Genome scans can potentially identify genetic loci involved in evolutionary processes such as local adaptation and gene flow. Here, we show that recombination rate variation across a neutrally ...evolving genome gives rise to mixed sampling distributions of mean FST (FST^), a common population genetic summary statistic. In particular, we show that in regions of low recombination the distribution of FST^ estimates has more variance and a longer tail than in more highly recombining regions. Determining outliers from the genome‐wide distribution without taking local recombination rate into consideration may therefore increase the frequency of false positives in low recombination regions and be overly conservative in more highly recombining ones. We perform genome scans on simulated and empirical Drosophila melanogaster data sets and, in both cases, find patterns consistent with this neutral model. Similar patterns are observed for other summary statistics used to capture variation in the coalescent process. Linked selection, particularly background selection, is often invoked to explain heterogeneity in FST^ across the genome, but here we point out that even under neutrality, statistical artefacts can arise due to variation in recombination rate. Our results highlight a flaw in the design of genome‐scan studies and suggest that without estimates of local recombination rate, interpreting the genomic landscape of any summary statistic that captures variation in the coalescent process will be very difficult.
see also the Perspective by Laurie S. Stevison and Suzanne E. McGaugh.
Population geneticists have long sought to understand the contribution of natural selection to molecular evolution. A variety of approaches have been proposed that use population genetics theory to ...quantify the rate and strength of positive selection acting in a species' genome. In this review we discuss methods that use patterns of between-species nucleotide divergence and within-species diversity to estimate positive selection parameters from population genomic data. We also discuss recently proposed methods to detect positive selection from a population's haplotype structure. The application of these tests has resulted in the detection of pervasive adaptive molecular evolution in multiple species.
Genetic diversity loss in the Anthropocene Exposito-Alonso, Moises; Booker, Tom R; Czech, Lucas ...
Science (American Association for the Advancement of Science),
09/2022, Letnik:
377, Številka:
6613
Journal Article
Recenzirano
Odprti dostop
Anthropogenic habitat loss and climate change are reducing species' geographic ranges, increasing extinction risk and losses of species' genetic diversity. Although preserving genetic diversity is ...key to maintaining species' adaptability, we lack predictive tools and global estimates of genetic diversity loss across ecosystems. We introduce a mathematical framework that bridges biodiversity theory and population genetics to understand the loss of naturally occurring DNA mutations with decreasing habitat. By analyzing genomic variation of 10,095 georeferenced individuals from 20 plant and animal species, we show that genome-wide diversity follows a mutations-area relationship power law with geographic area, which can predict genetic diversity loss from local population extinctions. We estimate that more than 10% of genetic diversity may already be lost for many threatened and nonthreatened species, surpassing the United Nations' post-2020 targets for genetic preservation.
Characterizing the distribution of fitness effects (DFE) for new mutations is central in evolutionary genetics. Analysis of molecular data under the McDonald-Kreitman test has suggested that adaptive ...substitutions make a substantial contribution to between-species divergence. Methods have been proposed to estimate the parameters of the distribution of fitness effects for positively selected mutations from the unfolded site frequency spectrum (uSFS). Such methods perform well when beneficial mutations are mildly selected and frequent. However, when beneficial mutations are strongly selected and rare, they may make little contribution to standing variation and will thus be difficult to detect from the uSFS. In this study, I analyze uSFS data from simulated populations subject to advantageous mutations with effects on fitness ranging from mildly to strongly beneficial. As expected, frequent, mildly beneficial mutations contribute substantially to standing genetic variation and parameters are accurately recovered from the uSFS. However, when advantageous mutations are strongly selected and rare, there are very few segregating in populations at any one time. Fitting the uSFS in such cases leads to underestimates of the strength of positive selection and may lead researchers to false conclusions regarding the relative contribution adaptive mutations make to molecular evolution. Fortunately, the parameters for the distribution of fitness effects for harmful mutations are estimated with high accuracy and precision. The results from this study suggest that the parameters of positively selected mutations obtained by analysis of the uSFS should be treated with caution and that variability at linked sites should be used in conjunction with standing variability to estimate parameters of the distribution of fitness effects in the future.
Genotype–environment association (GEA) studies have the potential to identify the genetic basis of local adaptation in natural populations. Specifically, GEA approaches look for a correlation between ...allele frequencies and putatively selective features of the environment. Genetic markers with extreme evidence of correlation with the environment are presumed to be tagging the location of alleles that contribute to local adaptation. In this study, we propose a new method for GEA studies called the Weighted‐Z Analysis (WZA) that combines information from closely linked sites into analysis windows in a way that was inspired by methods for calculating FST. Performing GEA methods in analysis windows has the advantage that it takes advantage of the increased linkage disequilibrium expected surrounding sites subject to local adaptation. We analyse simulations modelling local adaptation to heterogeneous environments to compare the WZA with existing methods. In the majority of cases we tested, the WZA either outperformed single‐SNP (single nucleotide polymorphism)‐based approaches or performed similarly. In particular, the WZA outperformed individual SNP approaches when a small number of individuals or demes were sampled. Particularly troubling, we found that some GEA methods exhibit very high false positive rates. We applied the WZA to previously published data from lodgepole pine and identified candidate loci that were identified in the original study alongside numerous loci that were not found in the original study.
see also the Perspective by Kathrin A. Otte
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 toward 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.
Quantifying the impact of human activity on the capacity of populations to persist is paramount to conservation biology, as numerous species and populations have already been driven to or beyond the ...brink of extinction. Those populations that persist are often a sobering example of the evolutionary power of human‐disturbance, such as the loss of tusks in African elephants resulting from ivory harvesting (Campbell‐Staton et al., 2021) and rapid life‐history evolution in northern Atlantic cod in response to fisheries (Olsen et al., 2004). These evolutionary responses reflect a delicate interplay between demographic and selective processes (e.g., evolutionary rescue: Bell & Gonzalez, 2009; Gomulkiewicz & Holt, 1995), both of which can modify genetic variation for fitness. While quantifying fitness remains a difficult challenge, generalizable insights into the evolutionary consequences of population collapse can be provided in systems with independent demographic shifts in response to human activity. Unfortunately, such was the case for sea otter populations across its range in the 18th and 19th centuries, where the fur‐trade had catastrophic, range‐wide effects on sea otter (Enhydra lutris) populations. In a From the Cover article in this issue of Molecular Ecology, Beichman et al. (2022) combine a population genomic spatiotemporal data set and theoretical simulations not only to quantify past demographic change in response to sea otter exploitation, but also to understand the consequences of population collapse on species persistence.
Background selection (BGS), the effect that purifying selection exerts on sites linked to deleterious alleles, is expected to be ubiquitous across eukaryotic genomes. The effects of BGS reflect the ...interplay of the rates and fitness effects of deleterious mutations with recombination. A fundamental assumption of BGS models is that recombination rates are invariant over time. However, in some lineages, recombination rates evolve rapidly, violating this central assumption. Here, we investigate how recombination rate evolution affects genetic variation under BGS. We show that recombination rate evolution modifies the effects of BGS in a manner similar to a localized change in the effective population size, potentially leading to underestimation or overestimation of the genome-wide effects of selection. Furthermore, we find evidence that recombination rate evolution in the ancestors of modern house mice may have impacted inferences of the genome-wide effects of selection in that species.
Many approaches for inferring adaptive molecular evolution analyze the unfolded site frequency spectrum (SFS), a vector of counts of sites with different numbers of copies of derived alleles in a ...sample of alleles from a population. Accurate inference of the high-copy-number elements of the SFS is difficult, however, because of misassignment of alleles as derived vs. ancestral. This is a known problem with parsimony using outgroup species. Here we show that the problem is particularly serious if there is variation in the substitution rate among sites brought about by variation in selective constraint levels. We present a new method for inferring the SFS using one or two outgroups that attempts to overcome the problem of misassignment. We show that two outgroups are required for accurate estimation of the SFS if there is substantial variation in selective constraints, which is expected to be the case for nonsynonymous sites in protein-coding genes. We apply the method to estimate unfolded SFSs for synonymous and nonsynonymous sites in a population of Drosophila melanogaster from phase 2 of the Drosophila Population Genomics Project. We use the unfolded spectra to estimate the frequency and strength of advantageous and deleterious mutations and estimate that ∼50% of amino acid substitutions are positively selected but that <0.5% of new amino acid mutations are beneficial, with a scaled selection strength of Nes ≈ 12.
A major goal of population genetics has been to determine the extent by which selection at linked sites influences patterns of neutral nucleotide diversity in the genome. Multiple lines of evidence ...suggest that diversity is influenced by both positive and negative selection. For example, in many species there are troughs in diversity surrounding functional genomic elements, consistent with the action of either background selection (BGS) or selective sweeps. In this study, we investigated the causes of the diversity troughs that are observed in the wild house mouse genome. Using the unfolded site frequency spectrum, we estimated the strength and frequencies of deleterious and advantageous mutations occurring in different functional elements in the genome. We then used these estimates to parameterize forward-in-time simulations of chromosomes, using realistic distributions of functional elements and recombination rate variation in order to determine whether selection at linked sites can explain the observed patterns of nucleotide diversity. The simulations suggest that BGS alone cannot explain the dips in diversity around either exons or conserved noncoding elements. A combination of BGS and selective sweeps produces deeper dips in diversity than BGS alone, but the inferred parameters of selection cannot fully explain the patterns observed in the genome. Our results provide evidence of sweeps shaping patterns of nucleotide diversity across the mouse genome and also suggest that infrequent, strongly advantageous mutations play an important role in this. The limitations of using the unfolded site frequency spectrum for inferring the frequency and effects of advantageous mutations are discussed.
PDF
