Combining population genomics and fitness QTLs to identify the genetics of local adaptation in Arabidopsis thaliana

A large-effect fitness trade-off across environments is explained by a single mutation affecting cold acclimation

Identifying the genetic basis of local adaptation and fitness trade-offs across environments is a central goal of evolutionary biology. Cold acclimation is an adaptive plastic response for surviving seasonal freezing, and costs of acclimation may be a general mechanism for fitness trade-offs across environments in temperate zone species. Starting with locally adapted ecotypes of Arabidopsis thaliana from Italy and Sweden, we examined the fitness consequences of a naturally occurring functional polymorphism in CBF2. This gene encodes a transcription factor that is a major regulator of cold-acclimated freezing tolerance and resides within a locus responsible for a genetic trade-off for long-term mean fitness. We estimated the consequences of alternate genotypes of CBF2 on 5-y mean fitness and fitness components at the native field sites by comparing near-isogenic lines with alternate genotypes of CBF2 to their genetic background ecotypes. The effects of CBF2 were validated at the nucleotide level using gene-edited lines in the native genetic backgrounds grown in simulated parental environments. The foreign CBF2 genotype in the local genetic background reduced long-term mean fitness in Sweden by more than 10%, primarily via effects on survival. In Italy, fitness was reduced by more than 20%, primarily via effects on fecundity. At both sites, the effects were temporally variable and much stronger in some years. The gene-edited lines confirmed that CBF2 encodes the causal variant underlying this genetic trade-off. Additionally, we demonstrated a substantial fitness cost of cold acclimation, which has broad implications for potential maladaptive responses to climate change.

The limits of stress-tolerance for zooplankton resting stages in freshwater ponds

In seasonal environments, many organisms evolve strategies such as diapause to survive stressful periods. Understanding the link between habitat stability and diapause strategy can help predict a population's survival in a changing world. Indeed, resting stages may be an important way freshwater organisms can survive periods of drought or freezing, and as the frequency and extent of drought or freezing vary strongly among habitats and are predicted to change with climate change, it raises questions about how organisms cope with, and survive, environmental stress. Using Daphnia magna as a model system, we tested the ability of resting stages from different populations to cope with stress during diapause. The combination of elevated temperatures and wet conditions during diapause shows to prevent hatching altogether. In contrast, hatching is relatively higher after a dry and warm diapause, but declines with rising temperatures, while time to hatch increases. Resting stages produced by populations from summer-dry habitats perform slightly, but consistently, better at higher temperatures and dryness, supporting the local adaptation hypothesis. A higher trehalose content in resting eggs from summer-dry habitat might explain such pattern. Considering that temperatures and summer droughts are projected to increase in upcoming years, it is fundamental to know how resting stages resist stressful conditions so as to predict and protect the ecological functioning of freshwater ecosystems.

Uncovering the genetic architecture of parallel evolution

Identifying the genetic architecture underlying adaptive traits is exceptionally challenging in natural populations. This is because associations between traits not only mask the targets of selection but also create correlated patterns of genomic divergence that hinder our ability to isolate causal genetic effects. Here, we examine the repeated evolution of components of the auxin pathway that have contributed to the replicated loss of gravitropism (i.e. the ability of a plant to bend in response to gravity) in multiple populations of the Senecio lautus species complex in Australia. We use a powerful approach which combines parallel population genomics with association mapping in a Multiparent Advanced Generation Inter-Cross (MAGIC) population to break down genetic and trait correlations to reveal how adaptive traits evolve during replicated evolution. We sequenced auxin and shoot gravitropism-related gene regions in 80 individuals from six natural populations (three parallel divergence events) and 133 individuals from a MAGIC population derived from two of the recently diverged natural populations. We show that artificial tail selection on gravitropism in the MAGIC population recreates patterns of parallel divergence in the auxin pathway in the natural populations. We reveal a set of 55 auxin gene regions that have evolved repeatedly during the evolution of the species, of which 50 are directly associated with gravitropism divergence in the MAGIC population. Our work creates a strong link between patterns of genomic divergence and trait variation contributing to replicated evolution by natural selection, paving the way to understand the origin and maintenance of adaptations in natural populations.

Quantitative genetic-by-soil microbiome interactions in a perennial grass affect functional traits

Plants interact with diverse microbiomes that can impact plant growth and performance. Recent studies highlight the potential beneficial aspects of plant microbiomes, including the possibility that microbes facilitate the process of local adaptation in their host plants. Microbially mediated local adaptation in plants occurs when local host genotypes have higher fitness than foreign genotypes because of their affiliation with locally beneficial microbes. Here, plant adaptation results from genetic interactions of the host with locally beneficial microbes (e.g. host genotype-by-microbiome interactions). We used a recombinant inbred line (RIL) mapping population derived from upland and lowland ecotypes of the diploid C4 perennial bunch grass Panicum hallii to explore quantitative genetic responses to soil microbiomes focusing on functional root and shoot traits involved in ecotypic divergence. We show that the growth and development of ecotypes and their trait divergence depends on soil microbiomes. Moreover, we find that the genetic architecture is modified by soil microbiomes, revealing important plant genotype-by-microbiome interactions for quantitative traits. We detected a number of quantitative trait loci (QTL) that interact with the soil microbiome. Our results highlight the importance of microbial interactions in ecotypic divergence and trait genetic architecture in C4 perennial grasses.

Genotype-environment associations to reveal the molecular basis of environmental adaptation

A fundamental goal in plant biology is to identify and understand the variation underlying plants' adaptation to their environment. Climate change has given new urgency to this goal, as society aims to accelerate adaptation of ecologically important plant species, endangered plant species, and crops to hotter, less predictable climates. In the pre-genomic era, identifying adaptive alleles was painstaking work, leveraging genetics, molecular biology, physiology, and ecology. Now, the rise of genomics and new computational approaches may facilitate this research. Genotype-environment associations (GEAs) use statistical associations between allele frequency and environment of origin to test the hypothesis that allelic variation at a given gene is adapted to local environments. Researchers may scan the genome for GEAs to generate hypotheses on adaptive genetic variants (environmental genome-wide association studies). Despite the rapid adoption of these methods, many important questions remain about the interpretation of GEA findings, which arise from fundamental unanswered questions on the genetic architecture of adaptation and limitations inherent to association-based analyses. We outline strategies to ground GEAs in the underlying hypotheses of genetic architecture and better test GEA-generated hypotheses using genetics and ecophysiology. We provide recommendations for new users who seek to learn about the molecular basis of adaptation. When combined with a rigorous hypothesis testing framework, GEAs may facilitate our understanding of the molecular basis of climate adaptation for plant improvement.

High-Density Genetic Linkage Map of the Southern Blue-ringed Octopus (Octopodidae: Hapalochlaena maculosa)

Genetic linkage maps provide a useful resource for non-model genomes and can aid in genome reassembly to form more contiguous pseudo-chromosomes. We present the first linkage map of any cephalopod, H. maculosa, composed of 47 linkage groups (LG). A total of 2166 single nucleotide polymorphisms and 2455 presence–absence variant loci were utilised by Lep-Map3 in linkage map construction. The map length spans 2016.62 cM with an average marker distance of 0.85 cM. Integration of the recent H. maculosa genome allowed 1151 scaffolds comprising 34% of the total genomic sequence to be orientated and/or placed using 1278 markers across all 47 LG. The linkage map generated provides a new perspective on HOX gene distribution in octopods. In the H. maculosa linkage map three (SCR, LOX4 and POST1) of six identified HOX genes (HOX1/LAB, SCR, LOX2, LOX4, LOX5, POST1) were located within the same LG (LG 9). The generation of a linkage map for H. maculosa has provided a valuable resource for understanding the evolution of cephalopod genomes and will provide a base for future work.

Utility and challenges of using whole‐genome resequencing to detect emerging insect and mite resistance in agroecosystems

Arthropods that invade agricultural ecosystems systematically evolve resistance to the control measures used against them, and this remains a significant and ongoing challenge for sustainable food production systems. Early detection of resistance evolution could prompt remedial action to slow the spread of resistance alleles in the landscape. Historical approaches used to detect emerging resistance included phenotypic monitoring of agricultural pest populations, as well as monitoring of allele frequency changes at one or a few candidate pesticide resistance genes. In this article, I discuss the successes and limitations of these traditional monitoring approaches and then consider whether whole‐genome scanning could be applied to samples collected from agroecosystems over time for resistance monitoring. I examine the qualities of agroecosystems that could impact application of this approach to pesticide resistance monitoring and describe a recent retrospective analysis where genome scanning successfully detected an oligogenic response to selection by pesticides years prior to pest management failure. I conclude by considering areas of further study that will shed light on the feasibility of applying whole‐genome scanning for resistance risk monitoring in agricultural pest species.

Genome-wide polymorphism and genic selection in feral and domesticated lineages of Cannabis sativa

A comprehensive understanding of the degree to which genomic variation is maintained by selection versus drift and gene flow is lacking in many important species such as Cannabis sativa (C. sativa), one of the oldest known crops to be cultivated by humans worldwide. We generated whole genome resequencing data across diverse samples of feralized (escaped domesticated lineages) and domesticated lineages of C. sativa. We performed analyses to examine population structure, and genome wide scans for FST, balancing selection, and positive selection. Our analyses identified evidence for sub-population structure and further support the Asian origin hypothesis of this species. Feral plants sourced from the U.S. exhibited broad regions on chromosomes 4 and 10 with high F̄ST which may indicate chromosomal inversions maintained at high frequency in this sub-population. Both our balancing and positive selection analyses identified loci that may reflect differential selection for traits favored by natural selection and artificial selection in feral versus domesticated sub-populations. In the U.S. feral sub-population, we found six loci related to stress response under balancing selection and one gene involved in disease resistance under positive selection, suggesting local adaptation to new climates and biotic interactions. In the marijuana sub-population, we identified the gene SMALLER TRICHOMES WITH VARIABLE BRANCHES 2 to be under positive selection which suggests artificial selection for increased tetrahydrocannabinol yield. Overall, the data generated, and results obtained from our study help to form a better understanding of the evolutionary history in C. sativa.

A Perspective on Plant Phenomics: Coupling Deep Learning and Near-Infrared Spectroscopy

The trait-based approach in plant ecology aims at understanding and classifying the diversity of ecological strategies by comparing plant morphology and physiology across organisms. The major drawback of the approach is that the time and financial cost of measuring the traits on many individuals and environments can be prohibitive. We show that combining near-infrared spectroscopy (NIRS) with deep learning resolves this limitation by quickly, non-destructively, and accurately measuring a suite of traits, including plant morphology, chemistry, and metabolism. Such an approach also allows to position plants within the well-known CSR triangle that depicts the diversity of plant ecological strategies. The processing of NIRS through deep learning identifies the effect of growth conditions on trait values, an issue that plagues traditional statistical approaches. Together, the coupling of NIRS and deep learning is a promising high-throughput approach to capture a range of ecological information on plant diversity and functioning and can accelerate the creation of extensive trait databases.

The influence of genetic architecture on responses to selection under drought in rice

Accurately predicting responses to selection is a major goal in biology and important for successful crop breeding in changing environments. However, evolutionary responses to selection can be constrained by such factors as genetic and cross‐environment correlations, linkage, and pleiotropy, and our understanding of the extent and impact of such constraints is still developing. Here, we conducted a field experiment to investigate potential constraints to selection for drought resistance in rice (Oryza sativa) using phenotypic selection analysis and quantitative genetics. We found that traits related to drought response were heritable, and some were under selection, including selection for earlier flowering, which could allow drought escape. However, patterns of selection generally were not opposite under wet and dry conditions, and we did not find individual or closely linked genes that influenced multiple traits, indicating a lack of evidence that antagonistic pleiotropy, linkage, or cross‐environment correlations would constrain selection for drought resistance. In most cases, genetic correlations had little influence on responses to selection, with direct and indirect selection largely congruent. The exception to this was seed mass under drought, which was predicted to evolve in the opposite direction of direct selection due to correlations. Because of this indirect effect on selection on seed mass, selection for drought resistance was not accompanied by a decrease in seed mass, and yield increased with fecundity. Furthermore, breeding lines with high fitness and yield under drought also had high fitness and yield under wet conditions, indicating that there was no evidence for a yield penalty on drought resistance. We found multiple genes in which expression influenced both water use efficiency (WUE) and days to first flowering, supporting a genetic basis for the trade‐off between drought escape and avoidance strategies. Together, these results can provide helpful guidance for understanding and managing evolutionary constraints and breeding stress‐resistant crops.

Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations

Integration of multi-omics data improved prediction accuracies of oat agronomic and seed nutritional traits in multi-environment trials and distantly related populations in addition to the single-environment prediction. Multi-omics prediction has been shown to be superior to genomic prediction with genome-wide DNA-based genetic markers (G) for predicting phenotypes. However, most of the existing studies were based on historical datasets from one environment; therefore, they were unable to evaluate the efficiency of multi-omics prediction in multi-environment trials and distantly related populations. To fill those gaps, we designed a systematic experiment to collect omics data and evaluate 17 traits in two oat breeding populations planted in single and multiple environments. In the single-environment trial, transcriptomic BLUP (T), metabolomic BLUP (M), G + T, G + M, and G + T + M models showed greater prediction accuracy than GBLUP for 5, 10, 11, 17, and 17 traits, respectively, and metabolites generally performed better than transcripts when combined with SNPs. In the multi-environment trial, multi-trait models with omics data outperformed both counterpart multi-trait GBLUP models and single-environment omics models, and the highest prediction accuracy was achieved when modeling genetic covariance as an unstructured covariance model. We also demonstrated that omics data can be used to prioritize loci from one population with omics data to improve genomic prediction in a distantly related population using a two-kernel linear model that accommodated both likely casual loci with large-effect and loci that explain little or no phenotypic variance. We propose that the two-kernel linear model is superior to most genomic prediction models that assume each variant is equally likely to affect the trait and can be used to improve prediction accuracy for any trait with prior knowledge of genetic architecture.

Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations

Integration of multi-omics data improved prediction accuracies of oat agronomic and seed nutritional traits in multi-environment trials and distantly related populations in addition to the single-environment prediction. Multi-omics prediction has been shown to be superior to genomic prediction with genome-wide DNA-based genetic markers (G) for predicting phenotypes. However, most of the existing studies were based on historical datasets from one environment; therefore, they were unable to evaluate the efficiency of multi-omics prediction in multi-environment trials and distantly related populations. To fill those gaps, we designed a systematic experiment to collect omics data and evaluate 17 traits in two oat breeding populations planted in single and multiple environments. In the single-environment trial, transcriptomic BLUP (T), metabolomic BLUP (M), G + T, G + M, and G + T + M models showed greater prediction accuracy than GBLUP for 5, 10, 11, 17, and 17 traits, respectively, and metabolites generally performed better than transcripts when combined with SNPs. In the multi-environment trial, multi-trait models with omics data outperformed both counterpart multi-trait GBLUP models and single-environment omics models, and the highest prediction accuracy was achieved when modeling genetic covariance as an unstructured covariance model. We also demonstrated that omics data can be used to prioritize loci from one population with omics data to improve genomic prediction in a distantly related population using a two-kernel linear model that accommodated both likely casual loci with large-effect and loci that explain little or no phenotypic variance. We propose that the two-kernel linear model is superior to most genomic prediction models that assume each variant is equally likely to affect the trait and can be used to improve prediction accuracy for any trait with prior knowledge of genetic architecture.

Flowering Times of Wild Arabidopsis Accessions From Across Norway Correlate With Expression Levels of FT, CO, and FLC Genes

Temperate species often require or flower most rapidly in the long daylengths, or photoperiods, experienced in summer or after prolonged periods of cold temperatures, referred to as vernalization. Yet, even within species, plants vary in the degree of responsiveness to these cues. In Arabidopsis thaliana, CONSTANS (CO) and FLOWERING LOCUS C (FLC) genes are key to photoperiod and vernalization perception and antagonistically regulate FLOWERING LOCUS T (FT) to influence the flowering time of the plants. However, it is still an open question as to how these genes vary in their interactions among wild accessions with different flowering behaviors and adapted to different microclimates, yet this knowledge could improve our ability to predict plant responses in variable natural conditions. To assess the relationships among these genes and to flowering time, we exposed 10 winter-annual Arabidopsis accessions from throughout Norway, ranging from early to late flowering, along with two summer-annual accessions to 14 weeks of vernalization and either 8- or 19-h photoperiods to mimic Norwegian climate conditions, then assessed gene expression levels 3-, 5-, and 8-days post vernalization. CO and FLC explained both FT levels and flowering time (days) but not rosette leaf number at flowering. The correlation between FT and flowering time increased over time. Although vernalization suppresses FLC, FLC was high in the late-flowering accessions. Across accessions, FT was expressed only at low FLC levels and did not respond to CO in the late-flowering accessions. We proposed that FT may only be expressed below a threshold value of FLC and demonstrated that these three genes correlated to flowering times across genetically distinct accessions of Arabidopsis.

Genetic trade-offs underlie divergent life history strategies for local adaptation in white clover

Local adaptation is common in plants, yet characterization of its underlying genetic basis is rare in herbaceous perennials. Moreover, while many plant species exhibit intraspecific chemical defence polymorphisms, their importance for local adaptation remains poorly understood. We examined the genetic architecture of local adaptation in a perennial, obligately-outcrossing herbaceous legume, white clover (Trifolium repens). This widespread species displays a well-studied chemical defence polymorphism for cyanogenesis (HCN release following tissue damage) and has evolved climate-associated cyanogenesis clines throughout its range. Two biparental F₂  mapping populations, derived from three parents collected in environments spanning the U.S. latitudinal species range (Duluth, MN, St. Louis, MO and Gainesville, FL), were grown in triplicate for two years in reciprocal common garden experiments in the parental environments (6,012 total plants). Vegetative growth and reproductive fitness traits displayed trade-offs across reciprocal environments, indicating local adaptation. Genetic mapping of fitness traits revealed a genetic architecture characterized by allelic trade-offs between environments, with 100% and 80% of fitness QTL in the two mapping populations showing significant QTL×E interactions, consistent with antagonistic pleiotropy. Across the genome there were three hotspots of QTL colocalization. Unexpectedly, we found little evidence that the cyanogenesis polymorphism contributes to local adaptation. Instead, divergent life history strategies in reciprocal environments were major fitness determinants: selection favoured early investment in flowering at the cost of multiyear survival in the southernmost site versus delayed flowering and multiyear persistence in the northern environments. Our findings demonstrate that multilocus genetic trade-offs contribute to contrasting life history characteristics that allow for local adaptation in this outcrossing herbaceous perennial.

Evolution and seed dormancy shape plant genotypic structure through a successional cycle

Dormancy has repeatedly evolved in plants, animals, and microbes and is hypothesized to facilitate persistence in the face of environmental change. Yet previous experiments have not tracked demography and trait evolution spanning a full successional cycle to ask whether early bouts of natural selection are later reinforced or erased during periods of population dormancy. In addition, it is unclear how well short-term measures of fitness predict long-term genotypic success for species with dormancy. Here, we address these issues using experimental field populations of the plant Oenothera biennis, which evolved over five generations in plots exposed to or protected from insect herbivory. While populations existed above ground, there was rapid evolution of defensive and life-history traits, but populations lost genetic diversity and crashed as succession proceeded. After >5 y of seed dormancy, we triggered germination from the seedbank and genotyped >3,000 colonizers. Resurrected populations showed restored genetic diversity that reduced earlier responses to selection and pushed population phenotypes toward the starting conditions of a decade earlier. Nonetheless, four defense and life-history traits remained differentiated in populations with insect suppression compared with controls. These findings capture key missing elements of evolution during ecological cycles and demonstrate the impact of dormancy on future evolutionary responses to environmental change.

Ecological factors influence balancing selection on leaf chemical profiles of a wildflower

Balancing selection is frequently invoked as a mechanism that maintains variation within and across populations. However, there are few examples of balancing selection operating on loci underpinning complex traits, which frequently display high levels of variation. We investigated mechanisms that may maintain variation in a focal polymorphism-leaf chemical profiles of a perennial wildflower (Boechera stricta, Brassicaceae)-explicitly interrogating multiple ecological and genetic processes including spatial variation in selection, antagonistic pleiotropy and frequency-dependent selection. A suite of common garden and greenhouse experiments showed that the alleles underlying variation in chemical profile have contrasting fitness effects across environments, implicating two ecological drivers of selection on chemical profile: herbivory and drought. Phenotype-environment associations and molecular genetic analyses revealed additional evidence of past selection by these drivers. Together, these data are consistent with balancing selection on chemical profile, probably caused by pleiotropic effects of secondary chemical biosynthesis genes on herbivore defence and drought response.

No support for the hereditarian hypothesis of the Black-White achievement gap using polygenic scores and tests for divergent selection

OBJECTIVES

Debate about the cause of IQ score gaps between Black and White populations has persisted within genetics, anthropology, and psychology. Recently, authors claimed polygenic scores provide evidence that a significant portion of differences in cognitive performance between Black and White populations are caused by genetic differences due to natural selection, the "hereditarian hypothesis." This study aims to show conceptual and methodological flaws of past studies supporting the hereditarian hypothesis.

MATERIALS AND METHODS

Polygenic scores for educational attainment were constructed for African and European samples of the 1000 Genomes Project. Evidence for selection was evaluated using an excess variance test. Education associated variants were further evaluated for signals of selection by testing for excess genetic differentiation (F_st ). Expected mean difference in IQ for populations was calculated under a neutral evolutionary scenario and contrasted to hereditarian claims.

RESULTS

Tests for selection using polygenic scores failed to find evidence of natural selection when the less biased within-family GWAS effect sizes were used. Tests for selection using F_st values did not find evidence of natural selection. Expected mean difference in IQ was substantially smaller than postulated by hereditarians, even under unrealistic assumptions that overestimate genetic contribution.

CONCLUSION

Given these results, hereditarian claims are not supported in the least. Cognitive performance does not appear to have been under diversifying selection in Europeans and Africans. In the absence of diversifying selection, the best case estimate for genetic contributions to group differences in cognitive performance is substantially smaller than hereditarians claim and is consistent with genetic differences contributing little to the Black-White gap.

Fitness effects of mutation in natural populations of Arabidopsis thaliana reveal a complex influence of local adaptation

Little is empirically known about the contribution of mutations to fitness in natural environments. However, Fisher's Geometric Model (FGM) provides a conceptual foundation to consider the influence of the environment on mutational effects. To quantify mutational properties in the field, we established eight sets of MA lines (7-10 generations) derived from eight founders collected from natural populations of Arabidopsis thaliana from French and Swedish sites, representing the range margins of the species in Europe. We reciprocally planted the MA lines and their founders at French and Swedish sites, allowing us to test predictions of FGM under naturally occurring environmental conditions. The performance of the MA lines relative to each other and to their respective founders confirmed some and contradicted other predictions of the FGM: the contribution of mutation to fitness variance increased when the genotype was in an environment where its fitness was low, that is, in the away environment, but mutations were more likely to be beneficial when the genotype was in its home environment. Consequently, environmental context plays a large role in the contribution of mutations to the evolutionary process and local adaptation does not guarantee that a genotype is at or close to its optimum.

Fitness variation across subtle environmental perturbations reveals local modularity and global pleiotropy of adaptation

Building a genotype-phenotype-fitness map of adaptation is a central goal in evolutionary biology. It is difficult even when adaptive mutations are known because it is hard to enumerate which phenotypes make these mutations adaptive. We address this problem by first quantifying how the fitness of hundreds of adaptive yeast mutants responds to subtle environmental shifts. We then model the number of phenotypes these mutations collectively influence by decomposing these patterns of fitness variation. We find that a small number of inferred phenotypes can predict fitness of the adaptive mutations near their original glucose-limited evolution condition. Importantly, inferred phenotypes that matter little to fitness at or near the evolution condition can matter strongly in distant environments. This suggests that adaptive mutations are locally modular - affecting a small number of phenotypes that matter to fitness in the environment where they evolved - yet globally pleiotropic - affecting additional phenotypes that may reduce or improve fitness in new environments.

Genome wide analysis reveals genetic divergence between Goldsinny wrasse populations

Background

Marine fish populations are often characterized by high levels of gene flow and correspondingly low genetic divergence. This presents a challenge to define management units. Goldsinny wrasse (Ctenolabrus rupestris) is a heavily exploited species due to its importance as a cleaner-fish in commercial salmonid aquaculture. However, at the present, the population genetic structure of this species is still largely unresolved. Here, full-genome sequencing was used to produce the first genomic reference for this species, to study population-genomic divergence among four geographically distinct populations, and, to identify informative SNP markers for future studies.

Results

After construction of a de novo assembly, the genome was estimated to be highly polymorphic and of ~600Mbp in size. 33,235 SNPs were thereafter selected to assess genomic diversity and differentiation among four populations collected from Scandinavia, Scotland, and Spain. Global F_ST among these populations was 0.015–0.092. Approximately 4% of the investigated loci were identified as putative global outliers, and ~ 1% within Scandinavia. SNPs showing large divergence (F_ST > 0.15) were picked as candidate diagnostic markers for population assignment. One hundred seventy-three of the most diagnostic SNPs between the two Scandinavian populations were validated by genotyping 47 individuals from each end of the species’ Scandinavian distribution range. Sixty-nine of these SNPs were significantly (p < 0.05) differentiated (mean F_{ST_173_loci} = 0.065, F_{ST_69_loci} = 0.140). Using these validated SNPs, individuals were assigned with high probability (≥ 94%) to their populations of origin.

Conclusions

Goldsinny wrasse displays a highly polymorphic genome, and substantial population genomic structure. Diversifying selection likely affects population structuring globally and within Scandinavia. The diagnostic loci identified now provide a promising and cost-efficient tool to investigate goldsinny wrasse populations further.

Gene flow as a simple cause for an excess of high-frequency-derived alleles

Most human populations exhibit an excess of high-frequency variants, leading to a U-shaped site-frequency spectrum (uSFS). This pattern has been generally interpreted as a signature of ongoing episodes of positive selection, or as evidence for a mis-assignment of ancestral/derived allelic states, but uSFS has also been observed in populations receiving gene flow from a ghost population, in structured populations, or after range expansions. In order to better explain the prevalence of high-frequency variants in humans and other populations, we describe here which patterns of gene flow and population demography can lead to uSFS by using extensive coalescent simulations. We find that uSFS can often be observed in a population if gene flow brings a few ancestral alleles from a well-differentiated population. Gene flow can either consist in single pulses of admixture or continuous immigration, but different demographic conditions are necessary to observe uSFS in these two scenarios. Indeed, an extremely low and recent gene flow is required in the case of single admixture events, while with continuous immigration, uSFS occurs only if gene flow started recently at a high rate or if it lasted for a long time at a low rate. Overall, we find that a neutral uSFS occurs under more restrictive conditions in populations having received single pulses of gene flow than in populations exposed to continuous gene flow. We also show that the uSFS observed in human populations from the 1000 Genomes Project can easily be explained by gene flow from surrounding populations without requiring past episodes of positive selection. These results imply that uSFS should be common in non-isolated populations, such as most wild or domesticated plants and animals.

The search for sexually antagonistic genes: Practical insights from studies of local adaptation and statistical genomics

Sexually antagonistic (SA) genetic variation-in which alleles favored in one sex are disfavored in the other-is predicted to be common and has been documented in several animal and plant populations, yet we currently know little about its pervasiveness among species or its population genetic basis. Recent applications of genomics in studies of SA genetic variation have highlighted considerable methodological challenges to the identification and characterization of SA genes, raising questions about the feasibility of genomic approaches for inferring SA selection. The related fields of local adaptation and statistical genomics have previously dealt with similar challenges, and lessons from these disciplines can therefore help overcome current difficulties in applying genomics to study SA genetic variation. Here, we integrate theoretical and analytical concepts from local adaptation and statistical genomics research-including F ST and F IS statistics, genome-wide association studies, pedigree analyses, reciprocal transplant studies, and evolve-and-resequence experiments-to evaluate methods for identifying SA genes and genome-wide signals of SA genetic variation. We begin by developing theoretical models for between-sex F ST and F IS, including explicit null distributions for each statistic, and using them to critically evaluate putative multilocus signals of sex-specific selection in previously published datasets. We then highlight new statistics that address some of the limitations of F ST and F IS, along with applications of more direct approaches for characterizing SA genetic variation, which incorporate explicit fitness measurements. We finish by presenting practical guidelines for the validation and evolutionary analysis of candidate SA genes and discussing promising empirical systems for future work.

Geographic variation in the genetic basis of resistance to leaf rust between locally adapted ecotypes of the biofuel crop switchgrass (Panicum virgatum)

Local adaptation is an important process in plant evolution, which can be impacted by differential pathogen pressures along environmental gradients. However, the degree to which pathogen resistance loci vary in effect across space and time is incompletely described. To understand how the genetic architecture of resistance varies across time and geographic space, we quantified rust (Puccinia spp.) severity in switchgrass (Panicum virgatum) plantings at eight locations across the central USA for 3 yr and conducted quantitative trait locus (QTL) mapping for rust progression. We mapped several variable QTLs, but two large-effect QTLs which we have named Prr1 and Prr2 were consistently associated with rust severity in multiple sites and years, particularly in northern sites. By contrast, there were numerous small-effect QTLs at southern sites, indicating a genotype-by-environment interaction in rust resistance loci. Interestingly, Prr1 and Prr2 had a strong epistatic interaction, which also varied in the strength and direction of effect across space. Our results suggest that abiotic factors covarying with latitude interact with the genetic loci underlying plant resistance to control rust infection severity. Furthermore, our results indicate that segregating genetic variation in epistatically interacting loci may play a key role in determining response to infection across geographic space.

Combining Experimental Evolution and Genomics to Understand How Seed Beetles Adapt to a Marginal Host Plant

Genes that affect adaptive traits have been identified, but our knowledge of the genetic basis of adaptation in a more general sense (across multiple traits) remains limited. We combined population-genomic analyses of evolve-and-resequence experiments, genome-wide association mapping of performance traits, and analyses of gene expression to fill this knowledge gap and shed light on the genomics of adaptation to a marginal host (lentil) by the seed beetle Callosobruchus maculatus. Using population-genomic approaches, we detected modest parallelism in allele frequency change across replicate lines during adaptation to lentil. Mapping populations derived from each lentil-adapted line revealed a polygenic basis for two host-specific performance traits (weight and development time), which had low to modest heritabilities. We found less evidence of parallelism in genotype-phenotype associations across these lines than in allele frequency changes during the experiments. Differential gene expression caused by differences in recent evolutionary history exceeded that caused by immediate rearing host. Together, the three genomic datasets suggest that genes affecting traits other than weight and development time are likely to be the main causes of parallel evolution and that detoxification genes (especially cytochrome P450s and beta-glucosidase) could be especially important for colonization of lentil by C. maculatus.

Using networks to identify and interpret natural variation

Studies on natural variation and network biology inherently work to summarize vast amounts of information and data. The combination of these two areas of study while creating datasets of immense complexity is critical to their mutual progress. Networks are necessary as a way to work to reduce the dimensionality inherent in natural variation with 100 s to 1000 s of genotypes. Correspondingly natural variation is essential for testing how networks may or may not be shared across individuals or species. Advances in this area of cross-fertilization including using networks directly as phenotypes and the use of networks to help in prioritizing candidate gene validation efforts. Interesting new observations on frequent presence-absence variation in gene content and adaptation is beginning to highlight the potential for natural variation in network presence-absence. This review attempts to delve into these new insights.

Small spaces, big impacts: contributions of micro-environmental variation to population persistence under climate change

Individuals within natural populations can experience very different abiotic and biotic conditions across small spatial scales owing to microtopography and other micro-environmental gradients. Ecological and evolutionary studies often ignore the effects of micro-environment on plant population and community dynamics. Here, we explore the extent to which fine-grained variation in abiotic and biotic conditions contributes to within-population variation in trait expression and genetic diversity in natural plant populations. Furthermore, we consider whether benign microhabitats could buffer local populations of some plant species from abiotic stresses imposed by rapid anthropogenic climate change. If microrefugia sustain local populations and communities in the short term, other eco-evolutionary processes, such as gene flow and adaptation, could enhance population stability in the longer term. We caution, however, that local populations may still decline in size as they contract into rare microhabitats and microrefugia. We encourage future research that explicitly examines the role of the micro-environment in maintaining genetic variation within local populations, favouring the evolution of phenotypic plasticity at local scales and enhancing population persistence under global change.

Genetic and genomic analysis for cocoon yield traits in silkworm

Domestic species provides a powerful model for examining genetic mechanisms in the evolution of yield traits. The domestic silkworm (Bombyx mori) is an important livestock species in sericulture. While the mechanisms controlling cocoon yield are largely unknown. Here, using B. mori and its wild relative B. mandarina as intercross parents, 100 BC₁ individuals were sequenced by restriction site-associated DNA sequencing (RAD-Seq). The linkage map contained 9,632 markers was constructed. We performed high-resolution quantitative trait locus (QTL) mapping for four cocoon yield traits. A total of 11 QTLs were identified, including one yield-enhancing QTL from wild silkworm. By integrating population genomics and transcriptomic analysis with QTLs, some favourable genes were revealed, including 14 domestication-related genes and 71 differentially expressed genes (DEGs) in the fifth-instar larval silk gland transcriptome between B. mori and B. mandarina. The relationships between the expression of two important candidate genes (KWMTBOMO04917 and KWMTBOMO12906) and cocoon yield were supported by quantitative real-time PCR (qPCR). Our results provide some new insights into the molecular mechanisms of complex yield traits in silkworm. The combined method might be an efficient approach for identifying putative causal genes in domestic livestock and wild relatives.

Selective Sweeps Under Dominance and Inbreeding

A major research goal in evolutionary genetics is to uncover loci experiencing positive selection. One approach involves finding 'selective sweeps' patterns, which can either be 'hard sweeps' formed by de novo mutation, or 'soft sweeps' arising from recurrent mutation or existing standing variation. Existing theory generally assumes outcrossing populations, and it is unclear how dominance affects soft sweeps. We consider how arbitrary dominance and inbreeding via self-fertilization affect hard and soft sweep signatures. With increased self-fertilization, they are maintained over longer map distances due to reduced effective recombination and faster beneficial allele fixation times. Dominance can affect sweep patterns in outcrossers if the derived variant originates from either a single novel allele, or from recurrent mutation. These models highlight the challenges in distinguishing hard and soft sweeps, and propose methods to differentiate between scenarios.

Pleiotropy facilitates local adaptation to distant optima in common ragweed (Ambrosia artemisiifolia)

Pleiotropy, the control of multiple phenotypes by a single locus, is expected to slow the rate of adaptation by increasing the chance that beneficial alleles also have deleterious effects. However, a prediction arising from classical theory of quantitative trait evolution states that pleiotropic alleles may have a selective advantage when phenotypes are distant from their selective optima. We examine the role of pleiotropy in regulating adaptive differentiation among populations of common ragweed (Ambrosia artemisiifolia); a species that has recently expanded its North American range due to human-mediated habitat change. We employ a phenotype-free approach by using connectivity in gene networks as a proxy for pleiotropy. First, we identify loci bearing footprints of local adaptation, and then use genotype-expression mapping and co-expression networks to infer the connectivity of the genes. Our results indicate that the putatively adaptive loci are highly pleiotropic, as they are more likely than expected to affect the expression of other genes, and they reside in central positions within the gene networks. We propose that the conditionally advantageous alleles at these loci avoid the cost of pleiotropy by having large phenotypic effects that are beneficial when populations are far from their selective optima. We further use evolutionary simulations to show that these patterns are in agreement with a model where populations face novel selective pressures, as expected during a range expansion. Overall, our results suggest that highly connected genes may be targets of positive selection during environmental change, even though they likely experience strong purifying selection in stable selective environments.

In the presence of population structure: From genomics to candidate genes underlying local adaptation

Understanding the genomic signatures, genes, and traits underlying local adaptation of organisms to heterogeneous environments is of central importance to the field evolutionary biology. To identify loci underlying local adaptation, models that combine allelic and environmental variation while controlling for the effects of population structure have emerged as the method of choice. Despite being evaluated in simulation studies, there has not been a thorough investigation of empirical evidence supporting local adaptation across these alleles. To evaluate these methods, we use 875 Arabidopsis thaliana Eurasian accessions and two mixed models (GEMMA and LFMM) to identify candidate SNPs underlying local adaptation to climate. Subsequently, to assess evidence of local adaptation and function among significant SNPs, we examine allele frequency differentiation and recent selection across Eurasian populations, in addition to their distribution along quantitative trait loci (QTL) explaining fitness variation between Italy and Sweden populations and cis-regulatory/nonsynonymous sites showing significant selective constraint. Our results indicate that significant LFMM/GEMMA SNPs show low allele frequency differentiation and linkage disequilibrium across locally adapted Italy and Sweden populations, in addition to a poor association with fitness QTL peaks (highest logarithm of odds score). Furthermore, when examining derived allele frequencies across the Eurasian range, we find that these SNPs are enriched in low-frequency variants that show very large climatic differentiation but low levels of linkage disequilibrium. These results suggest that their enrichment along putative functional sites most likely represents deleterious variation that is independent of local adaptation. Among all the genomic signatures examined, only SNPs showing high absolute allele frequency differentiation (AFD) and linkage disequilibrium (LD) between Italy and Sweden populations showed a strong association with fitness QTL peaks and were enriched along selectively constrained cis-regulatory/nonsynonymous sites. Using these SNPs, we find strong evidence linking flowering time, freezing tolerance, and the abscisic-acid pathway to local adaptation.

In the presence of population structure: From genomics to candidate genes underlying local adaptation

Understanding the genomic signatures, genes, and traits underlying local adaptation of organisms to heterogeneous environments is of central importance to the field evolutionary biology. To identify loci underlying local adaptation, models that combine allelic and environmental variation while controlling for the effects of population structure have emerged as the method of choice. Despite being evaluated in simulation studies, there has not been a thorough investigation of empirical evidence supporting local adaptation across these alleles. To evaluate these methods, we use 875 Arabidopsis thaliana Eurasian accessions and two mixed models (GEMMA and LFMM) to identify candidate SNPs underlying local adaptation to climate. Subsequently, to assess evidence of local adaptation and function among significant SNPs, we examine allele frequency differentiation and recent selection across Eurasian populations, in addition to their distribution along quantitative trait loci (QTL) explaining fitness variation between Italy and Sweden populations and cis-regulatory/nonsynonymous sites showing significant selective constraint. Our results indicate that significant LFMM/GEMMA SNPs show low allele frequency differentiation and linkage disequilibrium across locally adapted Italy and Sweden populations, in addition to a poor association with fitness QTL peaks (highest logarithm of odds score). Furthermore, when examining derived allele frequencies across the Eurasian range, we find that these SNPs are enriched in low-frequency variants that show very large climatic differentiation but low levels of linkage disequilibrium. These results suggest that their enrichment along putative functional sites most likely represents deleterious variation that is independent of local adaptation. Among all the genomic signatures examined, only SNPs showing high absolute allele frequency differentiation (AFD) and linkage disequilibrium (LD) between Italy and Sweden populations showed a strong association with fitness QTL peaks and were enriched along selectively constrained cis-regulatory/nonsynonymous sites. Using these SNPs, we find strong evidence linking flowering time, freezing tolerance, and the abscisic-acid pathway to local adaptation.

Functional variants of DOG1 control seed chilling responses and variation in seasonal life-history strategies in Arabidopsis thaliana

The seasonal timing of seed germination is critical for plant fitness in different climates. To germinate at the right time of year, seeds respond to seasonal environmental cues, such as cold temperatures. We characterized genetic variation in seed dormancy responses to cold across the geographic range of a widespread annual plant. Induction of secondary seed dormancy during winter conditions (which restricts germination to autumn) was positively correlated with flowering time, constructing winter and spring seasonal life-history strategies. Variation in seed chilling responses was strongly associated with functional variants of a known dormancy gene. These variants showed evidence of ancient diversification associated with Pleistocene glacial cycles, and were associated with climate gradients across the species’ geographical range.

The seasonal timing of seed germination determines a plant’s realized environmental niche, and is important for adaptation to climate. The timing of seasonal germination depends on patterns of seed dormancy release or induction by cold and interacts with flowering-time variation to construct different seasonal life histories. To characterize the genetic basis and climatic associations of natural variation in seed chilling responses and associated life-history syndromes, we selected 559 fully sequenced accessions of the model annual species Arabidopsis thaliana from across a wide climate range and scored each for seed germination across a range of 13 cold stratification treatments, as well as the timing of flowering and senescence. Germination strategies varied continuously along 2 major axes: 1) Overall germination fraction and 2) induction vs. release of dormancy by cold. Natural variation in seed responses to chilling was correlated with flowering time and senescence to create a range of seasonal life-history syndromes. Genome-wide association identified several loci associated with natural variation in seed chilling responses, including a known functional polymorphism in the self-binding domain of the candidate gene DOG1. A phylogeny of DOG1 haplotypes revealed ancient divergence of these functional variants associated with periods of Pleistocene climate change, and Gradient Forest analysis showed that allele turnover of candidate SNPs was significantly associated with climate gradients. These results provide evidence that A. thaliana’s germination niche and correlated life-history syndromes are shaped by past climate cycles, as well as local adaptation to contemporary climate.

Extreme genetic signatures of local adaptation during Lotus japonicus colonization of Japan

Colonization of new habitats is expected to require genetic adaptations to overcome environmental challenges. Here, we use full genome re-sequencing and extensive common garden experiments to investigate demographic and selective processes associated with colonization of Japan by Lotus japonicus over the past ~20,000 years. Based on patterns of genomic variation, we infer the details of the colonization process where L. japonicus gradually spread from subtropical conditions to much colder climates in northern Japan. We identify genomic regions with extreme genetic differentiation between northern and southern subpopulations and perform population structure-corrected association mapping of phenotypic traits measured in a common garden. Comparing the results of these analyses, we find that signatures of extreme subpopulation differentiation overlap strongly with phenotype association signals for overwintering and flowering time traits. Our results provide evidence that these traits were direct targets of selection during colonization and point to associated candidate genes.

Local adaptation contributes to plant colonization across extreme environmental gradients. Here, the authors reconstruct the colonization history of Lotus japonicus in Japan and identify extreme genetic signatures of local adaptation to a cold climate using genome resequencing and common garden experiments.

Signatures of selection for bonamiosis resistance in European flat oyster (Ostrea edulis): New genomic tools for breeding programs and management of natural resources

The European flat oyster (Ostrea edulis) is a highly appreciated mollusk with an important aquaculture production throughout the 20th century, in addition to playing an important role on coastal ecosystems. Overexploitation of natural beds, habitat degradation, introduction of non-native species, and epidemic outbreaks have severely affected this important resource, particularly, the protozoan parasite Bonamia ostreae, which is the main concern affecting its production and conservation. In order to identify genomic regions and markers potentially associated with bonamiosis resistance, six oyster beds distributed throughout the European Atlantic coast were sampled. Three of them have been exposed to this parasite since the early 1980s and showed some degree of innate resistance (long-term affected group, LTA), while the other three were free of B. ostreae at least until sampling date (naïve group, NV). A total of 14,065 SNPs were analyzed, including 37 markers from candidate genes and 14,028 from a medium-density SNP array. Gene diversity was similar between LTA and NV groups suggesting no genetic erosion due to long-term exposure to the parasite, and three population clusters were detected using the whole dataset. Tests for divergent selection between NV and LTA groups detected the presence of a very consistent set of 22 markers, located within a putative single genomic region, which suggests the presence of a major quantitative trait locus associated with B. ostreae resistance. Moreover, 324 outlier loci associated with factors other than bonamiosis were identified allowing fully discrimination of all the oyster beds. A practical tool which included the 84 highest discriminative markers for tracing O. edulis populations was developed and tested with empirical data. Results reported herein could assist the production of stocks with improved resistance to bonamiosis and facilitate the management of oyster beds for recovery production and ecosystem services provided by this species.

The potential of genomics for restoring ecosystems and biodiversity

Billions of hectares of natural ecosystems have been degraded through human actions. The global community has agreed on targets to halt and reverse these declines, and the restoration sector faces the important but arduous task of implementing programmes to meet these objectives. Existing and emerging genomics tools offer the potential to improve the odds of achieving these targets. These tools include population genomics that can improve seed sourcing, meta-omics that can improve assessment and monitoring of restoration outcomes, and genome editing that can generate novel genotypes for restoring challenging environments. We identify barriers to adopting these tools in a restoration context and emphasize that regulatory and ethical frameworks are required to guide their use.

Genomic Patterns of Local Adaptation under Gene Flow in Arabidopsis lyrata

Short-scale local adaptation is a complex process involving selection, migration, and drift. The expected effects on the genome are well grounded in theory but examining these on an empirical level has proven difficult, as it requires information about local selection, demographic history, and recombination rate variation. Here, we use locally adapted and phenotypically differentiated Arabidopsis lyrata populations from two altitudinal gradients in Norway to test these expectations at the whole-genome level. Demography modeling indicates that populations within the gradients diverged <2 kya and that the sites are connected by gene flow. The gene flow estimates are, however, highly asymmetric with migration from high to low altitudes being several times more frequent than vice versa. To detect signatures of selection for local adaptation, we estimate patterns of lineage-specific differentiation among these populations. Theory predicts that gene flow leads to concentration of adaptive loci in areas of low recombination; a pattern we observe in both lowland-alpine comparisons. Although most selected loci display patterns of conditional neutrality, we found indications of genetic trade-offs, with one locus particularly showing high differentiation and signs of selection in both populations. Our results further suggest that resistance to solar radiation is an important adaptation to alpine environments, while vegetative growth and bacterial defense are indicated as selected traits in the lowland habitats. These results provide insights into genetic architectures and evolutionary processes driving local adaptation under gene flow. We also contribute to understanding of traits and biological processes underlying alpine adaptation in northern latitudes.

QTL × environment interactions underlie adaptive divergence in switchgrass across a large latitudinal gradient

Understanding how individual genetic loci contribute to trait variation across geographic space is of fundamental importance for understanding evolutionary adaptations. Our study demonstrates that most loci underlying locally adaptive trait variation have beneficial effects in some geographic regions while conferring little or no detectable cost in other parts of the geographic range of switchgrass over two field seasons of study. Thus, loci that contribute to local adaptation vary in the degree to which they are costly in alternative environments but typically confer greater benefits than costs. Further, our study suggests that breeding locally adapted varieties of switchgrass will be a boon to the biofuel industry, as locally adaptive loci could be combined to increase local yields in switchgrass.

Local adaptation is the process by which natural selection drives adaptive phenotypic divergence across environmental gradients. Theory suggests that local adaptation results from genetic trade-offs at individual genetic loci, where adaptation to one set of environmental conditions results in a cost to fitness in alternative environments. However, the degree to which there are costs associated with local adaptation is poorly understood because most of these experiments rely on two-site reciprocal transplant experiments. Here, we quantify the benefits and costs of locally adaptive loci across 17° of latitude in a four-grandparent outbred mapping population in outcrossing switchgrass (Panicum virgatum L.), an emerging biofuel crop and dominant tallgrass species. We conducted quantitative trait locus (QTL) mapping across 10 sites, ranging from Texas to South Dakota. This analysis revealed that beneficial biomass (fitness) QTL generally incur minimal costs when transplanted to other field sites distributed over a large climatic gradient over the 2 y of our study. Therefore, locally advantageous alleles could potentially be combined across multiple loci through breeding to create high-yielding regionally adapted cultivars.

A Molecular View of Plant Local Adaptation: Incorporating Stress-Response Networks

Ecological specialization in plants occurs primarily through local adaptation to different environments. Local adaptation is widely thought to result in costly fitness trade-offs that result in maladaptation to alternative environments. However, recent studies suggest that such trade-offs are not universal. Further, there is currently a limited understanding of the molecular mechanisms responsible for fitness trade-offs associated with adaptation. Here, we review the literature on stress responses in plants to identify potential mechanisms underlying local adaptation and ecological specialization. We focus on drought, high and low temperature, flooding, herbivore, and pathogen stresses. We then synthesize our findings with recent advances in the local adaptation and plant molecular biology literature. In the process, we identify mechanisms that could cause fitness trade-offs and outline scenarios where trade-offs are not a necessary consequence of adaptation. Future studies should aim to explicitly integrate molecular mechanisms into studies of local adaptation.