Taking Quantitative Genomics into the Wild

A key goal in studies of ecology and evolution is understanding the causes of phenotypic diversity in nature. Most traits of interest, such as those relating to morphology, life-history, immunity and behaviour are quantitative, and phenotypic variation is driven by the cumulative effects of genetic and environmental variation. The field of quantitative genetics aims to quantify the additive genetic component of this trait variance (i.e. the "heritability"), often with the underlying assumption that trait variance is driven by many loci of infinitesimal effects throughout the genome. This approach allows us to understand the evolutionary potential of natural populations and can be extended to examine the genetic covariation with fitness to predict responses to selection. Therefore, quantitative genetic studies are fundamental to understanding evolution in the wild. Over the last two decades, there has been a wealth of studies investigating trait heritabilities and genetic correlations, but these were initially limited to long-term studies of pedigreed populations or common-garden experiments. However, genomic technologies have since allowed quantitative genetic studies in a more diverse range of wild systems and has increased the opportunities for addressing outstanding questions in ecology and evolution. In particular, genomic studies can uncover the genetic basis of fitness-related quantitative traits, allowing a better understanding of their evolutionary dynamics. We organised 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.Comment: 17 page (plus references) Editorial for a special issue of Proceedings of the Royal Society B: Biological Sciences. Revised submissio

Co-Variation between Seed Dormancy, Growth Rate and Flowering Time Changes with Latitude in Arabidopsis thaliana

Life-history traits controlling the duration and timing of developmental phases in the life cycle jointly determine fitness. Therefore, life-history traits studied in isolation provide an incomplete view on the relevance of life-cycle variation for adaptation. In this study, we examine genetic variation in traits covering the major life history events of the annual species Arabidopsis thaliana: seed dormancy, vegetative growth rate and flowering time. In a sample of 112 genotypes collected throughout the European range of the species, both seed dormancy and flowering time follow a latitudinal gradient independent of the major population structure gradient. This finding confirms previous studies reporting the adaptive evolution of these two traits. Here, however, we further analyze patterns of co-variation among traits. We observe that co-variation between primary dormancy, vegetative growth rate and flowering time also follows a latitudinal cline. At higher latitudes, vegetative growth rate is positively correlated with primary dormancy and negatively with flowering time. In the South, this trend disappears. Patterns of trait co-variation change, presumably because major environmental gradients shift with latitude. This pattern appears unrelated to population structure, suggesting that changes in the coordinated evolution of major life history traits is adaptive. Our data suggest that A. thaliana provides a good model for the evolution of trade-offs and their genetic basis.<br

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

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.Impact StatementPredicting whether and how organisms will adapt to environmental change is a crucial goal. However, this goal can be complicated because environmental change can alter traits, in a process called plasticity. The extent and fitness consequences of plasticity will have important effects on the adaptive process. In this study, we use adaptation to herbicide in the agricultural weed, the common morning glory, as a model for understanding the extent and fitness consequences of plasticity in gene expression. We find evidence that gene expression plasticity is adaptive in the presence of herbicide, suggesting that understanding plasticity is crucial for understanding how organisms adapt to new environments.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/168444/1/evl3241.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/168444/2/evl3241_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/168444/3/evl3241-sup-0003-figureS3.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/168444/4/evl3241-sup-0001-figureS1.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/168444/5/evl3241-sup-0002-figureS2.pd

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 (alpha) 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

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

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