Population Genomic Data Research Articles

Recommendations for improving statistical inference in population genomics.

The field of population genomics has grown rapidly in response to the recent advent of affordable, large-scale sequencing technologies. As opposed to the situation during the majority of the 20th century, in which the development of theoretical and statistical population genetic insights outpaced the generation of data to which they could be applied, genomic data are now being produced at a far greater rate than they can be meaningfully analyzed and interpreted. With this wealth of data has come a tendency to focus on fitting specific (and often rather idiosyncratic) models to data, at the expense of a careful exploration of the range of possible underlying evolutionary processes. For example, the approach of directly investigating models of adaptive evolution in each newly sequenced population or species often neglects the fact that a thorough characterization of ubiquitous nonadaptive processes is a prerequisite for accurate inference. We here describe the perils of these tendencies, present our consensus views on current best practices in population genomic data analysis, and highlight areas of statistical inference and theory that are in need of further attention. Thereby, we argue for the importance of defining a biologically relevant baseline model tuned to the details of each new analysis, of skepticism and scrutiny in interpreting model fitting results, and of carefully defining addressable hypotheses and underlying uncertainties.

Machine-Learning Prospects for Detecting Selection Signatures Using Population Genomics Data.

Natural selection has been given a lot of attention because it relates to the adaptation of populations to their environments, both biotic and abiotic. An allele is selected when it is favored by natural selection. Consequently, the favored allele increases in frequency in the population and neighboring linked variation diminishes, causing so-called selective sweeps. A high-throughput genomic sequence allows one to disentangle the evolutionary forces at play in populations. With the development of high-throughput genome sequencing technologies, it has become easier to detect these selective sweeps/selection signatures. Various methods can be used to detect selective sweeps, from simple implementations using summary statistics to complex statistical approaches. One of the important problems of these statistical models is the potential to provide inaccurate results when their assumptions are violated. The use of machine learning (ML) in population genetics has been introduced as an alternative method of detecting selection by treating the problem of detecting selection signatures as a classification problem. Since the availability of population genomics data is increasing, researchers may incorporate ML into these statistical models to infer signatures of selection with higher predictive accuracy and better resolution. This article describes how ML can be used to aid in detecting and studying natural selection patterns using population genomic data.

Human herpesvirus diversity is altered in HLA class I binding peptides

Herpesviruses are ubiquitous, genetically diverse DNA viruses, with long-term presence in humans associated with infrequent but significant pathology. Human leukocyte antigen (HLA) class I presents intracellularly derived peptide fragments from infected tissue cells to CD8+ T and natural killer cells, thereby directing antiviral immunity. Allotypes of highly polymorphic HLA class I are distinguished by their peptide binding repertoires. Because this HLA class I variation is a major determinant of herpesvirus disease, we examined if sequence diversity of virus proteins reflects evasion of HLA presentation. Using population genomic data from Epstein–Barr virus (EBV), human cytomegalovirus (HCMV), and Varicella–Zoster virus, we tested whether diversity differed between the regions of herpesvirus proteins that can be recognized, or not, by HLA class I. Herpesviruses exhibit lytic and latent infection stages, with the latter better enabling immune evasion. Whereas HLA binding peptides of lytic proteins are conserved, we found that EBV and HCMV proteins expressed during latency have increased peptide sequence diversity. Similarly, latent, but not lytic, herpesvirus proteins have greater population structure in HLA binding than nonbinding peptides. Finally, we found patterns consistent with EBV adaption to the local HLA environment, with less efficient recognition of EBV isolates by high-frequency HLA class I allotypes. Here, the frequency of CD8+ T cell epitopes inversely correlated with the frequency of HLA class I recognition. Previous analyses have shown that pathogen-mediated natural selection maintains exceptional polymorphism in HLA residues that determine peptide recognition. Here, we show that HLA class I peptide recognition impacts diversity of globally widespread pathogens.

The Bank Vole (Clethrionomys glareolus) as a Model System for Adaptive Phylogeography in the European Theater

The legacy of climatic changes during the Pleistocene glaciations allows inferences to be made about the patterns and processes associated with range expansion/colonization, including evolutionary adaptation. With the increasing availability of population genomic data, we have the opportunity to examine these questions in detail and in a variety of non-traditional model species. As an exemplar, here we review more than two decades of work by our group and others that illustrate the potential of a single “non-model model” mammal species - the bank vole (Clethrionomys glareolus), which is particularly well suited to illustrate the complexities that may be associated with range expansion and the power of genomics (and other datasets) to uncover them. We first summarize early phylogeographic work using mitochondrial DNA and then describe new phylogeographic insights gained from population genomic analysis of genome-wide SNP data to highlight the bank vole as one of the most compelling examples of a forest mammal, that survived in cryptic extra-Mediterranean (“northern”) glacial refugia in Europe, and as one of the species in which substantial replacement and mixing of lineages originating from different refugia occurred during end-glacial colonization. Our studies of bank vole hemoglobin structure and function, as well as our recent ecological niche modeling study examining differences among bank vole lineages, led us to develop the idea of “adaptive phylogeography.” This is what we call the study of the role of adaptive differences among populations in shaping phylogeographic patterns. Adaptive phylogeography provides a link between past population history and adaptation that can ultimately help predict the potential of future species responses to climate change. Because the bank vole is part of a community of organisms whose range has repeatedly contracted and then expanded in the past, what we learn from the bank vole will be useful for our understanding of a broad range of species.

Sex-specific distribution and classification of Wolbachia infections and mitochondrial DNA haplogroups in Aedes albopictus from the Indo-Pacific.

The arbovirus vector Aedes albopictus (Asian tiger mosquito) is common throughout the Indo-Pacific region, where most global dengue transmission occurs. We analysed population genomic data and tested for cryptic species in 160 Ae. albopictus sampled from 16 locations across this region. We found no evidence of cryptic Ae. albopictus but found multiple intraspecific COI haplotypes partitioned into groups representing three Asian lineages: East Asia, Southeast Asia and Indonesia. Papua New Guinea (PNG), Vanuatu and Christmas Island shared recent coancestry, and Indonesia and Timor-Leste were likely invaded from East Asia. We used a machine learning trained on morphologically sexed samples to classify sexes using multiple genetic features and then characterized the wAlbA and wAlbB Wolbachia infections in 664 other samples. The wAlbA and wAlbB infections as detected by qPCR showed markedly different patterns in the sexes. For females, most populations had a very high double infection incidence, with 67% being the lowest value (from Timor-Leste). For males, the incidence of double infections ranged from 100% (PNG) to 0% (Vanuatu). Only 6 females were infected solely by the wAlbA infection, while rare uninfected mosquitoes were found in both sexes. The wAlbA and wAlbB densities varied significantly among populations. For mosquitoes from Torres Strait and Vietnam, the wAlbB density was similar in single-infected and superinfected (wAlbA and wAlbB) mosquitoes. There was a positive association between wAlbA and wAlbB infection densities in superinfected Ae. albopictus. Our findings provide no evidence of cryptic species of Ae. albopictus in the region and suggest site-specific factors influencing the incidence of Wolbachia infections and their densities. We also demonstrate the usefulness of ddRAD tag depths as sex-specific mosquito markers. The results provide baseline data for the exploitation of Wolbachia-induced cytoplasmic incompatibility (CI) in dengue control.

An integrative skeletal and paleogenomic analysis of stature variation suggests relatively reduced health for early European farmers

Human culture, biology, and health were shaped dramatically by the onset of agriculture ∼12,000 y B.P. This shift is hypothesized to have resulted in increased individual fitness and population growth as evidenced by archaeological and population genomic data alongside a decline in physiological health as inferred from skeletal remains. Here, we consider osteological and ancient DNA data from the same prehistoric individuals to study human stature variation as a proxy for health across a transition to agriculture. Specifically, we compared “predicted” genetic contributions to height from paleogenomic data and “achieved” adult osteological height estimated from long bone measurements for 167 individuals across Europe spanning the Upper Paleolithic to Iron Age (∼38,000 to 2,400 B.P.). We found that individuals from the Neolithic were shorter than expected (given their individual polygenic height scores) by an average of −3.82 cm relative to individuals from the Upper Paleolithic and Mesolithic (P = 0.040) and −2.21 cm shorter relative to post-Neolithic individuals (P = 0.068), with osteological vs. expected stature steadily increasing across the Copper (+1.95 cm relative to the Neolithic), Bronze (+2.70 cm), and Iron (+3.27 cm) Ages. These results were attenuated when we additionally accounted for genome-wide genetic ancestry variation: for example, with Neolithic individuals −2.82 cm shorter than expected on average relative to pre-Neolithic individuals (P = 0.120). We also incorporated observations of paleopathological indicators of nonspecific stress that can persist from childhood to adulthood in skeletal remains into our model. Overall, our work highlights the potential of integrating disparate datasets to explore proxies of health in prehistory.

From genotype to phenotype: Genetic redundancy and the maintenance of an adaptive polymorphism in the context of high gene flow.

A central question in evolution is how several adaptive phenotypes are maintained within a species. Theory predicts that the genetic determination of a trait, and in particular the amounts of redundancy in the mapping of genotypes to phenotypes, mediates evolutionary outcomes of phenotypic selection. In Mediterranean wild thyme, numerous discrete chemical phenotypes (chemotypes) occur in close geographic proximity. Chemotypes are defined by the predominant monoterpene produced by individual plants in their essential oil. In this study, we analyze the ecological genetics of six chemotypes nested within two well‐established chemical families (hereafter ecotypes). Ecotypes, and chemotypes within ecotypes, are spatially segregated, and their distributions track local differences in the abiotic environment. By combining population genomic, phenotypic, and environmental data from 700 individuals, we show how the genetics of ecotype determination mediates this evolutionary response. Variation in three terpene‐synthase loci explains variation in ecotype identity, with one single locus accounting for as much as 78% of this variation. Phenotypic selection combined with low segregating genotypic redundancy of ecotypes leaves a clear footprint at the genomic level: alleles associated with ecotype identity track environmental variation despite extensive gene flow. Different chemotypes within each ecotype differentially track environmental variation. Their identity is determined by multiple loci and displays a wider range of genotypic redundancy that dilutes phenotypic selection on their characteristic alleles. Our study thus provides a novel illustration of how genetic redundancy of a phenotype modulates the ability of selection to maintain adaptive differentiation. Identifying the precise genetics of the chemical polymorphism in thyme is the next crucial step for our understanding of the origin and maintenance of a polymorphism that is present in many aromatic plants.

Therapeutic Targets for Heart Failure Identified Using Proteomics and Mendelian Randomization.

Background:Heart failure (HF) is a highly prevalent disorder for which disease mechanisms are incompletely understood. The discovery of disease-associated proteins with causal genetic evidence provides an opportunity to identify new therapeutic targets.Methods:We investigated the observational and causal associations of 90 cardiovascular proteins, which were measured using affinity-based proteomic assays. First, we estimated the associations of 90 cardiovascular proteins with incident heart failure by means of a fixed-effect meta-analysis of 4 population-based studies, composed of a total of 3019 participants with 732 HF events. The causal effects of HF-associated proteins were then investigated by Mendelian randomization, using cis-protein quantitative loci genetic instruments identified from genomewide association studies in more than 30 000 individuals. To improve the precision of causal estimates, we implemented an Mendelian randomization model that accounted for linkage disequilibrium between instruments and tested the robustness of causal estimates through a multiverse sensitivity analysis that included up to 120 combinations of instrument selection parameters and Mendelian randomization models per protein. The druggability of candidate proteins was surveyed, and mechanism of action and potential on-target side effects were explored with cross-trait Mendelian randomization analysis.Results:Forty-four of ninety proteins were positively associated with risk of incident HF (P<6.0×10–4). Among these, 8 proteins had evidence of a causal association with HF that was robust to multiverse sensitivity analysis: higher CSF-1 (macrophage colony-stimulating factor 1), Gal-3 (galectin-3) and KIM-1 (kidney injury molecule 1) were positively associated with risk of HF, whereas higher ADM (adrenomedullin), CHI3L1 (chitinase-3-like protein 1), CTSL1 (cathepsin L1), FGF-23 (fibroblast growth factor 23), and MMP-12 (matrix metalloproteinase-12) were protective. Therapeutics targeting ADM and Gal-3 are currently under evaluation in clinical trials, and all the remaining proteins were considered druggable, except KIM-1.Conclusions:We identified 44 circulating proteins that were associated with incident HF, of which 8 showed evidence of a causal relationship and 7 were druggable, including adrenomedullin, which represents a particularly promising drug target. Our approach demonstrates a tractable roadmap for the triangulation of population genomic and proteomic data for the prioritization of therapeutic targets for complex human diseases.

Identification of important genomic footprints using eight different selection signature statistics in domestic cattle breeds.

In the present study, the population genomic data of different cattle breeds were explored to decipher the genomic regions affected due to selective events and reflected in the productive, reproductive, thermo-tolerance, and health-related traits. To find out these genomic deviations due to selective sweeps, we used eight different statistical tools (Tajima's D, Fu & Li's D*, CLR, ROH, iHS, FST, FLK, and hapFLK) on seven indigenous and five exotic cattle breeds. We further performed composite analysis by comparing their covariance matrix. Several candidate genes were found to be related to milk production (ADARB, WDR70, and CA8), reproductive (PARN, FAM134B2, and ZBTB20), and health-related traits (SP110, CXCL2, CLXCL3, CXCL5, IRF8, and MYOM1). The outcome of this investigation provides a basis for detecting selective sweeps that explain the genetic variation of traits. They may possess functional importance for multiple cattle breeds in different subcontinents. However, further studies are required to improve the findings using high-density arrays or whole-genome sequencing with higher resolution and greater sample sizes.

Convergent consequences of parthenogenesis on stick insect genomes.

The shift from sexual reproduction to parthenogenesis has occurred repeatedly in animals, but how the loss of sex affects genome evolution remains poorly understood. We generated reference genomes for five independently evolved parthenogenetic species in the stick insect genus Timema and their closest sexual relatives. Using these references and population genomic data, we show that parthenogenesis results in an extreme reduction of heterozygosity and often leads to genetically uniform populations. We also find evidence for less effective positive selection in parthenogenetic species, suggesting that sex is ubiquitous in natural populations because it facilitates fast rates of adaptation. Parthenogenetic species did not show increased transposable element (TE) accumulation, likely because there is little TE activity in the genus. By using replicated sexual-parthenogenetic comparisons, our study reveals how the absence of sex affects genome evolution in natural populations, providing empirical support for the negative consequences of parthenogenesis as predicted by theory.

Integrative Reverse Genetic Analysis Identifies Polymorphisms Contributing to Decreased Antimicrobial Agent Susceptibility in Streptococcus pyogenes.

ABSTRACTIdentification of genetic polymorphisms causing increased antibiotic resistance in bacterial pathogens traditionally has proceeded from observed phenotype to defined mutant genotype. The availability of large collections of microbial genome sequences that lack antibiotic susceptibility metadata provides an important resource and opportunity to obtain new information about increased antimicrobial resistance by a reverse genotype-to-phenotype bioinformatic and experimental workflow. We analyzed 26,465 genome sequences of Streptococcus pyogenes, a human pathogen causing 700 million infections annually. The population genomic data identified amino acid changes in penicillin-binding proteins 1A, 1B, 2A, and 2X with signatures of evolution under positive selection as potential candidates for causing decreased susceptibility to β-lactam antibiotics. Construction and analysis of isogenic mutant strains containing individual amino acid replacements in penicillin-binding protein 2X (PBP2X) confirmed that the identified residues produced decreased susceptibility to penicillin. We also discovered the first chimeric PBP2X in S. pyogenes and show that strains containing it have significantly decreased β-lactam susceptibility. The novel integrative reverse genotype-to-phenotype strategy presented is broadly applicable to other pathogens and likely will lead to new knowledge about antimicrobial agent resistance, a massive public health problem worldwide.

Ribosomal protein S6 kinase beta-1 gene variants cause hypertrophic cardiomyopathy

BackgroundHypertrophic cardiomyopathy (HCM) is a genetic heart muscle disease with preserved or increased ejection fraction in the absence of secondary causes. Mutations in the sarcomeric protein-encoding genes predominantly cause HCM....

Genomic patterns and the evolutionary origin of an invasive fungal pathogen (Hymenoscyphus fraxineus) in Europe

In the 1990s, a highly pathogenic and invasive lineage of the Asian ascomycete Hymenoscyphus fraxineus appeared in Europe. Spreading across the continent, the fungal lineage devastates common ash (Fraxinus excelsior) populations and is a threat to ash species worldwide.In contrast to highly diverse reference populations in Japan and East Russia, the expanding European population is characterized by the persistence of a mosaic of two ancestral haplotypes, and presumed constraints to recombination. Four hypotheses were proposed for the evolutionary origin of the European lineage. The first hypothesis H1 states that two, and only two, pathogenic founder individuals were transferred from Asia to Europe and successfully established. Alternatively, in addition to the fungus arriving in Europe, a change in function might have occurred and only then the lineage became invasive. The cause of such a change might have been H2: one to two mutations during meiosis; H3: the lateral transfer of a European cytoplasmatic element modifying the behavior of H. fraxineus; or H4: a hybridization event with a closely related European ascomycete.Only the hybridization hypothesis seems to explain all of the genome-wide characteristics observed for the nuclear genome. Existing reconstructions of phylogenetic relationships at different taxonomic levels provide the evolutionary context of the lineages potentially involved. Vital information was gained from three configurations of nucleotide diversity and population differentiation associated with the genomic mosaic of monomorphic tracts and diversity islands.This synthesis points to the importance of identifying the taxonomic and geographic origins of the conserved two genomic ancestries within the European lineage of H. fraxineus and of reconstructing the mosaic of ancestral parental haplotypes (ancestry blocks) across the genome. Only with this information at hand will it be possible, to reliably interpret population-genomic data in the search for the functional elements underlying this unbalanced host-pathogen system.

QtlXplorer: an online systems genetics browser in the Eucalyptus Genome Integrative Explorer (EucGenIE)

BackgroundAffordable high-throughput DNA and RNA sequencing technologies are allowing genomic analysis of plant and animal populations and as a result empowering new systems genetics approaches to study complex traits. The availability of intuitive tools to browse and analyze the resulting large-scale genetic and genomic datasets remain a significant challenge. Furthermore, these integrative genomics approaches require innovative methods to dissect the flow and interconnectedness of biological information underlying complex trait variation. The Plant Genome Integrative Explorer (PlantGenIE.org) is a multi-species database and domain that houses online tools for model and woody plant species including Eucalyptus. Since the Eucalyptus Genome Integrative Explorer (EucGenIE) is integrated within PlantGenIE, it shares genome and expression analysis tools previously implemented within the various subdomains (ConGenIE, PopGenIE and AtGenIE). Despite the success in setting up integrative genomics databases, online tools for systems genetics modelling and high-resolution dissection of complex trait variation in plant populations have been lacking.ResultsWe have developed qtlXplorer (https://eucgenie.org/QTLXplorer) for visualizing and exploring systems genetics data from genome-wide association studies including quantitative trait loci (QTLs) and expression-based QTL (eQTL) associations. This module allows users to, for example, find co-located QTLs and eQTLs using an interactive version of Circos, or explore underlying genes using JBrowse. It provides users with a means to build systems genetics models and generate hypotheses from large-scale population genomics data. We also substantially upgraded the EucGenIE resource and show how it enables users to combine genomics and systems genetics approaches to discover candidate genes involved in biotic stress responses and wood formation by focusing on two multigene families, laccases and peroxidases.ConclusionsqtlXplorer adds a new dimension, population genomics, to the EucGenIE and PlantGenIE environment. The resource will be of interest to researchers and molecular breeders working in Eucalyptus and other woody plant species. It provides an example of how systems genetics data can be integrated with functional genetics data to provide biological insight and formulate hypotheses. Importantly, integration within PlantGenIE enables novel comparative genomics analyses to be performed from population-scale data.

Navigating the temporal continuum of effective population size

AbstractEffective population size, Ne, is a key evolutionary parameter that determines the levels of genetic variation and efficacy of selection. Estimation and interpretation of Ne are essential in diverse areas of evolutionary and conservation biology, ranging from assessing the evolutionary potential or extinction risk to empowering research on the genomic basis of adaptation.The diverse applications of the Ne concept resulted in largely independent methodological developments using different Ne definitions and focusing on the estimation of either contemporary or long‐term Ne. Recently, several novel approaches appeared that allow the estimation of temporal Ne trends at various parts of the temporal Ne continuum.Here, we provide an overview of the temporal aspects of Ne, asking whether the existing methods and available data provide Ne estimates for the entire temporal continuum. We outline the apparent division into contemporary and long‐term Ne existing in the literature, review Ne estimation methods across the temporal continuum and navigate the reader through it pointing to the important biological and methodological limitations inherent to the estimation procedures. Finally, we provide examples of recent research and future perspectives.We conclude that the entire temporal Ne continuum can now be inferred using several complementary approaches, bringing together contemporary and long‐term perspectives. Further rapid progress in the field is expected from the wide adoption of machine learning and transition to haplotype‐resolved population genomic data. The expected progress notwithstanding, interpretational difficulties are likely to remain, emphasising the need for a thorough understanding of the Ne theory behind the methods, their assumptions and inherent limitations.

PopHumanVar: an interactive application for the functional characterization and prioritization of adaptive genomic variants in humans.

Adaptive challenges that humans faced as they expanded across the globe left specific molecular footprints that can be decoded in our today's genomes. Different sets of metrics are used to identify genomic regions that have undergone selection. However, there are fewer methods capable of pinpointing the allele ultimately responsible for this selection. Here, we present PopHumanVar, an interactive online application that is designed to facilitate the exploration and thorough analysis of candidate genomic regions by integrating both functional and population genomics data currently available. PopHumanVar generates useful summary reports of prioritized variants that are putatively causal of recent selective sweeps. It compiles data and graphically represents different layers of information, including natural selection statistics, as well as functional annotations and genealogical estimations of variant age, for biallelic single nucleotide variants (SNVs) of the 1000 Genomes Project phase 3. Specifically, PopHumanVar amasses SNV-based information from GEVA, SnpEFF, GWAS Catalog, ClinVar, RegulomeDB and DisGeNET databases, as well as accurate estimations of iHS, nSL and iSAFE statistics. Notably, PopHumanVar can successfully identify known causal variants of frequently reported candidate selection regions, including EDAR in East-Asians, ACKR1 (DARC) in Africans and LCT/MCM6 in Europeans. PopHumanVar is open and freely available at https://pophumanvar.uab.cat.

AdmixSim 2: a forward-time simulator for modeling complex population admixture

BackgroundComputer simulations have been widely applied in population genetics and evolutionary studies. A great deal of effort has been made over the past two decades in developing simulation tools. However, there are not many simulation tools suitable for studying population admixture.ResultsWe here developed a forward-time simulator, AdmixSim 2, an individual-based tool that can flexibly and efficiently simulate population genomics data under complex evolutionary scenarios. Unlike its previous version, AdmixSim 2 is based on the extended Wright-Fisher model, and it implements many common evolutionary parameters to involve gene flow, natural selection, recombination, and mutation, which allow users to freely design and simulate any complex scenario involving population admixture. AdmixSim 2 can be used to simulate data of dioecious or monoecious populations, autosomes, or sex chromosomes. To our best knowledge, there are no similar tools available for the purpose of simulation of complex population admixture. Using empirical or previously simulated genomic data as input, AdmixSim 2 provides phased haplotype data for the convenience of further admixture-related analyses such as local ancestry inference, association studies, and other applications. We here evaluate the performance of AdmixSim 2 based on simulated data and validated functions via comparative analysis of simulated data and empirical data of African American, Mexican, and Uyghur populations.ConclusionsAdmixSim 2 is a flexible simulation tool expected to facilitate the study of complex population admixture in various situations.

Genome-Wide Association and Selective Sweep Studies Reveal the Complex Genetic Architecture of DMI Fungicide Resistance in Cercospora beticola.

The rapid and widespread evolution of fungicide resistance remains a challenge for crop disease management. The demethylation inhibitor (DMI) class of fungicides is a widely used chemistry for managing disease, but there has been a gradual decline in efficacy in many crop pathosystems. Reliance on DMI fungicides has increased resistance in populations of the plant pathogenic fungus Cercospora beticola worldwide. To better understand the genetic and evolutionary basis for DMI resistance in C. beticola, a genome-wide association study (GWAS) and selective sweep analysis were conducted for the first time in this species. We performed whole-genome resequencing of 190 C. beticola isolates infecting sugar beet (Beta vulgaris ssp. vulgaris). All isolates were phenotyped for sensitivity to the DMI tetraconazole. Intragenic markers on chromosomes 1, 4, and 9 were significantly associated with DMI fungicide resistance, including a polyketide synthase gene and the gene encoding the DMI target CbCYP51. Haplotype analysis of CbCYP51 identified a synonymous mutation (E170) and nonsynonymous mutations (L144F, I387M, and Y464S) associated with DMI resistance. Genome-wide scans of selection showed that several of the GWAS mutations for fungicide resistance resided in regions that have recently undergone a selective sweep. Using radial plate growth on selected media as a fitness proxy, we did not find a trade-off associated with DMI fungicide resistance. Taken together, we show that population genomic data from a crop pathogen can allow the identification of mutations conferring fungicide resistance and inform about their origins in the pathogen population.

Divergence in Body Mass, Wing Loading, and Population Structure Reveals Species-Specific and Potentially Adaptive Trait Variation Across Elevations in Montane Bumble Bees

AbstractBiogeographic clines in morphology along environmental gradients can illuminate forces influencing trait evolution within and between species. Latitude has long been studied as a driver of morphological clines, with a focus on body size and temperature. However, counteracting environmental pressures may impose constraints on body size. In montane landscapes, declines in air density with elevation can negatively impact flight performance in volant species, which may contribute to selection for reduced body mass despite declining temperatures. We examine morphology in two bumble bee (Hymenoptera: Apidae: Bombus Latreille) species, Bombus vancouverensis Cresson and Bombus vosnesenskii Radoszkowski, across mountainous regions of California, Oregon, and Washington, United States. We incorporate population genomic data to investigate the relationship between genomic ancestry and morphological divergence. We find that B. vancouverensis, which tends to be more specialized for high elevations, exhibits stronger spatial-environmental variation, being smaller in the southern and higher elevation parts of its range and having reduced wing loading (mass relative to wing area) at high elevations. Bombus vosnesenskii, which is more of an elevational generalist, has substantial trait variation, but spatial-environmental correlations are weak. Population structure is stronger in the smaller B. vancouverensis, and we find a significant association between elevation and wing loading after accounting for genetic structure, suggesting the possibility of local adaptation for this flight performance trait. Our findings suggest that some conflicting results for body size trends may stem from distinct environmental pressures that impact different aspects of bumble bee ecology, and that different species show different morphological clines in the same region.

Population structure, genomic diversity and demographic history of Komodo dragons inferred from whole-genome sequencing.

Population and conservation genetics studies have greatly benefited from the development of new techniques and bioinformatic tools associated with next‐generation sequencing. Analysis of extensive data sets from whole‐genome sequencing of even a few individuals allows the detection of patterns of fine‐scale population structure and detailed reconstruction of demographic dynamics through time. In this study, we investigated the population structure, genomic diversity and demographic history of the Komodo dragon (Varanus komodoensis), the world's largest lizard, by sequencing the whole genomes of 24 individuals from the five main Indonesian islands comprising the entire range of the species. Three main genomic groups were observed. The populations of the Island of Komodo and the northern coast of Flores, in particular, were identified as two distinct conservation units. Degrees of genomic divergence among island populations were interpreted as a result of changes in sea level affecting connectivity across islands. Demographic inference suggested that Komodo dragons probably experienced a relatively steep population decline over the last million years, reaching a relatively stable Ne during the Saalian glacial cycle (400–150 thousand years ago) followed by a rapid Ne decrease. Genomic diversity of Komodo dragons was similar to that found in endangered or already extinct reptile species. Overall, this study provides an example of how whole‐genome analysis of a few individuals per population can help define population structure and intraspecific demographic dynamics. This is particularly important when applying population genomics data to conservation of rare or elusive endangered species.