Previously unmeasured genetic diversity explains part of Lewontin's paradox in a k-mer-based meta-analysis of 112 plant species.

At the molecular level, most evolution is expected to be neutral. A key prediction of this expectation is that the level of genetic diversity in a population should scale with population size. However, as was noted by Richard Lewontin in 1974 and reaffirmed by later studies, the relationship between population size and diversity in nature is much weaker than expected. We hypothesize that one contributor to this apparent paradox is that current methods relying on single nucleotide polymorphisms (SNPs) called from aligning short reads to a reference genome underestimate levels of genetic diversity in many species. To test this idea, we calculated nucleotide diversity ( π ) and k-mer-based metrics of genetic diversity across 112 plant species, amounting to over 205 terabases of DNA sequencing data from 27,488 individual plants. We then compared how these different metrics correlated with proxies of population size that account for both range size and population density variation across species. We found that our population size proxies scaled anywhere from about 3 to over 20 times faster with k-mer diversity than nucleotide diversity after adjusting for evolutionary history, mating system, life cycle habit, cultivation status, and invasiveness. The relationship between k-mer diversity and population size proxies also remains significant after correcting for genome size, whereas the analogous relationship for nucleotide diversity does not. These results suggest that variation not captured by common SNP-based analyses explains part of Lewontin ' s paradox in plants.

Herbarium specimens reveal links between Capsella bursa-pastoris leaf shape and climate.

Studies into the evolution and development of leaf shape have connected variation in plant form, function, and fitness. For species with consistent leaf margin features, patterns in leaf architecture are related to both biotic and abiotic factors. However, for species with inconsistent leaf margin features, quantifying leaf shape variation and the effects of environmental factors on leaf shape has proven challenging. To investigate leaf shape variation in species with inconsistent shapes, we analyzed approximately 500 digitized Capsella bursa-pastoris specimens collected throughout the continental U.S. over a 100-year period with geometric morphometric modeling and deterministic techniques. We generated a morphospace of C. bursa-pastoris leaf shapes and modeled leaf shape as a function of environment and time. Our results suggest C. bursa-pastoris leaf shape variation is strongly associated with temperature over the C. bursa-pastoris growing season, with lobing decreasing as temperature increases. While we expected to see changes in variation over time, our results show that level of leaf shape variation is consistent over the 100-year period. Our findings showed that species with inconsistent leaf shape variation can be quantified using geometric morphometric modeling techniques and that temperature is the main environmental factor influencing leaf shape variation.

Weaker selection on genes with treatment-specific expression consistent with a limit on plasticity evolution in Arabidopsis thaliana.

Differential gene expression between environments often underlies phenotypic plasticity. However, environment-specific expression patterns are hypothesized to relax selection on genes, and thus limit plasticity evolution. We collated over 27 terabases of RNA-sequencing data on Arabidopsis thaliana from over 300 peer-reviewed studies and 200 treatment conditions to investigate this hypothesis. Consistent with relaxed selection, genes with more treatment-specific expression have higher levels of nucleotide diversity and divergence at nonsynonymous sites but lack stronger signals of positive selection. This result persisted even after controlling for expression level, gene length, GC content, the tissue specificity of expression, and technical variation between studies. Overall, our investigation supports the existence of a hypothesized trade-off between the environment specificity of a gene's expression and the strength of selection on said gene in A. thaliana. Future studies should leverage multiple genome-scale datasets to tease apart the contributions of many variables in limiting plasticity evolution.

Evaluating niche changes during invasion with seasonal models in Capsella bursa-pastoris.

PREMISE: Researchers often use ecological niche models to predict where species might establish and persist under future or novel climate conditions. However, these predictive methods assume species have stable niches across time and space. Furthermore, ignoring the time of occurrence data can obscure important information about species reproduction and ultimately fitness. Here, we assess compare ecological niche models generated from full-year averages to seasonal models. METHODS: In this study, we generate full-year and monthly ecological niche models for Capsella bursa-pastoris in Europe and North America to see if we can detect changes in the seasonal niche of the species after long-distance dispersal. RESULTS: We find full-year ecological niche models have low transferability across continents and there are continental differences in the climate conditions that influence the distribution of C. bursa-pastoris. Monthly models have greater predictive accuracy than full-year models in cooler seasons, but no monthly models can predict North American summer occurrences very well. CONCLUSIONS: The relative predictive ability of European monthly models compared to North American monthly models suggests a change in the seasonal timing between the native range to the non-native range. These results highlight the utility of ecological niche models at finer temporal scales in predicting species distributions and unmasking subtle patterns of evolution.

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.

Taking quantitative genomics into the wild.

We organized this special issue to highlight new work and review recent advances at the cutting edge of 'wild quantitative genomics'. In this editorial, we will present some history of wild quantitative genetic and genomic studies, before discussing the main themes in the papers published in this special issue and highlighting the future outlook of this dynamic field.

Local adaptation: Causal agents of selection and adaptive trait divergence.

Divergent selection across the landscape can favor the evolution of local adaptation in populations experiencing contrasting conditions. Local adaptation is widely observed in a diversity of taxa, yet we have a surprisingly limited understanding of the mechanisms that give rise to it. For instance, few have experimentally confirmed the biotic and abiotic variables that promote local adaptation, and fewer yet have identified the phenotypic targets of selection that mediate local adaptation. Here, we highlight critical gaps in our understanding of the process of local adaptation and discuss insights emerging from in-depth investigations of the agents of selection that drive local adaptation, the phenotypes they target, and the genetic basis of these phenotypes. We review historical and contemporary methods for assessing local adaptation, explore whether local adaptation manifests differently across life history, and evaluate constraints on local adaptation.

The important contribution of transposable elements to phenotypic variation and evolution.

Transposable elements (TEs) are responsible for significant genomic variation in plants. Our understanding of the evolutionary forces shaping TE polymorphism has lagged behind other mutations because of the difficulty of accurately identifying TE polymorphism in short-read population genomic data. However, new approaches allow us to quantify TE polymorphisms in population datasets and address fundamental questions about the evolution of these polymorphisms. Here, we discuss how insertional biases shape where, when, and how often TEs insert throughout the genome. Next, we examine mechanisms by which TEs can affect phenotype. Finally, we evaluate current evidence for selection on TE polymorphisms. All together, it is clear that TEs are important, but underappreciated, contributors to intraspecific phenotypic variation, and that understanding the dynamics governing TE polymorphism is crucial for evolutionary biologists interested in the maintenance of variation.

Selection on Accessible Chromatin Regions in Capsella grandiflora.

Accurate estimates of genome-wide rates and fitness effects of new mutations are essential for an improved understanding of molecular evolutionary processes. Although eukaryotic genomes generally contain a large noncoding fraction, functional noncoding regions and fitness effects of mutations in such regions are still incompletely characterized. A promising approach to characterize functional noncoding regions relies on identifying accessible chromatin regions (ACRs) tightly associated with regulatory DNA. Here, we applied this approach to identify and estimate selection on ACRs in Capsella grandiflora, a crucifer species ideal for population genomic quantification of selection due to its favorable population demography. We describe a population-wide ACR distribution based on ATAC-seq data for leaf samples of 16 individuals from a natural population. We use population genomic methods to estimate fitness effects and proportions of positively selected fixations (α) in ACRs and find that intergenic ACRs harbor a considerable fraction of weakly deleterious new mutations, as well as a significantly higher proportion of strongly deleterious mutations than comparable inaccessible intergenic regions. ACRs are enriched for expression quantitative trait loci (eQTL) and depleted of transposable element insertions, as expected if intergenic ACRs are under selection because they harbor regulatory regions. By integrating empirical identification of intergenic ACRs with analyses of eQTL and population genomic analyses of selection, we demonstrate that intergenic regulatory regions are an important source of nearly neutral mutations. These results improve our understanding of selection on noncoding regions and the role of nearly neutral mutations for evolutionary processes in outcrossing Brassicaceae species.

Adaptive and maladaptive expression plasticity underlying herbicide resistance in an agricultural weed.

Plastic phenotypic responses to environmental change are common, yet we lack a clear understanding of the fitness consequences of these plastic responses. Here, we use the evolution of herbicide resistance in the common morning glory (Ipomoea purpurea) as a model for understanding the relative importance of adaptive and maladaptive gene expression responses to herbicide. Specifically, we compare leaf gene expression changes caused by herbicide to the expression changes that evolve in response to artificial selection for herbicide resistance. We identify a number of genes that show plastic and evolved responses to herbicide and find that for the majority of genes with both plastic and evolved responses, plastic responses appear to be adaptive. We also find that selection for herbicide response increases gene expression plasticity. Overall, these results show the importance of adaptive plasticity for herbicide resistance in a common weed and that expression changes in response to strong environmental change can be adaptive.

Molecular Parallelism Underlies Convergent Highland Adaptation of Maize Landraces.

Convergent phenotypic evolution provides some of the strongest evidence for adaptation. However, the extent to which recurrent phenotypic adaptation has arisen via parallelism at the molecular level remains unresolved, as does the evolutionary origin of alleles underlying such adaptation. Here, we investigate genetic mechanisms of convergent highland adaptation in maize landrace populations and evaluate the genetic sources of recurrently selected alleles. Population branch excess statistics reveal substantial evidence of parallel adaptation at the level of individual single-nucleotide polymorphism (SNPs), genes, and pathways in four independent highland maize populations. The majority of convergently selected SNPs originated via migration from a single population, most likely in the Mesoamerican highlands, while standing variation introduced by ancient gene flow was also a contributor. Polygenic adaptation analyses of quantitative traits reveal that alleles affecting flowering time are significantly associated with elevation, indicating the flowering time pathway was targeted by highland adaptation. In addition, repeatedly selected genes were significantly enriched in the flowering time pathway, indicating their significance in adapting to highland conditions. Overall, our study system represents a promising model to study convergent evolution in plants with potential applications to crop adaptation across environmental gradients.

Local adaptation contributes to gene expression divergence in maize.

Gene expression links genotypes to phenotypes, so identifying genes whose expression is shaped by selection will be important for understanding the traits and processes underlying local adaptation. However, detecting local adaptation for gene expression will require distinguishing between divergence due to selection and divergence due to genetic drift. Here, we adapt a QST-FST framework to detect local adaptation for transcriptome-wide gene expression levels in a population of diverse maize genotypes. We compare the number and types of selected genes across a wide range of maize populations and tissues, as well as selection on cold-response genes, drought-response genes, and coexpression clusters. We identify a number of genes whose expression levels are consistent with local adaptation and show that genes involved in stress response show enrichment for selection. Due to its history of intense selective breeding and domestication, maize evolution has long been of interest to researchers, and our study provides insight into the genes and processes important for in local adaptation of maize.

Gene expression links genotype and phenotype during rapid adaptation.

Natural environments can change quickly and organisms living in these environments can either move, go extinct, or persist through rapid adaptation. Understanding the genetic and phenotypic changes that occur during rapid adaptation is crucial for predicting how populations will respond to sudden environmental changes. Since gene expression links genotype to phenotype, determining how rapid adaptation shapes the transcriptome will be useful for identifying both the traits and the genes important for adaptation, especially in cases where adaptation involves changes in quantitative traits. However, we lack a clear understanding of how rapid adaptation can cause and be caused by changes in gene expression. In this issue of Molecular Ecology, Hamann et al. (2020) investigate how gene expression has evolved during rapid adaptation to drought in two populations of the plant species Brassica rapa.

The Evolutionary Forces Shaping Cis- and Trans-Regulation of Gene Expression within a Population of Outcrossing Plants.

Understanding the persistence of genetic variation within populations has long been a goal of evolutionary biology. One promising route toward achieving this goal is using population genetic approaches to describe how selection acts on the loci associated with trait variation. Gene expression provides a model trait for addressing the challenge of the maintenance of variation because it can be measured genome-wide without information about how gene expression affects traits. Previous work has shown that loci affecting the expression of nearby genes (local or cis-eQTLs) are under negative selection, but we lack a clear understanding of the selective forces acting on variants that affect the expression of genes in trans. Here, we identify loci that affect gene expression in trans using genomic and transcriptomic data from one population of the obligately outcrossing plant, Capsella grandiflora. The allele frequencies of trans-eQTLs are consistent with stronger negative selection acting on trans-eQTLs than cis-eQTLs, and stronger negative selection acting on trans-eQTLs associated with the expression of multiple genes. However, despite this general pattern, we still observe the presence of a trans-eQTL at intermediate frequency that affects the expression of a large number of genes in the same coexpression module. Overall, our work highlights the different selective pressures shaping variation in cis- and trans-regulation.

Transposable Elements Are Important Contributors to Standing Variation in Gene Expression in Capsella Grandiflora.

Transposable elements (TEs) make up a significant portion of eukaryotic genomes and are important drivers of genome evolution. However, the extent to which TEs affect gene expression variation on a genome-wide scale in comparison with other types of variants is still unclear. We characterized TE insertion polymorphisms and their association with gene expression in 124 whole-genome sequences from a single population of Capsella grandiflora, and contrasted this with the effects of single nucleotide polymorphisms (SNPs). Population frequency of insertions was negatively correlated with distance to genes, as well as density of conserved noncoding elements, suggesting that the negative effects of TEs on gene regulation are important in limiting their abundance. Rare TE variants strongly influence gene expression variation, predominantly through downregulation. In contrast, rare SNPs contribute equally to up- and down-regulation, but have a weaker individual effect than TEs. An expression quantitative trait loci (eQTL) analysis shows that a greater proportion of common TEs are eQTLs as opposed to common SNPs, and a third of the genes with TE eQTLs do not have SNP eQTLs. In contrast with rare TE insertions, common insertions are more likely to increase expression, consistent with recent models of cis-regulatory evolution favoring enhancer alleles. Taken together, these results imply that TEs are a significant contributor to gene expression variation and are individually more likely than rare SNPs to cause extreme changes in gene expression.

Detecting Adaptive Differentiation in Structured Populations with Genomic Data and Common Gardens.

Adaptation in quantitative traits often occurs through subtle shifts in allele frequencies at many loci-a process called polygenic adaptation. While a number of methods have been developed to detect polygenic adaptation in human populations, we lack clear strategies for doing so in many other systems. In particular, there is an opportunity to develop new methods that leverage datasets with genomic data and common garden trait measurements to systematically detect the quantitative traits important for adaptation. Here, we develop methods that do just this, using principal components of the relatedness matrix to detect excess divergence consistent with polygenic adaptation, and using a conditional test to control for confounding effects due to population structure. We apply these methods to inbred maize lines from the United States Department of Agriculture germplasm pool and maize landraces from Europe. Ultimately, these methods can be applied to additional domesticated and wild species to give us a broader picture of the specific traits that contribute to adaptation and the overall importance of polygenic adaptation in shaping quantitative trait variation.

Determining the evolutionary forces shaping G × E.

Contents Summary 31 I. Introduction 31 II. The maintenance of genetic variation for plasticity 32 III. Why is there environmental variation for genetic effects? 33 IV. Conclusions 35 Acknowledgements 35 References 35 SUMMARY: Phenotypic plasticity is common in nature, yet we lack a comprehensive understanding of the evolutionary forces that shape genetic variation for plasticity. This endeavor is especially important because variation for plasticity will result in genotype-by-environment interactions (G × E), a crucial component of variation in quantitative traits. Here, I review our understanding of the evolutionary forces shaping G × E, focusing specifically on: what evolutionary forces maintain variation for plasticity; and what forces maintain different genetic architectures across environments. My specific goal is to show that genomic data can be leveraged to explain the maintenance of G × E by contrasting patterns of genetic variation for plasticity with neutral expectations.

The Relationship between Selection, Network Connectivity, and Regulatory Variation within a Population of Capsella grandiflora.

Interactions between genes can have important consequences for how selection shapes sequence variation at these genes. Specifically, genes that have pleiotropic effects by affecting the expression level of many other genes may be under stronger selective constraint. We used coexpression networks to measure connectivity between genes and investigated the relationship between gene connectivity and selection in a natural population of the plant Capsella grandiflora. We observed that network connectivity was negatively correlated with genetic divergence due to stronger negative selection on highly-connected genes even when controlling for variation in gene expression level. However, the presence of local regulatory variation for a gene's expression level was also associated with reduced negative selection and lower gene connectivity. While it is difficult to disentangle the causal relationships between these factors, our results show that both connectivity and local regulatory variation are important factors for explaining variation in selection between genes.

What can genome-wide association studies tell us about the evolutionary forces maintaining genetic variation for quantitative traits?

Contents 21 I. 21 II. 22 III. 24 IV. 25 V. 29 30 References 30 SUMMARY: Understanding the evolutionary forces that shape genetic variation within species has long been a goal of evolutionary biology. Integrating data for the genetic architecture of traits from genome-wide association mapping studies (GWAS) along with the development of new population genetic methods for identifying selection in sequence data may allow us to evaluate the roles of mutation-selection balance and balancing selection in shaping genetic variation at various scales. Here, we review the theoretical predictions for genetic architecture and additional signals of selection on genomic sequence for the loci that affect traits. Next, we review how plant GWAS have tested for the signatures of various selective scenarios. Limited evidence to date suggests that within-population variation is maintained primarily by mutation-selection balance while variation across the landscape is the result of local adaptation. However, there are a number of inherent biases in these interpretations. We highlight these challenges and suggest ways forward to further understanding of the maintenance of variation.