Population Genomic Datasets Research Articles

Thematic Layers of Genomic Susceptibility for Conservation Monitoring.

Population genomics has great potential to inform applied conservation management and associated policy. However, the bioinformatic analyses and interpretation of population genomic datasets can be daunting and difficult to convey to nonspecialists, including on-the-ground conservationists that work with many state, federal and international agencies. We think that individual population genomic metrics of interest can be interpolated and ultimately distilled into thematic GIS layers that represent spatiotemporal genomic potential (or conversely, susceptibility) in conservation monitoring. As examples relevant to ongoing conservation efforts, we use introgressive hybridisation and individual heterozygosity to illustrate a conceptual approach for mapping population genomic susceptibility. The general framework of thematic layers could be extended to integrate key genomic metrics (e.g., runs of homozygosity and genomic load) that are relevant to many conservation efforts.

Accurate Detection of scutellata‐Hybrids (Africanized Bees) Using a SNP‐Based Diagnostic Assay

ABSTRACTHybrid populations of Africanized honey bees (scutellata‐hybrids), notable for their defensive behaviour, have spread rapidly throughout South and North America since their unintentional introduction. Although their migration has slowed, the large‐scale trade and movement of honey bee queens and colonies raise concern over the accidental importation of scutellata‐hybrids to previously unoccupied areas. Therefore, developing an accurate and robust assay to detect scutellata‐hybrids is an important first step toward mitigating risk. Here, we used an extensive population genomic dataset to assess the genomic composition of Apis mellifera native populations and patterns of genetic admixture in North and South American commercial honey bees. We used this dataset to develop a SNP assay, where 80 markers, combined with machine learning classification, can accurately differentiate between scutellata‐hybrids and non‐scutellata‐hybrid commercial colonies. The assay was validated on 1263 individuals from colonies located in Canada, the United States, Australia and Brazil. Notably, we demonstrate that using a reduced SNP set of as few as 10 loci can still provide accurate results.

Population Genomic Scans for Natural Selection and Demography.

Uncovering the fundamental processes that shape genomic variation in natural populations is a primary objective of population genetics. These processes include demographic effects such as past changes in effective population size or gene flow between structured populations. Furthermore, genomic variation is affected by selection on nonneutral genetic variants, for example, through the adaptation of beneficial alleles or balancing selection that maintains genetic variation. In this article, we discuss the characterization of these processes using population genetic models, and we review methods developed on the basis of these models to unravel the underlying processes from modern population genomic data sets. We briefly discuss the conditions in which these approaches can be used to infer demography or identify specific nonneutral genetic variants and cases in which caution is warranted. Moreover, we summarize the challenges of jointly inferring demography and selective processes that affect neutral variation genome-wide.

Integrating genomic data and simulations to evaluate alternative species distribution models and improve predictions of glacial refugia and future responses to climate change

Climate change poses a threat to biodiversity, and it is unclear whether species can adapt to or tolerate new conditions, or migrate to areas with suitable habitats. Reconstructions of range shifts that occurred in response to environmental changes since the last glacial maximum (LGM) from species distribution models (SDMs) can provide useful data to inform conservation efforts. However, different SDM algorithms and climate reconstructions often produce contrasting patterns, and validation methods typically focus on accuracy in recreating current distributions, limiting their relevance for assessing predictions to the past or future. We modeled historically suitable habitat for the threatened North American tree green ash Fraxinus pennsylvanica using 24 SDMs built using two climate models, three calibration regions, and four modeling algorithms. We evaluated the SDMs using contemporary data with spatial block cross‐validation and compared the relative support for alternative models using a novel integrative method based on coupled demographic‐genetic simulations. We simulated genomic datasets using habitat suitability of each of the 24 SDMs in a spatially‐explicit model. Approximate Bayesian computation (ABC) was then used to evaluate the support for alternative SDMs through comparisons to an empirical population genomic dataset. Models had very similar performance when assessed with contemporary occurrences using spatial cross‐validation, but ABC model selection analyses consistently supported SDMs based on the CCSM climate model, an intermediate calibration extent, and the generalized linear modeling algorithm. Finally, we projected the future range of green ash under four climate change scenarios. Future projections using the SDMs selected via ABC suggest only minor shifts in suitable habitat for this species, while some of those that were rejected predicted dramatic changes. Our results highlight the different inferences that may result from the application of alternative distribution modeling algorithms and provide a novel approach for selecting among a set of competing SDMs with independent data.

The contribution of gene flow, selection, and genetic drift to five thousand years of human allele frequency change

Genomic time series from experimental evolution studies and ancient DNA datasets offer us a chance to directly observe the interplay of various evolutionary forces. We show how the genome-wide variance in allele frequency change between two time points can be decomposed into the contributions of gene flow, genetic drift, and linked selection. In closed populations, the contribution of linked selection is identifiable because it creates covariances between time intervals, and genetic drift does not. However, repeated gene flow between populations can also produce directionality in allele frequency change, creating covariances. We show how to accurately separate the fraction of variance in allele frequency change due to admixture and linked selection in a population receiving gene flow. We use two human ancient DNA datasets, spanning around 5,000 y, as time transects to quantify the contributions to the genome-wide variance in allele frequency change. We find that a large fraction of genome-wide change is due to gene flow. In both cases, after correcting for known major gene flow events, we do not observe a signal of genome-wide linked selection. Thus despite the known role of selection in shaping long-term polymorphism levels, and an increasing number of examples of strong selection on single loci and polygenic scores from ancient DNA, it appears to be gene flow and drift, and not selection, that are the main determinants of recent genome-wide allele frequency change. Our approach should be applicable to the growing number of contemporary and ancient temporal population genomics datasets.

Unveiling recent and ongoing adaptive selection in human populations.

Genome-wide scans for signals of selection have become a routine part of the analysis of population genomic variation datasets and have resulted in compelling evidence of selection during recent human evolution. This Essay spotlights methodological innovations that have enabled the detection of selection over very recent timescales, even in contemporary human populations. By harnessing large-scale genomic and phenotypic datasets, these new methods use different strategies to uncover connections between genotype, phenotype, and fitness. This Essay outlines the rationale and key findings of each strategy, discusses challenges in interpretation, and describes opportunities to improve detection and understanding of ongoing selection in human populations.

Whole-genome sequencing provides novel insights into the evolutionary history and genetic adaptation of reindeer populations in northern Eurasia

Domestic reindeer (Rangifer tarandus) play a vital role in the culture and livelihoods of indigenous people across northern Eurasia. These animals are well adapted to harsh environmental conditions, such as extreme cold, limited feed availability and long migration distances. Therefore, understanding the genomics of reindeer is crucial for improving their management, conservation and utilisation. In this study, we have generated a new genome assembly for the Fennoscandian domestic reindeer with high contiguity, making it the most complete reference genome for reindeer to date. The new genome assembly was utilised to explore genetic diversity, population structure and selective sweeps in Eurasian Rangifer tarandus populations which was based on the largest population genomic dataset for reindeer, encompassing 58 individuals from diverse populations. Phylogenetic analyses revealed distinct genetic clusters, with the Finnish wild forest reindeer (Rangifer tarandus fennicus) standing out as a unique subspecies. Divergence time estimates suggested a separation of ~ 52 thousand years ago (Kya) between the northern European Rangifer tarandus fennicus and Rangifer tarandus tarandus. Our study identified four main genetic clusters: Fennoscandian, the eastern/northern Russian and Alaskan group, the Finnish forest reindeer, and the Svalbard reindeer. Furthermore, two independent reindeer domestication processes were inferred, suggesting separate origins for the domestic Fennoscandian and eastern/northern Russian reindeer. Notably, shared genes under selection, including retroviral genes, point towards molecular domestication processes that aided adaptation of this species to diverse environments.

Y chromosome sequence and epigenomic reconstruction across human populations

Recent advances in long-read sequencing technologies have allowed the generation and curation of more complete genome assemblies, enabling the analysis of traditionally neglected chromosomes, such as the human Y chromosome (chrY). Native DNA was sequenced on a MinION Oxford Nanopore Technologies sequencing device to generate genome assemblies for seven major chrY human haplogroups. We analyzed and compared the chrY enrichment of sequencing data obtained using two different selective sequencing approaches: adaptive sampling and flow cytometry chromosome sorting. We show that adaptive sampling can produce data to create assemblies comparable to chromosome sorting while being a less expensive and time-consuming technique. We also assessed haplogroup-specific structural variants, which would be otherwise difficult to study using short-read sequencing data only. Finally, we took advantage of this technology to detect and profile epigenetic modifications among the considered haplogroups. Altogether, we provide a framework to study complex genomic regions with a simple, fast, and affordable methodology that could be applied to larger population genomics datasets.

HaploBlocks: Efficient Detection of Positive Selection in Large Population Genomic Datasets.

Genomic regions under positive selection harbor variation linked for example to adaptation. Most tools for detecting positively selected variants have computational resource requirements rendering them impractical on population genomic datasets with hundreds of thousands of individuals or more. We have developed and implemented an efficient haplotype-based approach able to scan large datasets and accurately detect positive selection. We achieve this by combining a pattern matching approach based on the positional Burrows-Wheeler transform with model-based inference which only requires the evaluation of closed-form expressions. We evaluate our approach with simulations, and find it to be both sensitive and specific. The computational resource requirements quantified using UK Biobank data indicate that our implementation is scalable to population genomic datasets with millions of individuals. Our approach may serve as an algorithmic blueprint for the era of "big data" genomics: a combinatorial core coupled with statistical inference in closed form.

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.

Evolutionary dynamics of pseudoautosomal region 1 in humans and great apes

BackgroundThe pseudoautosomal region 1 (PAR1) is a 2.7 Mb telomeric region of human sex chromosomes. PAR1 has a crucial role in ensuring proper segregation of sex chromosomes during male meiosis, exposing it to extreme recombination and mutation processes. We investigate PAR1 evolution using population genomic datasets of extant humans, eight populations of great apes, and two archaic human genome sequences.ResultsWe find that PAR1 is fast evolving and closer to evolutionary nucleotide equilibrium than autosomal telomeres. We detect a difference between substitution patterns and extant diversity in PAR1, mainly driven by the conflict between strong mutation and recombination-associated fixation bias at CpG sites. We detect excess C-to-G mutations in PAR1 of all great apes, specific to the mutagenic effect of male recombination. Despite recent evidence for Y chromosome introgression from humans into Neanderthals, we find that the Neanderthal PAR1 retained similarity to the Denisovan sequence. We find differences between substitution spectra of these archaics suggesting rapid evolution of PAR1 in recent hominin history. Frequency analysis of alleles segregating in females and males provided no evidence for recent sexual antagonism in this region. We study repeat content and double-strand break hotspot regions in PAR1 and find that they may play roles in ensuring the obligate X-Y recombination event during male meiosis.ConclusionsOur study provides an unprecedented quantification of population genetic forces governing PAR1 biology across extant and extinct hominids. PAR1 evolutionary dynamics are predominantly governed by recombination processes with a strong impact on mutation patterns across all species.

Robust inference of population size histories from genomic sequencing data.

Unraveling the complex demographic histories of natural populations is a central problem in population genetics. Understanding past demographic events is of general anthropological interest, but is also an important step in establishing accurate null models when identifying adaptive or disease-associated genetic variation. An important class of tools for inferring past population size changes from genomic sequence data are Coalescent Hidden Markov Models (CHMMs). These models make efficient use of the linkage information in population genomic datasets by using the local genealogies relating sampled individuals as latent states that evolve along the chromosome in an HMM framework. Extending these models to large sample sizes is challenging, since the number of possible latent states increases rapidly.Here, we present our method CHIMP (CHMM History-Inference Maximum-Likelihood Procedure), a novel CHMM method for inferring the size history of a population. It can be applied to large samples (hundreds of haplotypes) and only requires unphased genomes as input. The two implementations of CHIMP that we present here use either the height of the genealogical tree (TMRCA) or the total branch length, respectively, as the latent variable at each position in the genome. The requisite transition and emission probabilities are obtained by numerically solving certain systems of differential equations derived from the ancestral process with recombination. The parameters of the population size history are subsequently inferred using an Expectation-Maximization algorithm. In addition, we implement a composite likelihood scheme to allow the method to scale to large sample sizes.We demonstrate the efficiency and accuracy of our method in a variety of benchmark tests using simulated data and present comparisons to other state-of-the-art methods. Specifically, our implementation using TMRCA as the latent variable shows comparable performance and provides accurate estimates of effective population sizes in intermediate and ancient times. Our method is agnostic to the phasing of the data, which makes it a promising alternative in scenarios where high quality data is not available, and has potential applications for pseudo-haploid data.

Strong evidence for the adaptive walk model of gene evolution in Drosophila and Arabidopsis.

Understanding the dynamics of species adaptation to their environments has long been a central focus of the study of evolution. Theories of adaptation propose that populations evolve by "walking" in a fitness landscape. This "adaptive walk" is characterised by a pattern of diminishing returns, where populations further away from their fitness optimum take larger steps than those closer to their optimal conditions. Hence, we expect young genes to evolve faster and experience mutations with stronger fitness effects than older genes because they are further away from their fitness optimum. Testing this hypothesis, however, constitutes an arduous task. Young genes are small, encode proteins with a higher degree of intrinsic disorder, are expressed at lower levels, and are involved in species-specific adaptations. Since all these factors lead to increased protein evolutionary rates, they could be masking the effect of gene age. While controlling for these factors, we used population genomic data sets of Arabidopsis and Drosophila and estimated the rate of adaptive substitutions across genes from different phylostrata. We found that a gene's evolutionary age significantly impacts the molecular rate of adaptation. Moreover, we observed that substitutions in young genes tend to have larger physicochemical effects. Our study, therefore, provides strong evidence that molecular evolution follows an adaptive walk model across a large evolutionary timescale.

Genome assembly of the roundjaw bonefish (Albula glossodonta), avulnerable circumtropical sportfish.

The roundjaw bonefish, Albula glossodonta, is the most widespread albulid in the Indo-Pacific and is vulnerable to extinction. We assembled the genome of a roundjaw bonefish from Hawai'i, USA, which will be instrumental for effective transboundary management and conservation when paired with population genomics datasets. The 1.05 gigabase pair (Gbp) contig-level assembly had a 4.75 megabase pair (Mbp) NG50 and a maximum contig length of 28.2 Mbp. Scaffolding yielded an LG50 of 20 and an NG50 of 14.49 Mbp, with the longest scaffold reaching 42.29 Mbp. The genome comprised 6.5% repetitive elements and was annotated with 28.3 K protein-coding genes. We then evaluated population genetic connectivity between six atolls in the Western Indian Ocean with 38,355 SNP loci across 66 A. glossodonta individuals. We discerned shallow population structure and observed genetic homogeneity between atolls in Seychelles and reduced gene flow between Seychelles and Mauritius. The South Equatorial Current might be the limiting mechanism of this reduced gene flow. The genome assembly will be useful for addressing taxonomic uncertainties of bonefishes globally.

Patterns and Causes of Signed Linkage Disequilibria in Flies and Plants.

Most empirical studies of linkage disequilibrium (LD) study its magnitude, ignoring its sign. Here, we examine patterns of signed LD in two population genomic data sets, one from Capsella grandiflora and one from Drosophila melanogaster. We consider how processes such as drift, admixture, Hill–Robertson interference, and epistasis may contribute to these patterns. We report that most types of mutations exhibit positive LD, particularly, if they are predicted to be less deleterious. We show with simulations that this pattern arises easily in a model of admixture or distance-biased mating, and that genome-wide differences across site types are generally expected due to differences in the strength of purifying selection even in the absence of epistasis. We further explore how signed LD decays on a finer scale, showing that loss of function mutations exhibit particularly positive LD across short distances, a pattern consistent with intragenic antagonistic epistasis. Controlling for genomic distance, signed LD in C. grandiflora decays faster within genes, compared with between genes, likely a by-product of frequent recombination in gene promoters known to occur in plant genomes. Finally, we use information from published biological networks to explore whether there is evidence for negative synergistic epistasis between interacting radical missense mutations. In D. melanogaster networks, we find a modest but significant enrichment of negative LD, consistent with the possibility of intranetwork negative synergistic epistasis.

Population Genomic Analyses Confirm Male-Biased Mutation Rates in Snakes.

Male-biased mutation rates occur in a diverse array of organisms. The ratio of male-to-female mutation rate may have major ramifications for evolution across the genome, and for sex-linked genes in particular. In ZW species, the Z chromosome is carried by males two-thirds of the time, leading to the prediction that male-biased mutation rates will have a disproportionate effect on the evolution of Z-linked genes relative to autosomes and the W chromosome. Colubroid snakes (including colubrids, elapids, and viperids) have ZW sex determination, yet male-biased mutation rates have not been well studied in this group. Here we analyze a population genomic dataset from rattlesnakes to quantify genetic variation within and genetic divergence between species. We use a new method for unbiased estimation of population genetic summary statistics to compare variation between the Z chromosome and autosomes and to calculate net nucleotide differentiation between species. We find evidence for a 2.03-fold greater mutation rate in male rattlesnakes relative to females, corresponding to an average μZ/μA ratio of 1.1. Our results from snakes are quantitatively similar to birds, suggesting that male-biased mutation rates may be a common feature across vertebrate lineages with ZW sex determination.

Understanding genetic epidemiology and population disparities of inherited blood cancer syndromes from integrative analysis of population genomics datasets

Interpopulation differences in prevalence have been well documented for a number of diseases including genetic diseases. The availability of population scale datasets and well-annotated variant resources would therefore offer a new opportunity to understand the genetic factors linked with such population differences or disparities. In the present manuscript, we evaluated the allele frequencies of genetic variants in inherited blood cancer syndromes in population genomes to understand whether they could provide an insight into these population disparities. We analysed 10 inherited disorders, encompassing 20 different genes. Pathogenic variants were systematically collected, and reclassified using the ACMG & AMP guidelines. Variant prevalence was studied using the Genome Aggregation Database (gnomAD) database, which provided a unique population template to understand prevalence across major global populations. Our analysis highlights high-confidence Pathogenic variants that are significantly enriched across specific subpopulations. To the best of our knowledge, this is the first and comprehensive analysis of genetic variants in inherited blood cancer syndromes in global populations and one of the first to suggest allele specific disparities across global populations.

Elevated diversity in loci linked to facial morphology is consistent with the hypothesis that individual facial recognition is important across hominoids.

The ability to use visual signals to identify individuals is an important feature of primate social groups, including humans. Sheehan and Nachman (2014) showed that loci linked to facial morphology had elevated levels of diversity and interpreted this as evidence that the human face is under frequency-dependent selection to enhance individual recognition (Nature Communications 5). In our study, we tested whether this pattern is found in non-human ape species, to help understand whether individual recognition might also play a role in species other than humans. We examined levels of genetic diversity in an available population genomic dataset of humans, chimpanzees, bonobos, gorillas, and orangutans for three sets of loci, (1) loci linked to facial morphology, (2) loci linked to height, and (3) neutrally evolving regions. We tested whether loci linked to facial morphology were more variable than loci linked to height or neutrally evolving loci in each of these species. We found significantly elevated diversity in loci linked to facial morphology in chimpanzees, gorillas, and Sumatran and Bornean orangutans. Our findings closely parallel those of Sheehan and Nachman and are consistent with the idea that selection for facial diversity and individual recognition has not only shaped the evolution of the human face, but it has similarly shaped the evolution of most of our closest primate relatives. We also discuss alternative hypotheses for this pattern.

AdmixPipe: population analyses in Admixture for non-model organisms

BackgroundResearch on the molecular ecology of non-model organisms, while previously constrained, has now been greatly facilitated by the advent of reduced-representation sequencing protocols. However, tools that allow these large datasets to be efficiently parsed are often lacking, or if indeed available, then limited by the necessity of a comparable reference genome as an adjunct. This, of course, can be difficult when working with non-model organisms. Fortunately, pipelines are currently available that avoid this prerequisite, thus allowing data to be a priori parsed. An oft-used molecular ecology program (i.e., Structure), for example, is facilitated by such pipelines, yet they are surprisingly absent for a second program that is similarly popular and computationally more efficient (i.e., Admixture). The two programs differ in that Admixture employs a maximum-likelihood framework whereas Structure uses a Bayesian approach, yet both produce similar results. Given these issues, there is an overriding (and recognized) need among researchers in molecular ecology for bioinformatic software that will not only condense output from replicated Admixture runs, but also infer from these data the optimal number of population clusters (K).ResultsHere we provide such a program (i.e., AdmixPipe) that (a) filters SNPs to allow the delineation of population structure in Admixture, then (b) parses the output for summarization and graphical representation via Clumpak. Our benchmarks effectively demonstrate how efficient the pipeline is for processing large, non-model datasets generated via double digest restriction-site associated DNA sequencing (ddRAD). Outputs not only parallel those from Structure, but also visualize the variation among individual Admixture runs, so as to facilitate selection of the most appropriate K-value.ConclusionsAdmixPipe successfully integrates Admixture analysis with popular variant call format (VCF) filtering software to yield file types readily analyzed by Clumpak. Large population genomic datasets derived from non-model organisms are efficiently analyzed via the parallel-processing capabilities of Admixture. AdmixPipe is distributed under the GNU Public License and freely available for Mac OSX and Linux platforms at: https://github.com/stevemussmann/admixturePipeline.

Population Genomics of Transitions to Selfing in Brassicaceae Model Systems.

Many plants harbor complex mechanisms that promote outcrossing and efficient pollen transfer. These include floral adaptations as well as genetic mechanisms, such as molecular self-incompatibility (SI) systems. The maintenance of such systems over long evolutionary timescales suggests that outcrossing is favorable over a broad range of conditions. Conversely, SI has repeatedly been lost, often in association with transitions to self-fertilization (selfing). This transition is favored when the short-term advantages of selfing outweigh the costs, primarily inbreeding depression. The transition to selfing is expected to have major effects on population genetic variation and adaptive potential, as well as on genome evolution. In the Brassicaceae, many studies on the population genetic, gene regulatory, and genomic effects of selfing have centered on the model plant Arabidopsis thaliana and the crucifer genus Capsella. The accumulation of population genomics datasets have allowed detailed investigation of where, when and how the transition to selfing occurred. Future studies will take advantage of the development of population genetics theory on the impact of selfing, especially regarding positive selection. Furthermore, investigation of systems including recent transitions to selfing, mixed mating populations and/or multiple independent replicates of the same transition will facilitate dissecting the effects of mating system variation from processes driven by demography.