Biology subjects

Harpak, A.

Publications and source records attributed to Harpak, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Collapsed Site Coalescent Facilitates Detection of Adaptive Divergences Between Segmental Duplicates

Segmental duplications make up a substantial fraction of many genomes and are a recurrent source of evolutionary innovation, yet they have remained largely inaccessible to population genetic inference. One reason is that, until recently, their high sequence identity precluded reliable sequencing. Long-read sequencing now resolves population haplotypes across segmental duplications. Still lacking are appropriate population genetic methods to analyze population samples. A particular difficulty arises from frequent interlocus gene conversion (IGC), the copying of sequence from one duplicate into another. Such events correspond to coalescence of lineages across distinct sites--thereby violating a core assumption of standard coalescent theory and ancestral recombination graphs (ARGs). Here, we propose an approach that can facilitate ARG inference for segmental duplicates. We propose the Collapsed Site Coalescent (CSC), a model which treats each pair of paralogous sites as a single collapsed pseudo-site with a self-contained genealogy, so that the sequence of genealogies along a collapsed locus forms an ARG. Based on the CSC, we develop a haplotype-copying inference method that estimates IGC parameters from a population sample. In simulations, estimated parameters closely track true IGC rates. We further investigate the case where an adaptive difference between duplicates is sheltered by selection, protecting it from IGC that would otherwise erase the difference, a situation we call an IGC haven. When both IGC and selection are strong, our approach allows for the detection of IGC havens. In sum, this approach brings duplicated sequence within reach of population genetics inference, opening the way for tackling longstanding questions about the evolution of gene duplicates.

genomics↗

bioRxiv2026

trAIt: Species-by-Trait Data Retrieval using Large Language Models

Biological research often requires information about species traits. Manual literature collation can be time-consuming and miss parts of the literature. To address this gap, we developed trAIt, a publicly available software for the retrieval of characteristics of species from scientific literature catalogued in the Europe PubMed Central (PubMed) database. trAIt provides a graphical user interface (GUI) in which users specify species and characteristics of interest. Leveraging a large language model (LLM), trAIt retrieves relevant papers, combines their content through a consensus-based summarization model, and outputs a species-by-characteristic table. For a case study involving frog species, trAIt recovered 47.1% of trait-species combinations in 2.75 hours, while an expert curator independently recovered 62.4% over months. The consensus-based summarization substantially aids accuracy compared to single-source extraction. Across three case studies of vertebrate taxa, an expert confirmed the accuracy of 70.9% of trait-species entries recovered by trAIt. We observed considerable variation across taxa in trAIts accuracy, which is possibly due to heterogeneity in open-access literature availability and inconsistencies in species and trait terminology. In sum, our analysis suggests that LLM-based tools can accelerate biological data synthesis but should be used to support domain experts research, rather than replace their judgment.

bioinformatics↗

bioRxiv2026

Estimating uncertainty in family-based GWAS

Standard genome-wide association studies (GWASs) are vulnerable to confounding factors, including stratification, assortative mating, and dynastic effects. Family studies such as sibling-based GWAS (sib-GWAS) mitigate such confounding and are becoming the tool of choice for teasing apart direct genetic effects---causal effects of one's genotype on one's own phenotype---from other factors. However, due in part to their smaller sample sizes, sib-GWAS allelic effect estimates are much noisier than standard (i.e., population-based) GWAS estimates. The quantification of this estimation uncertainty is essential for many uses of sib-GWAS, including polygenic scoring, causal inference (e.g., Mendelian randomization), disentangling direct from indirect familial effects, and measuring assortative mating. Here, we investigate sources of uncertainty in sib-GWAS allelic effect estimators. We study their impacts on the biases of three uncertainty measurement methods, including two that are commonly used and a new resampling-based approach we propose. We find that heterogeneity in allelic effects or heteroskedasticity across families (e.g., due to variation in genetic backgrounds or environments) can bias existing methods, and that this bias is more severe for small samples and rare variants. In contrast, the resampling-based approach we propose is approximately unbiased under all scenarios we considered. We validate our theoretical predictions, as well as the importance of effect heterogeneity and heteroskedasticity, using simulations and empirical analysis in the UK Biobank. In sum, this study helps understand the sources of uncertainty in family-based genotype-phenotype association studies and provides a robust method to estimate uncertainty.

genetics↗

bioRxiv2026

Representation in genetic studies affects inference about genetic architecture

Knowledge of a trait's "genetic architecture," namely the joint distribution of allele frequencies of causal variants and the direction and magnitude of their effects, is essential to understanding its evolution and underlying biology. Inferences about genetic architecture are based on data collected in different ways in cohorts recruited through heterogeneous mechanisms. As a result, cohorts differ in genotype, environment, and trait distributions. For example, the UK Biobank (UKB) was designed for broad population representation, whereas FinnGen drew extensively from clinical registries enriched for diagnosed health conditions. Here, we asked whether representation in genetic studies influences inferences about genetic architectures. Using GWAS data from the UKB, All of Us (AoU), and FinnGen, we compared several summaries of genetic architecture. We find systematic differences in some summaries, such as SNP heritability, which were often (in 6/13 traits) significantly lower in AoU than in the UKB (and never the reverse), even when matching samples such that they have similar genetic ancestry compositions. This result aligns with other recent evidence that biobanks enriched for diagnosed health conditions, also sometimes characterized by less-standardized phenotyping, have lower heritability than population-based biobanks. We highlight a second case, where a summary of genetic architecture varies considerably but not systematically across traits and biobanks. Such is the case for the mean direction of allelic effects ("sign bias"). For example, 72% of rare minor alleles affecting type 2 diabetes risk are inferred to be risk-increasing based on AoU data, while nearly all (>99%) are inferred to be risk-increasing based on UKB data. We hypothesize that the inferred sign bias is heavily influenced by the skewness of the trait distribution in the study and otherwise largely independent of other study or trait characteristics, including whether the trait is binary or quantitative. We provide strong support for this hypothesis through simulations and data from the three biobanks: the variation in inferred sign bias for rare minor alleles across traits and biobanks is explained remarkably well (82% and 97% of variance explained for trait-associated SNPs and randomly selected SNPs, respectively) solely by the trait's skewness in the biobank, with residual biobank-specificity explaining little. Our findings suggest that inferences about the map between genetic and trait variation can depend on study design and participation in genetic studies in surprising ways.

genomics↗

bioRxiv2026

Drivers of systemic male-female allele frequency divergence in humans

Allele frequency differences between males and females in genetic studies have been interpreted as suggestive of sex differences in natural selection. However, systemic differences may also arise through sex differences in study participation as well as bioinformatic artifacts. To mitigate these confounding effects, we performed a meta-analysis of sex differences in allele frequencies across three genetic studies. We identify twelve concordant genes with cross-study evidence of sex differences in allele frequency. Using previous literature about the twelve candidates, we propose four hypotheses for how and when sex differences in allele frequencies arise and suggest which hypotheses are plausible for each candidate. For example, beyond candidates that plausibly reflect sex differences in viability, we also find candidate loci potentially underlying fitness differences between X/Y-carrying sperm. Taken together, our results offer a clearer delineation of the factors driving sex differences in allele frequencies, including the timing and mechanisms of natural selection.

evolutionary biology↗

bioRxiv2025

Escape from X inactivation drives sex differences and female trait variation

X chromosome inactivation (XCI) partially balances gene dosage between sexes, yet many genes are expressed from the inactive X (Xi) to a variable degree. In this study, we investigate whether variation in Xi expression among genes predicts transcriptional and phenotypic consequences of X-linked variation. We find that Xi expression levels are a strong linear predictor of female-male expression differences, suggesting that other compensatory or regulatory mechanisms play a more minor role in sex differences in X-linked gene expression. Among females, we identify traits--including BMI, estradiol, and testosterone levels--for which higher Xi expression correlates with the strength of evidence for either additive or dominance effects on the trait. We hypothesize that an underappreciated mechanism could generate dominance effects of X-linked variants on a trait--specifically when the variant influences skew in X inactivation. The work establishes Xi expression as important for understanding transcriptional sex differences and the physiological variation among females.

genomics↗

bioRxiv2025

A Litmus Test for Confounding in Polygenic Scores

Polygenic scores (PGSs) are being rapidly adopted for trait prediction in the clinic and beyond. PGSs are often thought of as capturing the direct genetic effect of one's genotype on one's phenotype. However, because PGSs are constructed from population-level associations, they are influenced by factors other than direct genetic effects, including stratification, assortative mating, and dynastic effects ("SAD effects"). Our interpretation and application of PGSs may hinge on the relative influence of SAD effects, since they may often be environmentally or culturally mediated. We developed a method to measure these influences, Partitioning Genetic Scores Using Siblings (PGSUS, pron. "Pegasus"). PGSUS leverages a comparison of a PGS of interest based on a standard GWAS with a PGS based on a sibling GWAS--which is largely immune to SAD effects--to partition variance in a PGS (in a given sample) into components due to direct effects, SAD effects, and their covariance. Using PGSUS, we found that in many cases direct genetic effects contribute relatively little to PGS variation--most pronouncedly so in PGSs for social or behavioral traits, such as educational attainment or neuroticism. PGSUS further breaks down variance components by axes of genetic ancestry, allowing for a nuanced interpretation of SAD effects. In particular, PGSUS can detect stratification along major axes of ancestry as well as SAD variance that is "isotropic" with respect to axes of ancestry. Applying PGSUS, we found evidence of stratification in PGSs constructed using large meta-analyses of height as well as in multiple PGSs constructed using the UK Biobank. We show that a given PGS can suffer from stratification along a major axis of ancestry in one sample but not in another (for example, in comparisons of prediction in samples from contemporary vs. ancient DNA samples). We further show that when axes of stratification are shared between GWAS and prediction samples, stratification can both aid and impede phenotypic prediction accuracy. In summary, PGSUS offers advances in interpretation towards more informed application of polygenic scores.

genomics↗

bioRxiv2024

Three Open Questions in Polygenic Score Portability

A major obstacle hindering the broad adoption of polygenic scores (PGS) is their lack of "portability" to people that differ--in genetic ancestry or other characteristics--from the GWAS samples in which genetic effects were estimated. Here, we use the UK Biobank to measure the change in PGS prediction accuracy as a continuous function of individuals genome-wide genetic dissimilarity to the GWAS sample ("genetic distance"). Our results highlight three gaps in our understanding of PGS portability. First, prediction accuracy is extremely noisy at the individual level and not well predicted by genetic distance. In fact, variance in prediction accuracy is explained comparably well by socioeconomic measures. Second, trends of portability vary across traits. For several immunity-related traits, prediction accuracy drops near zero quickly even at intermediate levels of genetic distance. This quick drop may reflect GWAS associations being more ancestry-specific in immunity-related traits than in other traits. Third, we show that even qualitative trends of portability can de pend on the measure of prediction accuracy used. For instance, for type 2 diabetes, precision stays roughly constant, while recall surprisingly increases, with genetic distance. Together, our results show that portability cannot be understood through global ancestry groupings alone. There are other, understudied factors influencing portability, such as the specifics of the evolution of the trait and its genetic architecture, social context, and the construction of the polygenic score. Addressing these gaps can aid in the development and application of PGS and inform more equitable genomic research.

genetics↗

bioRxiv2024

Does Sex-Differential Gene Expression Drive Sex-Differential Selection in Humans?

Sex differences in human transcriptomes have been argued to drive sex-differential natural selection (SDS). Here, we show that previous evidence supporting this hypothesis has been largely unfounded. We develop a new method to test for a genome-wide relationship between sex differences in expression and selection on expression-influencing alleles (eQTLs). We apply it across 39 human tissues and find no evidence for a general relationship. We offer possible explanations for the lack of evidence, including that it is due in part to eQTL ascertainment bias towards sites under weak selection. We conclude that the drivers of ongoing SDS in humans remain to be identified.

evolutionary biology↗

bioRxiv2024

The battle of the sexes in humans is highly polygenic

Sex-differential selection (SDS), which occurs when the fitness effects of alleles differ between males and females, can have profound impacts on the maintenance of genetic variation, disease risk, and other key aspects of natural populations. Because the sexes mix their autosomal genomes each generation, quantifying SDS is not possible using conventional population genetic approaches. Here, we introduce a novel method that exploits subtle sex differences in haplotype frequencies resulting from SDS acting in the current generation. Using data from 300K individuals in the UK Biobank, we estimate the strength of SDS throughout the genome. While only a handful of loci under SDS are individually significant, we uncover polygenic signals of genome-wide SDS for both viability and fecundity. An interesting life-history tradeoff emerges: alleles that increase viability more in one sex increase fecundity more in the other sex. Lastly, we find evidence of SDS on fecundity acting on alleles affecting arm fat-free mass. Taken together, our findings connect the long-standing evidence of SDS acting on human phenotypes with its impact on the genome. Significance statementSelection often acts differently on females and males, as evidenced by the striking sexual dimorphism found in many taxa. As a result, alleles can have different fitness effects in each sex. Consequences can include higher levels of genetic variation and higher disease burdens in populations. This study introduces a novel method to quantify this sex-differential selection (SDS) and reveals that it acts throughout the human genome. We discovered a life history tradeoff between survival and fecundity in females and males and that SDS on fecundity acts on alleles affecting arm fat-free mass.

evolutionary biology↗

bioRxiv2024

Does standard adjustment for genomic population structure capture direct genetic effects?

Contemporary genomic studies of complex traits, such as genome-wide association studies (GWASs) and polygenic index (PGI) analyses, often use the principal components of the genotype matrix (PCs) to adjust for population stratification. In this paper, we explore the extent to which we may be discounting direct genetic effects by adjusting for PCs. Using family-based models that control for parental genotype (obtained via Mendelian imputation), we test whether PCs have a direct genetic effect on nine complex phenotypes in the White British subsample of the UK Biobank. Further, we assess the extent to which estimates of polygenic effects meaningfully change when adjusting for PCs in within-family models. Across the nine traits, within-family effects of the top 40 PCs are highly similar to their population effects, suggesting that standard PC adjustments diminish, albeit to a small degree, detectable signals of direct genetic effects. Within family models also confirm that PCs have significant marginal effects on a few traits, most consistently, height and educational attainment. Nonetheless, the variance explained by the effects of PCs is modest, and adjusting for PCs does not appear to affect the magnitude and significance of PGI effects in within-family models.

genomics↗

bioRxiv2023

Tradeoffs in Modeling Context Dependency in Complex Trait Genetics

Genetic effects on complex traits may depend on context, such as age, sex, environmental exposures or social settings. However, it is often unclear if the extent of context dependency, or Gene-by-Environment interaction (GxE), merits more involved models than the additive model typically used to analyze data from genome-wide association studies (GWAS). Here, we suggest considering the utility of GxE models in GWAS as a tradeoff between bias and variance parameters. In particular, We derive a decision rule for choosing between competing models for the estimation of allelic effects. The rule weighs the increased estimation noise when context is considered against the potential bias when context dependency is ignored. In the empirical example of GxSex in human physiology, the increased noise of context-specific estimation often outweighs the bias reduction, rendering GxE models less useful when variants are considered independently. However, we argue that for complex traits, the joint consideration of context dependency across many variants mitigates both noise and bias. As a result, polygenic GxE models can improve both estimation and trait prediction. Finally, we exemplify (using GxDiet effects on longevity in fruit flies) how analyses based on independently ascertained "top hits" alone can be misleading, and that considering polygenic patterns of GxE can improve interpretation.

genomics↗

bioRxiv2022

1,000 ancient genomes uncover 10,000 years of natural selection in Europe

Ancient DNA has revolutionized our understanding of human population history. However, its potential to examine how rapid cultural evolution to new lifestyles may have driven biological adaptation has not been met, largely due to limited sample sizes. We assembled genome-wide data from 1,291 individuals from Europe over 10,000 years, providing a dataset that is large enough to resolve the timing of selection into the Neolithic, Bronze Age, and Historical periods. We identified 25 genetic loci with rapid changes in frequency during these periods, a majority of which were previously undetected. Signals specific to the Neolithic transition are associated with body weight, diet, and lipid metabolism-related phenotypes. They also include immune phenotypes, most notably a locus that confers immunity to Salmonella infection at a time when ancient Salmonella genomes have been shown to adapt to human hosts, thus providing a possible example of human-pathogen co-evolution. In the Bronze Age, selection signals are enriched near genes involved in pigmentation and immune-related traits, including at a key human protein interactor of SARS-CoV-2. Only in the Historical period do the selection candidates we detect largely mirror previously-reported signals, highlighting how the statistical power of previous studies was limited to the last few millennia. The Historical period also has multiple signals associated with vitamin D binding, providing evidence that lactase persistence may have been part of an oligogenic adaptation for efficient calcium uptake and challenging the theory that its adaptive value lies only in facilitating caloric supplementation during times of scarcity. Finally, we detect selection on complex traits in all three periods, including selection favoring variants that reduce body weight in the Neolithic. In the Historical period, we detect selection favoring variants that increase risk for cardiovascular disease plausibly reflecting selection for a more active inflammatory response that would have been adaptive in the face of increased infectious disease exposure. Our results provide an evolutionary rationale for the high prevalence of these deadly diseases in modern societies today and highlight the unique power of ancient DNA in elucidating biological change that accompanied the profound cultural transformations of recent human history.

genomics↗

bioRxiv2022

Amplification is the Primary Mode of Gene-by-Sex Interaction in Complex Human Traits

Sex differences in complex traits are suspected to be in part due to widespread gene-by-sex interactions (GxSex), but empirical evidence has been elusive. Here, we infer the mixture of ways polygenic effects on physiological traits covary between males and females. We find that GxSex is pervasive but acts primarily through systematic sex differences in the magnitude of many genetic effects ("amplification"), rather than in the identity of causal variants. Amplification patterns account for sex differences in trait variance. In some cases, testosterone may mediate amplification. Finally, we develop a population-genetic test linking GxSex to contemporary natural selection and find evidence for sexually antagonistic selection on variants affecting testosterone levels. Taken together, our results suggest that the amplification of polygenic effects is a common mode of GxSex that may contribute to sex differences and fuel their evolution. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=130 SRC="FIGDIR/small/490973v3_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@19c267eorg.highwire.dtl.DTLVardef@a32233org.highwire.dtl.DTLVardef@1a6daeorg.highwire.dtl.DTLVardef@116cbff_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2020

The mutability of demographic noise in microbial range expansions

Demographic noise, the change in the composition of a population due to random birth and death events, is an important driving force in evolution because it reduces the efficacy of natural selection. Demographic noise is typically thought to be set by the population size and the environment, but recent experiments with microbial range expansions have revealed substantial strain-level differences in demographic noise under the same growth conditions. Many genetic and phenotypic differences exist between strains; to what extent do single mutations change the strength of demographic noise? To investigate this question, we developed a high-throughput method for measuring demographic noise in colonies without the need for genetic manipulation. By applying this method to 191 randomly-selected single gene deletion strains from the E. coli Keio collection, we find that a typical single gene deletion mutation decreases demographic noise by 8% (maximal decrease: 81%). We find that the strength of demographic noise is an emergent trait at the population level that can be predicted by colony-level traits but not cell-level traits. The observed differences in demographic noise from single gene deletions can increase the establishment probability of beneficial mutations by almost an order of magnitude higher than the wild type. Our results show that single mutations can substantially alter adaptation through their effects on demographic noise and suggest that demographic noise can be an evolvable phenotype of a population.

evolutionary biology↗

bioRxiv2020

Concerted evolution reveals co-adapted amino acid substitutions in frogs that prey on toxic toads

Gene duplication is an important source of evolutionary innovation, but the adaptive division-of-labor between duplicates can be opposed by ongoing gene conversion between them. Here we document a tandem duplication of Na+,K+-ATPase subunit 1 (ATP1A1) shared by frogs in the genus Leptodactylus, a group of species that feeds on toxic toads. One ATP1A1 paralog evolved resistance to toad toxins while the other paralog retained ancestral susceptibility. We show that the two Leptodactylus paralogs are distinguished by 12 amino acid substitutions that were maintained by strong selection that counteracted the homogenizing effect of gene conversion. Protein-engineering experiments show that two major-effect substitutions confer toxin resistance, whereas the 10 additional substitutions mitigate deleterious pleiotropic effects on enzyme function. Our results highlight how trans-specific, neofunctionalized gene duplicates can provide unique insights into interactions between adaptive substitutions and the genetic backgrounds on which they arise. One Sentence SummarySelection counteracts gene conversion to maintain an adaptive division-of-labor between tandemly duplicated genes.

evolutionary biology↗

bioRxiv2020

Genetic Adaptation in New York City Rats

Brown rats (Rattus norvegicus) thrive in urban environments by navigating the anthropocentric environment and taking advantage of human resources and by-products. From the human perspective, rats are a chronic problem that causes billions of dollars in damage to agriculture, health and infrastructure. Did genetic adaptation play a role in the spread of rats in cities? To approach this question, we collected whole-genome sequences from 29 brown rats from New York City (NYC) and scanned for genetic signatures of adaptation. We tested for (i) high-frequency, extended haplotypes that could indicate selective sweeps and (ii) loci of extreme genetic differentiation between the NYC sample and a sample from the presumed ancestral range of brown rats in northeast China. We found candidate selective sweeps near or inside genes associated with metabolism, diet, the nervous system and locomotory behavior. Patterns of differentiation between NYC and Chinese rats at putative sweep loci suggests that many sweeps began after the split from the ancestral population. Together, our results suggest several hypotheses on adaptation in rats living in close proximity to humans.

evolutionary biology↗

bioRxiv2019

Variable prediction accuracy of polygenic scores within an ancestry group

Fields as diverse as human genetics and sociology are increasingly using polygenic scores based on genome-wide association studies (GWAS) for phenotypic prediction. However, recent work has shown that polygenic scores have limited portability across groups of different genetic ancestries, restricting the contexts in which they can be used reliably and potentially creating serious inequities in future clinical applications. Using the UK Biobank data, we demonstrate that even within a single ancestry group, the prediction accuracy of polygenic scores depends on characteristics such as the age or sex composition of the individuals in which the GWAS and the prediction were conducted, and on the GWAS study design. Our findings highlight both the complexities of interpreting polygenic scores and underappreciated obstacles to their broad use.

genetics↗