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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 toward 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.

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.

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, andH. 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 involved can still make substantial contributions without the benefit of recombination suppression. 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.

Methods using genomic information to forecast 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 two methods – Gradient Forests (GF) and the Risk Of Non-Adaptedness – 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 number of input populations as well as the source and number 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 genetic offset methods often better predict transplant performance than climatic or geographic distances. We also find that while GF models are surprisingly not improved using GEA outliers, they are sensitive to the populations included in analysis. Even with promising validation results, ambiguity of 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.

Many conifers have distributions that span wide ranges in both biotic and abiotic conditions, but the basis of response to biotic stress has received much less attention than response to abiotic stress. In this study, we investigated the gene expression response of lodgepole pine (Pinus contorta) to attack by the fungal pathogen Dothistroma septosporum, which causes Dothistroma needle blight, a disease that has caused severe climate-related outbreaks in northwestern British Columbia. We inoculated tolerant and susceptible pines with two D. septosporum isolates and analyzed the differentially expressed genes (DEGs), differential exon usage, and coexpressed gene modules using RNA-sequencing data. We found a rapid and strong transcriptomic response in tolerant lodgepole pine samples inoculated with one D. septosporum isolate, and a late and weak response in susceptible samples inoculated with another isolate. We mapped 43 of the DEG- or gene module–identified genes to the reference plant-pathogen interaction pathway deposited in the Kyoto Encyclopedia of Genes and Genomes database. These genes are present in PAMP-triggered and effector-triggered immunity pathways. Genes comprising pathways and gene modules had signatures of strong selective constraint, while the highly expressed genes in tolerant samples appear to have been favored by selection to counterattack the pathogen. We identified candidate resistance genes that may respond to D. septosporum effectors. Taken together, our results show that gene expression response to D. septosporum infection in lodgepole pine varies both among tree genotypes and pathogen strains and involves both known candidate genes and a number of genes with previously unknown functions. [Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Spatially varying selection promotes variance in allele frequencies, increasing genetic differentiation between the demes of a metapopulation. For that reason, outliers in the genome-wide distribution of summary statistics measuring genetic differentiation, such as FST, are often interpreted as evidence for alleles that contribute to local adaptation. However, theoretical studies have shown that in spatially structured populations the spread of beneficial mutations with spatially uniform fitness effects can also induce transient genetic differentiation. In recent years, numerous empirical studies have suggested that such species-wide, or global, adaptation makes a substantial contribution to molecular evolution. In this perspective, we discuss how commonly such global adaptation may influence the genome-wide distribution of FST and generate genetic differentiation patterns, which could be mistaken for local adaptation. To illustrate this, we use forward-in-time population genetic simulations assuming parameters for the rate and strength of beneficial mutations consistent with estimates from natural populations. We demonstrate that the spread of globally beneficial mutations in parapatric populations may frequently generate FST outliers, which could be misinterpreted as evidence for local adaptation. The spread of beneficial mutations causes selective sweeps at flanking sites, so in some cases, the effects of global versus local adaptation may be distinguished by examining patterns of nucleotide diversity within and between populations in addition to FST. However, when local adaptation has been only recently established, it may be much more difficult to distinguish from global adaptation, due to less accumulation of linkage disequilibrium at flanking sites. Through our discussion, we conclude that a large fraction of FST outliers that are presumed to arise from local adaptation may instead be due to global adaptation.

Theory predicts that local adaptation should favor the evolution of a concentrated genetic architecture, where the alleles driving adaptive divergence are tightly clustered on chromosomes. Adaptation to marine versus freshwater environments in threespine stickleback has resulted in an architecture that seems consistent with this prediction: divergence among populations is mainly driven by a few genomic regions harboring multiple quantitative trait loci for environmentally adapted traits, as well as candidate genes with well-established phenotypic effects. One theory for the evolution of these “genomic islands” is that rearrangements remodel the genome to bring causal loci into tight proximity, but this has not been studied explicitly. We tested this theory using synteny analysis to identify micro- and macro-rearrangements in the stickleback genome and assess their potential involvement in the evolution of genomic islands. To identify rearrangements, we conducted a de novo assembly of the closely related tubesnout (Aulorhyncus flavidus) genome and compared this to the genomes of threespine stickleback and two other closely related species. We found that small rearrangements, within-chromosome duplications, and lineage-specific genes (LSGs) were enriched around genomic islands, and that all three chromosomes harboring large genomic islands have experienced macro-rearrangements. We also found that duplicates and micro-rearrangements are 9.9× and 2.9× more likely to involve genes differentially expressed between marine and freshwater genotypes. While not conclusive, these results are consistent with the explanation that strong divergent selection on candidate genes drove the recruitment of rearrangements to yield clusters of locally adaptive loci.

Genes in plant secondary metabolic pathways enable biosynthesis of a range of medically and industrially important compounds, and are often clustered on chromosomes. Here, we study genomic clustering in the benzylisoquinoline alkaloid (BIA) pathway in opium poppy (Papaver somniferum), exploring relationships between gene expression, copy number variation, and metabolite production. We use Hi-C to improve the existing draft genome assembly, yielding chromosome-scale scaffolds that include 35 previously unanchored BIA genes. We find that co-expression of BIA genes increases within clusters and identify candidates with unknown function based on clustering and covariation in expression and alkaloid production. Copy number variation in critical BIA genes correlates with stark differences in alkaloid production, linking noscapine production with an 11-gene deletion, and increased thebaine/decreased morphine production with deletion of a T6ODM cluster. Our results show that the opium poppy genome is still dynamically evolving in ways that contribute to medically and industrially important phenotypes.

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.

The use of next-generation sequencing (NGS) data sets has increased dramatically over the last decade, but there have been few systematic analyses quantifying the accuracy of the commonly used variant caller programs. Here we used a familial design consisting of diploid tissue from a single lodgepole pine (Pinus contorta) parent and the maternally derived haploid tissue from 106 full-sibling offspring, where mismatches could only arise due to mutation or bioinformatic error. Given the rarity of mutation, we used the rate of mismatches between parent and offspring genotype calls to infer the single nucleotide polymorphism (SNP) genotyping error rates of FreeBayes, HaplotypeCaller, SAMtools, UnifiedGenotyper, and VarScan. With baseline filtering HaplotypeCaller and UnifiedGenotyper yielded more SNPs and higher error rates by one to two orders of magnitude, whereas FreeBayes, SAMtools and VarScan yielded lower numbers of SNPs and more modest error rates. To facilitate comparison between variant callers we standardized each SNP set to the same number of SNPs using additional filtering, where UnifiedGenotyper consistently produced the smallest proportion of genotype errors, followed by HaplotypeCaller, VarScan, SAMtools, and FreeBayes. Additionally, we found that error rates were minimized for SNPs called by more than one variant caller. Finally, we evaluated the performance of various commonly used filtering metrics on SNP calling. Our analysis provides a quantitative assessment of the accuracy of five widely used variant calling programs and offers valuable insights into both the choice of variant caller program and the choice of filtering metrics, especially for researchers using non-model study systems.

Studies of trait-mapping and local adaptation often identify signatures of genetically parallel evolution, where different species evolve similar phenotypes using the same genes. Such patterns appear incongruent with current estimations of quantitative trait architecture. With hundreds or thousands or genes contributing to a trait, why would selection make repeated use of the same genes? Here, we use individual-based simulations to explore a two-patch model with quantitative traits and pleiotropy to understand the parameters which may lead to repeated use of a particular locus during independent bouts of adaptation. We find that repeatability can be driven by increased phenotypic effect size, a reduction in trait dimensionality and a reduction in mutational correlations at a particular locus relative to other loci in the genome, and that these patterns are magnified by increased migration between demes. These results suggest that evolutionary convergence can arise from multiple characteristics of a locus, and provide a framework for the interpretation of quantitative signatures of convergence in empirical studies.

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.

The isomerization of neopinone to codeinone is a critical step in the biosynthesis of opiate alkaloids in opium poppy. Previously assumed to be spontaneous, the process is in fact catalyzed enzymatically by neopinone isomerase (NISO). Without NISO the primary metabolic products in the plant, in engineered microbes and in vitro are neopine and neomorphine, which are structural isomers of codeine and morphine, respectively. Inclusion of NISO in yeast strains engineered to convert thebaine to natural or semisynthetic opiates dramatically enhances formation of the desired products at the expense of neopine and neomorphine accumulation. Along with thebaine synthase, NISO is the second member of the pathogenesis-related 10 (PR10) protein family recently implicated in the enzymatic catalysis of a presumed spontaneous conversion in morphine biosynthesis.

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

Significance Temperate trees originating from warmer localities usually grow faster and acclimate to winter later than trees of the same species from colder localities. However, when trees are selected for faster growth, are climatically adaptive genes and traits affected? Our research demonstrates a simple, sensitive, broadly applicable, and elusive approach to integrating complex polygenic variation into applied environmental management. We show that selective breeding increases allele frequencies of many trait-associated genes, and that alleles that increase most display strong genetic linkage and potential trade-offs among traits. Increasing tree growth while maintaining adaptation is essential to reforestation in a changing climate, but this may be challenging because trait-associated genetic variation that underpins climate adaptation is responsive to selection for tree growth., Locally adapted temperate tree populations exhibit genetic trade-offs among climate-related traits that can be exacerbated by selective breeding and are challenging to manage under climate change. To inform climatically adaptive forest management, we investigated the genetic architecture and impacts of selective breeding on four climate-related traits in 105 natural and 20 selectively bred lodgepole pine populations from western Canada. Growth, cold injury, growth initiation, and growth cessation phenotypes were tested for associations with 18,600 single-nucleotide polymorphisms (SNPs) in natural populations to identify “positive effect alleles” (PEAs). The effects of artificial selection for faster growth on the frequency of PEAs associated with each trait were quantified in breeding populations from different climates. Substantial shifts in PEA proportions and frequencies were observed across many loci after two generations of selective breeding for height, and responses of phenology-associated PEAs differed strongly among climatic regions. Extensive genetic overlap was evident among traits. Alleles most strongly associated with greater height were often associated with greater cold injury and delayed phenology, although it is unclear whether potential trade-offs arose directly from pleiotropy or indirectly via genetic linkage. Modest variation in multilocus PEA frequencies among populations was associated with large phenotypic differences and strong climatic gradients, providing support for assisted gene flow polices. Relationships among genotypes, phenotypes, and climate in natural populations were maintained or strengthened by selective breeding. However, future adaptive phenotypes and assisted gene flow may be compromised if selective breeding further increases the PEA frequencies of SNPs involved in adaptive trade-offs among climate-related traits.

Many conifers have distributions that span wide ranges in both biotic and abiotic conditions, but the basis of response to biotic stress has received much less attention than response to abiotic stress. In this study, we investigated the gene expression response of lodgepole pine (

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.

The identification of genome rearrangements is of direct relevance for understanding their potential impacts on evolution and disease. However, available methods that detect or visualize rearrangements from deviations in gene order do not map them onto a genome of interest, complicating downstream analysis. In this work, we present rearrvisr, an R package that implements a novel algorithm for the identification and classification of rearrangements. In contrast to other software, it projects rearrangements onto a single genome, facilitating the localization of rearranged regions and estimation of their extent. We show that our tool achieves high precision and recall scores on simulated data, and illustrate the utility of our method by applying it to a data set generated from publicly available Drosophila genomes. The package is freely available from GitHub (https://github.com/dorolin/rearrvisr) and can be installed directly from R.

Genes in plant secondary metabolic pathways enable biosynthesis of a range of medically and industrially important compounds, and are often clustered on chromosomes. Here, we study genomic clustering in the benzylisoquinoline alkaloid (BIA) pathway in opium poppy (Papaver somniferum), exploring relationships between gene expression, copy number variation, and metabolite production. We use Hi-C to improve the existing draft genome assembly, yielding chromosome-scale scaffolds that include 35 previously unanchored BIA genes. We find that co-expression of BIA genes increases within clusters and identify candidates with unknown function based on clustering and covariation in expression and alkaloid production. Copy number variation in critical BIA genes correlates with stark differences in alkaloid production, linking noscapine production with an 11-gene deletion, and increased thebaine/decreased morphine production with deletion of a T6ODM cluster. Our results show that the opium poppy genome is still dynamically evolving in ways that contribute to medically and industrially important phenotypes., The opium poppy has been a source of painkilling drugs synthesized by the benzylisoquinoline alkaloid pathway. Here, the authors report an improved genome assembly and reveal gene clustering and copy number variation in alkaloid metabolic pathways.

Local adaptation is expected to cause high FSTat sites linked to a causal locus, however this pattern can also be driven by background or positive selection. Within-population nucleotide diversity could provide a means to differentiate these scenarios, as both background and positive selection deplete diversity, whereas some theoretical studies have shown that local adaptation increases it. However, it is unclear whether such theoretical predictions generalize to more complicated models. Here, we explore how local adaptation shapes genome-wide patterns in nucleotide diversity and FST, extending previous work to study the effect of variable degrees of polygenicity and genotypic redundancy in an adaptive trait, and different levels of population structure. We show that local adaptation produces two very different patterns depending on the relative strengths of migration and selection, either markedly decreasing or increasing within-population diversity at linked sites at equilibrium. When migration is low, regions of depleted diversity can extend large distances from the causal locus, with substantially more diversity eroded than expected with background selection. With higher migration, peaks occur over much smaller genomic distances but with much larger magnitude changes in diversity. In spatially extended clinal environments both patterns can be found within a single species, with increases in diversity at the center of the range and decreases towards the periphery. Our results demonstrate that there is no universal diagnostic signature of local adaptation based on nucleotide diversity, however, given that neither background nor positive selection inflate diversity, when peaks are found they strongly suggest local adaptation.

Genome scans can potentially identify genetic loci involved in evolutionary processes such as local adaptation and gene flow. Here, we show that recombination rate variation across a neutrally evolving genome gives rise to mixed sampling distributions of mean FST, a common population genetic summary statistic. In particular, we show that in regions of low recombination the distribution of estimates have more variance and a longer tail than in more highly recombining regions. Determining outliers from the genome-wide distribution without taking local recombination rate into consideration may therefore increase the frequency of false positives in low recombination regions and be overly conservative in more highly recombining ones. We perform genome-scans on simulated and empirical Drosophila melanogaster datasets and, in both cases, find patterns consistent with this neutral model. Similar patterns are observed for other summary statistics used to capture variation in the coalescent process. Linked selection, particularly background selection, is often invoked to explain heterogeneity in across the genome, but here we point out that even under neutrality, statistical artefacts can arise due to variation in recombination rate. Our results highlight a flaw in the design of genome scan studies and suggest that without estimates of local recombination rate, interpreting the genomic landscape of any summary statistic that captures variation in the coalescent process will be very difficult.

Species often include multiple ecotypes that are adapted to different environments 1. However, it is unclear how ecotypes arise and how their distinctive combinations of adaptive alleles are maintained despite hybridization with non-adapted populations 2-4. Here, by resequencing 1,506 wild sunflowers from 3 species (Helianthus annuus, Helianthus petiolaris and Helianthus argophyllus), we identify 37 large (1-100 Mbp in size), non-recombining haplotype blocks that are associated with numerous ecologically relevant traits, as well as soil and climate characteristics. Limited recombination in these haplotype blocks keeps adaptive alleles together, and these regions differentiate sunflower ecotypes. For example, haplotype blocks control a 77-day difference in flowering between ecotypes of the silverleaf sunflower H. argophyllus (probably through deletion of a homologue of FLOWERING LOCUS T (FT)), and are associated with seed size, flowering time and soil fertility in dune-adapted sunflowers. These haplotypes are highly divergent, frequently associated with structural variants and often appear to represent introgressions from other-possibly now-extinct-congeners. These results highlight a pervasive role of structural variation in ecotypic adaptation. Local adaptation is common in species that experience different environments across their range, often resulting in the formation of ecotypes-ecological races with distinct morphological and/or physiological characteristics that provide an environment-specific fitness advantage. Despite the prevalence of ecotypic differentiation, much remains to be understood about the genetic basis and evolutionary mechanisms that underlie its establishment and maintenance. In particular , a longstanding evolutionary question-dating to criticisms of Darwin's theories by his contemporaries 4-concerns how such ecological divergence can occur when challenged by hybridization with non-adapted populations 2. Local adaptation typically requires alleles at multiple loci that contribute to increased fitness in the same environment ; however, different ecotypes are often geographically close and interfertile, and hybridization between them should break up adaptive allelic combinations 3. To better understand the genetic basis of local adaptation and ecotypic differentiation, we conducted an in-depth study of genetic, phenotypic and environmental variation in three annual sunflower species, each of which includes multiple reproductively compatible ecotypes. Two species (H. annuus and H. petiolaris) have broad, overlapping distributions across North America. Helianthus annuus, the common sunflower, is generally found on mesic soils, but can grow in a variety of disturbed or extreme habitats, including semi-desert or frequently flooded areas. An especially well-characterized ecotype (formally known as H. annuus subsp. texanus) is adapted to the higher temperatures and herbivore pressures in Texas (USA) 5. Helianthus petiolaris, the prairie sunflower, prefers sandier soils; ecotypes of this species are adapted to sand sheets and dunes 6. The third species-H. argophyllus, the silverleaf sunflower-is endemic to southern Texas and includes both an early-flowering, coastal-island ecotype and a late-flowering inland ecotype 7. Population structure of wild sunflowers In a common garden experiment, we grew 10 plants from each of 151 populations of the 3 species, selected from across their native range (Fig. 1a); for each of these populations, we collected corresponding soil samples. We generated extensive records of developmental and morphological traits, and resequenced the genomes of 1,401 individual plants. We resequenced an additional 105 H. annuus plants to fill gaps in geographical coverage, as well as 12 outgroup taxa (Supplementary Table 1). Sunflower genomes are relatively large (H. annuus, 3.5 Gbp; https://doi.

Hybrid zones contain extensive standing genetic variation that facilitates rapid responses to selection. The Picea glauca × Picea engelmannii hybrid zone in western Canada is the focus of tree breeding programs that annually produce ~90 million reforestation seedlings. Understanding the direct and indirect effects of selective breeding on adaptive variation is necessary to implement assisted gene flow (AGF) polices in Alberta and British Columbia that match these seedlings with future climates. We decomposed relationships among hybrid ancestry, adaptive traits, and climate to understand the implications of selective breeding for climate adaptations and AGF strategies. The effects of selection on associations among hybrid index estimated from ~6,500 SNPs, adaptive traits, and provenance climates were assessed for ~2,400 common garden seedlings. Hybrid index differences between natural and selected seedlings within breeding zones were small in Alberta (average +2%), but larger and more variable in BC (average −7%, range −24% to +1%), slightly favoring P. glauca ancestry. The average height growth gain of selected seedlings over natural seedlings within breeding zones was 36% (range 12%–86%). Clines in growth with temperature‐related variables were strong, but differed little between selected and natural populations. Seedling hybrid index and growth trait associations with evapotranspiration‐related climate variables were stronger in selected than in natural seedlings, indicating possible preadaptation to drier future climates. Associations among cold hardiness, hybrid ancestry, and cold‐related climate variables dominated signals of local adaptation and were preserved in breeding populations. Strong hybrid ancestry–phenotype–climate associations suggest that AGF will be necessary to match interior spruce breeding populations with shifting future climates. The absence of antagonistic selection responses among traits and maintenance of cold adaptation in selected seedlings suggests breeding populations can be safely redeployed using AGF prescriptions similar to those of natural populations.

Genetic redundancy has been defined in many different ways at different levels of biological organization. Here, we briefly review the general concept of redundancy and focus on the evolutionary importance of redundancy in terms of the number of genotypes that give rise to the same phenotype. We discuss the challenges in determining redundancy empirically, with published experimental examples, and demonstrate the use of the C-score metric to quantify redundancy in evolution studies. We contrast the implicit assumptions of redundancy in quantitative versus population genetic models, show how this contributes to signatures of allele frequency shifts, and highlight how the rapid accumulation of genome-wide association data provides an avenue for further understanding the prevalence and role of redundancy in evolution.

Species often include multiple ecotypes that are adapted to different environments. But how do ecotypes arise, and how are their distinctive combinations of adaptive alleles maintained despite hybridization with non-adapted populations? Re-sequencing of 1506 wild sunflowers from three species identified 37 large (1-100 Mbp), non-recombining haplotype blocks associated with numerous ecologically relevant traits, and soil and climate characteristics. Limited recombination in these regions keeps adaptive alleles together, and we find that they differentiate several sunflower ecotypes; for example, they control a 77 day difference in flowering between ecotypes of silverleaf sunflower (likely through deletion of a FLOWERING LOCUS T homolog), and are associated with seed size, flowering time and soil fertility in dune-adapted sunflowers. These haplotypes are highly divergent, associated with polymorphic structural variants, and often appear to represent introgressions from other, possibly extinct, congeners. This work highlights a pervasive role of structural variation in maintaining complex ecotypic adaptation.

Spatially varying selection promotes variance in allele frequencies, increasing genetic differentiation between the demes of a metapopulation. For that reason, outliers in the genome wide distribution of summary statistics measuring genetic differentiation, such asFST, are often interpreted as evidence for alleles which contribute to local adaptation. However, in spatially structured populations, the spread of beneficial mutations with spatially uniform effects can also induce transient genetic differentiation and numerous theoretical studies have suggested that species-wide, or global, adaptation makes a substantial contribution to molecular evolution. In this study, we ask whether such global adaptation affects the genome-wide distribution ofFSTand generates statistical outliers which could be mistaken for local adaptation. Using forward-in-time population genetic simulations assuming parameters for the rate and strength of beneficial mutations similar to those that have been estimated for natural populations, we show the spread of globally beneficial in parapatric populations can readily generateFSToutliers, which may be misinterpreted as evidence for local adaptation. The spread of beneficial mutations causes selective sweeps at flanking sites, so the effects of global versus local adaptation may be distinguished by examining patterns of nucleotide diversity along withFST. Our study suggests that global adaptation should be considered in the interpretation of genome-scan results and the design of future studies aimed at understanding the genetic basis of local adaptation.

Self-fertilisation has consequences for variation across the genome as it reduces effective population size, effect recombination rates and pollen flow, with implications for local adaptation. We conducted simulations of divergent stabilising selection on a quantitative trait with drift, pollen flow, mutation, recombination and different outcrossing rates. We quantified trait divergence and the genetic architecture of adaptation. We conducted an FST outlier analysis to identify candidate loci and quantified the impact of mating system on detectability. Selfing promoted trait divergence mainly through reductions in pollen flow. Moreover, trait architecture became more diffuse with selfing. Average effect size of trait loci was lower, while the number of loci, and their clustering distance increased. The genetic architecture of selfers was also more diffuse than outcrossers for equivalent migration rates. However, when deleterious alleles were included, architectures became more concentrated in selfers, likely to be because of reductions in population size caused by mutational meltdown and impacts of background selection on Ne . Our simulations demonstrate that mating system has important impacts on adaptive divergence of traits and the genetic landscape underlying that divergence. Selfing has a significant effect on detectability of regions of the genome important for adaptation because of neutral divergence and diffuse trait architecture.

Background Lodgepole pine (Pinus contorta) and interior spruce (Picea glauca, Picea engelmannii, and their hybrids) are distantly related conifer species. Previous studies identified 47 genes containing variants associated with environmental variables in both species, providing evidence of convergent local adaptation. However, if the intensity of purifying selection varies with the environment, clines in nucleotide diversity could evolve through linked (background) selection that would yield allele frequency-environment signatures resembling local adaptation. If similar geographic patterns in the strength of purifying selection occur in these species, this could result in the convergent signatures of local adaptation, especially if the landscape of recombination is conserved. In the present study, we investigated whether spatially/environmentally varying purifying selection could give rise to the convergent signatures of local adaptation that had previously reported. Results We analyzed 86 lodgepole pine and 50 interior spruce natural populations spanning heterogeneous environments in western Canada where previous analyses had found signatures of convergent local adaptation. We estimated nucleotide diversity and Tajima’s D for each gene within each population and calculated the strength of correlations between nucleotide diversity and environmental variables. Overall, these estimates in the genes with previously identified convergent local adaptation signatures had no similar pattern between pine and spruce. Clines in nucleotide diversity along environmental variables were found for interior spruce, but not for lodgepole pine. In spruce, genes with convergent adaption signatures showed a higher strength of correlations than genes without convergent adaption signatures, but there was no such disparity in pine, which suggests the pattern in spruce may have arisen due to a combination of selection and hybridization. Conclusions The results rule out purifying/background selection as a driver of convergent local adaption signatures in lodgepole pine and interior spruce. Electronic supplementary material The online version of this article (10.1186/s12862-019-1438-8) contains supplementary material, which is available to authorized users.

Although social behavior can have a strong genetic component, it can also result in selection on genome structure and function, thereby influencing the evolution of the genome itself. Here we explore the bidirectional links between social behavior and genome architecture by considering variation in social and/or mating behavior among populations (social polymorphisms) and across closely related species. We propose that social behavior can influence genome architecture via associated demographic changes due to social living. We establish guidelines to exploit emerging whole-genome sequences using analytical approaches that examine genome structure and function at different levels (regulatory vs structural variation) from the perspective of both molecular biology and population genetics in an ecological context.

Genomic islands are clusters of loci with elevated divergence that are commonly found in population genomic studies of local adaptation and speciation. One explanation for their evolution is that linkage between selected alleles confers a benefit, which increases the establishment probability of new mutations that are linked to existing locally adapted polymorphisms. Previous theory suggested there is only limited potential for the evolution of islands via this mechanism, but involved some simplifying assumptions that may limit the accuracy of this inference. Here, we extend previous analytical approaches to study the effect of linkage on the establishment probability of new mutations and identify parameter regimes that are most likely to lead to evolution of islands via this mechanism. We show how the interplay between migration and selection affects the establishment probability of linked vs. unlinked alleles, the expected maximum size of genomic islands, and the expected time required for their evolution. Our results agree with previous studies, suggesting that this mechanism alone is unlikely to be a general explanation for the evolution of genomic islands. However, this mechanism could occur more readily if there were other pre-adaptations to reduce local rates of recombination or increase the local density of mutational targets within the region of the island. We also show that island formation via erosion following secondary contact is much more rapid than island formation from de novo mutations, suggesting that this mechanism may be more likely.

Convergent adaptation occurs at the genome scale when independently evolving lineages use the same genes to respond to similar selection pressures. These patterns of genetic repeatability provide insights into the factors that facilitate or constrain the diversity of genetic responses that contribute to adaptive evolution. A first step in studying such factors is to quantify the observed amount of repeatability relative to expectations under a null hypothesis. Here, we formulate a novel index to quantify the constraints driving the observed amount of repeated adaptation in pairwise contrasts based on the hypergeometric distribution, and then generalize this for simultaneous analysis of multiple lineages. This index is explicitly based on the probability of observing a given amount of repeatability by chance under a given null hypothesis and is readily compared among different species and types of trait. We also formulate an index to quantify the effective proportion of genes in the genome that have the potential to contribute to adaptation. As an example of how these indices can be used to draw inferences, we assess the amount of repeatability observed in existing datasets on adaptation to stress in yeast and climate in conifers. This approach provides a method to test a wide range of hypotheses about how different kinds of factors can facilitate or constrain the diversity of genetic responses observed during adaptive evolution., Author summary How many ways can evolution solve the same adaptive problem? While convergent adaptation is evident in many organisms at the phenotypic level, we are only beginning to understand how commonly this convergence extends to the genome scale. Quantifying the repeatability of adaptation at the genome scale is therefore critical for assessing how constraints affect the diversity of viable genetic responses. Here, we develop probability-based indices to quantify the deviation between observed repeatability and expectations under a range of null hypotheses, and an estimator of the proportion of loci in the genome that can contribute to adaptation. We demonstrate the usage of these indices with individual-based simulations and example datasets from yeast and conifers and discuss how they differ from previously developed approaches to studying repeatability. Because these indices are unitless, they provide a general approach to quantifying and comparing how constraints drive convergence at the genome scale across a wide range of traits and taxa.

Quantitative-genetic models of differentiation under migration-selection balance often rely on the assumption of normally distributed genotypic and phenotypic values. When a population is subdivided into demes with selection toward different local optima, migration between demes may result in asymmetric, or skewed, local distributions. Using a simplified two-habitat model, we derive formulas without a priori assuming a Gaussian distribution of genotypic values, and we find expressions that naturally incorporate higher moments, such as skew. These formulas yield predictions of the expected divergence under migration-selection balance that are more accurate than models assuming Gaussian distributions, which illustrates the importance of incorporating these higher moments to assess the response to selection in heterogeneous environments. We further show with simulations that traits with loci of large effect display the largest skew in their distribution at migration-selection balance.

Population genetic models predict that alleles with small selection coefficients may be swamped by migration and will not contribute to local adaptation. But if most alleles contributing to standing variation are of small effect, how does local adaptation proceed? Here I review predictions of population and quantitative genetic models and use individual-based simulations to illustrate how the architecture of local adaptation depends on the genetic redundancy of the trait, the maintenance of standing genetic variation (V(G)), and the susceptibility of alleles to swamping. Even when population genetic models predict swamping for individual alleles, considerable local adaptation can evolve at the phenotypic level if there is sufficient V(G). However, in such cases the underlying architecture of divergence is transient: F(ST) is low across all loci, and no locus makes an important contribution for very long. Because this kind of local adaptation is mainly due to transient frequency changes and allelic covariances, these architectures will be difficult--if not impossible--to detect using current approaches to studying the genomic basis of adaptation. Even when alleles are large and resistant to swamping, architectures can be highly transient if genetic redundancy and mutation rates are high. These results suggest that drift can play a critical role in shaping the architecture of local adaptation, both through eroding V(G) and affecting the rate of turnover of polymorphisms with redundant phenotypic effects.

When confronted with an adaptive challenge, such as extreme temperature, closely related species frequently evolve similar phenotypes using the same genes. Although such repeated evolution is thought to be less likely in highly polygenic traits and distantly related species, this has not been tested at the genome scale. We performed a population genomic study of convergent local adaptation among two distantly related species, lodgepole pine and interior spruce. We identified a suite of 47 genes, enriched for duplicated genes, with variants associated with spatial variation in temperature or cold hardiness in both species, providing evidence of convergent local adaptation despite 140 million years of separate evolution. These results show that adaptation to climate can be genetically constrained, with certain key genes playing nonredundant roles.

Background Mutation load is expected to be reduced in hybrids via complementation of deleterious alleles. While local adaptation of hybrids confounds phenotypic tests for reduced mutation load, it may be possible to assess variation in load by analyzing the distribution of putatively deleterious alleles. Here, we use this approach in the interior spruce (Picea glauca x P. engelmannii) hybrid complex, a group likely to suffer from high mutation load and in which hybrids exhibit local adaptation to intermediate conditions. We used PROVEAN to bioinformatically predict whether non-synonymous alleles are deleterious, based on conservation of the position and abnormality of the amino acid change. Results As expected, we found that predicted deleterious alleles were at lower average allele frequencies than alleles not predicted to be deleterious. We were unable to detect a phenotypic effect on juvenile growth rate of the many rare alleles predicted to be deleterious. Both the proportion of alleles predicted to be deleterious and the proportion of loci homozygous for predicted deleterious alleles were higher in P. engelmannii (Engelmann spruce) than in P. glauca (white spruce), due to higher diversity and frequencies of rare alleles in Engelmann. Relative to parental species, the proportion of alleles predicted to be deleterious was intermediate in hybrids, and the proportion of loci homozygous for predicted deleterious alleles was lowest. Conclusion Given that most deleterious alleles are recessive, this suggests that mutation load is reduced in hybrids due to complementation of deleterious alleles. This effect may enhance the fitness of hybrids. Electronic supplementary material The online version of this article (10.1186/s12864-017-4344-8) contains supplementary material, which is available to authorized users.

The paradox of high genetic variation observed in traits under stabilizing selection is a longstanding problem in evolutionary theory, as mutation rates are 10-100 times too low to explain observed levels of standing genetic variation under classic models of mutation-selection balance. Here, we use individual-based simulations to explore the effect of various types of environmental heterogeneity on the maintenance of genetic variation (VA) for a quantitative trait under stabilizing selection. We find that VAis maximized at intermediate migration rates in spatially heterogeneous environments, and that the observed patterns are robust to changes in population size. Spatial environmental heterogeneity increased variation by as much as 10-fold over mutation-selection-balance alone, whereas pure temporal environmental heterogeneity increased variance by only 45% at max. Our results show that some combinations of spatial heterogeneity and migration can maintain considerably more variation than mutation-selection balance, potentially reconciling the discrepancy between theoretical predictions and empirical observations. However, given the narrow regions of parameter space required for this effect, this is unlikely to provide a general explanation for the maintenance of variation. Nonetheless, our results suggest that habitat fragmentation may affect the maintenance of VAand thereby reduce the adaptive capacity of populations.

This preprint has been reviewed and recommended by Peer Community In Evolutionary Biology (https://doi.org/10.24072/pci.evolbiol.100050)BackgroundLinkage among genes experiencing different selection pressures can make natural selection less efficient. Theory predicts that when local adaptation is driven by complex and non-covarying stresses, increased linkage is favoured for alleles with similar pleiotropic effects, with increased recombination favoured among alleles with contrasting pleiotropic effects. Here, we introduce a framework to test these predictions with a co-association network analysis, which clusters loci based on differing associations. We use this framework to study the genetic architecture of local adaptation to climate in lodgepole pine (Pinus contorta), based on associations with environments.ResultsWe identified many clusters of candidate genes and SNPs associated with distinct environments (aspects of aridity, freezing, etc.), and discovered low recombination rates among some candidate genes in different clusters. Only a few genes contained SNPs with effects on more than one distinct aspect of climate. There was limited correspondence between co-association networks and gene regulatory networks. We further showed how associations with environmental principal components can lead to misinterpretation. Finally, simulations illustrated both benefits and caveats of co-association networks.ConclusionsOur results supported the prediction that different selection pressures favored the evolution of distinct groups of genes, each associating with a different aspect of climate. But our results went against the prediction that loci experiencing different sources of selection would have high recombination among them. These results give new insight into evolutionary debates about the extent of modularity, pleiotropy, and linkage in the evolution of genetic architectures.

The paradox of high genetic variation observed in traits under stabilizing selection is a long-standing problem in evolutionary theory, as mutation rates appear too low to explain observed levels of standing genetic variation under classic models of mutation-selection balance. Spatially or temporally heterogeneous environments can maintain more standing genetic variation within populations than homogeneous environments, but it is unclear whether such conditions can resolve the above discrepancy between theory and observation. Here, we use individual-based simulations to explore the effect of various types of environmental heterogeneity on the maintenance of genetic variation (VA ) for a quantitative trait under stabilizing selection. We find that VA is maximized at intermediate migration rates in spatially heterogeneous environments and that the observed patterns are robust to changes in population size. Spatial environmental heterogeneity increased variation by as much as 10-fold over mutation-selection balance alone, whereas pure temporal environmental heterogeneity increased variance by only 45% at max. Our results show that some combinations of spatial heterogeneity and migration can maintain considerably more variation than mutation-selection balance, potentially reconciling the discrepancy between theoretical predictions and empirical observations. However, given the narrow regions of parameter space required for this effect, this is unlikely to provide a general explanation for the maintenance of variation. Nonetheless, our results suggest that habitat fragmentation may affect the maintenance of VA and thereby reduce the adaptive capacity of populations.

Summary Species respond to environmental stress through a combination of genetic adaptation and phenotypic plasticity, both of which may be important for survival in the face of climatic change. By characterizing the molecular basis of plastic responses and comparing patterns among species, it is possible to identify how such traits evolve. Here, we used de novo transcriptome assembly and RNAseq to explore how patterns of gene expression differ in response to temperature, moisture, and light regime treatments in lodgepole pine (Pinus contorta) and interior spruce (a natural hybrid population of Picea glauca and Picea engelmannii). We found wide evidence for an effect of treatment on expression within each species, with 6413 and 11 658 differentially expressed genes identified in spruce and pine, respectively. Comparing patterns of expression among these species, we found that 74% of all orthologs with differential expression had a pattern that was conserved in both species, despite 140 million yr of evolution. We also found that the specific treatments driving expression patterns differed between genes with conserved versus diverged patterns of expression. We conclude that natural selection has probably played a role in shaping plastic responses to environment in these species.