Biology subjects

Sella, G.

Publications and source records attributed to Sella, G..

18 recordsLinked to original sources

bioRxiv2026

Dynamics of mutators of arbitrary dominance in humans

Recent findings in humans and other species have revealed the presence of "mutator" alleles that increase germline mutation rate across the genome. Such mutators are expected to be selected against because of the additional deleterious alleles that they generate, to a degree that will depend on how much they increase the mutation rate in heterozygotes and homozygotes. To describe their dynamics, we develop a population genetic model of mutation rate modifiers with arbitrary dominance coefficients, in which fitness effects stem from additional germline mutations. We then use it to interpret findings for the seven human mutators identified to date. For six of the seven known mutators, the observed frequencies are well fit by the model and thus consistent with purifying selection arising solely due to their effects on germline mutation rates, although also consistent with a wide range of parameters; in two, the observed frequencies are readily explained by purely recessive fitness effects. The exception is a variant in MUTYH, which is more common than predicted under plausible parameters, for reasons that remain unclear. We also use the model to explore what types of mutators are most likely to be discovered in parents, based on identifying offspring with unexpectedly high numbers of de novo mutations. Although the two first mutators identified by this approach seem to be recessive, our modeling suggests that, for the same effect size, semi-dominant mutators are much more likely to be detected. These findings therefore imply that there are many more modifier sites with recessive effects than semi-dominant ones. More generally, our model provides a framework for interpreting properties of mutators in humans and other species and for learning about the genetic architecture of germline mutation rate variation.

evolutionary biology↗

bioRxiv2026

Genetic architectures of brain-related traits are shaped by strong selective constraints

Genome-wide association studies (GWAS) have identified hundreds of significant loci for psychiatric disorders, yet the strength of these associations remains modest compared to other human complex traits with similar numbers of hits. Whether this pattern reflects statistical artifacts or real biological differences -- and, if the latter, what underlies it -- remains unclear. In addition to psychiatric disorders, we find that other traits with functional enrichment in the central nervous system (CNS), whether binary or quantitative, also share similar genetic architectures, characterized by GWAS hits of limited statistical significance and generally higher allele frequencies. To robustly compare traits that differ in GWAS statistical power, we demonstrate how binarizing a quantitative trait reduces power. This loss of power can be replicated by a matched "effective sample size" on the liability scale. After matching "effective sample sizes", we show that CNS-enriched traits have large mutational target sizes, with contributing variants and genes experiencing stronger selection than those for other traits. Our findings reveal heterogeneity among diseases and provide insights into traits that more effectively capture fitness-relevant processes. More broadly, our results suggest that the genetic architectures of complex traits are shaped by the tissues through which these traits are mediated.

evolutionary biology↗

bioRxiv2025

Regulatory network topology and the genetic architecture of gene expression

In human populations, most of the genetic variance in gene expression can be attributed to trans-acting expression quantitative trait loci (eQTLs) spread across the genome. However, in practice it is difficult to discover these eQTLs, and their cumulative effects on gene expression and complex traits are yet to be fully understood. Here, we assess how properties of the genetic architecture of gene expression constrain the space of plausible gene regulatory networks. We describe a structured causal model of gene expression regulation and consider how it interacts with biologically relevant properties of the gene regulatory network to alter the genomic distribution of expression heritability. Under our model, we find that the genetic architecture of gene expression is shaped in large part by local network motifs and by hub regulators that shorten paths through the network and act as key sources of trans-acting variance. Further, simulated networks with an enrichment of motifs and hub regulators best recapitulate the distribution of cis and trans heritability of gene expression as measured in a recent twin study. Taken together, our results suggest that the architecture of gene expression is sparser and more pleiotropic across genes than would be suggested by naive models of regulatory networks, which has important implications for future studies of complex traits.

genetics↗

bioRxiv2025

Principled measures and estimates of trait polygenicity

The polygenicity of traits is often invoked and sometimes quantified in quantitative, statistical, and human genetics. What do we mean by the polygenicity of a trait? We propose a principled definition that encompasses a range of polygenicity measures. We show that these measures satisfy certain mathematical properties, we argue that these properties are sensible if not necessary, and we show that, conversely, measures that satisfy these properties also satisfy our definition. We consider four specific measures in greater detail, describe how they differ and show that three of them can be estimated from GWAS summary statistics using an existing method, Fourier Mixture Regression. We estimate these measures for 36 traits in humans. We find a dearth of traits with polygenicity values that fall within the large gap between Mendelian and highly polygenic traits. We discuss the evolutionary and cellular processes underlying trait polygenicity.

genetics↗

bioRxiv2025

Mutation-selection-drift balance models of complex diseases

Genetic variation that influences complex disease susceptibility is introduced into the population by mutation and removed by natural selection and genetic drift. This mutation-selection-drift-balance (MSDB) shapes the prevalence of a disease and its genetic architecture. To date, however, MSDB has only been modeled for monogenic (Mendelian) diseases. Here, we develop a MSDB model for complex disease susceptibility: we assume that genotype relates to disease risk according to the canonical liability threshold model and that selection on variants affecting risk derives from the fitness cost of the disease, and focus on diseases that are highly polygenic, entail a substantial fitness cost, and are neither extremely common in the population nor exceedingly rare. Contrasting model predictions with GWAS and other findings in humans suggests that directional selection plays little role in shaping common genetic variation affecting complex disease susceptibility but might substantially affect rare, large effect variation. In turn, common variation affecting complex disease susceptibility appears to be dominated by pleiotropic stabilizing selection on other traits. Our results further suggest that current estimates of disease heritability are likely biased. More generally, our model provides a better understanding of the evolutionary processes that shape the architecture and prevalence of complex diseases.

genetics↗

bioRxiv2025

When should adaptation arise from a polygenic response versus few large effect changes?

The question of when adaptation involves genetic changes of large effect versus a polygenic response traces back to the foundation of evolutionary biology. While there are compelling reasons to expect polygenic adaptation to be common, direct evidence is still lacking. In turn, there are hundreds of examples of large effect adaptations across species, but it is unclear whether they are a common occurrence in any given species. Synthesizing the different lines of evidence is further complicated by differences in study designs. Here, we reframe this long-standing question in terms of properties of the trait under selection. We ask how the genetic basis of adaptation is expected to depend on key aspects of the genetic variation in the trait and on the extent of changes in selection pressures on it (i.e., the "traits ecology"). We consider a quantitative trait subject to stabilizing selection and model the response to selection when a population at mutation-selection-drift balance experiences a sudden shift in the optimal value. Using this model, we delimit how the contributions of large effect and polygenic changes to adaptation depend on the genetics and ecology of the trait, as well as on other salient factors. We thus formulate testable predictions about when different modes of adaptation are expected and outline a framework within which to interpret disparate sources of evidence about the genetic basis of adaptation.

evolutionary biology↗

bioRxiv2024

Specificity, length, and luck: How genes are prioritized by rare and common variant association studies

Standard genome-wide association studies (GWAS) and rare variant burden tests are essential tools for identifying trait-relevant genes. Although these methods are conceptually similar, we show by analyzing association studies of 209 quantitative traits in the UK Biobank that they systematically prioritize different genes. This raises the question of how genes should ideally be prioritized. We propose two prioritization criteria: 1) trait importance -- how much a gene quantitatively affects a trait; and 2) trait specificity -- a genes importance for the trait under study relative to its importance across all traits. We find that GWAS prioritize genes near trait-specific variants, while burden tests prioritize trait-specific genes. Because non-coding variants can be context specific, GWAS can prioritize highly pleiotropic genes, while burden tests generally cannot. Both study designs are also affected by distinct trait-irrelevant factors, complicating their interpretation. Our results illustrate that burden tests and GWAS reveal different aspects of trait biology and suggest ways to improve their interpretation and usage.

genomics↗

bioRxiv2024

Gene regulatory network structure informs the distribution of perturbation effects

Gene regulatory networks (GRNs) govern many core developmental and biological processes underlying human complex traits. Even with broad-scale efforts to characterize the effects of molecular perturbations and interpret gene coexpression, it remains challenging to infer the architecture of gene regulation in a precise and efficient manner. Key properties of GRNs, like hierarchical structure, modular organization, and sparsity, provide both challenges and opportunities for this objective. Here, we seek to better understand properties of GRNs using a new approach to simulate their structure and model their function. We produce realistic network structures with a novel generating algorithm based on insights from small-world network theory, and we model gene expression regulation using stochastic differential equations formulated to accommodate modeling molecular perturbations. With these tools, we systematically describe the effects of gene knockouts within and across GRNs, finding a subset of networks that recapitulate features of a recent genome-scale perturbation study. With deeper analysis of these exemplar networks, we consider future avenues to map the architecture of gene expression regulation using data from cells in perturbed and unperturbed states, finding that while perturbation data are critical to discover specific regulatory interactions, data from unperturbed cells may be sufficient to reveal regulatory programs.

genetics↗

bioRxiv2023

Disentangling sources of clock-like mutations in germline and soma

The rates of mutations vary across cell types. To identify causes of this variation, mutations are often decomposed into a combination of the single base substitution (SBS) "signatures" observed in germline, soma and tumors, with the idea that each signature corresponds to one or a small number of underlying mutagenic processes. Two such signatures turn out to be ubiquitous across cell types: SBS signature 1, which consists primarily of transitions at methylated CpG sites caused by spontaneous deamination, and the more diffuse SBS signature 5, which is of unknown etiology. In cancers, the number of mutations attributed to these two signatures accumulates linearly with age of diagnosis, and thus the signatures have been termed "clock-like." To better understand this clocklike behavior, we develop a mathematical model that includes DNA replication errors, unrepaired damage, and damage repaired incorrectly. We show that mutational signatures can exhibit clocklike behavior because cell divisions occur at a constant rate and/or because damage rates remain constant over time, and that these distinct sources can be teased apart by comparing cell lineages that divide at different rates. With this goal in mind, we analyze the rate of accumulation of mutations in multiple cell types, including soma as well as male and female germline. We find no detectable increase in SBS signature 1 mutations in neurons and only a very weak increase in mutations assigned to the female germline, but a significant increase with time in rapidly-dividing cells, suggesting that SBS signature 1 is driven by rounds of DNA replication occurring at a relatively fixed rate. In contrast, SBS signature 5 increases with time in all cell types, including post-mitotic ones, indicating that it accumulates independently of cell divisions; this observation points to errors in DNA repair as the key underlying mechanism. Thus, the two "clock-like" signatures observed across cell types likely have distinct origins, one set by rates of cell division, the other by damage rates.

genetics↗

bioRxiv2022

Simple scaling laws control the genetic architectures of human complex traits

Genome-wide association studies have revealed that the genetic architectures of complex traits vary widely, including in terms of the numbers, effect sizes, and allele frequencies of significant hits. However, at present we lack a principled way of understanding the similarities and differences among traits. Here, we describe a probabilistic model that combines mutation, drift, and stabilizing selection at individual sites with a genome-scale model of phenotypic variation. In this model, the architecture of a trait arises from the distribution of selection coefficients of mutations and from two scaling parameters. We fit this model for 95 diverse, highly polygenic quantitative traits from the UK Biobank. Notably, we infer similar distributions of selection coefficients across all these traits. This shared distribution implies that differences in architectures of highly polygenic traits arise mainly from the two scaling parameters: the mutational target size and heritability per site, which vary by orders of magnitude across traits. When these two scale factors are accounted for, the architectures of all 95 traits are nearly identical.

genetics↗

bioRxiv2022

Down the Penrose stairs: How selection for fewer recombination hotspots maintains their existence

In many species, meiotic recombination events tend to occur in narrow intervals of the genome, known as hotspots. In humans and mice, double strand break (DSB) hotspot locations are determined by the DNA-binding specificity of the zinc finger array of the PRDM9 protein, which is rapidly evolving at residues in contact with DNA. Previous models explained this rapid evolution in terms of the need to restore PRDM9 binding sites lost to gene conversion over time, under the assumption that more PRDM9 binding always leads to more DSBs. In recent experimental work, however, it has become apparent that PRDM9 binding on both homologs facilitates DSB repair, and moreover that, in the absence of enough symmetric binding, meiosis no longer progresses reliably. We therefore consider the possibility that the benefit of PRDM9 stems from its role in coupling DSB formation and efficient repair. To this end, we model the evolution of PRDM9 from first principles: from its binding dynamics to the population processes that govern the evolution of the zinc finger array and its binding sites in the genome. As we show, the loss of a small number of strong binding sites leads to the use of a greater number of weaker ones, resulting in a sharp reduction in symmetric binding and favoring new PRDM9 alleles that restore the use of a smaller set of strong binding sites. This decrease in PRDM9 binding symmetry and in its ability to promote DSB repair drive the rapid zinc finger turnover. These results imply that the advantage of new PRDM9 alleles is in limiting the number of binding sites used effectively, rather than in increasing net PRDM9 binding, as previously believed. By extension, our model suggests that the evolutionary advantage of hotspots may have been to increase the efficiency of DSB repair and/or homolog pairing.

evolutionary biology↗

bioRxiv2021

The impact of genetic modifiers on variation in germline mutation rates within and among human populations

Mutation rates and spectra differ among human populations. Here, we examine whether this variation could be explained by evolution at mutation modifiers. To this end, we consider genetic modifier sites at which mutations, "mutator alleles", increase genome-wide mutation rates and model their evolution under purifying selection due to the additional deleterious mutations that they cause, genetic drift, and demographic processes. We solve the model analytically for a constant population size and characterize how evolution at modifier sites impacts variation in mutation rates within and among populations. We then use simulations to study the effects of modifier sites under a plausible demographic model for Africans and Europeans. When comparing populations that evolve independently, weakly selected modifier sites (2Nes {approx} 1), which evolve slowly, contribute the most to variation in mutation rates. In contrast, when populations recently split from a common ancestral population, strongly selected modifier sites (2Nes >> 1), which evolve rapidly, contribute the most to variation between them. Moreover, a modest number of modifier sites (e.g., 10 per mutation type in the standard classification into 96 types) subject to moderate to strong selection (2Nes > 1) could account for the variation in mutation rates observed among human populations. If such modifier sites indeed underlie differences among populations, they should also cause variation in mutation rates within populations and their effects should be detectable in pedigree studies.

evolutionary biology↗

bioRxiv2021

Broad-scale variation in human genetic diversity levels is predicted by purifying selection on coding and non-coding elements

Analyses of genetic variation in many taxa have established that neutral genetic diversity is shaped by natural selection at linked sites. Whether the mode of selection is primarily the fixation of strongly beneficial alleles (selective sweeps) or purifying selection on deleterious mutations (background selection) remains unknown, however. We address this question in humans by fitting a model of the joint effects of selective sweeps and background selection to autosomal polymorphism data from the 1000 Genomes Project. After controlling for variation in mutation rates along the genome, a model of background selection alone explains [~]60% of the variance in diversity levels at the megabase scale. Adding the effects of selective sweeps driven by adaptive substitutions to the model does not improve the fit, and when both modes of selection are considered jointly, selective sweeps are estimated to have had little or no effect on linked neutral diversity. The regions under purifying selection are best predicted by phylogenetic conservation, with [~]80% of the deleterious mutations affecting neutral diversity occurring in non-exonic regions. Thus, background selection is the dominant mode of linked selection in humans, with marked effects on diversity levels throughout autosomes.

evolutionary biology↗

bioRxiv2019

Polygenic adaptation after a sudden change in environment

Polygenic adaptation is thought to be ubiquitous, yet remains poorly understood. Here, we model this process analytically, in the plausible setting of a highly polygenic, quantitative trait that experiences a sudden shift in the fitness optimum. We show how the mean phenotype changes over time, depending on the effect sizes of loci that contribute to variance in the trait, and characterize the allele dynamics at these loci. Importantly, we describe the two phases of the allele dynamics: a rapid phase in which directional selection introduces small frequency differences between alleles whose effects are aligned with or opposed to the shift, which ultimately lead to small differences in their probability of fixation during a second, longer phase, governed by stabilizing selection. As we discuss, our key results should hold in more general settings, and have important implications for efforts to identify the genetic basis of adaptation in humans and other species.

evolutionary biology↗

bioRxiv2019

Changes in life history and population size can explain relative neutral diversity levels on X and autosomes in extant human populations

In human populations, relative levels of neutral polymorphism on the X and autosomes differ markedly from each other and from the naive theoretical expectation of [3/4]. These differences have attracted considerable attention, with studies highlighting several potential causes, including male biased mutation and reproductive variance, historical changes in population size, and selection at linked loci. We revisit this question in light of our new theory about the effects of life history and given pedigree-based estimates of the dependence of human mutation rates on sex and age. We demonstrate that life history effects, particularly higher generation times in males than females, likely had multiple effects on human X-to-autosomes (X:A) polymorphism ratios, through the extent of male mutation bias, the equilibrium X:A ratios of effective population sizes, and differential responses to changes in population size. We also show that the standard approach of using divergence between species to correct for the male bias in mutation results in biased estimates of X:A effective population size ratios. We obtain alternative estimates using pedigree-based estimates of the male mutation bias, which reveal X:A ratios of effective population sizes to be considerably greater than previously appreciated. We then show that the joint effects of historical changes in life history and population size can explain X:A polymorphism ratios in extant human populations. Our results suggest that ancestral human populations were highly polygynous; that non-African populations experienced a substantial reduction in polygyny and/or increase in male-biased generation times around the out of Africa bottleneck; and that extant diversity levels were affected by fairly recent changes in sex-specific life history.\n\nSignificance StatementAll else being equal, the ratio of diversity levels on X and autosomes at selectively neutral sites should mirror the ratio of their numbers in the population and thus equal [3/4]. In reality, the ratios observed across human populations differ markedly from [3/4] and from each other. Because from a population perspective, autosomes spend an equal number of generations in both sexes while the X spends twice as many generations in females, these departures from the naive expectations likely reflect differences between male and female life histories and their effects on mutation processes. Indeed, we show that the ratios observed across human populations can be explained by demographic history, assuming plausible, sex-specific mutation rates, generation times and reproductive variances.

evolutionary biology↗

bioRxiv2018

Measuring intolerance to mutation in human genetics

In numerous applications, from working with animal models to mapping the genetic basis of human disease susceptibility, it is useful to know whether a single disrupting mutation in a gene is likely to be deleterious1-4. With this goal in mind, a number of measures have been developed to identify genes in which protein-truncating variants (PTVs), or other types of mutations, are absent or kept at very low frequency in large population samples--genes that appear \"intolerant to mutation\"3,5-9. One measure in particular, pLI, has been widely adopted7. By contrasting the observed versus expected number of PTVs, it aims to classify genes into three categories, labelled \"null\", \"recessive\" and \"haploinsufficient\"7. Such population genetic approaches can be useful in many applications. As we clarify, however, these measures reflect the strength of selection acting on heterozygotes, and not dominance for fitness or haploinsufficiency for other phenotypes.

genetics↗

bioRxiv2018

Evolutionary tradeoffs and the structure of allelic polymorphisms

Populations of organisms show prevalent genetic differences called polymorphisms. Understanding the effects of polymorphisms is of central importance in biology and medicine. Here, we ask which polymorphisms occur at high frequency when organisms evolve under tradeoffs between multiple tasks. Multiple tasks present a problem, because it is not possible to be optimal at all tasks simultaneously and hence compromises are necessary. Recent work indicates that tradeoffs lead to a simple geometry of phenotypes in the space of traits: phenotypes fall on the Pareto front, which is shaped as a polytope: a line, triangle, tetrahedron etc. The vertices of these polytopes are the optimal phenotypes for a single task. Up to now, work on this Pareto approach has not considered its genetic underpinnings. Here, we address this by asking how the polymorphism structure of a population is affected by evolution under tradeoffs. We simulate a multi-task selection scenario, in which the population evolves to the Pareto front: the line segment between two archetypes or the triangle between three archetypes. We find that polymorphisms that become prevalent in the population have pleiotropic phenotypic effects that align with the Pareto front. Similarly, epistatic effects between prevalent polymorphisms are parallel to the front. Alignment with the front occurs also for asexual mating. Alignment is reduced when drift or linkage is strong, and is replaced by a more complex structure in which many perpendicular allele effects cancel out. Aligned polymorphism structure allows mating to produce offspring that stand a good chance of being optimal multi-taskers in at least one of the locales available to the species.

systems biology↗

bioRxiv2017

Life history effects on neutral polymorphism levels of autosomes and sex chromosomes

All else being equal, the ratio of genetic diversity levels on X and autosomes at selectively neutral sites should mirror the ratio of their numbers in the population and thus equal [3/4]. Because X chromosomes spend twice as many generations in females as in males, however, the ratio of diversity levels is also affected by sex differences in life history. The effects of life history on diversity levels, notably those of sex-specific age structures and reproductive variances, have been studied for decades, yet existing theory relies on many parameters that are difficult to measure and lacks generality in ways that limit their applicability. We derive general yet simple expressions for these effects and show that life history effects on X-to-autosome (X:A) ratios of diversity levels depend only on sex-ratios of mutation rates, generation times, and reproductive variances. These results reveal that changing the sex-ratio of generation times has opposite effects on X:A ratios of polymorphism and divergence. They also explain how sex-specific life histories modulate the response of X:A polymorphism ratios to changes in population size. More generally, they clarify that sex-specific life history--generation times in particular--should have a marked effect on X:A polymorphism ratios in many taxa and enable the investigation of these effects.\n\nSignificance StatementUnderstanding the determinants of neutral diversity patterns on autosomes and sex chromosomes provides a bedrock for our interpretation of population genetic data. Sex-specific age-structure and variation in reproductive success have long been thought to affect neutral diversity, but theoretical descriptions of these effects were complicated and/or lacked in generality, stymying attempts to relate diversity patterns of species with their life history. We derive general yet simple expressions for these effects, which clarify how they impact neutral diversity and should enable studies of relative diversity levels on the autosomes and sex chromosomes in many taxa.

evolutionary biology↗