Site Frequency Spectrum Research Articles

Rapid vertebrate speciation via isolation, bottlenecks, and drift

Speciation is often driven by selective processes like those associated with viability, mate choice, or local adaptation, and "speciation genes" have been identified in many eukaryotic lineages. In contrast, neutral processes are rarely considered as the primary drivers of speciation, especially over short evolutionary timeframes. Here, we describe a rapid vertebrate speciation event driven primarily by genetic drift. The White Sands pupfish (Cyprinodon tularosa) is endemic to New Mexico's Tularosa Basin where the species is currently managed as two Evolutionarily significant units (ESUs) and is of international conservation concern (Endangered). Whole-genome resequencing data from each ESU showed remarkably high and uniform levels of differentiation across the entire genome (global FST ≈ 0.40). Despite inhabiting ecologically dissimilar springs and streams, our whole-genome analysis revealed no discrete islands of divergence indicative of strong selection, even when we focused on an array of candidate genes. Demographic modeling of the joint allele frequency spectrum indicates the two ESUs split only ~4 to 5 kya and that both ESUs have undergone major bottlenecks within the last 2.5 millennia. Our results indicate the genome-wide disparities between the two ESUs are not driven by divergent selection but by neutral drift due to small population sizes, geographic isolation, and repeated bottlenecks. While rapid speciation is often driven by natural or sexual selection, here we show that isolation and drift have led to speciation within a few thousand generations. We discuss these evolutionary insights in light of the conservation management challenges they pose.

Boundary Effects Cause False Signals of Range Expansions in Population Genomic Data.

Studying range expansions is central for understanding genetic variation through space and time as well as for identifying refugia and biological invasions. Range expansions are characterized by serial founder events causing clines of decreasing genetic diversity away from the center of origin and asymmetries in the two-dimensional allele frequency spectra. These asymmetries, summarized by the directionality index (ψ), are sensitive to range expansions and persist for longer than clines in genetic diversity. In continuous and finite meta-populations, genetic drift tends to be stronger at the edges of the species distribution in equilibrium populations and populations undergoing range expansions alike. Such boundary effects are expected to affect geographic patterns in genetic diversity and ψ. Here we demonstrate that boundary effects cause high false positive rates in equilibrium meta-populations when testing for range expansions. In the simulations, the absolute value of ψ (|ψ|) in equilibrium data sets was proportional to the fixation index (FST). By fitting signatures of range expansions as a function of ɛ |ψ|/FST and geographic clines in ψ, strong evidence for range expansions could be detected in data from a recent rapid invasion of the cane toad, Rhinella marina, in Australia, but not in 28 previously published empirical data sets from Australian scincid lizards that were significant for the standard range expansion tests. Thus, while clinal variation in ψ is still the most sensitive statistic to range expansions, to detect true signatures of range expansions in natural populations, its magnitude needs to be considered in relation to the overall levels of genetic structuring in the data.

A harmonized public resource of deeply sequenced diverse human genomes.

Underrepresented populations are often excluded from genomic studies owing in part to a lack of resources supporting their analyses. The 1000 Genomes Project (1kGP) and Human Genome Diversity Project (HGDP), which have recently been sequenced to high coverage, are valuable genomic resources because of the global diversity they capture and their open data sharing policies. Here, we harmonized a high-quality set of 4094 whole genomes from 80 populations in the HGDP and 1kGP with data from the Genome Aggregation Database (gnomAD) and identified over 153 million high-quality SNVs, indels, and SVs. We performed a detailed ancestry analysis of this cohort, characterizing population structure and patterns of admixture across populations, analyzing site frequency spectra, and measuring variant counts at global and subcontinental levels. We also show substantial added value from this data set compared with the prior versions of the component resources, typically combined via liftOver and variant intersection; for example, we catalog millions of new genetic variants, mostly rare, compared with previous releases. In addition to unrestricted individual-level public release, we provide detailed tutorials for conducting many of the most common quality-control steps and analyses with these data in a scalable cloud-computing environment and publicly release this new phased joint callset for use as a haplotype resource in phasing and imputation pipelines. This jointly called reference panel will serve as a key resource to support research of diverse ancestry populations.

Evolutionary history and biological adaptation of Han Chinese people on the Mongolian Plateau

Complex demographic processes and natural selection pressures are critical to resolving patterns of the molecular genetic basis of adaptive traits or complex diseases. Recent ancient genome data allow us to trace how key traits evolved in different human populations over time, connecting ancient population history with disease susceptibility in western Eurasians. To fill this gap in eastern Eurasians and provide deep insights into the evolutionary history of the population-specific genetic basis of complex biological traits, we explored the evolutionary basis of adaptive traits in one integrative modern and ancient genomic database, including 225 out of 5583 genomes first reported here. We comprehensively characterized the demographic history and biological adaptation of Han Chinese individuals on the Mongolian Plateau based on the allele frequency spectrum and haplotype-resolved fragments. We found strong genetic homogeneity among geographically different Han populations from Inner Mongolia (IMH). We reconstructed their admixture models and demographic events, revealing that IMH had a close genetic relationship with ancient millet farmers and obtained additional gene flow from Altaic-speaking populations. The enrichment of selected candidate genes suggested that essential metabolism-related genes promoted the rapid adaptation of eastern Eurasians to ancient environmental shifts and dietary changes during agricultural innovations. Evolutionary trajectory reconstruction of metabolism-related genes of methylenetetrahydrofolate reductase (MTHFR) and fatty acid desaturase 1 (FADS1) suggested that ancient dietary shifts during the Neolithic transition period promoted the differentiated metabolic rate of folate and fatty acid. We revealed the polygenicity of biological adaptation traits and the pleiotropy of adaptive genes, indicating that complex recent polygenic adaptations, genetic interactions, and genotype–phenotype correlations have contributed to the genetic architecture of complex traits in eastern Eurasians.

Computationally Efficient Demographic History Inference from Allele Frequencies with Supervised Machine Learning.

Inferring past demographic history of natural populations from genomic data is of central concern in many studies across research fields. Previously, our group had developed dadi, a widely used demographic history inference method based on the allele frequency spectrum (AFS) and maximum composite-likelihood optimization. However, dadi's optimization procedure can be computationally expensive. Here, we present donni (demography optimization via neural network inference), a new inference method based on dadi that is more efficient while maintaining comparable inference accuracy. For each dadi-supported demographic model, donni simulates the expected AFS for a range of model parameters then trains a set of Mean Variance Estimation neural networks using the simulated AFS. Trained networks can then be used to instantaneously infer the model parameters from future genomic data summarized by an AFS. We demonstrate that for many demographic models, donni can infer some parameters, such as population size changes, very well and other parameters, such as migration rates and times of demographic events, fairly well. Importantly, donni provides both parameter and confidence interval estimates from input AFS with accuracy comparable to parameters inferred by dadi's likelihood optimization while bypassing its long and computationally intensive evaluation process. donni's performance demonstrates that supervised machine learning algorithms may be a promising avenue for developing more sustainable and computationally efficient demographic history inference methods.

FastDFE: Fast and Flexible Inference of the Distribution ofFitness Effects.

Estimating the distribution of fitness effects (DFE) of new mutations is of fundamental importance in evolutionary biology, ecology, and conservation. However, existing methods for DFE estimation suffer from limitations, such as slow computation speed and limited scalability. To address these issues, we introduce fastDFE, a Python-based software package, offering fast, and flexible DFE inference from site-frequency spectrum (SFS) data. Apart from providing efficient joint inference of multiple DFEs that share parameters, it offers the feature of introducing genomic covariates that influence the DFEs and testing their significance. To further simplify usage, fastDFE is equipped with comprehensive VCF-to-SFS parsing utilities. These include options for site filtering and stratification, as well as site-degeneracy annotation and probabilistic ancestral-allele inference. fastDFE thereby covers the entire workflow of DFE inference from the moment of acquiring a raw VCF file. Despite its Python foundation, fastDFE incorporates a full R interface, including native R visualization capabilities. The package is comprehensively tested and documented at fastdfe.readthedocs.io.

Malaria resistance-related biological adaptation and complex evolutionary footprints inferred from one integrative Tai-Kadai-related genomic resource

Pathogen‒host adaptative interactions and complex population demographical processes, including admixture, drift, and Darwen selection, have considerably shaped the Neolithic-to-Modern Western Eurasian population structure and genetic susceptibility to modern human diseases. However, the genetic footprints of evolutionary events in East Asia remain unknown due to the underrepresentation of genomic diversity and the design of large-scale population studies. We reported one aggregated database of genome-wide SNP variations from 796 Tai-Kadai (TK) genomes, including that of Bouyei first reported here, to explore the genetic history, population structure, and biological adaptative features of TK people from southern China and Southeast Asia. We found geography-related population substructure among TK people using the state-of-the-art population genetic structure reconstruction techniques based on the allele frequency spectrum and haplotype-resolved phased fragments. We found that the northern TK people from Guizhou harbored one TK-dominant ancestry maximized in the Bouyei people, and the southern TK people from Thailand were more influenced by Southeast Asians and indigenous people. We reconstructed fitted admixture models and demographic graphs, which showed that TK people received gene flow from ancient southern rice farmer-related lineages related to the Hmong-Mien and Austroasiatic people and from northern millet farmers associated with the Sino-Tibetan people. Biological adaptation focused on our identified unique TK lineages related to Bouyei, which showed many adaptive signatures conferring Malaria resistance and low-rate lipid metabolism. Further gene enrichment, the allele frequency distribution of derived alleles, and their correlation with the incidence of Malaria further confirmed that CR1 played an essential role in the resistance of Malaria in the ancient "Baiyue" tribes.

Temporal challenges in detecting balancing selection from population genomic data.

The role of balancing selection in maintaining genetic variation remains an open question in population genetics. Recent years have seen numerous studies identifying candidate loci potentially experiencing balancing selection, most predominantly in human populations. There are however numerous alternative evolutionary processes that may leave similar patterns of variation, thereby potentially confounding inference, and the expected signatures of balancing selection additionally change in a temporal fashion. Here we use forward-in-time simulations to quantify expected statistical power to detect balancing selection using both site frequency spectrum- and linkage disequilibrium-based methods under a variety of evolutionarily realistic null models. We find that whilst site frequency spectrum-based methods have little power immediately after a balanced mutation begins segregating, power increases with time since the introduction of the balanced allele. Conversely, linkage disequilibrium-based methods have considerable power whilst the allele is young, and power dissipates rapidly as the time since introduction increases. Taken together, this suggests that site frequency spectrum-based methods are most effective at detecting long-term balancing selection (>25N generations since the introduction of the balanced allele) whilst linkage disequilibrium-based methods are effective over much shorter timescales (<1N generations), thereby leaving a large time frame over which current methods have little power to detect the action of balancing selection. Finally, we investigate the extent to which alternative evolutionary processes may mimic these patterns, and demonstrate the need for caution in attempting to distinguish the signatures of balancing selection from those of both neutral processes (e.g. population structure and admixture) as well as of alternative selective processes (e.g. partial selective sweeps).

The expected sample allele frequencies from populations of changing size via orthogonal polynomials

In this article, discrete and stochastic changes in (effective) population size are incorporated into the spectral representation of a biallelic diffusion process for drift and small mutation rates. A forward algorithm inspired by Hidden-Markov-Model (HMM) literature is used to compute exact sample allele frequency spectra for three demographic scenarios: single changes in (effective) population size, boom-bust dynamics, and stochastic fluctuations in (effective) population size. An approach for fully agnostic demographic inference from these sample allele spectra is explored, and sufficient statistics for stepwise changes in population size are found. Further, convergence behaviours of the polymorphic sample spectra for population size changes on different time scales are examined and discussed within the context of inference of the effective population size. Joint visual assessment of the sample spectra and the temporal coefficients of the spectral decomposition of the forward diffusion process is found to be important in determining departure from equilibrium. Stochastic changes in (effective) population size are shown to shape sample spectra particularly strongly.

Estimating multiplicity of infection, allele frequencies, and prevalences accounting for incomplete data.

Molecular surveillance of infectious diseases allows the monitoring of pathogens beyond the granularity of traditional epidemiological approaches and is well-established for some of the most relevant infectious diseases such as malaria. The presence of genetically distinct pathogenic variants within an infection, referred to as multiplicity of infection (MOI) or complexity of infection (COI) is common in malaria and similar infectious diseases. It is an important metric that scales with transmission intensities, potentially affects the clinical pathogenesis, and a confounding factor when monitoring the frequency and prevalence of pathogenic variants. Several statistical methods exist to estimate MOI and the frequency distribution of pathogen variants. However, a common problem is the quality of the underlying molecular data. If molecular assays fail not randomly, it is likely to underestimate MOI and the prevalence of pathogen variants. A statistical model is introduced, which explicitly addresses data quality, by assuming a probability by which a pathogen variant remains undetected in a molecular assay. This is different from the assumption of missing at random, for which a molecular assay either performs perfectly or fails completely. The method is applicable to a single molecular marker and allows to estimate allele-frequency spectra, the distribution of MOI, and the probability of variants to remain undetected (incomplete information). Based on the statistical model, expressions for the prevalence of pathogen variants are derived and differences between frequency and prevalence are discussed. The usual desirable asymptotic properties of the maximum-likelihood estimator (MLE) are established by rewriting the model into an exponential family. The MLE has promising finite sample properties in terms of bias and variance. The covariance matrix of the estimator is close to the Cramér-Rao lower bound (inverse Fisher information). Importantly, the estimator's variance is larger than that of a similar method which disregards incomplete information, but its bias is smaller. Although the model introduced here has convenient properties, in terms of the mean squared error it does not outperform a simple standard method that neglects missing information. Thus, the new method is recommendable only for data sets in which the molecular assays produced poor-quality results. This will be particularly true if the model is extended to accommodate information from multiple molecular markers at the same time, and incomplete information at one or more markers leads to a strong depletion of sample size.

The influence of GC-biased gene conversion on non-adaptive sequence evolution in short introns of Drosophila melanogaster.

Population genetic inference of selection on the nucleotide sequence level often proceeds by comparison to a reference sequence evolving only under mutation and population demography. Among the few candidates for such a reference sequence is the 5' part of short introns (5SI) in Drosophila. In addition to mutation and population demography, however, there is evidence for a weak force favouring GC bases, likely due to GC-biased gene conversion (gBGC), and for the effect of linked selection. Here, we use polymorphism and divergence data of Drosophila melanogaster to detect and describe the forces affecting the evolution of the 5SI. We separately analyse mutation classes, compare them between chromosomes, and relate them to recombination rate frequencies. GC-conservative mutations seem to be mainly influenced by mutation and drift, with linked selection mostly causing differences between the central and the peripheral (i.e., telomeric and centromeric) regions of the chromosome arms. Comparing GC-conservative mutation patterns between autosomes and the X chromosome showed differences in mutation rates, rather than linked selection, in the central chromosomal regions after accounting for differences in effective population sizes. On the other hand, GC-changing mutations show asymmetric site frequency spectra, indicating the presence of gBGC, varying among mutation classes and in intensity along chromosomes, but approximately equal in strength in autosomes and the X chromosome.

Structural genomic variation in the inbred Scandinavian wolf population contributes to the realized genetic load but is positively affected by immigration.

When populations decrease in size and may become isolated, genomic erosion by loss of diversity from genetic drift and accumulation of deleterious mutations is likely an inevitable consequence. In such cases, immigration (genetic rescue) is necessary to restore levels of genetic diversity and counteract inbreeding depression. Recent work in conservation genomics has studied these processes focusing on the genetic diversity of single nucleotide polymorphisms. In contrast, our knowledge about structural genomic variation (insertions, deletions, duplications and inversions) in endangered species is limited. We analysed whole-genome, short-read sequences from 212 wolves from the inbred Scandinavian population and from neighbouring populations in Finland and Russia, and detected >35,000 structural variants (SVs) after stringent quality and genotype frequency filtering; >26,000 high-confidence variants remained after manual curation. The majority of variants were shorter than 1 kb, with a distinct peak in the length distribution of deletions at 190 bp, corresponding to insertion events of SINE/tRNA-Lys elements. The site frequency spectrum of SVs in protein-coding regions was significantly shifted towards rare alleles compared to putatively neutral variants, consistent with purifying selection. The realized genetic load of SVs in protein-coding regions increased with inbreeding levels in the Scandinavian population, but immigration provided a genetic rescue effect by lowering the load and reintroducing ancestral alleles at loci fixed for derived SVs. Our study shows that structural variation comprises a common type of in part deleterious mutations in endangered species and that establishing gene flow is necessary to mitigate the negative consequences of loss of diversity.

Exploring the genetic factors behind the discrepancy in resistance to bovine tuberculosis between African zebu cattle and European taurine cattle

Caused by the pathogenic agent Mycobacterium bovis, bovine tuberculosis (bTB) is a major concern in cattle breeding due to both its zoonotic potential and economic impact. Greater resistance to this disease has been reported in certain African zebu breeds compared to European taurine breeds. However the genetic basis for the lower susceptibility to bTB infection observed in zebu cattle remains poorly explored. This study was conducted on whole genome sequencing data of three bTB infection-resistant African zebu breeds and two bTB infection-susceptible taurine breeds to decipher the genetic background. A set of four selection signature statistics based on linkage disequilibrium, site frequency spectrum, and population differentiation were used on SNPs whereas between population variance based VST and t-test were used on CNVs. As a complement, genes from previous literature reported as candidate genes for bTB resistance were also inspected to identify genetic variations. Interestingly, the resulting nine candidate genes had deleterious missense variants (SHC3, IFNGR1, TLR2, TLR6, IL1A, LRRK2, EP300 and IRAK4) or a CNV difference (CD48) segregating between the groups. The genes found in the study play a role in immune pathways activated during Mycobacterium infection, contributing to the proliferation of immune cells and the granuloma formation, ultimately modulating the outcome of the infectious event. In particular, a deleterious variant in the LRRK2 gene, whose deficiency has been linked to improved prognosis upon tuberculosis infection, was found in the bTB infection-resistant zebu breeds. Therefore, these genes constitute credible candidates in explaining the discrepancy in Mycobacterium bovis infection susceptibility among different breed.

Differentiated genomic footprints suggest isolation and long-distance migration of Hmong-Mien populations

BackgroundThe underrepresentation of Hmong-Mien (HM) people in Asian genomic studies has hindered our comprehensive understanding of the full landscape of their evolutionary history and complex trait architecture. South China is a multi-ethnic region and indigenously settled by ethnolinguistically diverse HM, Austroasiatic (AA), Tai-Kadai (TK), Austronesian (AN), and Sino-Tibetan (ST) people, which is regarded as East Asia’s initial cradle of biodiversity. However, previous fragmented genetic studies have only presented a fraction of the landscape of genetic diversity in this region, especially the lack of haplotype-based genomic resources. The deep characterization of demographic history and natural-selection-relevant genetic architecture of HM people was necessary.ResultsWe reported one HM-specific genomic resource and comprehensively explored the fine-scale genetic structure and adaptative features inferred from the genome-wide SNP data of 440 HM individuals from 33 ethnolinguistic populations, including previously unreported She. We identified solid genetic differentiation between HM people and Han Chinese at 7.64‒15.86 years ago (kya) and split events between southern Chinese inland (Miao/Yao) and coastal (She) HM people in the middle Bronze Age period and the latter obtained more gene flow from Ancient Northern East Asians. Multiple admixture models further confirmed that extensive gene flow from surrounding ST, TK, and AN people entangled in forming the gene pool of Chinese coastal HM people. Genetic findings of isolated shared unique ancestral components based on the sharing alleles and haplotypes deconstructed that HM people from the Yungui Plateau carried the breadth of previously unknown genomic diversity. We identified a direct and recent genetic connection between Chinese inland and Southeast Asian HM people as they shared the most extended identity-by-descent fragments, supporting the long-distance migration hypothesis. Uniparental phylogenetic topology and network-based phylogenetic relationship reconstruction found ancient uniparental founding lineages in southwestern HM people. Finally, the population-specific biological adaptation study identified the shared and differentiated natural selection signatures among inland and coastal HM people associated with physical features and immune functions. The allele frequency spectrum of cancer susceptibility alleles and pharmacogenomic genes showed significant differences between HM and northern Chinese people.ConclusionsOur extensive genetic evidence combined with the historical documents supported the view that ancient HM people originated from the Yungui regions associated with ancient “Three-Miao tribes” descended from the ancient Daxi-Qujialing-Shijiahe people. Then, some have recently migrated rapidly to Southeast Asia, and some have migrated eastward and mixed respectively with Southeast Asian indigenes, Liangzhu-related coastal ancient populations, and incoming southward ST people. Generally, complex population migration, admixture, and adaptation history contributed to the complicated patterns of population structure of geographically diverse HM people.

Measures of genetic diversification in somatic tissues at bulk and single-cell resolution

Intra-tissue genetic heterogeneity is universal to both healthy and cancerous tissues. It emerges from the stochastic accumulation of somatic mutations throughout development and homeostasis. By combining population genetics theory and genomic information, genetic heterogeneity can be exploited to infer tissue organization and dynamics in vivo. However, many basic quantities, for example the dynamics of tissue-specific stem cells remain difficult to quantify precisely. Here, we show that single-cell and bulk sequencing data inform on different aspects of the underlying stochastic processes. Bulk-derived variant allele frequency spectra (VAF) show transitions from growing to constant stem cell populations with age in samples of healthy esophagus epithelium. Single-cell mutational burden distributions allow a sample size independent measure of mutation and proliferation rates. Mutation rates in adult hematopietic stem cells are higher compared to inferences during development, suggesting additional proliferation-independent effects. Furthermore, single-cell derived VAF spectra contain information on the number of tissue-specific stem cells. In hematopiesis, we find approximately 2 × 105 HSCs, if all stem cells divide symmetrically. However, the single-cell mutational burden distribution is over-dispersed compared to a model of Poisson distributed random mutations. A time-associated model of mutation accumulation with a constant rate alone cannot generate such a pattern. At least one additional source of stochasticity would be needed. Possible candidates for these processes may be occasional bursts of stem cell divisions, potentially in response to injury, or non-constant mutation rates either through environmental exposures or cell-intrinsic variation.

Measures of genetic diversification in somatic tissues at bulk and single-cell resolution.

Intra-tissue genetic heterogeneity is universal to both healthy and cancerous tissues. It emerges from the stochastic accumulation of somatic mutations throughout development and homeostasis. By combining population genetics theory and genomic information, genetic heterogeneity can be exploited to infer tissue organization and dynamics in vivo. However, many basic quantities, for example the dynamics of tissue-specific stem cells remain difficult to quantify precisely. Here, we show that single-cell and bulk sequencing data inform on different aspects of the underlying stochastic processes. Bulk-derived variant allele frequency spectra (VAF) show transitions from growing to constant stem cell populations with age in samples of healthy esophagus epithelium. Single-cell mutational burden distributions allow a sample size independent measure of mutation and proliferation rates. Mutation rates in adult hematopietic stem cells are higher compared to inferences during development, suggesting additional proliferation-independent effects. Furthermore, single-cell derived VAF spectra contain information on the number of tissue-specific stem cells. In hematopiesis, we find approximately 2 × 105 HSCs, if all stem cells divide symmetrically. However, the single-cell mutational burden distribution is over-dispersed compared to a model of Poisson distributed random mutations. A time-associated model of mutation accumulation with a constant rate alone cannot generate such a pattern. At least one additional source of stochasticity would be needed. Possible candidates for these processes may be occasional bursts of stem cell divisions, potentially in response to injury, or non-constant mutation rates either through environmental exposures or cell-intrinsic variation.

Pangenome graphs improve the analysis of structural variants in rare genetic diseases

Rare DNA alterations that cause heritable diseases are only partially resolvable by clinical next-generation sequencing due to the difficulty of detecting structural variation (SV) in all genomic contexts. Long-read, high fidelity genome sequencing (HiFi-GS) detects SVs with increased sensitivity and enables assembling personal and graph genomes. We leverage standard reference genomes, public assemblies (n = 94) and a large collection of HiFi-GS data from a rare disease program (Genomic Answers for Kids, GA4K, n = 574 assemblies) to build a graph genome representing a unified SV callset in GA4K, identify common variation and prioritize SVs that are more likely to cause genetic disease (MAF < 0.01). Using graphs, we obtain a higher level of reproducibility than the standard reference approach. We observe over 200,000 SV alleles unique to GA4K, including nearly 1000 rare variants that impact coding sequence. With improved specificity for rare SVs, we isolate 30 candidate SVs in phenotypically prioritized genes, including known disease SVs. We isolate a novel diagnostic SV in KMT2E, demonstrating use of personal assemblies coupled with pangenome graphs for rare disease genomics. The community may interrogate our pangenome with additional assemblies to discover new SVs within the allele frequency spectrum relevant to genetic diseases.

Phenome-wide association study of population-differentiating genetic variants around gene ACSL1.

Demographic dynamics and natural selection during human evolution shaped the present-day patterns of genetic variations, and geographically varying genetic factors contribute to different disease prevalences across human populations. This study aims to evaluate the presence of positive selection on the gene encoding long-chain fatty acyl-CoA synthetase 1 (ACSL1) and the phenotypic impacts of population-differentiating genetic variants around this gene. Three types of statistical tests for positive selection, based on site frequency spectrum, extended haplotype homozygosity and population differentiation, were applied to the whole-genome sequencing data from the 1000 Genomes Project. A phenome-wide association study of ACSL1 was performed with published genome-wide association studies (GWAS) and transcriptome-wide association studies, including phenome-wide studies in biobanks. Genetic variants associated with ACSL1 expression in various tissues exhibit geographically varying allele frequencies. Three types of statistical tests consistently supported the presence of positive selection on the coding and regulatory regions of ACSL1 in African, European, South Asian and East Asian populations. A phenome-wide association study of ACSL1 revealed associations with type 2 diabetes, blood glucose, age at menopause, mean platelet volume and mean reticulocyte volume. The top allele associated with lower diabetes risk has the highest frequency in European populations, whereas the top allele associated with later menopause has the highest frequency in African populations. Positive selection on ACSL1 resulted in geographically varying genetic variants, which may contribute to differential phenotypes across human populations, including type 2 diabetes and age at menopause.

Widespread Deviant Patterns of Heterozygosity in Whole-Genome Sequencing Due to Autopolyploidy, Repeated Elements, and Duplication.

Most population genomic tools rely on accurate single nucleotide polymorphism (SNP) calling and filtering to meet their underlying assumptions. However, genomic complexity, resulting from structural variants, paralogous sequences, and repetitive elements, presents significant challenges in assembling contiguous reference genomes. Consequently, short-read resequencing studies can encounter mismapping issues, leading to SNPs that deviate from Mendelian expected patterns of heterozygosity and allelic ratio. In this study, we employed the ngsParalog software to identify such deviant SNPs in whole-genome sequencing (WGS) data with low (1.5×) to intermediate (4.8×) coverage for four species: Arctic Char (Salvelinus alpinus), Lake Whitefish (Coregonus clupeaformis), Atlantic Salmon (Salmo salar), and the American Eel (Anguilla rostrata). The analyses revealed that deviant SNPs accounted for 22% to 62% of all SNPs in salmonid datasets and approximately 11% in the American Eel dataset. These deviant SNPs were particularly concentrated within repetitive elements and genomic regions that had recently undergone rediploidization in salmonids. Additionally, narrow peaks of elevated coverage were ubiquitous along all four reference genomes, encompassed most deviant SNPs, and could be partially associated with transposons and tandem repeats. Including these deviant SNPs in genomic analyses led to highly distorted site frequency spectra, underestimated pairwise FST values, and overestimated nucleotide diversity. Considering the widespread occurrence of deviant SNPs arising from a variety of sources, their important impact in estimating population parameters, and the availability of effective tools to identify them, we propose that excluding deviant SNPs from WGS datasets is required to improve genomic inferences for a wide range of taxa and sequencing depths.

A phenome-wide scan reveals convergence of common and rare variant associations

BackgroundCommon and rare variants contribute to the etiology of complex traits. However, the extent to which the phenotypic effects of common and rare variants involve shared molecular mediators remains poorly understood. The question is essential to the basic and translational goals of the science of genomics, with critical basic-science, methodological, and clinical consequences.MethodsLeveraging the latest release of whole-exome sequencing (WES, for rare variants) and genome-wide association study (GWAS, for common variants) data from the UK Biobank, we developed a metric, the COmmon variant and RAre variant Convergence (CORAC) signature, to quantify the convergence for a broad range of complex traits. We characterized the relationship between CORAC and effective sample size across phenome-wide association studies.ResultsWe found that the signature is positively correlated with effective sample size (Spearman ρ = 0.594, P < 2.2e − 16), indicating increased functional convergence of trait-associated genetic variation, across the allele frequency spectrum, with increased power. Sensitivity analyses, including accounting for heteroskedasticity and varying the number of detected association signals, further strengthened the validity of the finding. In addition, consistent with empirical data, extensive simulations showed that negative selection, in line with enhancing polygenicity, has a dampening effect on the convergence signature. Methodologically, leveraging the convergence leads to enhanced association analysis.ConclusionsThe presented framework for the convergence signature has important implications for fine-mapping strategies and drug discovery efforts. In addition, our study provides a blueprint for the expectation from future large-scale whole-genome sequencing (WGS)/WES and sheds methodological light on post-GWAS studies.