Biology subjects

Berg, J. J.

Publications and source records attributed to Berg, J. J..

14 recordsLinked to original sources

bioRxiv2025

Quantifying the susceptibility of polygenic scores to ancestry stratification

Polygenic scores aim to predict phenotypes from genetic data, yet they remain vulnerable to spurious correlations arising from environmental variation that covaries with population structure. While standard methods like Principal Component Analysis (PCA) and Linear Mixed Models (LMMs) mitigate this, quantifying the residual risk for specific applications remains challenging. Here, we develop a theoretical framework that quantifies the proportion of genetic variance in a GWAS panel explained by an external ancestry gradient (H), providing a direct measure of stratification susceptibility. We show that this baseline risk is amplified by the ascertainment process itself, which creates a directional bias ({Phi}) that is particularly strong for variants with intermediate probabilities of ascertainment. Applying this framework to the UK Biobank, we find that while uncorrected susceptibility is drastically higher in diverse cohorts, PCA correction effectively flattens this disparity. We observe that the residual susceptibility (H') in corrected diverse panels is often comparable to, or marginally lower than, that found in restricted homogeneous subsets, suggesting that sample diversity need not compromise stratification control. However, for both study designs, residual structure often remains just above or indistinguishable from the theoretical limit of detection. Because even undetectable levels of structure can accumulate to produce significant bias in highly polygenic scores, we introduce a diagnostic to calculate the critical magnitude of environmental confounding required to explain an observed signal. Using this diagnostic, we find that both the well-known divergence in height scores between Sardinia and mainland Europe and novel signals of divergence in systolic blood pressure scores within the British Isles appear relatively robust to residual stratification, albeit for different reasons. While the Sardinia signal would require moderate-to-strong environmental confounding to align with a vanishingly small residual ancestry axis, the systolic blood pressure signals would require implausibly large environmental effects to be explained as artifacts.

genetics↗

bioRxiv2025

The impact of background selection in mutation-selection-drift balance models of complex trait evolution

Background selection (BGS)--the reduction of linked neutral diversity via the purging of deleterious mutations--is a pervasive force in genomic evolution. However, its impact on complex phenotypes remains poorly understood because classical theory treats fitness effects as fixed rather than emerging from a phenotype-to-fitness map. Here, we investigate the impact of BGS across three phenotypic selection frameworks: exponential directional, a liability threshold model, and stabilizing selection. First, we develop an effectively non-recombining block approximation for the site frequency spectrum (SFS) and show that this framework accurately describes the skew in the site frequency spectrum in the weak mutation regime typical of humans. Second, we show that phenotypic impacts of BGS depend on how selection is coupled across loci. In the liability threshold model, strong synergistic epistasis generates a global compensation mechanism--driven by tiny shifts in the mean phenotype-- that propagates BGS effects to strongly selected variants otherwise immune to linked selection. This coupling reduces genetic variance across almost the entire effect-size distribution by an amount determined by a non-linear average of local effective population size reductions across the genome. Conversely, under stabilizing selection, BGS can counter-intuitively increase genetic variance. This occurs because BGS shifts strongly selected sites into the weakly selected underdominant regime where they persist at intermediate frequencies longer than in the equivalent directional selection model. Our results inform both longstanding evolutionary conversations regarding synergistic epistasis and efforts to model the impact of background selection on individual variants in recombining genomes. SignificanceNatural selection against deleterious mutations reshapes genetic variation through background selection, a process typically modeled as a local reduction in effective population size. We show that the consequences of this reduction for genetic architecture are mediated by the relationship between phenotype and fitness. Extending background selection theory to complex traits, we show that in models with strong synergistic epistasis, a global compensation mechanism propagates the effects of selection to strongly selected variants. Furthermore, we find that background selection can counter-intuitively increase genetic variation under stabilizing selection. Our work shows that the total impact of background selection on functional variation thus depends on the answers to longstanding questions about the ultimate sources of variance in fitness.

evolutionary biology↗

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

A Litmus Test for Confounding in Polygenic Scores

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

genomics↗

bioRxiv2024

Extinction vortices are driven more by a shortage of beneficial mutations than by deleterious mutation accumulation

Habitat loss contributes to extinction risk in multiple ways. Genetically, small populations can face an "extinction vortex" -- a positive feedback loop between declining fitness and declining population size. Two distinct genetic mechanisms can drive a long-term extinction vortex: i) ineffective selection in small populations allows deleterious mutations to fix, driving "mutational meltdown", and ii) smaller populations generate fewer beneficial mutations essential for long-term adaptation, a mechanism we term "mutational drought". To determine their relative importance, we ask whether, for a population near its critical size for persistence, changes in population size have a larger effect on the beneficial vs. deleterious component of fitness flux. In stable environments, we find that mutational drought is nearly as significant as mutational meltdown. Drought is more important than meltdown when populations must also adapt to a changing environment, unless the beneficial mutation rate is extremely high. Linkage disequilibria from background selection under realistically high deleterious mutation rates modestly increase the importance of mutational drought. Long-term conservation efforts should consider adaptive potential, not just deleterious load.

evolutionary biology↗

bioRxiv2024

Allele ages provide limited information about the strength of negative selection

For many problems in population genetics, it is useful to characterize the distribution of fitness effects (DFE) of de novo mutations among a certain class of sites. A DFE is typically estimated by fitting an observed site frequency spectrum (SFS) to an expected SFS given a hypothesized distribution of selection coefficients and demographic history. The development of tools to infer gene trees from haplotype alignments, along with ancient DNA resources, provides us with additional information about the frequency trajectories of segregating mutations. Here, we ask how useful this additional information is for learning about the DFE, using the joint distribution on allele frequency and age to summarize information about the trajectory. To this end, we introduce an accurate and efficient numerical method for computing the density on the age of a segregating variant found at a given sample frequency, given the strength of selection and an arbitrarily complex population size history. We then use this framework to show that the unconditional age distribution of negatively selected alleles is very closely approximated by re-weighting the neutral age distribution in terms of the negatively selected SFS, suggesting that allele ages provide very little information about the DFE beyond that already contained in the present day frequency. To confirm this prediction, we extended the standard Poisson Random Field (PRF) method to incorporate the joint distribution of frequency and age in estimating selection coefficients, and test its performance using simulations. We find that when the full SFS is observed and the true allele ages are known, including ages in the estimation provides only small increases in the accuracy of estimated selection coefficients. However, if only sites with frequencies above a certain threshold are observed, then the true ages can provide substantial information about the selection coefficients, especially when the selection coefficient is large. When ages are estimated from haplotype data using state-of-the-art tools, uncertainty about the age abrogates most of the additional information in the fully observed SFS case, while the neutral prior assumed in these tools when estimating ages induces a downward bias in the case of the thresholded SFS.

evolutionary biology↗

bioRxiv2023

Testing for differences in polygenic scores in the presence of confounding

Polygenic scores have become an important tool in human genetics, enabling the prediction of individuals phenotypes from their genotypes. Understanding how the pattern of differences in polygenic score predictions across individuals intersects with variation in ancestry can provide insights into the evolutionary forces acting on the trait in question, and is important for understanding health disparities. However, because most polygenic scores are computed using effect estimates from population samples, they are susceptible to confounding by both genetic and environmental effects that are correlated with ancestry. The extent to which this confounding drives patterns in the distribution of polygenic scores depends on patterns of population structure in both the original estimation panel and in the prediction/test panel. Here, we use theory from population and statistical genetics, together with simulations, to study the procedure of testing for an association between polygenic scores and axes of ancestry variation in the presence of confounding. We use a general model of genetic relatedness to describe how confounding in the estimation panel biases the distribution of polygenic scores in a way that depends on the degree of overlap in population structure between panels. We then show how this confounding can bias tests for associations between polygenic scores and important axes of ancestry variation in the test panel. Specifically, for any given test, there exists a single axis of population structure in the GWAS panel that needs to be controlled for in order to protect the test. Based on this result, we propose a new approach for directly estimating this axis of population structure in the GWAS panel. We then use simulations to compare the performance of this approach to the standard approach in which the principal components of the GWAS panel genotypes are used to control for stratification. Author SummaryComplex traits are influenced by both genetics and the environment. Human geneticists increasingly use polygenic scores, calculated as the weighted sum of trait-associated alleles, to predict genetic effects on a phenotype. Differences in polygenic scores across groups would therefore seem to indicate differences in the genetic basis of the trait, which are of interest to researchers across disciplines. However, because polygenic scores are usually computed using effect sizes estimated using population samples, they are susceptible to confounding due to both the genetic background and the environment. Here, we use theory from population and statistical genetics, together with simulations, to study how environmental and background genetic effects can confound tests for association between polygenic scores and axes of ancestry variation. We then develop a simple method to protect these tests from confounding, which we evaluate, alongside standard methods, across a range of possible situations. Our work helps clarify how bias in the distribution of polygenic scores is produced and provides insight to researchers wishing to protect their analyses from confounding.

genetics↗

bioRxiv2021

Polygenic score accuracy in ancient samples: quantifying the effects of allelic turnover

Polygenic scores link the genotypes of ancient individuals to their phenotypes, which are often unobservable, offering a tantalizing opportunity to reconstruct complex trait evolution. In practice, however, interpretation of ancient polygenic scores is subject to numerous assumptions. For one, the genome-wide association (GWA) studies from which polygenic scores are derived, can only estimate effect sizes for loci segregating in contemporary populations. Therefore, a GWA study may not correctly identify all loci relevant to trait variation in the ancient population. In addition, the frequencies of trait-associated loci may have changed in the intervening years. Here, we devise a theoretical framework to quantify the effect of this allelic turnover on the statistical properties of polygenic scores as functions of population genetic dynamics, trait architecture, power to detect significant loci, and the age of the ancient sample. We model the allele frequencies of loci underlying trait variation using the Wright-Fisher diffusion, and employ the spectral representation of its transition density to find analytical expressions for several error metrics, including the correlation between an ancient individuals polygenic score and true phenotype, referred to as polygenic score accuracy. Our theory also applies to a two-population scenario and demonstrates that allelic turnover alone may explain a substantial percentage of the reduced accuracy observed in cross-population predictions, akin to those performed in human genetics. Finally, we use simulations to explore the effects of recent directional selection, a bias-inducing process, on the statistics of interest. We find that even in the presence of bias, weak selection induces minimal deviations from our neutral expectations for the decay of polygenic score accuracy. By quantifying the limitations of polygenic scores in an explicit evolutionary context, our work lays the foundation for the development of more sophisticated statistical procedures to analyze both temporally and geographically resolved polygenic scores.

genetics↗

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

Detecting adaptive differentiation in structured populations with genomic data and common gardens

Adaptation in quantitative traits often occurs through subtle shifts in allele frequencies at many loci, a process called polygenic adaptation. While a number of methods have been developed to detect polygenic adaptation in human populations, we lack clear strategies for doing so in many other systems. In particular, there is an opportunity to develop new methods that leverage datasets with genomic data and common garden trait measurements to systematically detect the quantitative traits important for adaptation. Here, we develop methods that do just this, using principal components of the relatedness matrix to detect excess divergence consistent with polygenic adaptation and using a conditional test to control for confounding effects due to population structure. We apply these methods to inbred maize lines from the USDA germplasm pool and maize landraces from Europe. Ultimately, these methods can be applied to additional domesticated and wild species to give us a broader picture of the specific traits that contribute to adaptation and the overall importance of polygenic adaptation in shaping quantitative trait variation.

evolutionary biology↗

bioRxiv2018

Reduced signal for polygenic adaptation of height in UK Biobank

Several recent papers have reported strong signals of selection on European polygenic height scores. These analyses used height effect estimates from the GIANT consortium and replication studies. Here, we describe a new analysis based on the the UK Biobank (UKB), a large, independent dataset. We find that the signals of selection using UKB effect-size estimates for height are strongly attenuated or absent. We also provide evidence that previous analyses were confounded by population stratification Therefore, the conclusion of strong polygenic adaptation now lacks support. Moreover, these discrepancies highlight (1) that methods for correcting for population stratification in GWAS may not always be sufficient for polygenic trait analyses, and (2) that claims of differences in polygenic scores between populations should be treated with caution until these issues are better understood.

evolutionary biology↗

bioRxiv2017

Polygenic Adaptation has Impacted Multiple Anthropometric Traits

Our understanding of the genetic basis of human adaptation is biased toward loci of large pheno-typic effect. Genome wide association studies (GWAS) now enable the study of genetic adaptation in polygenic phenotypes. We test for polygenic adaptation among 187 world-wide human populations using polygenic scores constructed from GWAS of 34 complex traits. We identify signals of polygenic adaptation for anthropometric traits including height, infant head circumference (IHC), hip circumference and waist-to-hip ratio (WHR). Analysis of ancient DNA samples indicates that a north-south cline of height within Europe and and a west-east cline across Eurasia can be traced to selection for increased height in two late Pleistocene hunter gatherer populations living in western and west-central Eurasia. Our observation that IHC and WHR follow a latitudinal cline in Western Eurasia support the role of natural selection driving Bergmanns Rule in humans, consistent with thermoregulatory adaptation in response to latitudinal temperature variation.\n\nAuthors Note on Failure to ReplicateAfter this preprint was posted, the UK Biobank dataset was released, providing a new and open GWAS resource. When attempting to replicate the height selection results from this preprint using GWAS data from the UK Biobank, we discovered that we could not. In subsequent analyses, we determined that both the GIANT consortium height GWAS data, as well as another dataset that was used for replication, were impacted by stratification issues that created or at a minimum substantially inflated the height selection signals reported here. The results of this second investigation, written together with additional coauthors, have now been published (https://elifesciences.org/articles/39725 along with another paper by a separate group of authors, showing similar issues https://elifesciences.org/articles/39702). A preliminary investigation shows that the other non-height based results may suffer from similar issues. We stand by the theory and statistical methods reported in this paper, and the paper can be cited for these results. However, we have shown that the data on which the major empirical results were based are not sound, and so should be treated with caution until replicated.

evolutionary biology↗

bioRxiv2017

Detecting polygenic adaptation in admixture graphs

An open question in human evolution is the importance of polygenic adaptation: adaptive changes in the mean of a multifactorial trait due to shifts in allele frequencies across many loci. In recent years, several methods have been developed to detect polygenic adaptation using loci identified in genome-wide association studies (GWAS). Though powerful, these methods suffer from limited interpretability: they can detect which sets of populations have evidence for polygenic adaptation, but are unable to reveal where in the history of multiple populations these processes occurred. To address this, we created a method to detect polygenic adaptation in an admixture graph, which is a representation of the historical divergences and admixture events relating different populations through time. We developed a Markov chain Monte Carlo (MCMC) algorithm to infer branch-specific parameters reflecting the strength of selection in each branch of a graph. Additionally, we developed a set of summary statistics that are fast to compute and can indicate which branches are most likely to have experienced polygenic adaptation. We show via simulations that this method - which we call PolyGraph - has good power to detect polygenic adaptation, and applied it to human population genomic data from around the world. We also provide evidence that variants associated with several traits, including height, educational attainment, and self-reported unibrow, have been influenced by polygenic adaptation in different populations during human evolution.

evolutionary biology↗

bioRxiv2017

Parallel Altitudinal Clines Reveal Adaptive Evolution Of Genome Size In Zea mays

While the vast majority of genome size variation in plants is due to differences in repetitive sequence, we know little about how selection acts on repeat content in natural populations. Here we investigate parallel changes in intraspecific genome size and repeat content of domesticated maize (Zea mays) landraces and their wild relative teosinte across altitudinal gradients in Mesoamerica and South America. We combine genotyping, low coverage whole-genome sequence data, and flow cytometry to test for evidence of selection on genome size and individual repeat abundance. We find that population structure alone cannot explain the observed variation, implying that clinal patterns of genome size are maintained by natural selection. Our modeling additionally provides evidence of selection on individual heterochromatic knob repeats, likely due to their large individual contribution to genome size. To better understand the phenotypes driving selection on genome size, we conducted a growth chamber experiment using a population of highland teosinte exhibiting extensive variation in genome size. We find weak support for a positive correlation between genome size and cell size, but stronger support for a negative correlation between genome size and the rate of cell production. Reanalyzing published data of cell counts in maize shoot apical meristems, we then identify a negative correlation between cell production rate and flowering time. Together, our data suggest a model in which variation in genome size is driven by natural selection on flowering time across altitudinal clines, connecting intraspecific variation in repetitive sequence to important differences in adaptive phenotypes.\n\nAuthor summaryGenome size in plants can vary by orders of magnitude, but this variation has long been considered to be of little to no functional consequence. Studying three independent adaptations to high altitude in Zea mays, we find that genome size experiences parallel pressures from natural selection, causing a linear reduction in genome size with increasing altitude. Though reductions in repetitive content are responsible for the genome size change, we find that only those individual loci contributing most to the variation in genome size are individually targeted by selection. To identify the phenotype influenced by genome size, we study how variation in genome size within a single teosinte population impacts leaf growth and cell division. We find that genome size variation correlates negatively with the rate of cell division, suggesting that individuals with larger genomes require longer to complete a mitotic cycle. Finally, we reanalyze data from maize inbreds to show that faster cell division is correlated with earlier flowering, connecting observed variation in genome size to an important adaptive phenotype.

evolutionary biology↗