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Durvasula, A.

Publications and source records attributed to Durvasula, A..

13 recordsLinked to original sources
bioRxiv2026

Accelerated Evolution of Context-Dependent Gene Expression in Primate Immune Cells

Regulatory variation is widely recognized as a driver of evolution within and between species, including in the primate lineage. Evidence for potentially adaptive regulatory evolution in primates comes from both sequence-based tests for selection and cross-species comparisons of gene expression. However, comparative studies of gene expression have tended to focus on baseline cellular states, despite growing evidence that context-dependent gene regulation is important. Here, we investigate the evolution of gene expression in lymphoblastoid cell lines (LCLs) from 6 primate lineages (humans, chimpanzees, bonobos, western gorillas, Sumatran orangutans, and rhesus macaques) using data from both control and 5 perturbed cellular conditions (n=22-25 individuals per condition). Analyzing baseline expression data, we find that genes involved in immunity often exhibit lineage-specific transcriptional shifts, and that these genes occur near regions with lineage-specific sequence acceleration and lineage-specific epigenomic states. However, the subset of genes that respond to in vitro perturbations have evolved at significantly higher rates than environmentally-insensitive genes, a finding we replicate in an independent four-species dataset of primary white blood cells (n=26). Together, our findings support the argument that focusing on baseline states alone provides a biased view of gene regulatory evolution, and suggest that studying gene regulation in dynamic contexts can enrich our understanding of primate evolution.

evolutionary biology↗
bioRxiv2025

Determining the driving factors shaping genetic architecture of complex traits in recently admixed populations

Understanding the genetic architecture of complex traits in admixed populations remains challenging due to heterogeneous genetic backgrounds and demographic histories. Mischaracterizing admixture can bias genetic association estimates and limit the generalizability of biomedical findings. Here, we systematically evaluate how evolutionary forces--including admixture, natural selection, and demographic history--jointly shape complex trait architecture and influence genome-wide association study (GWAS) outcomes using a simulation-based framework complemented by empirical analyses. We model five human admixture scenarios and vary the correlation between causal variant effect sizes and selection coefficients to reflect different trait-fitness relationships. This framework enables simulation of complex trait phenotypes with environmental variance, allowing comprehensive assessment of GWAS power and fine-mapping precision across evolutionary contexts. We find that GWAS power is strongly modulated by both genetic architecture and demographic history. Traits with weak coupling between fitness and effect size, such as anthropometric traits, exhibit higher GWAS power than traits under stronger negative selection, including early-onset diseases. Because rare variants contribute substantially to heritability yet are poorly captured by GWAS, bottlenecked populations with fewer rare variants show enhanced power. Despite large differences in GWAS power, fine-mapping precision remains relatively consistent across traits and populations, improving primarily in regions of high recombination. Empirical analyses of diverse cohorts, the All of Us Research Program, support these patterns. Our findings highlight how evolutionary and demographic forces shape the genetic basis of complex traits in admixed populations and underscore the need for tailored study designs to improve GWAS accuracy and fine-mapping performance in diverse cohorts.

genomics↗
bioRxiv2024

Accurate inference of population history in the presence of background selection

1All published methods for learning about demographic history make the simplifying assumption that the genome evolves neutrally, and do not seek to account for the effects of natural selection on patterns of variation. This is a major concern, as ample work has demonstrated the pervasive effects of natural selection and in particular background selection (BGS) on patterns of genetic variation in diverse species. Simulations and theoretical work have shown that methods to infer changes in effective population size over time (Ne(t)) become increasingly inaccurate as the strength of linked selection increases. Here, we introduce an extension to the Pairwise Sequentially Markovian Coalescent (PSMC) algorithm, PSMC+, which explicitly co-models demographic history and natural selection. We benchmark our method using forward-in-time simulations with BGS and find that our approach improves the accuracy of effective population size inference. Leveraging a high resolution map of BGS in humans, we infer considerable changes in the magnitude of inferred effective population size relative to previous reports. Finally, we separately infer Ne(t) on the X chromosome and on the autosomes in diverse great apes without making a correction for selection, and find that the inferred ratio fluctuates substantially through time in a way that differs across species, showing that uncorrected selection may be an important driver of signals of genetic difference on the X chromosome and autosomes.

genetics↗
bioRxiv2022

The lingering effects of Neanderthal introgression on human complex traits

The mutations introduced into the ancestors of modern humans from interbreeding with Neanderthals have been suggested to contribute an unexpected extent to complex human traits. However, testing this hypothesis has been challenging due to the idiosyncratic population genetic properties of introgressed mutations. We developed rigorous methods to assess the contribution of introgressed Neanderthal mutations to heritable trait variation relative to that of modern human variants. We applied these methods to analyze 235,592 introgressed Neanderthal mutations and 96 distinct phenotypes measured in about 300,000 unrelated white British individuals in the UK Biobank. Introgressed Neanderthal mutations have a significant contribution to trait variation consistent with the polygenic architecture of complex phenotypes (contributing 0.1% of heritable variation averaged across phenotypes; p = 9.59x10-9). However, the contribution of introgressed mutations tends to be significantly depleted relative to modern human mutations matched for allele frequency and linkage disequilibrium (about 57% depletion on average), consistent with purifying selection on introgressed mutations. Different from previous studies (McArthur 2021), we find no evidence for elevated heritability across the phenotypes examined. We identified 348 independent significant associations of introgressed Neanderthal mutations with 64 phenotypes (p < 1 x10-10). Previous work (Skov 2021) has suggested that a majority of such associations are likely driven by statistical association with nearby modern human variants that are the true causal variants. We therefore developed a customized statistical fine-mapping methodology for introgressed mutations that led us to identify 112 regions (at a false discovery proportion of 16%) across 47 phenotypes containing 4,303 unique genetic variants where introgressed mutations are highly likely to have a phenotypic effect. Examination of these mutations reveal their substantial impact on genes that are important for the immune system, development, and metabolism. Our results provide the first rigorous basis for understanding how Neanderthal introgression modulates complex trait variation in present-day humans.

genetics↗
bioRxiv2022

MaLAdapt reveals novel targets of adaptive introgression from Neanderthals and Denisovans in worldwide human populations

Adaptive introgression (AI) facilitates local adaptation in a wide range of species. Many state-of-the-art methods detect AI with ad-hoc approaches that identify summary statistic outliers or intersect scans for positive selection with scans for introgressed genomic regions. Although widely used, these outlier-based approaches are vulnerable to a high false-negative rate as the power of different methods vary, especially for complex introgression events. Moreover, population genetic processes unrelated to AI, such as background selection or heterosis, may create similar genomic signals as AI, compromising the reliability of methods that rely on neutral null distributions. In recent years, machine learning (ML) methods have been increasingly applied to population genetic questions. Here, we present an ML-based method called MaLAdapt for identifying AI loci from genome-wide sequencing data. Using an Extra-Trees Classifier algorithm, our method combines information from a large number of biologically meaningful summary statistics to capture a powerful composite signature of AI across the genome. In contrast to existing methods, MaLAdapt is especially well-powered to detect AI with mild beneficial effects, including selection on standing archaic variation, and is robust to non-AI selection sweeps, heterosis, and demographic misspecifications. Further, MaLAdapt outperforms existing methods for detecting AI based on the analysis of simulated data and on a validation of empirical signals through visual impaction of haplotype patterns. We apply MaLAdapt to the 1000 Genomes Project human genomic data, and discover novel AI candidate regions in non-African populations, including genes that are enriched in functionally important biological pathways regulating metabolism and immune responses.

evolutionary biology↗
bioRxiv2022

Genome-wide selection inference at short tandem repeats

Short tandem repeats (STRs) comprising repeated sequences of 1-6 bp are one of the largest sources of genetic variation in humans. STRs are known to contribute to a variety of disorders, including Mendelian diseases, complex traits, and cancer. Based on their functional importance, mutations at some STRs are likely to introduce negative effects on reproductive fitness over evolutionary time. We previously developed SISTR (Selection Inference at STRs), a population genetics framework to measure negative selection against individual STR alleles. Here, we extend SISTR to enable joint estimation of the distribution of selection coefficients across a set of STRs. This method (SISTR2) allows for more accurate analysis of a broader range of STRs, including loci with low mutation rates. We apply SISTR2 to explore the range of feasible mutation parameters and demonstrate substantial variation in mutation and selection parameters across different classes of STRs. Finally, we show that de novo STR mutations tend to confer a greater selective burden compared to standing STR variation in the population and measure the relative burden of STRs vs. single nucleotide variants in a typical genome. Overall, we anticipate that the evolutionary insights gained from this study will be important for future studies of variation at STRs and their role in evolution and disease.

genomics↗
bioRxiv2021

Advancing admixture graph estimation via maximum likelihood network orientation

MotivationAdmixture, the interbreeding between previously distinct populations, is a pervasive force in evolution. The evolutionary history of populations in the presence of admixture can be modeled by augmenting phylogenetic trees with additional nodes that represent admixture events. While enabling a more faithful representation of evolutionary history, admixture graphs present formidable inferential challenges, and there is an increasing need for methods that are accurate, fully automated, and computationally efficient. One key challenge arises from the size of the space of admixture graphs. Given that exhaustively evaluating all admixture graphs can be prohibitively expensive, heuristics have been developed to enable efficient search over this space. One heuristic, implemented in the popular method TreeMix, consists of adding edges to a starting tree while optimizing a suitable objective function. ResultsHere, we present a demographic model (with one admixed population incident to a leaf) where TreeMix and any other starting-tree-based maximum likelihood heuristic using its likelihood function is guaranteed to get stuck in a local optimum and return an incorrect network topology. To address this issue, we propose a new search strategy that we term maximum likelihood network orientation (MLNO). We augment TreeMix with an exhaustive search for a MLNO, referring to this approach as OrientA-Graph. In evaluations including previously published admixture graphs, OrientAGraph outperformed TreeMix on 4/8 models (there are no differences in the other cases). Overall, OrientAGraph found graphs with higher likelihood scores and topological accuracy while remaining computationally efficient. Lastly, our study reveals several directions for improving maximum likelihood admixture graph estimation. AvailabilityOrientAGraph is available under the GNU General Public License v3.0 on Github (https://github.com/sriramlab/OrientAGraph).

evolutionary biology↗
bioRxiv2019

A community-maintained standard library of population genetic models

The explosion in population genomic data demands ever more complex modes of analysis, and increasingly these analyses depend on sophisticated simulations. Recent advances in population genetic simulation have made it possible to simulate large and complex models, but specifying such models for a particular simulation engine remains a difficult and error-prone task. Computational genetics researchers currently re-implement simulation models independently, leading to inconsistency and duplication of effort. This situation presents a major barrier to empirical researchers seeking to use simulations for power analyses of upcoming studies or sanity checks on existing genomic data. Population genetics, as a field, also lacks standard benchmarks by which new tools for inference might be measured. Here we describe a new resource, stdpopsim, that attempts to rectify this situation. Stdpopsim is a community-driven open source project, which provides easy access to a growing catalog of published simulation models from a range of organisms and supports multiple simulation engine backends. This resource is available as a well-documented python library with a simple command-line interface. We share some examples demonstrating how stdpopsim can be used to systematically compare demographic inference methods, and we encourage a broader community of developers to contribute to this growing resource.

evolutionary biology↗
bioRxiv2019

Negative selection on complex traits limits genetic risk prediction accuracy between populations

Accurate genetic risk prediction is a key goal for medical genetics and great progress has been made toward identifying individuals with extreme risk across several traits and diseases (Collins and Varmus, 2015). However, many of these studies are done in predominantly European populations (Bustamante et al., 2011; Popejoy and Fullerton, 2016). Although GWAS effect sizes correlate across ancestries (Wojcik et al., 2019), risk scores show substantial reductions in accuracy when applied to non-European populations (Kim et al., 2018; Martin et al., 2019; Scutari et al., 2016). We use simulations to show that human demographic history and negative selection on complex traits result in population specific genetic architectures. For traits under moderate negative selection, ~50% of the heritability can be accounted for by variants in Europe that are absent from Africa. We show that this directly leads to poor performance in risk prediction when using variants discovered in Europe to predict risk in African populations, especially in the tails of the risk distribution. To evaluate the impact of this effect in genomic data, we built a Bayesian model to stratify heritability between European-specific and shared variants and applied it to 43 traits and diseases in the UK Biobank. Across these phenotypes, we find ~50% of the heritability comes from European-specific variants, setting an upper bound on the accuracy of genetic risk prediction in non-European populations using effect sizes discovered in European populations. We conclude that genetic association studies need to include more diverse populations to enable to utility of genetic risk prediction in all populations.

genetics↗
bioRxiv2018

Recovering signals of ghost archaic admixture in the genomes of present-day Africans

While introgression from Neanderthals and Denisovans has been well-documented in modern humans outside Africa, the contribution of archaic hominins to the genetic variation of present-day Africans remains poorly understood. Using 405 whole-genome sequences from four sub-Saharan African populations, we provide complementary lines of evidence for archaic introgression into these populations. Our analyses of site frequency spectra indicate that these populations derive 2-19% of their genetic ancestry from an archaic population that diverged prior to the split of Neanderthals and modern humans. Using a method that can identify segments of archaic ancestry without the need for reference archaic genomes, we built genome-wide maps of archaic ancestry in the Yoruba and the Mende populations that recover about 482 and 502 megabases of archaic sequence, respectively. Analyses of these maps reveal segments of archaic ancestry at high frequency in these populations that represent potential targets of adaptive introgression. Our results reveal the substantial contribution of archaic ancestry in shaping the gene pool of present-day African populations. One sentence summaryMultiple present-day African populations inherited genes from an unknown archaic population that diverged before modern humans and Neanderthals split.

genomics↗
bioRxiv2017

Natural selection interacts with the local recombination rate to shape the evolution of hybrid genomes

While hybridization between species is increasingly appreciated to be a common occurrence, little is known about the forces that govern the subsequent evolution of hybrid genomes. We considered this question in three independent, naturally-occurring hybrid populations formed between swordtail fish species Xiphophorus birchmanni and X. malinche. To this end, we built a fine-scale genetic map and inferred patterns of local ancestry along the genomes of 690 individuals sampled from the three populations. In all three cases, we found hybrid ancestry to be more common in regions of high recombination and where there is linkage to fewer putative targets of selection. These same patterns are also apparent in a reanalysis of human-Neanderthal admixture. Our results lend support to models in which ancestry from the \"minor\" parental species persists only where it is rapidly uncoupled from alleles that are deleterious in hybrids, and show the retention of hybrid ancestry to be at least in part predictable from genomic features. Our analyses further indicate that in swordtail fish, the dominant source of selection on hybrids stems from deleterious combinations of epistatically-interacting alleles.\n\nOne sentence summaryThe persistence of hybrid ancestry is predictable from local recombination rates, in three replicate hybrid populations as well as in humans.

evolutionary biology↗
bioRxiv2017

Gene expression drives the evolution of dominance

Dominance is a fundamental concept in molecular genetics and has implications for understanding patterns of genetic variation, evolution, and complex traits. However, despite its importance, the degree of dominance has yet to be quantified in natural populations. Here, we leverage multiple mating systems in natural populations of Arabidopsis to co-estimate the distribution of fitness effects and dominance coefficients of new amino acid changing mutations. We find that more deleterious mutations are more likely to be recessive than less deleterious mutations. Further, this pattern holds across gene categories, but varies with the connectivity and expression patterns of genes. Our work argues that dominance arose as the inevitable consequence of the functional importance of genes and their optimal expression levels.\n\nOne sentence summaryWe use population genomic data to characterize the degree of dominance for new mutations and develop a new theory for its evolution.

genomics↗