Biology subjects

Schraiber, J. G.

Publications and source records attributed to Schraiber, J. G..

16 recordsLinked to original sources

bioRxiv2026

Accounting for recurrent mutation in the frequency spectrum of rare alleles

As whole-genome and whole-exome datasets increase in size, they uncover alleles at lower and lower frequencies in the population. Samples of rare alleles often include recurrent mutations, where derived alleles are identical by state and not by descent. As a result, the site frequency spectrum (SFS) becomes challenging to analyze because it is strongly dependent on the mutation rate. To overcome this hurdle, we define the single mutation frequency spectrum (SMFS), which is the frequency spectrum of alleles descendant from a single mutational event. For rare alleles, the SFS with recurrent mutation is then a weighted sum of the convolutions of the SMFS with itself. This simple, yet powerful, model decouples recurrent mutation from the population genetic processes giving rise to the SMFS, such as genetic drift and selection. We show how both forward-in-time and backward-in-time models with recurrent mutations can be recast in terms of the SMFS. We then develop a method for combinatorial hierarchic estimation of the SMFS (which we name CHES). We apply this simple, yet robust, method to a human exome sequencing dataset to show that the SMFS with recurrent mutation can account for SFS differences between low and high mutation rate sites. The inferred SMFS shows an approximate scaling law with allele frequencies inconsistent with both a constant population size and an exponentially growing population model. Lastly, we use our model to compare the expected and observed proportions of missense and stop-gain mutations in the human exome, using this disparity to infer the strength of selection on these classes of mutations. Our combined results show how the SMFS can explain the dependency of the SFS on the mutation rate and how it reflects human demographic and evolutionary history.

genetics↗

bioRxiv2025

Characterizing and mitigating confounding by unreplicated evolutionary events in phylogenetic regression

When researchers want to test for a direct effect of one attribute on another using interspecific data, they need to account for the confounding influence of other attributes that have phylogenetic structure. Accordingly, a suite of approaches have been developed for this purpose, mostly notably the use of phylogenetic regression. However, it has been shown that such methods can be misled by evolutionary events with large effects. We suggest that one widely-applicable solution to this problem can be found by borrowing both from the statistical genetics literature and by digging into the past of phylogenetic comparative methods. In conventional phylogenetic regression, the phylogenetic variance-covariance matrix is included as a random-effect term; here, we argue that it can be advantageous to also include the leading eigenvectors of this matrix as fixed effects. Building on the recent results of Schraiber et al. (2024), we use mathematical analysis and simulations to show under which scenarios this hybrid strategy is effective. We develop a novel approach for visualizing the contributions of different branches of the phylogeny to the eigenvectors of the variance-covariance matrix. We also illustrate how quantile-quantile plots can be used to assess whether the phylogenetic structure has been effectively controlled. We then apply the mixed approach to investigate the co-evolution of gene expression levels of different genes across the phylogeny of cichlid fishes; we show that including the leading eigenvectors likely reduces the false positive rate. To facilitate the use of our visualization approach and of including eigenvectors in phylogenetic regression models, we present a new R package called EIGER. We argue that the approaches we explore here can both help address the statistical problems that arise from large, historical events and more generally, provide richer insights into the nature of phylogenetic confounding.

evolutionary biology↗

bioRxiv2025

Observational epidemiological studies can mitigate genetic confounding with the genetic relatedness matrix

Observational studies are commonly used in psychology and epidemiology to identify risk factors correlated with health outcomes. However, these studies are vulnerable to confounding when shared genetic variation influences both the putative risk factor and outcome. Researchers have historically controlled for this type of genetic confounding using polygenic scores, but these scores are often noisy and biased estimators of a traits genetic component. Here, we develop a method that leverages a genetic relatedness matrix (GRM) to control genetic confounding when testing for non-genetic risk factors. In simulations, we find that our method outperforms existing approaches, particularly at sample sizes that are large by the standards of much human research but smaller than datasets often used in human genetics. We also demonstrate that existing methods are susceptible to poor GWAS portability, whereas our method is inherently robust to such concerns. Finally, we apply our method to the UK Biobank to re-analyze social risk factors for health outcomes in previously understudied cohorts. Significance statementLarge observational datasets are often used in epidemiology or social sciences to identify risk factors associated with health outcomes. However, these studies can be misleading when the putative risk factor and outcome are both complex genetic traits: a correlated genetic basis can create spurious associations between a putative risk factor and outcome. Existing approaches to address this problem typically rely on GWAS summary statistics, which require biobank-scale datasets. In this work, we introduce an alternative method that uses a genetic relatedness matrix (GRM) to control for genetic confounding directly. We show that our approach is closely related to existing methods but outperforms them in smaller datasets, making it an especially valuable tool for researchers who work on understudied traits and populations.

genetics↗

bioRxiv2025

Biophysical constraints on mRNA decay rates shape macroevolutionary divergence in steady-state abundances

Interspecific comparisons of cell-type-specific gene expression levels can provide information about the evolutionary processes that drove divergence between species. From these comparisons, it is now evident that the predominant mode of gene expression evolution has been stabilizing selection, both on the steady-state (mean) protein levels as well as on the mRNA levels with additional lineage-specific shifts resulting from directional selection. However, as all previous work has used bulk RNA measurements, it has been impossible to determine which of the many cellular processes that contribute to mean abundances are highly constrained and which are more evolutionary labile. Assessing this is further complicated by the expectation that components of complex systems will evolve over time independent of changes in the selective regime so long as the net output of a system (i.e., mean expression) remains near the evolutionary optima. This process, known as evolutionary systems drift (ESD), has been frequently invoked as a non-adaptive explanation for changes in cellular phenotypes but has never been quantitatively tested or accounted for in any statistical test for selective constraints. Here, we develop a new paradigm that addresses both of these open problems simultaneously. Using single-cell expression data and biophysical models, we estimate mRNA transcriptional bursting rates, splicing rates, and decay rates across multiple vertebrate species. We then derive new mathematical results that describe how these various biophysical parameters are expected to co-evolve under ESD and then test whether we need additional evolutionary constraints to explain the divergences in these parameters. We find evidence that the biophysical parameters are indeed evolving in a coordinated manner as predicted by ESD and that there are additional strong constraints on transcriptional bursting, likely as a consequence of selection to reduce noise in expression. More broadly, this work opens up a whole new approach for studying the evolutionary dynamics of complex cellular systems.

evolutionary biology↗

bioRxiv2024

Estimation of demography and mutation rates from one million haploid genomes

As genetic sequencing costs have plummeted, datasets with sizes previously un-thinkable have begun to appear. Such datasets present new opportunities to learn about evolutionary history, particularly via rare alleles that record the very recent past. However, beyond the computational challenges inherent in the analysis of many large-scale datasets, large population-genetic datasets present theoretical problems. In particular, the majority of population-genetic tools require the assumption that each mutant allele in the sample is the result of a single mutation (the "infinite sites" assumption), which is violated in large samples. Here, we present DR EVIL, a method for estimating mutation rates and recent demographic history from very large samples. DR EVIL avoids the infinite-sites assumption by using a diffusion approximation to a branching-process model with recurrent mutation. The branching-process approach limits the method to rare alleles, but, along with recent results, renders tractable likelihoods with recurrent mutation. We show that DR EVIL performs well in simulations and apply it to rare-variant data from a million haploid samples, identifying a signal of mutation-rate heterogeneity within commonly analyzed classes and predicting that in modern sample sizes, most rare variants at sites with high mutation rates represent the descendants of multiple mutation events.

genomics↗

bioRxiv2024

Convergent expansions of keystone gene families drive metabolic innovation in a major eukaryotic clade

Many remarkable innovations have repeatedly occurred across vast evolutionary distances. When convergent traits emerge on the tree of life, they are sometimes driven by the same underlying gene families, while other times many different gene families are involved. Conversely, a gene family may be repeatedly recruited for a single trait or many different traits. To understand the general rules governing convergence at both genomic and phenotypic levels, we systematically tested associations between 56 binary metabolic traits and gene count in 14,710 gene families from 993 species of Saccharomycotina yeasts. Using a recently developed phylogenetic approach that reduces spurious correlations, we discovered that gene family expansion and contraction was significantly linked to trait gain and loss in 45/56 (80%) of traits. While 601/746 (81%) of significant gene families were associated with only one trait, we also identified several keystone gene families that were significantly associated with up to 13/56 (23%) of all traits. These results indicate that metabolic innovations in yeasts are governed by a narrow set of major genetic elements and mechanisms.

evolutionary biology↗

bioRxiv2024

Phylogenetic analysis reveals how selection and mutation shape the coevolution of mRNA and protein abundances

The regulatory mechanisms that shape mRNA and protein abundances are intensely studied. Much less is known about the evolutionary processes that shape the relationship between these two levels of gene expression. To disentangle the contributions of mutational and selective processes, we derive a novel phylogenetic model and fit it to multi-species data from mammalian skin tissue. We find that over macroevolutionary time: 1) there has been strong stabilizing selection on protein abundances; 2) mutations impacting mRNA abundances have minimal influence on protein abundances; 3) mRNA abundances are under selection to track protein abundances, and 4) mRNA abundances adapt more quickly than protein abundances due to increased mutational opportunity. We find additional support for these findings by comparing gene-specific parameter estimates from our model to human functional genomic data. More broadly, our new phylogenetic approach provides a foundation for testing hypotheses about the processes that led to divergence in gene expression.

evolutionary biology↗

bioRxiv2024

Estimating allele frequencies, ancestry proportions and genotype likelihoods in the presence of mapping bias

Population genomic analyses rely on an accurate and unbiased characterization of the genetic composition of the studied population. For short-read, high-throughput sequencing data, mapping sequencing reads to a linear reference genome can bias population genetic inference due to mismatches in reads carrying non-reference alleles. In this study, we investigate the impact of mapping bias on allele frequency estimates from pseudohaploid data, commonly used in ultra-low coverage ancient DNA sequencing. To mitigate mapping bias, we propose an empirical adjustment to genotype likelihoods. Using data from the 1000 Genomes Project, we find that our new method improves allele frequency estimation. To test a downstream application, we simulate ancient DNA data with realistic post-mortem damage to compare widely used methods for estimating ancestry proportions under different scenarios, including reference genome selection, population divergence, and sequencing depth. Our findings reveal that mapping bias can lead to differences in estimated admixture proportion of up to 4% depending on the reference population. However, the choice of method has a much stronger impact, with some methods showing differences of 10%. qpAdm appears to perform best at estimating simulated ancestry proportions, but it is sensitive to mapping bias and its applicability may vary across species due to its requirement for additional populations beyond the sources and target population. Our adjusted genotype likelihood approach largely mitigates the effect of mapping bias on genome-wide ancestry estimates from genotype likelihood-based tools. However, it cannot account for the bias introduced by the method itself or the noise in individual site allele frequency estimates due to low sequencing depth. Overall, our study provides valuable insights for obtaining more precise estimates of allele frequencies and ancestry proportions in empirical studies.

evolutionary biology↗

bioRxiv2024

A path integral approach for allele frequency dynamics under polygenic selection

Many phenotypic traits have a polygenic genetic basis, making it challenging to learn their genetic architectures and predict individual phenotypes. One promising avenue to resolve the genetic basis of complex traits is through evolve-and-resequence experiments, in which laboratory populations are exposed to some selective pressure and trait-contributing loci are identified by extreme frequency changes over the course of the experiment. However, small laboratory populations will experience substantial random genetic drift, and it is difficult to determine whether selection played a roll in a given allele frequency change. Predicting how much allele frequencies change under drift and selection had remained an open problem well into the 21st century, even those contributing to simple, monogenic traits. Recently, there have been efforts to apply the path integral, a method borrowed from physics, to solve this problem. So far, this approach has been limited to genic selection, and is therefore inadequate to capture the complexity of quantitative, highly polygenic traits that are commonly studied. Here we extend one of these path integral methods, the perturbation approximation, to selection scenarios that are of interest to quantitative genetics. In particular, we derive analytic expressions for the transition probability (i.e., the probability that an allele will change in frequency from x, to y in time t) of an allele contributing to a trait subject to stabilizing selection, as well as that of an allele contributing to a trait rapidly adapting to a new phenotypic optimum. We use these expressions to characterize the use of allele frequency change to test for selection, as well as explore optimal design choices for evolve-and-resequence experiments to uncover the genetic architecture of polygenic traits under selection.

evolutionary biology↗

bioRxiv2024

Unifying approaches from statistical genetics and phylogenetics for mapping phenotypes in structured populations

In both statistical genetics and phylogenetics, a major goal is to identify correlations between genetic loci or other aspects of the phenotype or environment and a focal trait. In these two fields, there are sophisticated but disparate statistical traditions aimed at these tasks. The disconnect between their respective approaches is becoming untenable as questions in medicine, conservation biology, and evolutionary biology increasingly rely on integrating data from within and among species, and once-clear conceptual divisions are becoming increasingly blurred. To help bridge this divide, we derive a general model describing the covariance between the genetic contributions to the quantitative phenotypes of different individuals. Taking this approach shows that standard models in both statistical genetics (e.g., Genome-Wide Association Studies; GWAS) and phylogenetic comparative biology (e.g., phylogenetic regression) can be interpreted as special cases of this more general quantitative-genetic model. The fact that these models share the same core architecture means that we can build a unified understanding of the strengths and limitations of different methods for controlling for genetic structure when testing for associations. We develop intuition for why and when spurious correlations may occur using analytical theory and conduct population-genetic and phylogenetic simulations of quantitative traits. The structural similarity of problems in statistical genetics and phylogenetics enables us to take methodological advances from one field and apply them in the other. We demonstrate this by showing how a standard GWAS technique--including both the genetic relatedness matrix (GRM) as well as its leading eigenvectors, corresponding to the principal components of the genotype matrix, in a regression model--can mitigate spurious correlations in phylogenetic analyses. As a case study of this, we re-examine an analysis testing for co-evolution of expression levels between genes across a fungal phylogeny, and show that including covariance matrix eigenvectors as covariates decreases the false positive rate while simultaneously increasing the true positive rate. More generally, this work provides a foundation for more integrative approaches for understanding the genetic architecture of phenotypes and how evolutionary processes shape it.

genomics↗

bioRxiv2023

Heritability within groups is uninformative about differences among groups: cases from behavioral, evolutionary, and statistical genetics

Without the ability to control or randomize environments (or genotypes), it is difficult to determine the degree to which observed phenotypic differences between two groups of individuals are due to genetic vs. environmental differences. However, some have suggested that these concerns may be limited to pathological cases, and methods have appeared that seem to give--directly or indirectly--some support to claims that aggregate heritable variation within groups can be related to heritable variation among groups. We consider three families of approaches: the "between-group heritability" sometimes invoked in behavior genetics, the statistic PST used in empirical work in evolutionary quantitative genetics, and methods based on variation in ancestry in an admixed population, used in anthropological and statistical genetics. We take up these examples to show mathematically that information on within-group genetic and phenotypic information in the aggregate cannot separate among-group differences into genetic and environmental components, and we provide simulation results that support our claims. We discuss these results in terms of the long-running debate on this topic.

genetics↗

bioRxiv2023

A global catalog of whole-genome diversity from 233 primate species

The rich diversity of morphology and behavior displayed across primate species provides an informative context in which to study the impact of genomic diversity on fundamental biological processes. Analysis of that diversity provides insight into long-standing questions in evolutionary and conservation biology, and is urgent given severe threats these species are facing. Here, we present high coverage whole-genome data from 233 primate species representing 86% of genera and all 16 families. This dataset was used, together with fossil calibration, to create a nuclear DNA phylogeny and to reassess evolutionary divergence times among primate clades. We found within-species genetic diversity across families and geographic regions to be associated with climate and sociality, but not with extinction risk. Furthermore, mutation rates differ across species, potentially influenced by effective population sizes. Lastly, we identified extensive recurrence of missense mutations previously thought to be human-specific. This study will open a wide range of research avenues for future primate genomic research. One-Sentence SummaryThe whole genome sequences of 233 primate species provide insight into the determinants of genetic diversity, phylogenomics, and human uniqueness.

genomics↗

bioRxiv2023

Tree-based QTL mapping with expected local geneticrelatedness matrices

Understanding the genetic basis of complex phenotypes is a central pursuit of genetics. Genome-wide Association Studies (GWAS) are a powerful way to find genetic loci associated with phenotypes. GWAS are widely and successfully used, but they face challenges related to the fact that variants are tested for association with a phenotype independently, whereas in reality variants at different sites are correlated because of their shared evolutionary history. One way to model this shared history is through the ancestral recombination graph (ARG), which encodes a series of local coalescent trees. Recent computational and methodological breakthroughs have made it feasible to estimate approximate ARGs from large-scale samples. Here, we explore the potential of an ARG-based approach to quantitative-trait locus (QTL) mapping, echoing existing variance-components approaches. We propose a framework that relies on the conditional expectation of a local genetic relatedness matrix given the ARG (local eGRM). Simulations show that our method is especially beneficial for finding QTLs in the presence of allelic heterogeneity. By framing QTL mapping in terms of the estimated ARG, we can also facilitate the detection of QTLs in understudied populations. We use local eGRM to identify a large-effect BMI locus, the CREBRF gene, in a sample of Native Hawaiians in which it was not previously detectable by GWAS because of a lack of population-specific imputation resources. Our investigations can provide intuition about the benefits of using estimated ARGs in population- and statistical-genetic methods in general.

genetics↗

bioRxiv2020

Ancestry Inference Using Reference Labeled Clusters of Haplotypes

We present ARCHes, a fast and accurate haplotype-based approach for inferring an individuals ancestry composition. Our approach works by modeling haplotype diversity from a large, admixed cohort of hundreds of thousands, then annotating those models with population information from reference panels of known ancestry. The running time of ARCHes does not depend on the size of a reference panel because training and testing are separate processes, and the inferred population-annotated haplotype models can be written to disk and reused to label large test sets in parallel (in our experiments, it averages less than one minute to assign ancestry from 32 populations to 1,001 sections of a genotype using 10 CPU). We test ARCHes on public data from the 1,000 Genomes Project and HGDP as well as simulated examples of known admixture. Our results demonstrate that ARCHes outperforms RFMix at correctly assigning both global and local ancestry at finer population scales regardless of the amount of population admixture. Author SummaryHuman DNA is inherited from ancestors that come from different populations across the globe and across time. Being able to identify which of those populations make up an individuals DNA, how much they contribute, and on which chromosomes, is currently an important open research problem with many applications in the study of human diversity and history. As DNA sequencing and genotyping technology has developed, we have greater and greater amounts of data, which allows for the development of new sophisticated machine learning methods to approach this problem, and presents a need to process large amounts of data efficiently. These methods learn from examples of DNA data from known populations, and must be robust to differences in size and diversity among those reference populations. We present a new approach to this problem called ARCHes (Ancestry inference using Reference labeled Clusters of Haplotypes), that models the global diversity of small segments of human DNA sequence ("haplotypes"), and the extent to which these haplotypes are associated with each of a set of population reference panels. It then computes the most likely population assignments and the points along the genome where the populations change. Our experiments show that ARCHes has superior accuracy compared to a state-of-the-art method in identifying source populations and their locations on the genome, regardless of the number of different populations present in the genome, how closely related those populations are. ARCHes is also able to model populations despite having a small amount of population reference DNA data.

genomics↗

bioRxiv2018

Evolutionary inferences about quantitative traits are affected by underlying genealogical discordance

Modern phylogenetic methods used to study how traits evolve often require a single species tree as input, and do not take underlying gene tree discordance into account. Such approaches may lead to errors in phylogenetic inference because of hemiplasy -- the process by which single changes on discordant trees appear to be homoplastic when analyzed on a fixed species tree. Hemiplasy has been shown to affect inferences about discrete traits, but it is still unclear whether complications arise when quantitative traits are analyzed. In order to address this question and to characterize the effect of hemiplasy on traits controlled by a large number of loci, we present a multispecies coalescent model for quantitative traits evolving along a species tree. We demonstrate theoretically and through simulations that hemiplasy decreases the expected covariances in trait values between more closely related species relative to the covariances between more distantly related species. This effect leads to an overestimation of a traits evolutionary rate parameter, to a decrease of the traits phylogenetic signal, and to increased false positive rates in comparative methods such as the phylogenetic ANOVA. We also show that hemiplasy affects discrete, threshold traits that have an underlying continuous liability, leading to false inferences of convergent evolution. The number of loci controlling a quantitative trait appears to be irrelevant to the trends reported, for all analyses. Our results demonstrate that gene tree discordance and hemiplasy are a problem for all types of traits, across a wide range of methods. Our analyses also point to the conditions under which hemiplasy is most likely to be a factor, and suggest future approaches that may mitigate its effects.

evolutionary biology↗

bioRxiv2017

Punctuated evolution shaped modern vertebrate diversity

The relative importance of different modes of evolution in shaping phenotypic diversity remains a hotly debated question. Fossil data suggest that stasis may be a common mode of evolution, while modern data suggest very fast rates of evolution. One way to reconcile these observations is to imagine that evolution is punctuated, rather than gradual, on geological time scales. To test this hypothesis, we developed a novel maximum likelihood framework for fitting Levy processes to comparative morphological data. This class of stochastic processes includes both a gradual and punctuated component. We found that a plurality of modern vertebrate clades examined are best fit by punctuated processes over models of gradual change, gradual stasis, and adaptive radiation. When we compare our results to theoretical expectations of the rate and speed of regime shifts for models that detail fitness landscape dynamics, we find that our quantitative results are broadly compatible with both microevolutionary models and with observations from the fossil record.

evolutionary biology↗