diphosphate (GPP), which is converted to the monoterpene (+)-sabinene by (+)-sabinene synthase (Foster et al. 2013), followed by hydroxylation to (+)-trans-sabinol by the cytochrome P450 (+)-sabinene hydroxylase (Gesell et al. 2015). The two proposed remaining steps are oxidation of the (+)-trans-sabinol intermediate to (+)-sabinone, which is subsequently reduced to both the major (α-thujone) and minor (β-thujone) products; these reactions are catalysed by a putative alcohol dehydrogenase and aldo/keto reductase, respectively (Karp and Croteau 1982; Gesell et al. 2015). We found one putative alcohol dehydrogenase and two putative aldo/keto reductase genes containing SNPs associated with α-thujone. Meanwhile, one alcohol dehydrogenase gene was associated with β-thujone, while no aldo/keto reductase genes were associated with β-thujone. For all SNPs associated with each trait, 596 SNPs were shared between α- and β-thujone, of which 348 were not also shared with sabinene (Figure 4.4B). Of those not shared with sabinene, 256 were in 177 genes. Of SNPs shared between all three foliar terpenes, 173 were in 113 genes (Dataset 4.1). Interestingly, many more SNPs were shared between α-thujone and sabinene (821) than between β-thujone and sabinene (492); this may suggest shared regulatory factors between sabinene and α-thujone not associated with β-thujone, providing a possible explanation for why α-thujone is produced in much greater abundance than β-thujone. For large effect SNPs, only eight SNPs were shared between α- and β-thujone, and none were shared between all three foliar terpenes. Of the eight, four were in four different genes, including a putative methyltransferase and glutamine amidotransferase. 173 For wood thujaplicins, we were interested in SNPs shared between α- and β-thujaplicin and between γ-thujaplicin and β-thujaplicinol, as these have significant genetic correlations, and between all four wood thujaplicins (Figure 4.4C). There were 614 SNPs shared between α- and β-thujaplicin, 428 of which were in 278 genes. γ-thujaplicin and β-thujaplicinol, which had the highest genetic correlation of all traits (0.76), shared 706 SNPs, 520 of which were in 353 genes. Only 97 SNPs were shared between all four wood thujaplicins; however, 221 were shared between just α- and γ-thujaplicin and β-thujaplicinol, indicating that β-thujaplicin synthesis may involve different genes or it may be synthesized earlier in a shared pathway when compared to the other wood thujaplicins. This is consistent with the large number of SNPs unique to β-thujaplicin among wood thujaplicins (1,634), and the relatively low genetic correlation with γ-thujaplicin and β-thujaplicinol (~ 0.2). Height and DBH, expectedly, had by far the largest number of shared SNPs (1,619) – likely in large part due to their strong genetic correlation (0.75) and the larger number of SNPs overall associated with these traits (Figure 4.4D). Of these SNPs, 1,086 were in 662 genes, indicating that almost a third of SNPs associated with both of these traits are in intergenic regions of the genome. This pattern is similar for all SNPs associated with each of these growth traits. 174 Localization of trait-associated SNPs Using a putative linkage map for WRC established in Chapter 3, we mapped SNPs for each trait onto their respective LGs, to determine how these SNPs are distributed across the genome, and where SNPs of greatest ES are located. LG 2 had the largest proportion of SNPs (14.8%), closely followed by LG 1 and LG 4 with 12.9% and 10.8% of all SNPs each, respectively. Standardized to the size of each putative giant sequoia chromosome, LG 2 still had the highest proportion of all SNPs (Table 4.6). Large effect SNPs for α-thujone were distributed across all putative LGs, with more than 20% on LG 2, though this was not significantly more than would have been expected given the distribution of all SNPs (p = 0.0614, Fisher’s Exact Test) (Figure 4.5). This includes one SNP with an estimated ES nearly seven times larger than any other, which is located in a gene putatively annotated as encoding for a ubiquitin associated domain (UBA/TN-S). The same pattern and significance level were observed for β-thujone. Sabinene, on the other hand, had over 30% of its large effect SNPs on LG 10, which is significantly more than expected (p = 1.35×10-4). For β-thujone and sabinene, the SNPs with the largest ES were in intergenic regions on LG 8 and 10, respectively. Large effect SNPs for α-thujaplicin were significantly over-represented on LG 2 (p = 0.0202), while large effect SNPs for γ-thujaplicin and β-thujaplicinol were significantly over-represented on LG 9 (p < 8×10-4). Interestingly, the SNPs with the largest ES for α- and β-thujaplicin were both on LG 9, while for γ-thujaplicin and β-thujaplicinol, they were on LG 10 175 and LG 6, respectively. The largest effect SNPs for α-thujaplicin and β-thujaplicin were on LG 9, downstream of a gene encoding for a plant protein of unknown function, and in a gene putatively encoding for an ABC family transporter, respectively. The largest effect SNPs for γ-thujaplicin and β-thujaplicinol were in intergenic regions on LG 10 and 6, respectively. ES for height and DBH SNPs were generally small, even for large effect SNPs. This may be expected, due to the polygenic nature of these traits. The SNPs with the largest ES for height and DBH were found on LG 3 and 8, respectively, and were both in intergenic regions. 4.4.6 Inbreeding has little impact on growth and chemistry traits even during extreme selfing WRC is known to resist inbreeding depression, and readily self-fertilizes to produce viable offspring (Russell et al. 2003; Wang and Russell 2006; Russell and Ferguson 2008, Chapter 3). Using our TP, we aimed to test whether inbreeding depression was significant for WRC growth and terpene chemistry traits of interest. The trees in our TP had a wide range of estimates for the inbreeding coefficient F, with a mean of 0.23 and some as high as 0.85 (Figure 4.6). This was somewhat expected, given WRC’s low genetic diversity, and the known presence of self-pollinated and closely related individuals in the TP (Chapter 3, Gamal El-Dien et al., accepted). We found no significant effect of F on any of the tested traits, with only height and DBH showing an effect larger than the standard error. A similar trend was observed in our SLs, with no significant effect of F on height or foliar monoterpene BVs (Table 4.7). 176 Selection for growth remains viable during selfing despite large inbreeding coefficients Given the lack of apparent inbreeding depression even during selfing, we explored effect of selection for height growth during selfing. Overall F increased similarly in both select and random lines (Figure 4.7A). However, overall height increased each generation in the select lines, while decreasing in the random lines (Figure 4.7B). There was a significant interaction of F and line type, with height declining with increasing F in random lines, but increasing in select lines (Figure 4.7C). Thus, the only significant predictor of height growth during complete selfing was whether a line was select or random, indicating that despite a stark increase in F, selection for height growth is viable in WRC (Table 4.8). Terpene and growth trait loci associated with selfing In Chapter 3, we identified a set of 1,298 SNP loci putatively under selection during selfing in WRC, 80 of which were in genes. From this set, we identified 53 SNPs associated with foliar terpene traits, 52 associated with wood terpene traits, and 35 associated with growth traits, in 46 different genes – more than half of the genes containing outlier SNPs (Table 4.9). Out of all traits, only one large effect SNP each for α-thujone, which had no annotation, and α-thujaplicin, which was intergenic, were found, with the rest being small effect or polygenic loci. One gene involved in self pollen recognition was associated with both α- and β-thujaplicin, while β-177 thujaplicinol had one gene involved in terpene synthase (TPS) activity. Genes associated with growth included cytochrome P450, oxidoreductase, and RNA polymerase genes. 4.5 Discussion Understanding the genetic basis of traits is fundamental to a wide array of applications, from crop and animal breeding to targeted medicine (Isik et al. 2015; Timpson et al. 2018). Here, we examine the breeding population of an economically important conifer species, WRC, and shed light on the genetic architecture of important chemical traits involved in pest and pathogen resistance as well as key growth traits. In addition, using our breeding population and an array of selfing lines, we demonstrate that WRC does not display inbreeding depression for any of these traits. 4.5.1 Genetic architecture varies even within similar traits Our approach to understanding the genetic architecture of specialized terpene chemistry and growth traits in WRC involved a Bayesian model framework as implemented in BayesR (Moser et al. 2015). Genome-wide association studies (GWAS), where individual SNPs are regressed against the phenotype, are commonly performed to assess significant loci for traits in large populations. However, due to the large number of SNPs being assessed and the potentially polygenic nature of many complex traits, these studies run the risk of being perpetually underpowered to give a significant p-value to SNPs that in fact do have some effect (Yang et al. 178 2010; Boyle et al. 2017). The approach of BayesR sidesteps this limitation by fitting all SNPs simultaneously to the model, and assigning them to different effect sizes based on their posterior probability of tracking quantitative trait loci associated with the trait of interest (Goddard et al. 2016). Indeed, many complex traits have been found to be more complex than once thought, especially growth traits, which appear to have a large polygenic component (Yang et al. 2015; Boyle et al. 2017; Wray et al. 2018; Watanabe et al. 2019). Less is known about the genetic architecture of specialized chemistry traits, although many of the biochemical pathways for the synthesis of metabolites such as terpenoids have been partially or fully elucidated, suggesting a certain measure of major gene control (Keeling and Bohlmann 2006b; Nützmann and Osbourn 2014; Tholl 2015). TPS, in particular, have been especially well characterized (Chen et al. 2011; Shalev et al. 2018; Booth et al. 2020). Thus, we initially expected terpene traits to be mostly impacted by a few major loci of large effect, while growth traits would be mostly polygenic. Though this was largely true of the growth traits surveyed here, there was some variability among terpene traits. In particular, sabinene had the highest proportion of polygenic effect SNPs of any trait examined, and all terpene traits had between 30 – 50% of their variance explained by small or polygenic effect SNPs. This suggests that, despite the presence of major genes controlling which metabolite is synthesized, these traits are complex and phenotypic variation can be polygenic. We also observed some clustering of large effect SNPs based on our putative linkage map of the WRC genome (Chapter 3). Although SNPs with some effect were distributed across all LGs for each trait, certain LGs had greater proportions of large effect SNPs; for example, LG 2 for α- and β-179 thujone and α-thujaplicin, LG 9 for γ-thujaplicin and β-thujaplicinol, and LG 8 for sabinene. Overall, the more polygenic a trait was, the more evenly distributed large effect SNPs were across all LGs, whereas major effect loci were more likely to be localized to specific LGs, suggesting either linked groups of genes, or potentially genetic hitch-hiking due to the high LD in WRC (Chapter 3). The importance of the terpene traits examined in this study to defense against deer-browse and heartwood rot, and thus to WRC breeding, has been previously described (Vourc’h et al. 2002b; Morris and Stirling 2012; Russell and Yanchuk 2012); given the observed differences in genetic and phenotypic correlations for these traits, fully developing the biosynthetic pathways of these terpenes will be fundamental to understanding the mechanisms controlling their expression. The trait-associated SNPs identified in this study will be of use in further characterizing and understanding the biosynthetic pathways of specialized chemistry traits in WRC, as well as developing further insight into critical selection for growth and terpene traits in operational breeding programs. 4.5.2 WRC is not affected by inbreeding depression for chemistry and growth traits One of the most unique aspects of WRC biology is a mixed mating system characterized by relatively low outcrossing rates in nature (~ 70%; El-Kassaby et al. 1994; O’Connell et al. 2001, 2004). This is contrary to observations in other conifers, such as pines (Sorensen and Miles 1974; Remington and O’Malley 2000; Williams 2008), firs (Sorensen 1982, 1999) and douglas-fir (Sorensen and Miles 1974; Sorensen 1999), which all display high levels of outcrossing and 180 strong inbreeding depression. Molecular evidence suggests that during the most recent glacial period WRC was isolated to a single refugium near the southern end of its current range (O’Connell et al. 2008, Chapter 3). Given the pattern and rate of migration and continuous distribution of WRC, it is likely that its successful dispersal since the end of the last glaciation is related to its unique mating system, allowing for colonisation of new areas while isolated from other conspecifics (Baker 1955; Pannell and Barrett 1998). Previous studies have found inbreeding depression for seed traits to be practically nonexistent, while the effect on growth and productivity appears to be low (Russell et al. 2003; Wang and Russell 2006). Using a pedigreed, genotyped and extensively phenotyped TP for genomic selection, we were able to model and assess whether inbreeding depression is significant in a real-world scenario for WRC breeding. Our estimates of F from genotyping ranged widely, with some individuals having estimates as high as 0.85. Despite a high mean F (0.23), inbreeding depression was not significant for any of the growth or chemistry traits we tested. Russell and Ferguson (2008) found previously that selection for height growth is possible even under complete selfing. Using genotyping data, we show that this response to selection occurs despite sharp increases in F with each selfing generation, and that even in random lines inbreeding depression is not a significant predictor of height. Selfing rates tend to be higher in seed orchards (up to 60%; El-Kassaby et al. 1994; Glaubitz et al. 2000) than in natural populations, and there has been some debate over the merit of attempting to reduce these rates during breeding for operational forestry (Russell et al. 2003; Wang and Russell 2006; Ritland et al. 2020). Wang and Russell (2006) estimate that 8.1% of merchantable volume on average will 181 be lost by age 60 due to selfing; however, they also suggest that this effect may be mitigated by planting site improvement and sufficient selection intensity. Recently, Gamal El-Dien et al. (accepted) were able to use the same TP from this study to successfully generate genomic selection predictions which capture LD for implementation of genomic selection in WRC, which is ongoing. We add further that even individuals with high F estimates can be effective targets for selection and breeding, underscoring the importance of understanding the interactions of causal loci influencing the expression of key traits and increasing selection intensity for these loci, rather than attempting to drastically reduce inbreeding in WRC breeding populations. 182 4.6 Tables Table 4.1: Models used to predict breeding values (BVs) and estimate adjusted phenotypes for the genomic selection training population. For prediction of BVs, the inverse genomic realized relationship matrix (GRRM) was included as a random effect in the model. Model terms preceded with ‘idh’ used a block diagonal (heterogeneous) variance structure to split the variance for that term. Trait category Trait Fixed effects Random effects Foliar terpenes α-thujone site idh(site)×replicate + idh(site)×replicate×block β-thujone site site×replicate + idh(site)×replicate×block Sabinene site site×replicate + site×replicate×block Total foliar monoterpenes ǂ site idh(site)×replicate + site×replicate×block Wood terpenes α-thujaplicin site + pith age + SHR site×replicate + site×replicate×block β-thujaplicin site - γ-thujaplicin site + pith age site×replicate + site×replicate×block + β-thujaplicinol site + pith age + SHR - Total wood thujaplicins ǂ site + DBH15 + pith age idh(site)×replicate + site×replicate×block Growth HT15 * site idh(site)×replicate + idh(site)×replicate×block DBH15 * site site×replicate + site×replicate×block Dendrochronology Sapwood ǂ site + DBH15 + pith age idh(site)×replicate + idh(site)×replicate×block Heartwood ǂ site + DBH15 + pith age idh(site)×replicate + idh(site)×replicate×block Sapwood to heartwood ratio ǂ site + DBH15 + pith age idh(site)×replicate + idh(site)×replicate×block 183 HT15 – Height at 15 years DBH15 – Diameter at breast height at 15 years CLB15 – Cedar leaf blight incidence at 15 years SHR – Sapwood to heartwood ratio * – Also used to estimate inbreeding depression, with inbreeding coefficient F added as a fixed effect ǂ – Only used for estimation of inbreeding depression 184 Table 4.2: Student’s t-test for differences in height and DBH for the three different test sites in the training population. Two-sample one-tailed t-tests were performed to assess whether height and DBH were significantly greater at the Powell River site when compared to the Jordan River and Port McNeill sites. Site Average height (cm) (SD) Site Comparison t p-value (1) Jordan River 761.75 (102.12) 2/1 39.44 < 2×10-16 (2) Powell River 1084.85 (156.43) 2/3 45.84 < 2×10-16 (3) Port McNeill 683.39 (119.25) - - - Site Average DBH (mm) (SD) Site Comparison t p-value (1) Jordan River 109.93 (24.44) 2/1 18.51 < 2×10-16 (2) Powell River 143.07 (32.72) 2/3 23.16 < 2×10-16 (3) Port McNeill 98.67 (27.87) - - - 185 Table 4.3: Narrow-sense heritability (h2) estimates from genomic realized relationships (G) and pedigree (A). Trait G h2 (SE) A h2 (SE) α-thujone 0.27 (0.047) 0.35 (0.075) β-thujone 0.16 (0.041) 0.14 (0.043) Sabinene 0.27 (0.045) 0.39 (0.078) α-thujaplicin 0.26 (0.045) 0.26 (0.061) β-thujaplicin 0.30 (0.044) 0.21 (0.049) γ-thujaplicin 0.19 (0.038) 0.20 (0.051) β-thujaplicinol 0.13 (0.032) 0.11 (0.034) Height 0.14 (0.034) 0.15 (0.044) DBH 0.10 (0.032) 0.10 (0.036) 186 Table 4.4: Genetic (lower triangle) and phenotypic (upper triangle) correlations between all study traits estimated using genomic realized relationships (A) and pedigree (B). A Height DBH α-thujone β-thujone Sabinene α-thujaplicin β-thujaplicin γ-thujaplicin β-thujaplicinol Height - 0.85 ++ 0.055 + -0.043 + -0.011 -0.032 + -0.11 ++ -0.011 -0.12 ++ DBH 0.75 ++ - 0.098 ++ -0.036 + 0.033 + -0.10 ++ -0.15 ++ -0.067 ++ -0.18 ++ α-thujone 0.031 0.19 + - 0.60 ++ 0.66 ++ -0.027 -0.020 -0.033 + -0.061 ++ β-thujone 0.20 + 0.39 + 0.65 ++ - 0.32 ++ -0.035 + 0.019 -0.0050 -0.026 + Sabinene -0.076 -0.30 0.35 ++ 0.30 ++ - 0.022 -0.0097 -0.023 -0.027 α-thujaplicin 0.082 -0.051 0.11 0.12 0.31 ++ - 0.54 ++ 0.54 ++ 0.47 ++ β-thujaplicin 0.027 0.12 0.17 + 0.10 0.058 0.46 ++ - 0.41 ++ 0.36 ++ γ-thujaplicin 0.030 0.028 0.071 -0.023 0.074 0.53 ++ 0.22 + - 0.76 ++ β-thujaplicinol -0.10 -0.069 -0.20 + -0.20 + -0.0090 0.35 ++ 0.20 + 0.76 ++ - 187 B Height DBH α-thujone β-thujone Sabinene α-thujaplicin β-thujaplicin γ-thujaplicin β-thujaplicinol Height - 0.84 ++ 0.056 + -0.031 + -0.011 -0.025 -0.10 ++ -0.014 -0.13 DBH 0.72 ++ - 0.11 ++ -0.015 0.031 -0.10 ++ -0.14 ++ -0.060 ++ -0.18 ++ α-thujone 0.082 0.29 + - 0.61 ++ 0.63 ++ -0.018 -0.0056 -0.042 + -0.076 ++ β-thujone 0.28 + 0.43 + 0.73 ++ - 0.32 ++ -0.031 + 0.026 -0.015 -0.029 + Sabinene 0.042 0.032 0.43 ++ 0.36 ++ - 0.071 ++ -0.0075 -0.014 -0.028 α-thujaplicin 0.084 -0.18 0.074 0.038 0.59 ++ - 0.53 ++ 0.54 ++ 0.46 ++ β-thujaplicin -0.021 0.097 0.33 + 0.19 0.16 0.36 ++ - 0.40 ++ 0.34 ++ γ-thujaplicin -0.074 0.084 -0.16 -0.23 + 0.12 0.56 ++ 0.25 + - 0.75 ++ β-thujaplicinol -0.25 + -0.32 + -0.45 ++ -0.23 + -0.060 0.21 + 0.045 0.67 ++ - +: Estimate larger than the standard error ++: Estimate more than twice as large as the standard error 188 Table 4.5: Trait-associated SNPs and effect sizes for each trait estimated using the approach of BayesR (Moser et al. 2015). Of the 36,262 SNPs used in the study, 23,913 were associated with traits. Of these, 10,178 were in genic regions and 3,933 were in intergenic regions. Trait Trait-associated SNPs Large effect SNPs (10-2×𝝈𝝈𝑳𝑳𝟐𝟐) Small effect SNPs (10-3×𝝈𝝈𝑳𝑳𝟐𝟐) Polygenic SNPs (10-4×𝝈𝝈𝑳𝑳𝟐𝟐) No effect SNPs (0×𝝈𝝈𝑳𝑳𝟐𝟐) Annotated genes α-thujone 2,206 60 249 1,897 34,056 929 β-thujone 2,704 54 270 2,380 33,558 1,085 Sabinene 3,602 28 445 3,129 32,660 1,417 α-thujaplicin 2,508 55 279 2,174 33,754 1,017 β-thujaplicin 2,559 52 319 2,188 33,703 1,018 γ-thujaplicin 1,787 67 169 1,551 34,475 757 β-thujaplicinol 2,026 70 191 1,765 34,236 930 Height 3,016 47 280 2,689 33,246 1,133 DBH 3,505 46 303 3,155 32,758 1,344 189 Table 4.6: Number of SNPs in each putative linkage group derived from the Sequoiadendron giganteum (giant sequoia) draft genome (Scott et al. 2020). Percentage of total was scaled by the length of each putative giant sequoia chromosome. Linkage group # SNPs % Total Scaled % total 1 4475 12.9 9.40 2 5149 14.8 12.2 3 3023 8.69 7.43 4 3741 10.7 10.7 5 2512 7.22 7.54 6 3014 8.67 9.23 7 2907 8.36 9.14 8 2875 8.27 9.17 9 3401 9.78 11.0 10 2178 6.26 7.14 11 1493 4.29 6.98 190 Table 4.7: Estimates of inbreeding depression derived from mixed linear models for training population data (A) and OLS for selfing lines (B). A Trait Inbreeding depression (SE) F-statistic (Wald’s test) P-value Total foliar monoterpenes (µg/g DW) 373.8 (2389.6) 0.00 0.876 Total wood thujaplicins (µg/g CW) -106.9 (138.0) 0.60 0.439 Height (cm) -41.1 (21.8) 3.50 0.0599 DBH (mm) -8.61 (5.29) 2.60 0.104 Sapwood (mm) 0.349 (1.22) 0.10 0.775 Heartwood (mm) 0.959 (1.96) 0.20 0.626 Sapwood to heartwood ratio 0.0188 (0.0339) 0.30 0.579 B Trait Inbreeding depression (SE) F-statistic P-value Total foliar monoterpene breeding values (µg/g DW) -395.0 (1818.0) 0.0472 0.828 Height breeding values (cm) -0.0127 (0.813) 0.00020 0.988 191 Table 4.8: In four generations of complete selfing, deliberate selection for seedling height was the only significant predictor of height, despite an increase in the inbreeding coefficient F with each selfing generation (one-way ANOVA; OLS linear model). Moreover, there was a significant interaction effect of F and type, as predicted height BVs of random lines declined while those of select lines increased (Figure 4.7C). Variable Effect (SE) F-statistic P-value Type (Select/Random) 0.0532 (0.679) 27.8 5.18×10-7 F -2.69 (1.06) 0.0003 0.987 Type:F (interaction) 4.89 (1.45) 11.4 9.42×10-4 192 Table 4.9: Loci putatively under selection during selfing identified in Chapter 3 associated with terpene and growth traits. Trait All SNPs with effect Large effect SNPs (10-2×𝝈𝝈𝒈𝒈𝟐𝟐 ) Small effect SNPs (10-3×𝝈𝝈𝒈𝒈𝟐𝟐 ) Polygenic SNPs (10-4×𝝈𝝈𝒈𝒈𝟐𝟐 ) Annotated genes α-thujone 13 1 2 10 2 β-thujone 16 0 1 15 8 Sabinene 32 0 4 28 16 Total foliar terpene (unique SNPs) 53 1 7 47 22 α-thujaplicin 19 1 5 13 6 β-thujaplicin 27 0 2 25 6 γ-thujaplicin 8 0 0 8 1 β-thujaplicinol 12 0 2 10 3 Total wood terpene (unique SNPs) 52 1 8 48 13 Height 22 0 1 21 14 DBH 26 0 1 25 18 Total growth (unique SNPs) 35 0 2 35 23 Total 120 2 17 111 46 193 4.7 Figures Figure 4.1: Density plots of the amounts of different foliar (A) and wood (B) monoterpenes in the training population. Density indicates the proportion of observations of a range of terpene concentrations. 194 Figure 4.2: Distribution of height (A) and diameter at breast height (DBH; B) across the three progeny test sites in the training population. 195 Figure 4.3: Variance explained by each of three mixture distributions (σ2g×10-2 = large effect; σ2g×10-3 = small effect; σ2g×10-4 = polygenic effect) for each trait as determined by the approach of BayesR (Moser et al. 2015). 196 197 Figure 4.4: Overlap of trait-associated SNPs for growth and chemistry traits in WRC. A) Overlap of SNPs with large effect between all assessed traits. Blue: Foliar terpene traits; Yellow: wood terpene traits; Red: growth traits; Purple: traits of different categories. B) Overlap of all trait-associated SNPs for foliar terpene traits. C) Overlap of all trait-associated SNPs for wood terpene traits. D) Overlap of all trait-associated SNPs for growth traits. 198 Figure 4.5: Distribution of SNPs of three putative effect sizes as determined using BayesR (Moser et al. 2015), mapped across putative linkage groups based on the draft genome of Sequoiadendron giganteum (giant sequoia) (Scott et al. 2020). 199 Figure 4.6: Distribution of the inbreeding coefficient F as determined by the approach of Yang et al. (2011) in the training population. 200 Figure 4.7: Despite a noticeable increase in the inbreeding coefficient F in all selfing line trees with each successive selfing generation, the only significant predictor of height growth was whether a line was under deliberate artificial selection for height. A) Change in F during selfing. F increases with each selfing generation similarly within both random and select lines. B) Change in height BV during selfing. Height BVs increased with each selfing generation in select lines, while height BVs in random lines declined somewhat. Black line represents the regression of height BV on generation, indicating that change in height is not significant when line type is ignored. C) Interaction plot of predicted height BV and F. In the absence of selection, increasing values of F are negatively correlated with growth. However, under selection height BV increases despite an increase in F, indicating the effects of inbreeding are readily mitigated by moderate selective pressures in WRC. 201 4.8 Datasets Dataset 4.1: Master phenotypic dataset containing all study design, foliar chemistry, wood chemistry, growth, dendrochronology, and inbreeding data for the training population in this study. Dataset 4.2: Adjusted phenotypes for all assessed foliar terpene, wood terpene, and growth traits. Dataset 4.3: Inbreeding, height BV, foliar terpene, and sample collection data for the selfing lines in this study. Dataset 4.4: Final filtered SNP dataset (n = 36, 262) for the training population. Dataset 4.5: Putative trait-associated SNPs for all assessed traits in the study.202 Conclusion Over the past two decades, next-generation sequencing has driven the development of genomic resources for an abundance of organisms, and as costs decline and sequencing approaches advance, this includes rapidly increasing numbers of non-model species. Despite the challenges inherent to their large, complex genomes, much effort has gone into better understanding and developing conifer genomic resources, and thus their evolutionary history and ecology. Given the importance of conifers on an ecological, economic, and cultural scale, this understanding will be critical as we face future challenges of climate change, increased demand for lumber products, and the ever-increasing need for conservation of these long-lived giants. In this thesis, I have demonstrated how the use of highly homozygous fifth-generation selfed trees can facilitate the process of sequencing and assembly in western redcedar (WRC), allowing for very high quality, complete transcriptome and genome assemblies. These resources were then leveraged to further develop our understanding of the evolutionary history, genetic diversity, mating system, and complex growth and chemistry traits of WRC. 5.1 Selfing lines facilitate transcriptome and genome assembly The complexity and size of conifer genomes have made developing approaches to assembly of genomic resources equally complex. This has been further compounded by the overall high heterozygosity of conifers, which provide a major challenge for de Bruijn graph-based assembler 203 algorithms for de novo assembly of high-throughput short read sequencing runs (Pryszcz and Gabaldón 2016; Asalone et al. 2020; see also, for example Nystedt et al. 2013; Zimin et al. 2014; Warren et al. 2015; Stevens et al. 2016; Neale et al. 2017). By using a combination of multiple assembly algorithms on RNA-seq data from homozygous fifth-generation selfed trees, I was able to generate a highly complete, contiguous, accurate, low-redundancy reference transcriptome for WRC. We estimated transcriptome gene set BUSCO completeness at ~ 89%, with only ~ 6% duplicated sequences. Using a selfed tree from the same lines, a highly complete genome assembly was generated from short read, mate-pair read, and 10× Chromium large molecule linked-read sequencing libraries. With an N50 of 2.31 Mbp and an NG50 of 1.71 Mbp, the draft WRC genome is one of the most contiguous conifer genome assemblies available. New developments in resequencing and assembly methods have led to conifer genomes with near-chromosome-level assemblies (Scott et al. 2020; Neale et al. 2022; Niu et al. 2022). However, this appears to come at a cost of accuracy and completeness. The WRC genome assembly has a BUSCO completeness of 86%, the highest observed for any conifer genome assembly. Moreover, a comprehensive annotation of the genome yielded a gene set completeness of ~ 91%, also the highest of any current conifer gene set (Table 3.5). Despite the numerous benefits of achieving a chromosome-level scale of contiguity, the importance of retaining accuracy and completeness remains paramount. Thus; addressing heterozygosity remains a critical step in accurate assembly of conifer genomes. 204 5.2 Terpene biosynthesis in western redcedar Terpenes are one of the largest classes of specialized metabolites, and although the general biosynthesis of terpene precursors and many terpene products has been well documented, the pathways of terpene biosynthesis have not been well characterized in WRC. Moreover, little is known about the complexity and quantitative genetics of terpene traits. Terpenes are important in the defense of plants against invading pests and pathogens; thus, a better understanding of how they are controlled at the gene level is fundamental for better understanding and improving plant defense systems, especially in the case of important forestry and crop species (Keeling and Bohlmann 2006a, 2006b; Zulak and Bohlmann 2010). 5.2.1 Transcriptome mining uncovers novel Cupressaceae terpene synthases (TPS) and other genes of terpene biosynthesis and confirms the phylogenetic position of Cupressaceae and gymnosperm TPS In the biosynthesis of terpenes, terpene synthases (TPS) are major drivers of the structural diversity that has led to the vast size of this class of specialized metabolite (Bohlmann et al. 1998; Chen et al. 2011). Using a reduced-redundancy gene set generated from the transcriptome of selfed trees, I was able to isolate 33 putative full-length TPS genes from WRC. By running a comprehensive phylogenetic analysis, I found that WRC TPS genes, together with all other gymnosperm TPS genes of specialized metabolism, clustered within the TPS-d clade of TPS, consistent with earlier results examining TPS of angiosperms and gymnosperms (Chen et al. 205 2011), and contrary to recent results incorporating data from P. orientalis (Hu et al. 2016) (Figure 2.6). Furthermore, we found that WRC TPS sequences clustered separately from Pinaceae TPS, indicating that a divergence in these sequences likely occurred during the evolutionary split of Cupressaceae and Pinaceae (Figures 2.6 and 2.7). This appears to be at least partially due to differences in established conserved sequence motifs between the two families. In addition, I was able to identify 90 alcohol dehydrogenase and 70 aldo-keto reductase genes, which could be further explored to decipher the remaining steps of thujone biosynthesis in WRC foliage. This list has already been further narrowed down via differential expression analysis of a unique ecotype of WRC which does not form foliar oil glands with standard, gland-forming trees (Shalev, unpublished) and functional characterization undertaken by Kshatriya (2020), as well as through the identification of shared associated single nucleotide polymorphism (SNP) loci for thujone pathway metabolites in Chapter 5. 5.2.2 Terpene biosynthesis is a complex trait It is well established that biosynthetic pathways progress through multiple intermediate molecules, the conversion between which is catalysed by enzymes encoded by different genes, resulting in a final product. As explained previously, the biosynthetic pathways of many terpene products have been well characterized. What is less understood is the genetic architecture of these traits and the quantitative nature which underlies the natural variation in these traits observed in a population. 206 By utilizing a large genomic selection (GS) training population and SNP genotyping data captured from genomic and transcriptomic data, I was able to develop predictive models for key foliar and wood terpene traits in WRC and identify putatively causal loci for each trait by estimating SNP effect sizes via a mixture distribution-based Bayesian approach. I found that terpene traits had associations with between 1,800 and 3,600 SNP loci, indicating that despite major control by genes encoding biosynthetic pathway enzymes there remain thousands of genes and regulatory regions putatively associated with each trait. Further, genetic architecture was not consistent across traits – despite all having a large major gene component, only 23% of the variance in sabinene was explained by SNPs with large effect, whereas in other terpene traits SNPs with large effect explained between 45% and 67% of the variance. These large effect SNPs tend to cluster on specific linkage groups (LGs), suggesting specific regions of the genome could be targeted when using SNP data for GS on specific traits. Moreover, most traits had positive genetic correlations, which should allow for multiple selection of traits of interest. 5.3 Genetic diversity and inbreeding in western redcedar WRC’s unique mating system has been cited as a likely driver for its rapid expansion across the Pacific Northwest of North America since the end of the last glaciation (O’Connell et al. 2001, 2008; O’Connell 2003). Given the high self-compatibility of WRC and the observed range expansion, it has been expected that genetic diversity may be low, though this has not been assessed with modern next-generation sequencing (NGS) approaches (Copes 1981; Glaubitz et 207 al. 2000; O’Connell et al. 2008). Further, despite observed selfing behaviour and hypothesized low genetic diversity it is not known how WRC remains responsive to selection and how significant inbreeding depression is for growth and chemistry traits in supposedly outcrossed breeding populations or during deliberate complete selfing. 5.3.1 High self-compatibility and balancing selection may drive adaptability and responsiveness to selection despite low genetic diversity in western redcedar To assess genetic diversity in WRC, I used SNP data for a population of trees sampled from across the geographic range of the species. I found only weak population structure between trees from across the range, and overall genetic differentiation. This was expected given the continuous distribution of the population. However, overall nucleotide diversity π was low and consistent with tree species of drastically reduced range, and the ratio of π0/π4 was moderately high, suggesting weak purifying selection. Recently, there has been some debate over whether the current range of WRC originated from a single glacial refugium in the southern part of the range, or from multiple glacial refugia. Principal component analysis (PCA) showed only 3% of the variation in the population was explained by the first principal component (PC), corresponding with latitude, while only 1.5% was explained by the second PC. Despite trees from the south of the range showing slightly more differentiation, the variation between geographic groups is so small that all trees effectively constitute the same population. The site frequency spectrum (SFS) of the population suggests 208 that effective population size Ne declined dramatically during the last glaciation, and demographic analysis is consistent with expansion from a single glacial refugium, supporting previous results. Given WRC’s unique mating system, I investigated how selective pressures may affect WRC during selfing by using SNP data for an array of selfing lines that had undergone complete self-fertilization for up to five generations. I found that heterozygosity declined slightly slower than expected during selfing, partially due to SNPs remaining heterozygous even after multiple generations. This outcome, together with observed weak purifying selection suggest that WRC’s ability to adapt and respond to selection may be driven by balancing selection, despite low genetic diversity. In addition, some SNPs putatively under selection during selfing are found in genes associated with self-pollen recognition, suggesting that WRC may be under selective pressure to maintain self-compatibility. 5.3.2 No significant inbreeding depression for terpene chemistry and growth traits in WRC To evaluate the extent of inbreeding depression for traits of commercial interest, I utilized SNP data for the GS training population and selfing lines, together with growth, terpene chemistry and dendrochronological traits. I found that WRC does not exhibit inbreeding depression for any of the examined traits. Further, in selfing lines that had been selected for height growth, I found no significant effect of inbreeding. Rather, selection for growth was the only significant predictor of 209 growth, suggesting that with reasonable selection intensity, inbreeding may not have significant detrimental impacts on breeding populations of WRC. 5.4 Conclusion In this thesis, I have developed new genomic resources for WRC and used them to answer fundamental questions on the genetic diversity, demography, mating system, and important terpene chemistry traits in WRC, thus meeting and fulfilling the objectives of the thesis. The genomic, transcriptomic, and trait resources developed and expanded upon in this thesis will be important for further investigating the population genetics of WRC and its unique mating system, and for improving application of GS in operational forestry. 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Thesis/Dissertation
eng
Genome Science and Technology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Bohlmann, Jörg
Genomics of western redcedar (Thuja plicata)
Text