Biology subjects

Whitlock, M. C.

Publications and source records attributed to Whitlock, M. C..

7 recordsLinked to original sources

bioRxiv2026

The genetic architecture of local adaptation is historically contingent

Revealing the genetic basis of local adaptation is a common goal of evolutionary biology, but despite theoretical progress, general expectations for the genetic architecture of local adaptation are still unclear. Theoretical analyses usually model simplified ecologies or simplified genetic architectures of adaptive traits, so the interplay of these factors is missing from our understanding. In this study, we use simulations to explore how the interplay of ecological and genetic parameters influences the evolution and genetic architecture of local adaptation. With these simulations, we ask: i) What are the features of alleles that made the largest contribution to local adaptation, and how are they affected by polygenicity of adaptive traits, migration rates, demographic history, and the spatial pattern of the environment? And ii) does allele age moderate the confounding effect from population structure in genotype-environmental associations (GEA)? We find that the frequency, number, and phenotypic effect size of locally adaptive alleles are sensitive to trait polygenicity and demographic history, and that these factors shape the evolutionary dynamics of local adaptation. We find that population expansions can leave legacies in the genetic architecture of local adaptation, reducing the expected number of adaptive alleles relative to models with constant population size, and this effect is long-lasting. Compared to range expansion, other ecological variables known to affect the genetic basis of local adaptation had limited effects. Finally, allele age moderated the confounding effect of population structure and modified the causal effect of environmental variables on genotypes. Alleles that arose around the time of environmental changes often made large contributions to local adaptation, but young alleles often had the highest false positive rates and were the most common age category. We describe how incorporating allele age and its interactions with population structure and environmental variables may increase the sensitivity and specificity of GEA analysis. Overall, this work demonstrates the critical importance that a species demographic history can have on its genetic architecture of local adaptation.

genetics↗

bioRxiv2024

FST and genetic diversity in an island model with background selection

Background selection, by which selection on deleterious alleles reduces diversity at linked neutral sites, influences patterns of total neutral diversity,{pi} T, and genetic differentiation, FST, in structured populations. The theory of background selection may be split into two regimes: the background selection regime, where selection pressures are strong and mutation rates are sufficiently low such that deleterious alleles are at a deterministic mutation-selection balance, and the interference selection regime, where selection pressures are weak and mutation rates are sufficiently high that deleterious alleles accumulate and interfere with another, leading to selective interference. Previous work has quantified the effects of background selection on{pi} T and FST only for deleterious alleles in the background selection regime. Furthermore, there is evidence to suggest that migration reduces the effects of background selection on FST, but this has not been fully explained. Here, we derive novel theory to predict the effects of migration on background selection experienced by a subpopulation, and extend previous theory from the interference selection regime to make predictions in an island model. Using simulations, we show that this theory best predicts FST and{pi} T. Moreover, we demonstrate that background selection from weakly deleterious alleles may generate minimal increases in FST, because migration reduces correlated effects on fitness over generations within subpopulations. However, we show that background selection may still cause substantial reductions in{pi} T, particularly for metapopulations with a larger effective population size. Our work further extends the theory of background selection into structured populations, and suggests that background selection will minimally confound locus-to-locus FST scans. Author SummaryMost mutations that affect fitness incur deleterious effects and are ultimately removed via natural selection. Consequently, nearby neutral variants may also experience the effects of selection; this is termed background selection. Background selection greatly influences patterns of genetic diversity both between and within populations among virtually all extant species, and is therefore of great interest to geneticists. Previous models of background selection have been primarily restricted to populations with completely random mating. However, it is well known that most natural populations exhibit some form of spatial structure. Here, we explore the effects of background selection in spatially structured populations, and we find that migration between subpopulations may attenuate the effects of background selection acting to increase genetic differentiation among populations. We derive novel theory to account for this effect by considering that individuals with deleterious alleles may migrate out of a local subpopulation prior to being removed by the population via selection. Our work demonstrates that, when migration rates are high, background selection does not substantially influence genetic differentiation among populations. Despite this, we find that background selection may greatly decrease genetic diversity within subpopulations and in the whole metapopulation.

evolutionary biology↗

bioRxiv2023

How useful is genomic data for predicting maladaptation to future climate?

Methods using genomic information to forecast potential population maladaptation to climate change are becoming increasingly common, yet the lack of model validation poses serious hurdles toward their incorporation into management and policy. Here, we compare the validation of maladaptation estimates derived from two methods - Gradient Forests (GFoffset) and the Risk Of Non-Adaptedness (RONA) - using exome capture pool-seq data from 35 to 39 populations across three conifer taxa: two Douglas-fir varieties and jack pine. We evaluate sensitivity of these algorithms to the source of input loci (markers selected from genotype-environment associations [GEA] or those selected at random). We validate these methods against two-year and 52-year growth and mortality measured in independent transplant experiments. Overall, we find that both methods often better predict transplant performance than climatic or geographic distances. We also find that GFoffset and RONA models are surprisingly not improved using GEA candidates. Even with promising validation results, variation in model projections to future climates makes it difficult to identify the most maladapted populations using either method. Our work advances understanding of the sensitivity and applicability of these approaches, and we discuss recommendations for their future use.

evolutionary biology↗

bioRxiv2022

Using genome scans to identify genes used repeatedly for adaptation

Adaptation occurring in similar genes or genomic regions in distinct lineages provides evolutionary biologists with a glimpse at the fundamental opportunities for and constraints to diversification. With the widespread availability of high throughput sequencing technologies and the development of population genetic methods to identify the genetic basis of adaptation, studies have begun to compare the evidence for adaptation at the molecular level among distinct lineages. However, methods to study repeated adaptation are often oriented towards genome-wide testing to identify a set of genes with signatures of repeated use, rather than evaluating the significance at the level of an individual gene. In this study, we propose PicMin, a novel statistical method derived from the theory of order statistics that can test for repeated molecular evolution to estimate significance at the level of an individual gene, using the results of genome scans. This method is generalizable to any number of lineages and indeed, statistical power to detect repeated adaptation increases with the number of lineages that have signals of repeated adaptation of a given gene in multiple lineages. An implementation of the method written for R can be downloaded from https://github.com/TBooker/PicMin.

evolutionary biology↗

bioRxiv2019

Global adaptation confounds the search for local adaptation

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

evolutionary biology↗

bioRxiv2018

Background selection and the statistics of population differentiation: consequences for detecting local adaptation

Background selection is a process whereby recurrent deleterious mutations cause a decrease in the effective population size and genetic diversity at linked loci. Several authors have suggested that variation in the intensity of background selection could cause variation in FST across the genome, which could confound signals of local adaptation in genome scans. We performed realistic simulations of DNA sequences, using parameter estimates from humans and sticklebacks, to investigate how variation in the intensity of background selection affects different statistics of population differentiation. We show that, in populations connected by gene flow, Weir & Cockerhams (1984) estimator of FST is largely insensitive to locus-to-locus variation in the intensity of background selection. Unlike FST, however, dXY is negatively correlated with background selection. We also show that background selection does not greatly affect the false positive rate in FST outlier studies. Overall, our study indicates that background selection will not greatly interfere with finding the variants responsible for local adaptation.

evolutionary biology↗

bioRxiv2018

Quantifying how constraints limit the diversity of viable routes to adaptation

Convergent adaptation can occur at the genome scale when independently evolving lineages use the same genes to respond to similar selection pressures. These patterns provide insights into the factors that facilitate or constrain the diversity of genetic responses that contribute to adaptive evolution. A first step in studying such factors is to quantify the observed amount of repeatability relative to expectations under a null hypothesis. Here, we formulate a novel metric to quantify the constraints driving the observed amount of repeated adaptation in pairwise contrasts based on the hypergeometric distribution, and then generalize this for simultaneous analysis of multiple lineages. This metric is explicitly based on the probability of observing a given amount of repeatability by chance under an arbitrary null hypothesis, and is readily compared among different species and types of trait. We also formulate a metric to quantify the effective proportion of genes in the genome that have the potential to contribute to adaptation. As an example of how these metrics can be used to draw inferences, we assess the amount of repeatability observed in existing datasets on adaptation to antibiotics in yeast and climate in conifers. This approach provides a method to test a wide range of hypotheses about how different kinds of factors can facilitate or constrain the diversity of genetic responses observed during adaptive evolution.

evolutionary biology↗