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

Methods using genomic information to forecast potential population maladaptation to climate change or new environments 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 2- 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.

The WZA: A window-based method for characterizing genotype-environment associations.

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.

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 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.

Disentangling the genetic consequences of demographic change.

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.

Genetic diversity loss in the Anthropocene.

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.

Background selection under evolving recombination rates.

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.

The immediate costs and long-term benefits of assisted gene flow in large populations.

With the genetic health of many plant and animal populations deteriorating due to climate change outpacing adaptation, interventions, such as assisted gene flow (AGF), may provide genetic variation necessary for populations to adapt to climate change. We ran genetic simulations to mimic different AGF scenarios in large populations and measured their outcomes on population-level fitness to determine circumstances in which it is worthwhile to perform AGF. In the absence of inbreeding depression, AGF was beneficial within a few generations only when introduced genotypes had much higher fitness than local individuals and traits affecting fitness were controlled by a few genes of large effect. AGF was harmful over short periods (e.g., first ∼10-20 generations) if there was strong outbreeding depression or introduced deleterious genetic variation. When the adaptive trait was controlled by many loci of small effect, the benefits of AGF took over 10 generations to realize-potentially too long for most climate-related management scenarios. The genomic integrity of the recipient population typically remained intact following AGF; the amount of genetic material from the donor population usually constituted no more of the recipient population's genome than the fraction of the population introduced. Significant genomic turnover (e.g., >50% replacement) only occurred when the selective advantage of the adaptive trait and translocation fraction were extremely high. Our results will be useful when adaptive management is used to maintain the genetic health and productivity of large populations under climate change.

Con el deterioro de la salud genética de muchas poblaciones de plantas y animales debido a la ventaja que le lleva el cambio climático a la adaptación, algunas intervenciones, como el flujo génico asistido (FGA), pueden proporcionar la variación genética necesaria para que las poblaciones se adapten al cambio climático. Simulamos diferentes escenarios de FGA aplicado en poblaciones grandes y medimos los resultados en la aptitud a nivel poblacional para determinar las circunstancias en las que merece la pena realizar FGA. Cuando no hubo depresión endogámica, el FGA produjo un beneficio en pocas generaciones sólo cuando se introdujeron genotipos que tenían una aptitud mucho mayor que los individuos locales y cuando unos cuantos genes de gran efecto controlaron los rasgos que afectaban a la aptitud. El flujo génico asistido fue dañino en periodos cortos (p.ej.: las primeras 10-20 generaciones) si existía una fuerte depresión exogámica o una variación genética deletérea introducida. Cuando muchos loci de pequeño efecto controlaron el rasgo adaptativo, los beneficios del FGA tardaron más de 10 generaciones en aparecer - un tiempo potencialmente muy largo para la mayoría de la gestión relacionada con el clima. La integridad genómica de la población receptora casi siempre permaneció intacta después del FGA; es decir, la cantidad de material genético de la población donante generalmente no constituyó más que la fracción de población introducida en el genoma de la población receptora. La rotación genómica significativa (p.ej.: reemplazos >50%) sólo ocurrió cuando la ventaja selectiva del rasgo adaptativo y la fracción de reubicación fueron extremadamente elevadas. Nuestros resultados serán útiles cuando se use la gestión adaptativa para mantener la salud genética y la productividad de las poblaciones grandes bajo el cambio climático.

Haploid, diploid, and pooled exome capture recapitulate features of biology and paralogy in two non-model tree species.

Despite their suitability for studying evolution, many conifer species have large and repetitive giga-genomes (16-31 Gbp) that create hurdles to producing high coverage SNP data sets that capture diversity from across the entirety of the genome. Due in part to multiple ancient whole genome duplication events, gene family expansion and subsequent evolution within Pinaceae, false diversity from the misalignment of paralog copies creates further challenges in accurately and reproducibly inferring evolutionary history from sequence data. Here, we leverage the cost-saving benefits of pool-seq and exome-capture to discover SNPs in two conifer species, Douglas-fir (Pseudotsuga menziesii var. menziesii (Mirb.) Franco, Pinaceae) and jack pine (Pinus banksiana Lamb., Pinaceae). We show, using minimal baseline filtering, that allele frequencies estimated from pooled individuals show a strong, positive correlation with those estimated by sequencing the same population as individuals (r > .948), on par with such comparisons made in model organisms. Further, we highlight the utility of haploid megagametophyte tissue for identifying sites that are probably due to misaligned paralogs. Together with additional minor filtering, we show that it is possible to remove many of the loci with large frequency estimate discrepancies between individual and pooled sequencing approaches, improving the correlation further (r > .973). Our work addresses bioinformatic challenges in non-model organisms with large and complex genomes, highlights the use of megagametophyte tissue for the identification of paralogous artefacts, and suggests the combination of pool-seq and exome capture to be robust for further evolutionary hypothesis testing in these systems.

Global adaptation complicates the interpretation of genome scans 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 that contribute to local adaptation. However, theoretical studies have shown that in spatially structured populations the spread of beneficial mutations with spatially uniform fitness effects can also induce transient genetic differentiation. In recent years, numerous empirical studies have suggested that such species-wide, or global, adaptation makes a substantial contribution to molecular evolution. In this perspective, we discuss how commonly such global adaptation may influence the genome-wide distribution of FST and generate genetic differentiation patterns, which could be mistaken for local adaptation. To illustrate this, we use forward-in-time population genetic simulations assuming parameters for the rate and strength of beneficial mutations consistent with estimates from natural populations. We demonstrate that the spread of globally beneficial mutations in parapatric populations may frequently generate FST outliers, which could be misinterpreted as evidence for local adaptation. The spread of beneficial mutations causes selective sweeps at flanking sites, so in some cases, the effects of global versus local adaptation may be distinguished by examining patterns of nucleotide diversity within and between populations in addition to FST . However, when local adaptation has been only recently established, it may be much more difficult to distinguish from global adaptation, due to less accumulation of linkage disequilibrium at flanking sites. Through our discussion, we conclude that a large fraction of FST outliers that are presumed to arise from local adaptation may instead be due to global adaptation.

Using genetic relatedness to understand heterogeneous distributions of urban rat-associated pathogens.

Urban Norway rats (Rattus norvegicus) carry several pathogens transmissible to people. However, pathogen prevalence can vary across fine spatial scales (i.e., by city block). Using a population genomics approach, we sought to describe rat movement patterns across an urban landscape and to evaluate whether these patterns align with pathogen distributions. We genotyped 605 rats from a single neighborhood in Vancouver, Canada, and used 1,495 genome-wide single nucleotide polymorphisms to identify parent-offspring and sibling relationships using pedigree analysis. We resolved 1,246 pairs of relatives, of which only 1% of pairs were captured in different city blocks. Relatives were primarily caught within 33 meters of each other leading to a highly leptokurtic distribution of dispersal distances. Using binomial generalized linear mixed models, we evaluated whether family relationships influenced rat pathogen status with the bacterial pathogens Leptospira interrogans, Bartonella tribocorum, and Clostridium difficile, and found that an individual's pathogen status was not predicted any better by including disease status of related rats. The spatial clustering of related rats and their pathogens lends support to the hypothesis that spatially restricted movement promotes the heterogeneous patterns of pathogen prevalence evidenced in this population. Our findings also highlight the utility of evolutionary tools to understand movement and rat-associated health risks in urban landscapes.

Variation in recombination rate affects detection of outliers in genome scans under neutrality.

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.

Inferring Parameters of the Distribution of Fitness Effects of New Mutations When Beneficial Mutations Are Strongly Advantageous and Rare.

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.

Understanding the Factors That Shape Patterns of Nucleotide Diversity in the House Mouse Genome.

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.

Detecting positive selection in the genome.

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.

The Recombination Landscape in Wild House Mice Inferred Using Population Genomic Data.

Characterizing variation in the rate of recombination across the genome is important for understanding several evolutionary processes. Previous analysis of the recombination landscape in laboratory mice has revealed that the different subspecies have different suites of recombination hotspots. It is unknown, however, whether hotspots identified in laboratory strains reflect the hotspot diversity of natural populations or whether broad-scale variation in the rate of recombination is conserved between subspecies. In this study, we constructed fine-scale recombination rate maps for a natural population of the Eastern house mouse, Mus musculus castaneus We performed simulations to assess the accuracy of recombination rate inference in the presence of phase errors, and we used a novel approach to quantify phase error. The spatial distribution of recombination events is strongly positively correlated between our castaneus map, and a map constructed using inbred lines derived predominantly from M. m. domesticus Recombination hotspots in wild castaneus show little overlap, however, with the locations of double-strand breaks in wild-derived house mouse strains. Finally, we also find that genetic diversity in M. m. castaneus is positively correlated with the rate of recombination, consistent with pervasive natural selection operating in the genome. Our study suggests that recombination rate variation is conserved at broad scales between house mouse subspecies, but it is not strongly conserved at fine scales.

Inferring the Frequency Spectrum of Derived Variants to Quantify Adaptive Molecular Evolution in Protein-Coding Genes of Drosophila melanogaster.

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.