Biology subjects

Yeaman, S.

Publications and source records attributed to Yeaman, S..

At least 19 recordsLinked to original sources

bioRxiv2026

A simulation-based method for genotype-environment association analysis

Genotype-environment association (GEA) analyses are widely used to identify loci underlying local adaptation by examining correlations between allele frequencies and environmental variables across a species' range. A major challenge for this approach is distinguishing true adaptive signals from spurious associations arising from population structure. Several methods have been developed to account for population structure, but these methods can suffer from reduced statistical power or increased false positives under some conditions. To address this, we introduce a new GEA method, termed SimGEA. In essence, SimGEA infers a neutral evolutionary model that reproduces the population structure observed in empirical data and uses this model to simulate neutral alleles. By applying the same GEA statistic to both the empirical and simulated data, SimGEA evaluates the significance of observed associations against neutral expectations that account for population structure. We compared the performance of SimGEA with that of existing GEA methods, including LFMM2 and BayPass, using simulations of local adaptation in two-dimensional space. We found that SimGEA consistently controlled the false discovery rate without substantially sacrificing statistical power across the scenarios examined. These results suggest that calibrating statistics using neutral simulations provides a robust and flexible approach for accounting for population structure in GEA analyses.

genetics↗

bioRxiv2026

A conundrum of pleiotropy: GWAS-derived pleiotropy is weakly negatively associated with co-expression network centrality in Arabidopsis thaliana

Pleiotropy, the influence of a single gene on multiple phenotypic traits, is a fundamental feature of genetic systems but remains difficult to quantify at genome-wide scales. Genome-wide association studies (GWAS) provide one avenue to characterize pleiotropy through multi-trait genetic associations, while functional genomic approaches such as gene co-expression networks offer indirect predictions of gene importance and pleiotropic potential based on network centrality statistics. However, the relationship between GWAS-derived pleiotropy and network-based measures of gene importance remains unclear. Here, we quantified gene-level pleiotropy in Arabidopsis thaliana using gene-trait associations from the AraGWAS Catalog and AraPheno databases and compared them to previous estimates of pleiotropy based on gene co-expression networks. GWAS pleiotropy was measured using the Hill number of order q = 1, an entropy-based metric adapted from ecological diversity theory. Across 2,179 genes, pleiotropy exhibited a right-skewed distribution, with most genes influencing relatively few traits and a smaller subset exhibiting broad multi-trait effects. Contrary to expectations, pleiotropy was weakly negatively correlated with multiple measures of co-expression network centrality, including degree, strength, closeness, and betweenness. Additional analyses showed that GWAS-associated genes were enriched within the co-expression network, exhibited broader expression across tissues, and were modestly enriched among conserved BUSCO genes. Together, these findings reveal a conundrum: although genes identified by GWAS are broadly expressed and integrated into functional networks, the most pleiotropic genes occupy relatively peripheral positions within those networks. This unexpected pattern suggests that GWAS-derived pleiotropy captures a distinct aspect of gene function than co-expression network centrality.

evolutionary biology↗

bioRxiv2026

Polygenic and redundant architectures of climate-adaptive traits may complicate genomic predictions of maladaptation

SignificanceUnderstanding the genetic basis of local adaptation is an important step to predict species responses to climate change. Current predictions of maladaptation to climate in trees, however, rely mostly on genotype-environment associations, which overlooks the complexity of genetic architectures underpinning climate-adaptive traits. Based on genotype-phenotype associations, we unveil key aspects of the polygenic architecture of drought tolerance and cold hardiness in two lineages of a widespread conifer. Our results suggest that predictions based solely on climate-associated alleles may mischaracterize maladaptation to future climates due to mismatches between the distribution of climate-associated alleles and alleles contributing to adaptive traits. We also show that cold hardiness and drought tolerance evolved largely independently in the two lineages, and with little pleiotropy between traits. Understanding the genetic architecture of climate-adaptive traits can help predict responses to climate change and inform management strategies that mitigate the effects of future climates. By characterizing the genetic basis of adaptation to spatially varying climate conditions, models can predict response to temporal change in climate by extrapolating from spatial patterns of variation. However, such methods are predicated upon a simple genetic basis, with a linear relationship between allele frequency, phenotype, and environment. Here, we explore the nature of the mapping between genotype, phenotype, and climate adaptation in Douglas-fir, an important North American conifer. Using two large common garden experiments combined with exome sequencing and case-control genome-wide association tests, we found that drought tolerance and cold hardiness in two Douglas-fir varieties (Pseudotsuga menziesii var. menziesii and var. glauca) are highly polygenic with little pleiotropy between the two traits. The two varieties showed little repeated evolution in the genetic basis of drought tolerance and none for cold hardiness. Allelic clines were observed along geographic and climatic gradients across 74 natural populations, but their direction of effect was often inconsistent with phenotypic clines. Our findings reveal that the genomic variation underlying climate-adaptive traits is remarkably complex, with redundancy likely playing a key role in the evolution of the divergent polygenetic architectures of abiotic stress tolerance. This suggests that predicting maladaptation to climate based solely on climate-associated alleles may misrepresent future adaptive responses. Genomic predictions and management practices should focus on individual lineages and consider the particularities of the genetic architecture of climate-adaptive traits.

evolutionary biology↗

bioRxiv2025

The Genomic Architecture of Local Adaptation in Two Connected Populations of Three-Spined Stickleback.

Populations often adapt to their local environments despite the homogenizing effects of gene flow, but the genomic mechanisms enabling this process remain unclear. Theory predicts that adaptive divergence under high connectivity is favored when beneficial alleles cluster in regions of reduced recombination, a pattern that can be reinforced by structural variants (SVs). We investigated this in three-spined sticklebacks (Gasterosteus aculeatus) from the St. Lawrence Estuary, where distinct freshwater and marine ecotypes meet and interbreed along a short ecological gradient. Using long- and short-read whole-genome sequencing, we mapped fine-scale recombination landscapes, catalogued SVs, and examined their relationship with adaptive genomic regions. Recombination landscapes differed between populations, with population-specific shifts in recombination rate estimated by an LD-based method. Putatively adaptive regions were not confined to low-recombination regions, yet SVs (inversions, insertions, and deletions) frequently coincided with local recombination suppression and elevated differentiation, suggesting they may contribute to local adaptation. Differentiated regions also overlapped disproportionately with previously-identified regions involved in repeated local adaptation across the species range, which tended to be strongly enriched on chromosomes IV, VII and XXI. These repeated regions were associated with lower recombination rates, suggesting that recombination suppression may contribute to their reuse across populations. As found in stickleback populations from other regions, the St. Lawrence populations exhibit elements suggestive of concentrated architectures clustered in a few genomic regions, along with relatively diffuse patterns of highly differentiated regions distributed genome-wide, across a wide range of recombination rates. These results highlight the intertwined roles of recombination variation and structural variation in shaping evolutionary trajectories in connected populations. Article SummaryThis study investigates how recombination and structural variants shape local adaptation in the three-spined stickleback (Gasterosteus aculeatus). Using long- and short-read genome sequencing, the authors compared recombination landscapes and structural variants between marine and freshwater populations from the St. Lawrence Estuary. They found population-specific changes in recombination rate and frequent overlap between structural variants, reduced recombination, and genomic regions showing high differentiation. These findings suggest that variation in recombination and structural variants jointly influence how adaptation proceeds in connected populations, providing new insights into the genomic mechanisms that maintain diversity despite ongoing gene flow.

evolutionary biology↗

bioRxiv2025

The genomic basis of local adaptation in the white x Engelmann spruce hybrid zone

Hybridization between species can occur along repeated zones of contact, providing a natural laboratory for studying the interplay between migration and selection, and identifying loci involved in adaptation and reproductive isolation. However, interpreting how evolutionary processes shape genomic patterns can be challenging: repeatability of genotype-environment association alone is not strong evidence for selection, as hybrid zones derived from the same parental species are not evolutionarily independent. Conversely, processes that operate within the middle of each hybrid zone, such as selection driving directional introgression, may be more evolutionarily independent, and therefore provide stronger evidence of selection. Here we compared hybridization and local adaptation patterns between two replicated regions within the western Canada interior spruce hybrid zone: a broad latitudinal transect with gradual environmental variation and a narrow elevational transect with substantial topographical and environmental variation. We discovered a complex pattern of introgression, with strong differences in ancestry maintained even across small spatial scales at several locations along the elevational transect. Despite differences in their spatial scales, the elevational and latitudinal transects revealed strikingly similar genome-wide patterns of differentiation and adaptation, and consistent patterns of directional introgression. We explore the extent to which the evolutionary non-independence of these hybrid zones allows inferences about the role of natural selection and drift in shaping these patterns. Consistent with theory, we found longer genomic tracts in the elevational transect, likely because the steeper environmental gradients over shorter distances limit the rate of mixing by migration and recombination relative to drift and selection.

evolutionary biology↗

bioRxiv2025

Maintenance of polymorphism in spatially heterogeneous environments

Local adaptation occurs when species adapt to spatially heterogeneous environments. The stability of local adaptation is determined by selection-migration-drift balance: selection favours adaptive divergence whereas migration and random genetic drift cause the collapse of the divergence. How this balance determines the dynamics of local adaptation has been extensively studied. However, owing to the complexity of multi-population models, most previous theories used models with simple population structure and environmental variation, limiting their applicability to complex situations in nature. To address this issue, we developed a new theoretical method to analyze complex multi-population models, allowing heterogeneity in selection, migration, and population density. In essence, our method approximates a spatial model by a panmictic one-population model while retaining the core stochastic structure, enabling the application of conventional diffusion methods. By comparing with simulations, we confirmed that our method accurately describes stochastic evolutionary dynamics in various spatial models, provided sufficient levels of migration. This method is then applied to examine the effect of environmental distribution in 2D space. Assuming landscapes with different levels of the spatial autocorrelation of the environment, we found that the maintenance of locally adaptive alleles is significantly promoted when the spatial autocorrelation is high. These results highlight how complex spatial heterogeneity, as seen in nature, could affect the qualitative outcome of evolution.

genetics↗

bioRxiv2024

The persistence of locally adapted polymorphisms under mutation swamping

Locally adapted traits can exhibit a wide range of genetic architectures, from pronounced divergence at a few loci to small allele frequency shifts at many loci. The type of architecture that evolves depends strongly on migration rate, as weakly selected loci experience swamping and do not make stable contributions to divergence. Simulations from previous studies showed that even when mutations are strongly selected and should resist migration swamping, the architecture of adaptation can collapse and become transient at high mutation rates. Here, we use an analytical two-population model to study how this "mutation swamping" phenomenon depends upon population size, strength of selection, and parameters determining mutation effects. To do this, we developed a mathematical theory based on the diffusion approximation to predict the threshold mutation rate above which swamping occurs, and find that this performs well across wide range of parameter space, based on comparisons with individual-based simulations. The mutation swamping threshold depends most strongly on the average effect size of mutations, and weakly on the strength of selection, but is only minimally affected by population size. Across a wide range of parameter space, we observe that mutation swamping occurs when the trait-wide mutation rate is 10-3-10-2, suggesting that this phenomenon is potentially relevant to complex traits with a large mutational target. On the other hand, based on the apparent stability of genetic architecture in many classic examples of local adaptation, our theory suggests that per-trait mutation rates are often relatively low.

evolutionary biology↗

bioRxiv2024

Genetic architecture underlying response to the fungal pathogen Dothistroma septosporum in lodgepole pine, jack pine, and their hybrids

In recent decades, Dothistroma needle blight (DNB), a pine tree disease caused by the fungal pathogen Dothistroma septosporum, has severely damaged lodgepole pine (Pinus contorta Dougl. ex. Loud.) in British Columbia, Canada, and raised health concerns for jack pine (Pinus banksiana Lamb.). The pathogen has already shown signs of host shift eastward to the hybrid populations between lodgepole pine and jack pine (Pinus contorta x P. banksiana), and possibly into pure jack pine. However, we have little knowledge about mechanisms of resistance to D. septosporum, especially the underlying genetic basis of variation in pines. In this study, we conducted controlled inoculations to induce infection by D. septosporum and performed a genome-wide case-control association study with pooled sequencing (pool-seq) data to dissect the genetic architecture underlying response in lodgepole pine, jack pine, and their hybrids. We identified candidate genes associated with D. septosporum response in lodgepole pine and in hybrid samples. We also assessed genetic structure in hybrid populations and inferred how introgression may affect the distribution of genetic variation involved in D. septosporum response in the studied samples. These results can be used to develop genomic tools to evaluate DNB risk, guide forest management strategies, and potentially select for resistant genotypes.

plant biology↗

bioRxiv2024

Repeated global adaptation across plant species

Global adaptation occurs when all populations of a species undergo selection toward a common optimum. This can occur by a hard selective sweep with the emergence of a new globally advantageous allele that spreads throughout a species natural range until reaching fixation. This evolutionary process leaves a temporary trace in the region affected, which is detectable using population genomic methods. While selective sweeps have been identified in many species, there have been few comparative and systematic studies of the genes involved in global adaptation. Building upon recent findings showing repeated genetic basis of local adaptation across independent populations and species, we asked whether certain genes play a more significant role in driving global adaptation across plant species. To address this question, we scanned the genomes of 17 plant species to identify signals of repeated global selective sweeps. Despite the substantial evolutionary distance between the species analysed, we identified several gene families with strong evidence of repeated positive selection. These gene families tend to be enriched for reduced pleiotropy, consistent with predictions from Fishers evolutionary model and the cost of complexity hypothesis. We also found that genes with repeated sweeps exhibit elevated levels of gene duplication. Our findings contrast with recent observations of increased pleiotropy in genes driving local adaptation, consistent with predictions based on the theory of migration-selection balance. SignificanceGlobal adaptation occurs when a species undergoes selection toward a common optimum throughout its natural range. While instances of global adaptation are widespread in the literature, there is a shortage of comparative studies aimed at understanding its genetic architecture and how it contrasts with that of local adaptation. This research compares global selective sweeps across 17 plant species to uncover the attributes of the genetic loci repeatedly involved in adaptation. We show that global adaptation tends to rely on genes with reduced pleiotropy and is characterized by increased levels of gene duplication. This finding contrasts with recent observations of increased pleiotropy in genes driving local adaptation, reflecting the opposing dynamics underlying these two evolutionary processes.

evolutionary biology↗

bioRxiv2023

Conditionally deleterious mutation load accumulates in genomic islands but can be purged with sufficient genotypic redundancy

Local adaptation frequently evolves in patches or environments that are connected via migration. In these cases, genomic regions that are linked to an adaptive locus experience reduced effective migration rates. Via individual-based simulations of a two-patch system, we show that this reduced effective migration results in the accumulation of conditionally deleterious mutations, but not universally deleterious mutations, adjacent to adaptive loci. When there is redundancy in the genetic basis of local adaptation (i.e. genotypic redundancy), turnover of adaptive loci allows conditionally deleterious mutation load to be purged. The amount of mutational load that accumulates adjacent to adaptive loci is dependent on redundancy, recombination rate, migration rate, population size, strength of selection, and the phenotypic effect size of adaptive alleles. Our results highlight the need to be cautious when interpreting patterns of local adaptation at the level of phenotype or fitness, as the genetic basis of local adaptation can be transient, and evolution may confer a degree of maladaptation to non-local environments.

evolutionary biology↗

bioRxiv2023

Repeatability of adaptation in sunflowers: genomic regions harbouring inversions also drive adaptation in species lacking an inversion

Local adaptation commonly involves alleles of large effect, which experience fitness advantages when in positive linkage disequilibrium (LD). Because segregating inversions suppress recombination and facilitate the maintenance of LD between locally adapted loci, they are also commonly found to be associated with adaptive divergence. However, it is unclear what fraction of an adaptive response can be attributed to inversions and alleles of large effect, and whether the loci within an inversion could still drive adaptation in the absence of its recombination-suppressing effect. Here, we use genome-wide association studies to explore patterns of local adaptation in three species of sunflower: Helianthus annuus, H. argophyllus, and H. petiolaris, which each harbour a large number of species-specific inversions. We find evidence of significant genome-wide repeatability in signatures of association to phenotypes and environments, which are particularly enriched within regions of the genome harbouring an inversion in one species. This shows that while inversions may facilitate local adaptation, at least some of the loci can still harbour mutations that make substantial contributions without the benefit of recombination suppression in species lacking a segregating inversion. While a large number of genomic regions show evidence of repeated adaptation, most of the strongest signatures of association still tend to be species-specific, indicating substantial genotypic redundancy for local adaptation in these species.

evolutionary biology↗

bioRxiv2023

How useful is genomic data for predicting maladaptation to future climate?

Methods using genomic information to forecast potential population maladaptation to climate change are becoming increasingly common, yet the lack of model validation poses serious hurdles toward their incorporation into management and policy. Here, we compare the validation of maladaptation estimates derived from two methods - Gradient Forests (GFoffset) and the Risk Of Non-Adaptedness (RONA) - using exome capture pool-seq data from 35 to 39 populations across three conifer taxa: two Douglas-fir varieties and jack pine. We evaluate sensitivity of these algorithms to the source of input loci (markers selected from genotype-environment associations [GEA] or those selected at random). We validate these methods against two-year and 52-year growth and mortality measured in independent transplant experiments. Overall, we find that both methods often better predict transplant performance than climatic or geographic distances. We also find that GFoffset and RONA models are surprisingly not improved using GEA candidates. Even with promising validation results, variation in model projections to future climates makes it difficult to identify the most maladapted populations using either method. Our work advances understanding of the sensitivity and applicability of these approaches, and we discuss recommendations for their future use.

evolutionary biology↗

bioRxiv2022

Genomic signatures of selection associated with benzimidazole drug treatments in Haemonchus contortus field populations

Genome-wide methods offer a powerful approach to detect signatures of drug selection in parasite populations in the field. However, their application to parasitic nematodes has been limited because of both a lack of suitable reference genomes and the difficulty of obtaining field populations with sufficiently well-defined drug selection histories. Consequently, there is little information on the genomic signatures of drug selection for parasitic nematodes in the field and on how best to detect them. This study was designed to address these knowledge gaps using field populations of Haemonchus contortus with well-defined and contrasting benzimidazole-selection histories, leveraging a recently completed chromosomal-scale reference genome assembly. We generated a panel of 49,393 ddRADseq markers and used this resource to genotype 20 individual H. contortus adult worms from each of four H. contortus populations: two from closed sheep flocks that had an approximately 20-year history of frequent treatment exclusively with benzimidazole drugs, and two populations with a history of little or no drug treatment. The populations were chosen from the same geographical region to limit population structure in order to maximize the sensitivity of the approach. A clear signature of selection was detected on the left arm of chromosome I centered on the isotype-1 {beta}-tubulin gene in the benzimidazole-selected but not the unselected populations. Two additional, but weaker, signatures of selection were detected; one near the middle of chromosome I and one near the isotype-2 {beta}-tubulin locus on chromosome II. We examined genetic differentiation between populations, and nucleotide diversity and linkage disequilibrium within populations to define these two additional regions as encompassing five genes and a single gene. We also compared the relative power of using pooled versus individual worm sequence data to detect genomic selection signatures and how sensitivity is impacted by sequencing depth, worm number, and population structure. In summary, this study used H. contortus field populations with well-defined drug selection histories to provide the first direct genome-wide evidence for any parasitic nematode that the isotype-1 {beta}-tubulin gene is the quantitatively most important benzimidazole resistance locus. It also identified two additional genomic regions that likely contain benzimidazole-resistance loci of secondary importance. Finally, this study provides an experimental framework to maximize the power of genome-wide approaches to detect signatures of selection driven by anthelmintic drug treatments in field populations of parasitic nematodes. AUTHOR SUMMARYBenzimidazoles are important anthelmintic drugs for human and animal parasitic nematode control with [~]0.5 billion children at risk of infection treated annually worldwide. Drug resistance is common in livestock parasites and a growing concern in humans. Haemonchus contortus is the most important model parasite system used to study anthelmintic resistance and a significant livestock pathogen. It is also one of the few parasitic nematodes with a chromosomal-scale genome assembly. We have undertaken genome-wide scans using a dense RADseq marker panel on worms from natural field populations under differing levels of benzimidazole selection. We show that there is a single predominant genomic signature of selection in H. contortus associated with benzimidazole selection centred on the isotype-1 {beta}-tubulin locus. We also identify two weaker signatures of selection indicative of secondary drug resistance loci. Additionally, we assess the minimum data requirements for parameters including worm number, sequence depth, marker density needed to detect the signatures of selection and compare individual to Poolseq analysis. This work is the first genome-wide study in a parasitic nematode to provide direct evidence of the isotype-1 {beta}-tubulin locus being the single predominant benzimidazole resistance locus and provides an experimental framework for future population genomic studies on anthelmintic resistance.

genomics↗

bioRxiv2022

Using genome scans to identify genes used repeatedly for adaptation

Adaptation occurring in similar genes or genomic regions in distinct lineages provides evolutionary biologists with a glimpse at the fundamental opportunities for and constraints to diversification. With the widespread availability of high throughput sequencing technologies and the development of population genetic methods to identify the genetic basis of adaptation, studies have begun to compare the evidence for adaptation at the molecular level among distinct lineages. However, methods to study repeated adaptation are often oriented towards genome-wide testing to identify a set of genes with signatures of repeated use, rather than evaluating the significance at the level of an individual gene. In this study, we propose PicMin, a novel statistical method derived from the theory of order statistics that can test for repeated molecular evolution to estimate significance at the level of an individual gene, using the results of genome scans. This method is generalizable to any number of lineages and indeed, statistical power to detect repeated adaptation increases with the number of lineages that have signals of repeated adaptation of a given gene in multiple lineages. An implementation of the method written for R can be downloaded from https://github.com/TBooker/PicMin.

evolutionary biology↗

bioRxiv2021

Pleiotropy drives repeatability in the genetic basis of adaptation

Observations of genetically repeated evolution (repeatability) in complex organisms are incongruent with the Fisher-Orr model, which implies that repeated use of the same gene should be rare when mutations are pleiotropic (i.e., affect multiple traits). When spatially divergent selection occurs in the presence of migration, mutations of large effect are more strongly favoured, and hence repeatability is more likely, but it is unclear whether this observation is limited by pleiotropy. Here, we explore this question using individual-based simulations of a two-patch model incorporating multiple quantitative traits governed by mutations with pleiotropic effects. We explore the relationship between fitness trade-offs and repeatability by varying the alignment between mutation effect and spatial variation in trait optima. While repeatability decreases with increasing trait dimensionality, trade-offs in mutation effects on traits do not strongly limit the contribution of a locus of large effect to repeated adaptation, particularly under increased migration. These results suggest that repeatability will be more pronounced for local rather than global adaptation. Whereas pleiotropy limits repeatability in a single-population model, when there is local adaptation with gene flow, repeatability can occur if some loci are able to produce alleles of large effect, even when there are pleiotropic trade-offs. Article summaryClassical evolutionary theory predicts that genetically repeated evolution should be vanishingly rare in organisms where mutations affect multiple traits. In this article, we use simulations to study such pleiotropic mutations, and explore their effects on local adaptation in two patches under divergent selection. We find that migration between patches increases the likelihood of repeated evolution, even when there are fitness trade-offs imposed by pleiotropy.

genetics↗

bioRxiv2021

The WZA: A window-based method for characterizing genotype-environment association

Genotype environment association (GEA) studies have the potential to elucidate the genetic basis of local adaptation in natural populations. Specifically, GEA approaches look for a correlation between allele frequencies and putatively selective features of the environment. Genetic markers with extreme evidence of correlation with the environment are presumed to be tagging the location of alleles that contribute to local adaptation. In this study, we propose a new method for GEA studies called the weighted-Z analysis (WZA) that combines information from closely linked sites into analysis windows in a way that was inspired by methods for calculating FST. We analyze simulations modelling local adaptation to heterogeneous environments either using a GEA method that controls for population structure or an uncorrected approach. In the majority of cases we tested, the WZA either outperformed single-SNP based approaches or performed similarly. The WZA outperformed individual SNP approaches when the measured environment is not perfectly correlated with the true selection pressure or when a small number of individuals or demes was sampled. We apply the WZA to previously published data from lodgepole pine and identified candidate loci that were not found in the original study.

evolutionary biology↗

bioRxiv2021

Partial genome assembly of the medicinal plant Ephedra sinica.

Ephedra sinica is a high-value medicinal plant that produces important phenylpropylamino alkaloids pseudoephedrine and ephedrine. Few genomics resources exist for E. sinica, which has been characterized as a tetraploid with a monoploid genome size of 8.56 Gb. Here we reported a partial genome assembly of E. sinica (12.8 Gb) based on Illumina short-read sequencing at low coverage.

genomics↗

bioRxiv2020

Haploid, diploid, and pooled exome capture recapitulate features of biology and paralogy in two non-model tree species

Despite their suitability for studying evolution, many conifer species have large and repetitive giga-genomes (16-31Gbp) that create hurdles to producing high coverage SNP datasets that captures diversity from across the entirety of the genome. Due in part to multiple ancient whole genome duplication events, gene family expansion and subsequent evolution within Pinaceae, false diversity from the misalignment of paralog copies create further challenges in accurately and reproducibly inferring evolutionary history from sequence data. Here, we leverage the cost-saving benefits of pool-seq and exome-capture to discover SNPs in two conifer species, Douglas-fir (Pseudotsuga menziesii var. menziesii (Mirb.) Franco, Pinaceae) and jack pine (Pinus banksiana Lamb., Pinaceae). We show, using minimal baseline filtering, that allele frequencies estimated from pooled individuals show a strong positive correlation with those estimated by sequencing the same population as individuals (r > 0.948), on par with such comparisons made in model organisms. Further, we highlight the use of haploid megagametophyte tissue in identifying sites that are likely due to misaligned paralogs. Together with additional minor filtering, we show that it is possible to remove many of the loci with large frequency estimate discrepancies between individual and pooled sequencing approaches, improving the correlation further (r > 0.973). Our work addresses bioinformatic challenges in non-model organisms with large and complex genomes, highlights the use of megagametophyte tissue for the identification of paralog sites when sequencing large numbers of populations, and suggests the combination of pool-seq and exome capture to be robust for further evolutionary hypothesis testing in these systems.

genomics↗