Genomic evidence for domestication selection in three hatchery populations of Chinook salmon, Oncorhynchus tshawytscha.

Fish hatcheries are widely used to enhance fisheries and supplement declining wild populations. However, substantial evidence suggests that hatchery fish are subject to differential selection pressures compared to their wild counterparts. Domestication selection, or adaptation to the hatchery environment, poses a risk to wild populations if traits specific to success in the hatchery environment have a genetic component and there is subsequent introgression between hatchery and wild fish. Few studies have investigated domestication selection in hatcheries on a genomic level, and even fewer have done so in parallel across multiple hatchery-wild population pairs. In this study, we used low-coverage whole-genome sequencing to investigate signals of domestication selection in three separate hatchery populations of Chinook salmon, Oncorhynchus tshawytscha, after approximately seven generations of divergence from their corresponding wild progenitor populations. We sequenced 192 individuals from populations across Southeast Alaska and estimated genotype likelihoods at over six million loci. We discovered a total of 14 outlier peaks displaying high genetic differentiation (F ST) between hatchery-wild pairs, although no peaks were shared across the three comparisons. Peaks were small (53 kb on average) and often displayed elevated absolute genetic divergence (D xy) and linkage disequilibrium, suggesting some level of domestication selection has occurred. Our study provides evidence that domestication selection can lead to genetic differences between hatchery and wild populations in only a few generations. Additionally, our data suggest that population-specific adaptation to hatchery environments likely occurs through different genetic pathways, even for populations with similar standing genetic variation. These results highlight the need to collect paired genotype-phenotype data to understand how domestication may be affecting fitness and to identify potential management practices that may mitigate genetic risks despite multiple pathways of domestication.

Comparative study highlights how gene flow shapes adaptive genomic architecture.

Adaptation to environmental conditions, and the mechanisms underlying these adaptations, can vary greatly among species. This variation can be attributed to a variety of factors including the strength of evolutionary processes like selection, gene flow, time since divergence, and/or genetic drift, as well as the interactions between these processes. A number of simulation and theoretical studies have helped elucidate the role of these processes on the genomic basis of adaptation (Schaal et al., 2022; Yeaman et al., 2016). However, complementary empirical studies to test these theoretical expectations for within-species adaptation have been limited due to the challenging nature of evaluating these processes in a comparative framework. To do this effectively, it is necessary to have systems where the range of environmental variation is similar between species, but where one or more of these evolutionary processes vary. In a From the Cover article in this issue of Molecular Ecology, Shi et al. (2022) provide an excellent example of a freshwater system where rates of gene flow differ between populations of six riverine species due to variation in spawning strategies (i.e., broadcast spawners = high gene flow, nest spawners = low gene flow), but all experience the same variation in environmental conditions across their distributions. The authors take a multivariate approach to evaluate the genomic basis of adaptation by using a combination of differentiation-based and genotype-environment association (GEA) methods. By comparing the amount of gene flow between species and the resulting genomic basis of local adaptation, they are able to infer how genomic architecture may be shaped by rates of gene flow. Their results identify a general pattern of increased genomic clustering in species with increasing levels of gene flow. However, two of six species did not follow this pattern, which could be due to additional factors not assessed. Additionally, they provide convincing evidence that the underlying evolutionary mechanisms that formed genomic clusters within each species vary. These deviations from a general pattern highlight how difficult evaluating these processes in natural populations are, particularly because species-specific responses can vary dramatically. Taken together, their comparative framework for assessing the genomic architecture of adaptation is unique, sheds important light on how evolutionary processes can impact adaptation, and provides robust empirical support of foundational theoretical and simulation studies.

Inversion invasions: when the genetic basis of local adaptation is concentrated within inversions in the face of gene flow.

Across many species where inversions have been implicated in local adaptation, genomes often evolve to contain multiple, large inversions that arise early in divergence. Why this occurs has yet to be resolved. To address this gap, we built forward-time simulations in which inversions have flexible characteristics and can invade a metapopulation undergoing spatially divergent selection for a highly polygenic trait. In our simulations, inversions typically arose early in divergence, captured standing genetic variation upon mutation, and then accumulated many small-effect loci over time. Under special conditions, inversions could also arise late in adaptation and capture locally adapted alleles. Polygenic inversions behaved similarly to a single supergene of large effect and were detectable by genome scans. Our results show that characteristics of adaptive inversions found in empirical studies (e.g. multiple large, old inversions that are FST outliers, sometimes overlapping with other inversions) are consistent with a highly polygenic architecture, and inversions do not need to contain any large-effect genes to play an important role in local adaptation. By combining a population and quantitative genetic framework, our results give a deeper understanding of the specific conditions needed for inversions to be involved in adaptation when the genetic architecture is polygenic. This article is part of the theme issue 'Genomic architecture of supergenes: causes and evolutionary consequences'.

minotaur: A platform for the analysis and visualization of multivariate results from genome scans with R Shiny.

Genome scans are widely used to identify 'outliers' in genomic data: loci with different patterns compared with the rest of the genome due to the action of selection or other nonadaptive forces of evolution. These genomic data sets are often high dimensional, with complex correlation structures among variables, making it a challenge to identify outliers in a robust way. The Mahalanobis distance has been widely used, but has the major limitation of assuming that data follow a simple parametric distribution. Here, we develop three new metrics that can be used to identify outliers in multivariate space, while making no strong assumptions about the distribution of the data. These metrics are implemented in the R package minotaur, which also includes an interactive web-based application for visualizing outliers in high-dimensional data sets. We illustrate how these metrics can be used to identify outliers from simulated genetic data and discuss some of the limitations they may face in application.

Genome scans for the contemporary response to selection in quantitative traits.

Genome scans have been an important approach for discovering historical signatures of selection in both model and nonmodel species. An exciting new experimental design for genome scans is to measure the change in allele frequency before and after contemporary selection within a generation, from a single population. The most widely-used methods, however, have two major limitations: they are based on testing one locus at a time, and they only have power to uncover loci that have evolved under relatively strong selection. On the other hand, complex quantitative traits are common in nature and are caused by several loci of small effect. Selection on a quantitative trait at the phenotypic level is predicted to be accompanied by subtle allele frequency changes in many loci that covary (a polygenic soft sweep), rather than a large, single-effect allele (a selective sweep). In this issue of Molecular Ecology, Bourret et al. (2014) measure the contemporary response to natural selection across the genome in multiple cohorts of Atlantic salmon during their first year at sea. They introduce a multilocus framework based on groups of markers that covary in their genotypic distribution. While the traditional, single-locus approach did not find evidence for repeated patterns of selection, the multivariate approach found that a group of covarying SNPs was selected for in different cohorts at one site. Their multilocus framework has potential to be a more fruitful approach for uncovering the genomic basis of adaptation in quantitative traits, although caution should be applied as the framework has yet to be validated with simulated data.