Biology subjects

Lohmueller, K. E.

Publications and source records attributed to Lohmueller, K. E..

At least 19 recordsLinked to original sources

bioRxiv2026

Robustness of the inference of the distribution of fitness effects to selective sweeps

The distribution of fitness effects (DFE) describes the proportion of new mutations with certain selective strengths. It is a crucial quantity for the study of complex disease and evolutionary genetics. Population genetics approaches infer the DFE using the site frequency spectrum (SFS), which summarizes allele frequencies within a sample. These approaches first fit a demographic model to an SFS of putatively neutral sites, then infer the DFE from an SFS of selected sites conditional on that demographic model. This approach assumes that sites are independent and that population size changes affect selected and neutral sites equally. Selective sweeps may violate these assumptions. As a beneficial mutation rises in frequency, nearby sites hitchhike alongside the beneficial mutation. This distortion might differ between neutral and selected sites. Here, we evaluate the performance of the DFE inference method, fitdadi, in the presence of selective sweeps. We simulate genomic segments containing neutral and deleterious mutations drawn from a known DFE under varying selective sweep conditions, and test whether fitdadi can recover the true DFE with a flexible demographic model. When SFS are sampled immediately after fixation of a beneficial mutation, the demographic models absorb the sweep's effect as a population expansion, and the DFE is recovered relatively well. Performance declines at lower recombination rates and when using the unfolded SFS, leading to an overestimation of the strength of purifying selection. We then model recurrent selective sweeps at rates consistent with human polymorphism and divergence data. The demographic model captures these distortions, and the DFE is unbiased. Our results suggest that DFE inferences are unlikely to be substantially biased by selective sweeps, but that future studies should consider using the folded SFS and excluding regions of low recombination rate.

evolutionary biology↗

bioRxiv2026

The effects of hitchhiking and repulsion of deleterious alleles on linkage disequilibrium during a selective sweep

Deleterious mutations are abundant in nature. Consequently, when an adaptive mutation arises, it will likely land on a haplotype with several deleterious mutations. While deleterious mutations may hitchhike with the adaptive variant, deleterious mutations are less likely to be found on the same haplotype with each other due to repulsion (i.e. Hill Robertson interference). Both hitchhiking and repulsion shape patterns of linkage disequilibrium (LD), but their simultaneous impact on LD have not been fully understood, leaving a gap in accurate inference of selection from genomic data. Here, we investigate patterns of LD during a sweep using simulations across a wide range of evolutionary parameters. On average, LD is elevated between pairs of intermediate frequency nonsynonymous variants relative to synonymous variants. This occurs because nonsynonymous variants, which are presumably deleterious, are more likely to rise to high frequency via hitchhiking rather than drift and thus will be predominantly found on the sweeping haplotype. However, we find that this elevation does not occur across all distances: when nonsynonymous variants are either too close to each other or too far from the adaptive locus, repulsion between deleterious variants results in lower LD among nonsynonymous variants. This results in a characteristic "criss-crossing" of LD. We find that criss-crossing is a feature of empirical data from Clostridioides difficile, a pathogen that has undergone several selective sweeps. However, some sweeps show elevation of nonsynonymous LD over synonymous LD across all distances. These sweeps have been previously identified to bear complex adaptations, and moreover, harbor elevated nucleotide diversity. Consistent with this, we demonstrate that lack of criss-crossing can arise when adaptation proceeds via epistatic, multi-locus architecture or introgression.

genetics↗

bioRxiv2026

Long-term isolation and introgression shape the genomic distinctiveness of Rice's Whale

Species complex demographic history, including population size changes, isolation and gene flow, shapes patterns of genetic variation and deleterious load. In small and declining populations, understanding these processes is critical for predicting inbreeding depression and extinction risk. The Rices whale (Balaenoptera ricei) is a newly described baleen whale species resident to the heavily industrialized Gulf of Mexico, with a current abundance estimate of 51 (95 % CI: 20-130) individuals, making it one of the most endangered baleen whales globally. Using whole-genome sequences from 25 individuals, we reconstructed the evolutionary and demographic history of Rices whale and assessed its genomic health. Our analyses reinforce its distinctiveness from Brydes whales, and suggest that Rices whale has persisted as a small and isolated population in the Gulf of Mexico for tens of thousands of years. Despite its long-term small effective population size, genomes show modest impacts of inbreeding, including few long runs of homozygosity. We detected a distinct pulse of introgression [~]350 years ago from a Brydes whale-like lineage that resulted in windows of elevated heterozygosity in Rices whale, though it did not alter the burden of deleterious variation. Forward simulations indicate that a recent population collapse to [~]100 breeding individuals places the species at high risk of future inbreeding and genomic erosion unless population growth occurs. These findings highlight that while gene flow can increase genetic diversity, demographic recovery is essential to mitigate long-term genomic risks, underscoring the importance of management actions that promote sustained population growth.

genomics↗

bioRxiv2026

Causal inference clarifies the roles of background selection and mutation rate variation in shaping human genetic diversity

Decades of theoretical and empirical work have struggled to reconcile competing views on how evolution shapes patterns of genetic variation. We now understand the genome as a mosaic molded by both neutral and selective forces, but quantifying their relative contributions remains an open challenge. A major obstacle has been the tendency to analyze each evolutionary process in isolation. But different processes may leave similar signatures on genetic variation, making it challenging to draw conclusions from correlations alone. To address this gap, we make predictions of the landscape of diversity based on background selection and mutation rate variation. We then develop structural equation models describing how mutation, recombination and selection jointly shape the genomic landscape of diversity. This approach offers a more realistic representation of biological interactions and enables rigorous evaluation of hypothesized causal structures, marking both conceptual and practical improvements over previous studies. Analyses of human data reveal large variation in the explanatory power of candidate models across chromosomes. We find that previous studies likely overestimated the predictive accuracy of background selection; although it emerges as the overall main driver of diversity, in some chromosomes mutation rate variation has a comparable impact. We also show that recombination increases diversity more strongly through its influence on background selection than through its mutagenic effect, resolving a longstanding debate. This work demonstrates that modeling variation inherent in genome biology substantially improves our ability to explain human genetic diversity. At the same time, evidence for unmodeled covariance between mutation rates and density of constrained sites reinvigorates an ongoing discussion about the evolution of the mutation landscape, although additional work is needed to determine its origins.

evolutionary biology↗

bioRxiv2026

Inference of population demographic history captures differing evolutionary signals based on the number of individuals in the dataset

Accurate estimation of population demographic history is central to population genetics yet remains challenging due to the sensitivity of inference methods to the number of individuals and the demographic scenario assumed in inference. The site-frequency spectrum (SFS) of neutral variants, a widely used summary statistic of genetic variation, is particularly sensitive to demographic processes, but studies have shown that qualitative results from demographic inference, i.e., population expansion vs. contraction, can depend strongly on the number of individuals in the dataset. Here, we analyzed two simulated datasets and one empirical dataset characterized by an ancient population bottleneck followed by a recent population expansion. Fitting a two-epoch demographic model across a range of sample sizes, we found that inference shifted from signals of ancient population contraction at small sample sizes to signals of recent population expansion at large sample sizes. Other summary statistics, including Tajimas D and the proportion of singletons, also changed with sample size. We found that these changes of inferred evolutionary signals under a two-epoch model can be explained by the epoch which contributes the highest mean proportion of coalescent branch lengths. Our results highlight that demographic inference depends critically on the number of individuals analyzed and suggest that analyzing datasets at multiple sample sizes can reveal complementary aspects of population history.

evolutionary biology↗

bioRxiv2025

The landscape of fitness effects of putatively functional noncoding mutations in humans

While annotations of noncoding regions in the human genome are increasing, the fitness effects of mutations in these regions remain unclear. Here, we leverage these functional genomic annotations and human polymorphism data to infer the distributions of fitness effects of new noncoding mutations in humans. Our novel approach controls for mutation rate variation and linked selection along the genome. We find distinct patterns of selection in putative enhancers, promoters, and conserved noncoding regions. While mutations in enhancers are often neutral, approximately 30% of mutations in promoters are deleterious. The most conserved noncoding regions, showing reduced divergence across mammals and primates, have the highest proportion of deleterious mutations. Notably, while we infer the most conserved sites across mammals and primates are enriched for deleterious mutations, such conserved sites only account for a minority of the deleterious mutations in noncoding regions. For example, the top 5% of conserved noncoding sites encompass fewer than 20% of deleterious mutations, indicating that functional noncoding regions vary widely in the distribution of their evolutionary constraint. Our findings highlight the dynamic evolution of gene regulation and shifting selection pressures over deep evolutionary timescales. Consistent with this finding, we infer mutations in [~]7-9% of the noncoding genome are deleterious. These insights have broad implications for using comparative genomics to identify non-neutrally evolving sequences in the human genome.

evolutionary biology↗

bioRxiv2025

The distribution of fitness effects varies phylogenetically across animals

1The distribution of fitness effects (DFE) describes the selection coefficients (s) of newly arising mutations and fundamentally influences population genetic processes. However, the extent and mechanisms of DFE variation have not been systematically investigated across species with divergent phylogenetic histories and ecological functions. Here, we inferred the DFE in natural populations of eleven animal (sub)species, including humans, mice, fin whales, vaquitas, wolves, collared flycatchers, pied flycatchers, halictid bees, Drosophila, and mosquitoes. We find that the DFE co-varies with phylogeny, where the expected mutation effects are more similar in closely related species (Pagels{lambda} = 0.84, P = 0.01). Additionally, mammals have a higher proportion of strongly deleterious mutations (22% to 47% in mammals; 0.0% to 5.4% in insects and birds) and a lower proportion of weakly deleterious mutations than insects and birds. Population size is significantly negatively correlated with the expected impact of new deleterious mutations (PGLS{lambda}, P = 0.03), and the proportion of new beneficial mutations ([Formula], P < 0.001). These findings align with Fishers Geometric Model (FGM), which defines organismal complexity as the number of phenotypes under selection. Consistent with the FGMs predictions, we observe that mutations are more deleterious in complex organisms, while beneficial mutations occur more frequently in smaller populations to compensate for the drift load. Our study demonstrates strong phylogenetic constraints in the evolution of a fundamental population genetics parameter, and proposes that, through mechanisms of global epistasis, long-term population size and organismal complexity drive variation in the DFE across animals. 2 Significance StatementUnderstanding how mutations affect fitness is fundamental in evolution, but little is known about how and why the distribution of fitness effects (DFE) varies across species. In this study, we examine the DFE in diverse animal populations and show that closely related species exhibit similar patterns of mutation effects, with new mutations being more deleterious in mammals compared with birds and insects. Our findings corroborate Fishers Geometric Model, which explains the variation in the DFE across species as a function of organismal complexity and long-term population size. By connecting organismal complexity and population size with the DFE, we offer a phylogenetic view into the selective forces shaping species adaptation and evolution.

evolutionary biology↗

bioRxiv2025

Neanderthal introgressed ancestry reveals human genomic regions enriched with recessive deleterious mutations

Negative selection on deleterious mutations plays a key role in shaping human genetic variation, yet the dominance effects of these mutations remain poorly understood because existing statistical methods often cannot distinguish dominance effects from the overall selective effects. In this work, we take a fundamentally different approach by leveraging the distribution of Neanderthal ancestry across the human genome. Simulations show that recessive deleterious mutations can increase archaic introgressed ancestry through heterosis in the absence of positive selection, in contrast to the depletion expected under additive effects. We use this signal to develop DominL, a machine learning classifier trained on simulations of human demography with Neanderthal introgression to identify megabase-scale genomic windows enriched with recessive mutations. DominL demonstrates robust accuracy, with particularly high power in exon-dense regions. Applied to 7 non-African populations from the 1000 Genomes Project, DominL identifies approximately 3-9% of the genome as enriched for recessive mutations, with most high-confidence regions shared across populations. Predicted regions show patterns consistent with expected signatures from recessive mutations, including weakened background selection, depletion of runs of homozygosity, and enrichment of non-additive trait-associated variants. These regions also contain genes associated with metabolic and immune-related functions.

evolutionary biology↗

bioRxiv2025

Accessible, realistic genome simulation with selection using stdpopsim

Selection is a fundamental evolutionary force that shapes patterns of genetic variation across species. However, simulations incorporating realistic selection along heterogeneous genomes in complex demographic histories are challenging, limiting our ability to benchmark statistical methods aimed at detecting selection and to explore theoretical predictions. stdpopsim is a community-maintained simulation library that already provides an extensive catalog of species-specific population genetic models. Here we present a major extension to the stdpopsim framework that enables simulation of various modes of selection, including background selection, selective sweeps, and arbitrary distributions of fitness effects (DFE) acting on annotated subsets of the genome (for instance, exons). This extension maintains stdpopsims core principles of reproducibility and accessibility while adding support for species-specific genomic annotations and published DFE estimates. We demonstrate the utility of this framework by comparing methods for demographic inference, DFE estimation, and selective sweep detection across several species and scenarios. Our results demonstrate the robustness of demographic inference methods to selection on linked sites, reveal the sensitivity of DFE-inference methods to model assumptions, and show how genomic features, like recombination rate and functional sequence density, influence power to detect selective sweeps. This extension to stdpopsim provides a powerful new resource for the population genetics community to explore the interplay between selection and other evolutionary forces in a reproducible, user-friendly framework.

evolutionary biology↗

bioRxiv2024

Evolutionary consequences of domestication on the selective effects of new amino acid changing mutations in canids

The domestication of wild canids led to dogs no longer living in the wild but instead residing alongside humans. Extreme changes in behavior and diet associated with domestication may have led to the relaxation of the selective pressure on traits that may be less important in the domesticated context. Thus, here we hypothesize that strongly deleterious mutations may have become less deleterious in domesticated populations. We test this hypothesis by estimating the distribution of fitness effects (DFE) for new amino acid changing mutations using whole-genome sequence data from 24 gray wolves and 61 breed dogs. We find that the DFE is strikingly similar across canids, with 26-28% of new amino acid changing mutations being neutral/nearly neutral (|s| < 1e-5), and 41-48% under strong purifying selection (|s| > 1e-2). Our results are robust to different model assumptions suggesting that the DFE is stable across short evolutionary timescales, even in the face of putative drastic changes in the selective pressure caused by artificial selection during domestication and breed formation. On par with previous works describing DFE evolution, our data indicate that the DFE of amino acid changing mutations depends more strongly on genome structure and organismal characteristics, and less so on shifting selective pressures or environmental factors. Given the constant DFE and previous data showing that genetic variants that differentiate wolf and dog populations are enriched in regulatory elements, we speculate that domestication may have had a larger impact on regulatory variation than on amino acid changing mutations. Significance StatementDomestication of dogs to live alongside humans resulted in a dramatic shift in the pressures of natural selection. Thus, comparing dogs and wolves offers a unique opportunity to assess how these shifts in selective pressures have impacted the fitness effects of individual mutations. In this project, we use patterns of genetic variation in dogs and wolves to estimate the distribution of fitness effects (DFE), or the proportions of amino acid changing mutations with varying fitness effects throughout the genome. Overall, we find that the DFE for amino acid changing mutations is similar between dogs and wolves. Even genes thought to be most affected by domestication show a similar DFE, suggesting that the DFE has remained stable over evolutionary time.

genetics↗

bioRxiv2024

Constraining models of dominance for nonsynonymous mutations in the human genome

Dominance is a fundamental parameter in genetics, determining the dynamics of natural selection on deleterious and beneficial mutations, the patterns of genetic variation in natural populations, and the severity of inbreeding depression in a population. Despite this importance, dominance parameters remain poorly known, particularly in humans or other non-model organisms. A key reason for this lack of information about dominance is that it is extremely challenging to disentangle the selection coefficient (s) of a mutation from its dominance coefficient (h). Here, we explore dominance and selection parameters in humans by fitting models to the site frequency spectrum (SFS) for nonsynonymous mutations. When assuming a single dominance coefficient for all nonsynonymous mutations, we find that numerous h values can fit the data, so long as h is greater than [~]0.15. Moreover, we also observe that theoretically-predicted models with a negative relationship between h and s can also fit the data well, including models with h=0.05 for strongly deleterious mutations. Finally, we use our estimated dominance and selection parameters to inform simulations revisiting the question of whether the out-of-Africa bottleneck has led to differences in genetic load between African and non-African human populations. These simulations suggest that the relative burden of genetic load in non-African populations depends on the dominance model assumed, with slight increases for more weakly recessive models and slight decreases shown for more strongly recessive models. Moreover, these results also demonstrate that models of partially recessive nonsynonymous mutations can explain the observed severity of inbreeding depression in humans, bridging the gap between molecular population genetics and direct measures of fitness in humans. Our work represents a comprehensive assessment of dominance and deleterious variation in humans, with implications for parameterizing models of deleterious variation in humans and other mammalian species. Author SummaryThe dominance coefficient (h) of a mutation determines its impact on organismal fitness when heterozygous. For instance, fully recessive mutations (h=0) have no effects on fitness when heterozygous whereas additive mutations (h=0.5) have an effect that is intermediate to the two heterozygous mutations. The extent to which deleterious mutations may be recessive, additive, or dominant is a key area of study in evolutionary genetics. However, dominance parameters remain poorly known in humans and most other organisms due to a variety of technical challenges. In this study, we aim to constrain the possible set of dominance and selection parameters for amino acid changing mutations in humans. We find that a wide range of models are possible, including models with a theoretically-predicted relationship between h and s. We then use a range of plausible selection and dominance models to explore how deleterious variation may have been shaped by the out-of-Africa bottleneck in humans. Our results highlight the subtle influence of dominance on patterns of genetic load in humans and demonstrate that models of partially recessive mutations at amino-acid-changing sites can explain the observed effects of inbreeding on mortality in humans.

genetics↗

bioRxiv2024

The impact of non-neutral synonymous mutations when inferring selection on non-synonymous mutations

The distribution of fitness effects (DFE) describes the proportions of new mutations that have different effects on reproductive fitness. Accurate measurements of the DFE are important because the DFE is a fundamental parameter in evolutionary genetics and has implications for our understanding of other phenomena like complex disease or inbreeding depression. Current computational methods to infer the DFE for nonsynonymous mutations from natural variation first estimate demographic parameters from synonymous variants to control for the effects of demography and background selection. Then, conditional on these parameters, the DFE is then inferred for nonsynonymous mutations. This approach relies on the assumption that synonymous variants are neutrally evolving. However, some evidence points toward synonymous mutations having measurable effects on fitness. To test whether selection on synonymous mutations affects inference of the DFE of nonsynonymous mutations, we simulated several possible models of selection on synonymous mutations using SLiM and attempted to recover the DFE of nonsynonymous mutations using Fit{partial}a{partial}i, a common method for DFE inference. Our results show that the presence of selection on synonymous variants leads to incorrect inferences of recent population growth. Furthermore, under certain parameter combinations, inferences of the DFE can have an inflated proportion of highly deleterious nonsynonymous mutations. However, this bias can be eliminated if the correct demographic parameters are used for DFE inference instead of the biased ones inferred from synonymous variants. Our work demonstrates how unmodeled selection on synonymous mutations may affect downstream inferences of the DFE.

evolutionary biology↗

bioRxiv2023

Inference of the demographic histories and selective effects of human gut commensal microbiota over the course of human history

Despite the importance of gut commensal microbiota to human health, there is little knowledge about their evolutionary histories, including their population demographic histories and their distributions of fitness effects (DFE) of new mutations. Here, we infer the demographic histories and DFEs of 27 of the most highly prevalent and abundant commensal gut microbial species in North Americans over timescales exceeding human generations using a collection of lineages inferred from a panel of healthy hosts. We find overall reductions in genetic variation among commensal gut microbes sampled from a Western population relative to an African rural population. Additionally, some species in North American microbiomes display contractions in population size and others expansions, potentially occurring at several key historical moments in human history. DFEs across species vary from highly to mildly deleterious, with accessory genes experiencing more drift compared to core genes. Within genera, DFEs tend to be more congruent, reflective of underlying phylogenetic relationships. Taken together, these findings suggest that human commensal gut microbes have distinct evolutionary histories, possibly reflecting the unique roles of individual members of the microbiome.

evolutionary biology↗

bioRxiv2023

The influence of gene flow on population viability in an isolated urban caracal population

Wildlife populations are becoming increasingly fragmented by anthropogenic development. Such small and isolated populations often face an elevated risk of extinction, in part due to inbreeding depression. Here, we examine the genomic consequences of urbanization in a caracal (Caracal caracal) population that has become isolated in the Cape Peninsula region of the city of Cape Town, South Africa and is thought to number [~]50 individuals. We document low levels of migration into the population over the past [~]75 years, with an estimated rate of 1.3 effective migrants per generation. As a consequence of this isolation and small population size, levels of inbreeding are elevated in the contemporary Cape Peninsula population (mean FROH>1Mb=0.20). Inbreeding primarily manifests as long runs of homozygosity >10Mb, consistent with the effects of isolation due to the rapid recent growth of Cape Town. To explore how reduced migration and elevated inbreeding may impact future population dynamics, we parameterized an eco-evolutionary simulation model. We find that if migration rates do not change in the future, the population is expected to decline only slightly, with a low projected risk of extinction. However, if migration rates decline or anthropogenic mortality rates increase, the potential risk of extinction is greatly elevated. To avert a population decline, we suggest that translocating migrants into the Cape Peninsula to initiate a genetic rescue may be warranted in the near future. Our analysis highlights the utility of genomic datasets coupled with computational simulation models for investigating the influence of gene flow on population viability.

evolutionary biology↗

bioRxiv2022

Using computational simulations to quantify genetic load and predict extinction risk

Small and isolated wildlife populations face numerous threats to extinction, among which is the deterioration of fitness due to an accumulation of deleterious genetic variation. Genomic tools are increasingly used to quantify the impacts of deleterious variation in small populations; however, these approaches remain limited by an inability to accurately predict the selective and dominance effects of individual mutations. Computational simulations of deleterious genetic variation offer an alternative and complementary tool that can help overcome these limitations, though such approaches have yet to be widely employed. In this Perspective, we aim to encourage conservation genomics researchers to adopt greater use of computational simulations to aid in quantifying and predicting the threat that deleterious genetic variation poses to extinction. We first provide an overview of the components of a simulation of deleterious genetic variation, describing the key parameters involved in such models. Next, we clarify several misconceptions about an essential simulation parameter, the distribution of fitness effects (DFE) of new mutations, and review recent debates over what the most appropriate DFE parameters are. We conclude by comparing modern simulation tools to those that have long been employed in population viability analysis, weighing the pros and cons of a genomics-informed simulation approach, and discussing key areas for future research. Our aim is that this Perspective will facilitate broader use of computational simulations in conservation genomics, enabling a deeper understanding of the threat that deleterious genetic variation poses to biodiversity.

evolutionary biology↗

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↗

bioRxiv2022

Quantifying the fraction of new mutations that are recessive lethal

The presence and impact of recessive lethal mutations has been widely documented in diploid outcrossing species. However, precise estimates in different species of the proportion of mutations that are recessive lethal remain limited. Here, we attempt to quantify the fraction of new mutations that are recessive lethal using Fit{partial}a{partial}i, a commonly-used method for inferring the distribution of fitness effects (DFE) using the site frequency spectrum. Using simulations, we demonstrate that Fit{partial}a{partial}i cannot accurately estimate the fraction of recessive lethal mutations, as expected given that Fit{partial}a{partial}i assumes that all mutations are additive by default. Consistent with the idea that mis-specification of the dominance model can explain this performance, we find that Fit{partial}a{partial}i can accurately infer the fraction of additive lethal mutations. Moreover, we demonstrate that in both additive and recessive cases, inference of the deleterious non-lethal portion of the DFE is minimally impacted by a small proportion (<10%) of lethal mutations. Finally, as an alternative approach to estimate the proportion of mutations that are recessive lethal, we employ models of mutation-selection-drift balance using existing genomic parameters and segregating recessive lethals estimates for humans and Drosophila melanogaster. In both species, we find that the segregating recessive lethal load can be explained by a very small fraction (<1%) of new nonsynonymous mutations being recessive lethal. Our results refute recent assertions of a much higher recessive lethal mutation fraction (4-5%), while highlighting the need for additional information on the joint distribution of selection and dominance coefficients.

evolutionary biology↗