Biology subjects

Mathieson, I.

Publications and source records attributed to Mathieson, I..

At least 19 recordsLinked to original sources

bioRxiv2026

Local ancestry inference identifies robust evidence of selection in Neolithic Europe

During the European Neolithic, migrating Anatolian farmers admixed with local hunter-gatherers, coinciding with major shifts in diet, environment, and lifestyle that imposed strong selective pressures. Local ancestry inference is widely used to detect selection following admixture, but most methods were developed and validated on present-day populations. Their performance in ancient DNA - where reference panels are smaller, data are sparser, and admixture is more ancient - remains unresolved. We benchmark eight local ancestry inference methods on 176 imputed Neolithic genomes. While individual-level ancestry estimates are highly correlated across methods, inferred tract lengths and admixture time estimates vary by an order of magnitude. Overall, we recommend Gnomix or RFMix for general use. We also investigated our ability to detect natural selection using LAI. Integrating results across methods and replicating across methods and in two independent datasets (n=378 and 1,121) we identify a robust ancestry deviation at FADS1/2, consistent with adaptation on metabolism. We also identify IRAK4 (innate immunity) as a candidate locus, but with less consistent signal across methods. Finally, we replicate previous reports of excess hunter-gatherer ancestry at the HLA, but these results are inconsistent across methods and suggest that they may be affected by bias in local ancestry inference. Our findings demonstrate that while local ancestry inference recovers biologically meaningful signals in ancient genomes, results can be sensitive to the methods used for inference, particularly in complex regions like the HLA. Method choice critically influences inferred ancestry patterns and selection signals, underscoring the importance of multi-method validation.

genomics↗

bioRxiv2026

Choice of phenotype scale is critical in biobank-based GxE tests

The importance of gene-environment interactions (GxE) for complex human traits is heavily debated. Recently, biobank-based GWAS have revealed many statistically significant GxE signals, though most lack clear evidence of biological significance. Here, we partly explain this discrepancy by showing that many GxE signals simplify to additive effects on a different phenotype scale, a classical concern that is currently underappreciated. Our results clearly distinguish GxSex effects on height, which vanish on the log scale, from GxSex effects on testosterone, where the log scale uncovers biologically meaningful female-specific effects. Across 32 phenotypes in UK Biobank, we find that scaling by a power transformation can explain 46% of PGSxSex interactions, and that simple log transformation can explain 23%, with similar results for other environments. We also show that phenotype scale can substantially impact GWAS discovery and the construction and evaluation of polygenic scores. Finally, we provide a set of guidelines to consider and choose phenotype scale in modern genetic studies.

genetics↗

bioRxiv2026

Global patterns of natural selection inferred using ancient DNA

Ancient DNA has revolutionized our understanding of human history, and is now yielding important insights into evolution and natural selection. However, studies of selection using ancient DNA have largely been limited to Europe, excluding populations in other parts of the world. While many selective pressures were local to specific populations others, for example those related to the development of agriculture, may have been universal. By studying a broader range of global populations, we can identify examples of local adaption but also more general principles of adaptation to climatic, social and technological changes. We therefore leverage ancient DNA to test for selection in 7244 individuals from 13 ancient and 19 present-day populations across five regions: Europe, East Asia, South Asia, Africa and the Americas. In each region, we tested for selection using multiple approaches that account for complex demographic histories. We identify 31 genome-wide significant signals of selection, including both known and novel loci. We find a high degree of shared signal across regions, suggesting extensive parallel or shared adaptation. Using a novel admixture-aware time series method, we find that the strength of selection on many variants changed over time, for example decreasing selection at LCT in Europe and increasing selection at ADH1B in East Asia over the past few thousand years. Finally, we developed a test for polygenic selection on complex traits by modeling the frequencies of trait-associated alleles identified in GWAS. We tested for selection jointly across regions, avoiding the confounding effect of population stratification by excluding the European or East Asian GWAS population from the selection test. We find evidence for directional selection on pigmentation and immune traits, and that strong stabilizing selection on female waist-hip ratio was universal across human populations suggesting a fundamental constraint on human morphology.

genomics↗

bioRxiv2025

Effects of ancestry, agriculture, and lactase persistence on the stature of prehistoric Europeans

Ancient DNA has revolutionized our understanding of human evolutionary history, but studies focusing solely on genetic variation tell an incomplete story by neglecting phenotypic outcomes. The relationships between genotype and phenotype can change over time, making it desirable to study them directly in ancient populations rather than present-day data. Here, we present a large-scale integration of ancient genomic and phenotypic data, analyzing femur length as a proxy for stature in 568 individuals with published whole-genome ancient DNA data across western Eurasia. Polygenic scores derived from modern European and East Asian genome-wide association studies retain predictive power in ancient populations, explaining up to 10% of phenotypic variance. Contrary to longstanding archaeological hypotheses, we find that Neolithic populations were only modestly shorter than preceding Mesolithic groups, with differences at least partly attributable to genetic rather than environmental factors, challenging narratives of systematic stature decline following the transition to agriculture. Finally, we find that the lactase persistence allele had a large positive effect on stature in ancient individuals (0.24 standard deviations), even though it shows no association with height in modern populations. This gene-environment interaction highlights the limitation of using present-day genetic data to infer past phenotypic relationships. Our results underscore the value of integrating genetic and morphological data from ancient populations to reconstruct the dynamics of human adaptation.

genetics↗

bioRxiv2025

Hidden structure in polygenic scores and the challenge of disentangling ancestry interactions in admixed populations

The extent to which genetic effects vary across ancestries remains a central question in human genetics, with direct implications for improving polygenic prediction. Recent studies have found that average causal effects are similar across ancestries, but empirical evidence and theoretical considerations suggest that interactions may still contribute to poor portability of polygenic scores. Here, we leverage the ancestry mosaicism of admixed individuals to develop models of genetic interactions with local and global ancestry. We show that these models capture epistasis but are also consistent with highly similar average causal effects, clarifying that the latter observation does not exclude the possibility of gene-ancestry interaction. Next, we investigate how models of local and global interactions differ in polygenic prediction. Focusing on continuous traits, we show that if causal variants are known and causal effects differ across ancestries, then the global and local models can be differentiated using partial polygenic scores -- scores computed on ancestry-specific genomic segments -- while standard polygenic scores remain indistinguishable under both models. However, if causal variants are unknown, differences in linkage disequilibrium (LD) patterns confound this analysis, and the models are not practically distinguishable without knowledge of the LD between causal and tagging variants. Our findings motivate future developments of model-sensitive strategies for individual-level genetic risk prediction.

genetics↗

bioRxiv2025

Methylation-associated mutagenesis underlies variation in the mutation spectrum across eukaryotes

Mutation spectra vary across genetic and environmental contexts, leading to differences between and within species. Most research on mutation spectrum has focused on the trinucleotide (3-mer) mutation types in mammals, limiting the breadth and depth of variation surveyed. In this study, we use whole-genome resequencing data across 108 eukaryotic species - including mammals, fish, plants, and invertebrates - to characterize pentanucleotide (5-mer) non-coding mutation spectra using a Bayesian approach. Our findings reveal cytosine transition mutability at CpG and (among plants) at CHG sites as the main drivers of variation in mutation spectra across eukaryotes, correlating strongly with genomic CpG and CHG depletion. However, despite the influence of methylation on CpG mutability, genome-wide average CpG methylation levels do not predict CpG transition rates across species and CHG methylation does not predict CHG transition rate, indicating unknown genetic or environmental factors influencing mutation rates at methylated cytosines. Together, our results illustrate the pivotal role of mutagenesis in shaping genome composition across eukaryotes and highlight a gap in knowledge about the mechanisms governing mutation rates.

evolutionary biology↗

bioRxiv2023

Predicting functional consequences of recent natural selection in Britain

Ancient DNA can directly reveal the contribution of natural selection to human genomic variation. However, while the analysis of ancient DNA has been successful at identifying genomic signals of selection, inferring the phenotypic consequences of that selection has been more difficult. Most trait-associated variants are non-coding, so we expect that a large proportion of the phenotypic effects of selection will also act through non-coding variation. Since we cannot measure gene expression directly in ancient individuals, we used an approach (Joint-Tissue Imputation; JTI) developed to predict gene expression from genotype data. We tested for changes in the predicted expression of 17,384 protein coding genes over a time transect of 4500 years using 91 present-day and 616 ancient individuals from Britain. We identified 28 genes at seven genomic loci with significant (FDR < 0.05) changes in predicted expression levels in this time period. We compared the results from our transcriptome-wide scan to a genome-wide scan based on estimating per-SNP selection coefficients from time series data. At five previously identified loci, our approach allowed us to highlight small numbers of genes with evidence for significant shifts in expression from peaks that in some cases span tens of genes. At two novel loci (SLC44A5 and NUP85), we identify selection on gene expression not captured by scans based on genomic signatures of selection. Finally we show how classical selection statistics (iHS and SDS) can be combined with JTI models to incorporate functional information into scans that use present-day data alone. These results demonstrate the potential of this type of information to explore both the causes and consequences of natural selection.

evolutionary biology↗

bioRxiv2023

Insufficient evidence for natural selection associated with the Black Death

Klunk et al. analyzed ancient DNA data from individuals in London and Denmark before, during and after the Black Death [1], and argued that allele frequency changes at immune genes were too large to be produced by random genetic drift and thus must reflect natural selection. They also identified four specific variants that they claimed show evidence of selection including at ERAP2, for which they estimate a selection coefficient of 0.39-several times larger than any selection coefficient on a common human variant reported to date. Here we show that these claims are unsupported for four reasons. First, the signal of enrichment of large allele frequency changes in immune genes comparing people in London before and after the Black Death disappears after an appropriate randomization test is carried out: the P value increases by ten orders of magnitude and is no longer significant. Second, a technical error in the estimation of allele frequencies means that none of the four originally reported loci actually pass the filtering thresholds. Third, the filtering thresholds do not adequately correct for multiple testing. Finally, in the case of the ERAP2 variant rs2549794, which Klunk et al. show experimentally may be associated with a host interaction with Y. pestis, we find no evidence of significant frequency change either in the data that Klunk et al. report, or in published data spanning 2,000 years. While it remains plausible that immune genes were subject to natural selection during the Black Death, the magnitude of this selection and which specific genes may have been affected remains unknown.

genomics↗

bioRxiv2023

Interpreting Generative Adversarial Networks to Infer Natural Selection from Genetic Data

AO_SCPLOWBSTRACTC_SCPLOWUnderstanding natural selection in humans and other species is a major focus for the use of machine learning in population genetics. Existing methods rely on computationally intensive simulated training data. Unlike efficient neutral coalescent simulations for demographic inference, realistic simulations of selection typically requires slow forward simulations. Because there are many possible modes of selection, a high dimensional parameter space must be explored, with no guarantee that the simulated models are close to the real processes. Mismatches between simulated training data and real test data can lead to incorrect inference. Finally, it is difficult to interpret trained neural networks, leading to a lack of understanding about what features contribute to classification. Here we develop a new approach to detect selection that requires relatively few selection simulations during training. We use a Generative Adversarial Network (GAN) trained to simulate realistic neutral data. The resulting GAN consists of a generator (fitted demographic model) and a discriminator (convolutional neural network). For a genomic region, the discriminator predicts whether it is "real" or "fake" in the sense that it could have been simulated by the generator. As the "real" training data includes regions that experienced selection and the generator cannot produce such regions, regions with a high probability of being real are likely to have experienced selection. To further incentivize this behavior, we "fine-tune" the discriminator with a small number of selection simulations. We show that this approach has high power to detect selection in simulations, and that it finds regions under selection identified by state-of-the art population genetic methods in three human populations. Finally, we show how to interpret the trained networks by clustering hidden units of the discriminator based on their correlation patterns with known summary statistics. In summary, our approach is a novel, efficient, and powerful way to use machine learning to detect natural selection.

genomics↗

bioRxiv2023

Socio-cultural practices affect sexual dimorphism in stature in Early Neolithic Europe

AbstractThe rules and structure of human culture impact health and disease as much as genetics or the natural environment. To study the origin and evolution of these patterns, we take a multidisciplinary approach combining ancient DNA, skeletal metrics, paleopathology, and stable isotopes. Our analysis focuses on cultural, environmental, and genetic contributions to variation in stature in four populations of Early Neolithic Europe. In Central Europe, low female stature is likely due to male preference in resource allocation under conditions of stress. In contrast, shorter male stature in Mediterranean populations may reflect a lack of preference. Our analysis suggests that biological consequences of sex-specific inequities can be linked to culture as early as 7000 years before present. Understanding these patterns is key to interpreting the evolution of genetic and socio-cultural determinants of health, and our results show that culture, more than environment or genetics, drove height disparities in Early Neolithic Europe.

genetics↗

bioRxiv2022

A gene-level test for directional selection on gene expression

Most variants identified in human genome-wide association studies and scans for selection are non-coding. Interpretation of these variants effects and understanding of the way in which they contribute to phenotypic variation and adaptation in human populations is therefore limited by our understanding of gene regulation and by the difficulty in confidently linking non-coding variants to genes. To overcome this, we developed a gene-by-gene test for population-specific selection based on combinations of regulatory variants. We extended the QX test for polygenic selection to test for selection on regulatory variants for 17,388 protein-coding genes across 2,504 individuals. We identified 45 genes with significant evidence (FDR <0.1) for selection, including FADS1, KHK, SULT1A2, ITGAM, and genes in the HLA region. We further confirm that significant selection signals do correspond to plausible population-level differences in predicted expression. However, we find that very few (0.2%) genes have strong evidence for directional, population-specific selection on the component of their expression that is predicted by cis-regulatory variants. While this is consistent with most cis-regulatory variation evolving under genetic drift or stabilizing selection, it is also possible that any effects are smaller than we can detect, or that population-specific selection is driven by tissue-specific or trans effects. Our gene-level QX score is independent of other methods for detecting selection based on genomic variation, may therefore be useful when used in combination with more traditional selection tests to specifically identify selection on regulatory variation. Overall, our results demonstrate the utility of one approach to combining population-level information with functional data to understand the evolution of gene expression.

genomics↗

bioRxiv2022

Regularized sequence-context mutational trees capture variation in mutation rates across the human genome

Germline mutation is the mechanism by which genetic variation in a population is created. Inferences derived from mutation rate models are fundamental to many population genetics inference methods. Previous models have demonstrated that nucleotides flanking polymorphic sites - the local sequence context - explain variation in the probability that a site is polymorphic. However, limitations to these models exist as the size of the local sequence context window expands. These include a lack of robustness to data sparsity at typical sample sizes, lack of regularization to generate parsimonious models and lack of quantified uncertainty in estimated rates to facilitate comparison between models. To address these limitations, we developed Baymer, a regularized Bayesian hierarchical tree model that captures the heterogeneous effect of sequence contexts on polymorphism probabilities. Baymer implements an adaptive Metropolis-within-Gibbs Markov Chain Monte Carlo sampling scheme to estimate the posterior distributions of sequence-context based probabilities that a site is polymorphic. We show that Baymer accurately infers polymorphism probabilities and well-calibrated posterior distributions, robustly handles data sparsity, appropriately regularizes to return parsimonious models, and scales computationally at least up to 9-mer context windows. We demonstrate application of Baymer in three ways - first, identifying differences in polymorphism probabilities between continental populations in the 1000 Genomes Phase 3 dataset, second, in a sparse data setting to examine the use of polymorphism models as a proxy for de novo mutation probabilities as a function of variant age, sequence context window size, and demographic history, and third, comparing model concordance between different great ape species. We find a shared context-dependent mutation rate architecture underlying our models, enabling a transfer-learning inspired strategy for modeling germline mutations. In summary, Baymer is an accurate polymorphism probability estimation algorithm that automatically adapts to data sparsity at different sequence context levels, thereby making efficient use of the available data. AUTHOR SUMMARYMany biological questions rely on accurate estimates of where and how frequently mutations arise in populations. One factor that has been shown to predict the probability that a mutation occurs is the local DNA sequence surrounding a potential site for mutation. It has been shown that increasing the size of local DNA sequence immediately surrounding a site improves prediction of where, what type, and how frequently the site is mutated. However, current methods struggle to take full advantage of this trend as well as capturing how certain our estimates are, in practice. We have designed a model, implemented in software (named Baymer), that is able to use large windows of sequence context to accurately model mutation probabilities in a computationally efficient manner. We use Baymer to identify specific DNA sequences that have the biggest impacts on mutability and apply the model to find motifs that have potentially evolved mutability between different human populations. We also apply it to show that germline mutations observed as polymorphic sites in humans - those that have occurred in our recent evolutionary history - can model very young mutations (de novo mutations) as well as polymorphism observed in populations of closely related great ape species.

genomics↗

bioRxiv2022

Hunter-gatherer admixture facilitated natural selection in Neolithic European farmers

Ancient DNA has revealed multiple episodes of admixture in human prehistory during geographic expansions associated with cultural innovations. One important example is the expansion of Neolithic agricultural groups out of the Near East into Europe, and their consequent admixture with Mesolithic hunter-gatherers. Ancient genomes from this period provide an opportunity to study the role of admixture in providing new genetic variation for selection to act upon, and also to identify genomic regions that resisted hunter-gatherer introgression and may thus contribute to agricultural adaptations. We used genome-wide DNA from 728 individuals spanning Mesolithic and Neolithic Europe to infer ancestry deviations in the genomes of admixed individuals, and to test for natural selection after admixture using a new method based on testing for deviations from a genome-wide null distribution. We find that the region around the pigmentation-associated gene SLC24A5 shows the greatest overrepresentation of Neolithic ancestry in the genome (|Z| = 3.45). In contrast, we find the greatest overrepresentation of Mesolithic local ancestry across the key immunity locus that is the Major Histocompatibility Complex (MHC; |Z| > 4) which also shows allele frequency deviations indicative of a selective sweep following admixture (p =1x10-29). This could reflect negative frequency dependent selection on MHC alleles common in Neolithic populations, or that Mesolithic alleles were positively selected for and facilitated adaptation by Neolithic populations to pathogens, new diets, or other environmental factors. Our results extend previous results that highlight immune function and pigmentation as targets of adaptation in more recent populations to selection processes in the Stone Age, and demonstrate that admixture facilitated selection by contributing new genetic variation.

evolutionary biology↗

bioRxiv2022

Direct detection of natural selection in Bronze Age Britain

We developed a novel method for efficiently estimating time-varying selection coefficients from genome-wide ancient DNA data. In simulations, our method accurately recovers selective trajectories, and is robust to mis-specification of population size. We applied it to a large dataset of ancient and present-day human genomes from Britain, and identified seven loci with genome-wide significant evidence of selection in the past 4500 years. Almost all of them are related to increased vitamin D or calcium levels, and we conclude that lack of vitamin D and consequent low calcium was consistently the most important selective pressure in Britain since the Bronze Age. However, the strength of selection on individual loci varied substantially over time, suggesting that cultural or environmental factors moderated the genetic response to this pressure. Of 28 complex anthropometric and metabolic traits, skin pigmentation was the only one with significant evidence of polygenic selection, further underscoring the importance of phenotypes related to vitamin D. Our approach illustrates the power of ancient DNA to characterize selection in human populations and illuminates the recent evolutionary history of Britain.

evolutionary biology↗

bioRxiv2021

Tracing the evolution of human gene regulation and its association with shifts in environment

As humans spread throughout the world, they adapted to variation in many environmental factors, including climate, diet, and pathogens. Because many of these adaptations were likely mediated by multiple non-coding variants with small effects on gene regulation, it has been difficult to link genomic signals of selection to specific genes, and to describe the regulatory response to selection. To overcome this challenge, we adapted PrediXcan, a machine learning method for imputing gene regulation from genotype data, to analyze low-coverage ancient human DNA (aDNA). First, we used simulated genomes to benchmark strategies for adapting gene regulatory prediction to increase robustness to incomplete aDNA data. Applying the resulting models to 490 ancient Eurasians, we found that genes with the strongest divergent regulation among ancient populations with hunter-gatherer, pastoralist, and agricultural lifestyles are enriched for metabolic and immune functions. Next, we explored the contribution of divergent gene regulation to two traits with strong evidence of recent adaptation: dietary metabolism and skin pigmentation. We found enrichment for divergent regulation among genes previously proposed to be involved in diet-related local adaptation, and in many cases, the predicted effects on regulation provide explanations for previously observed signals of selection, e.g., at FADS1, GPX1, and LEPR. For skin pigmentation, we applied new models trained in melanocytes to a time series of 2999 ancient Europeans spanning ~38,000 years BP. In contrast to diet, skin pigmentation genes show little regulatory change over time, suggesting that adaptation mainly involved large-effect coding variants. This work demonstrates how aDNA can be combined with present-day genomes to shed light on the biological differences among ancient populations, the role of gene regulation in adaptation, and the relationship between ancient genetic diversity and the present-day distribution of complex traits.

genomics↗

bioRxiv2021

Predicting skeletal stature using ancient DNA

ObjectivesAncient DNA provides an opportunity to separate the genetic and environmental bases of complex traits by allowing direct estimation of genetic values in ancient individuals. Here, we test whether genetic scores for height in ancient individuals are predictive of their actual height, as inferred from skeletal remains. We estimate the contributions of genetic and environmental variables to observed phenotypic variation as a first step towards quantifying individual sources of morphological variation. Materials and MethodsWe collected stature estimates and femur lengths from West Eurasian skeletal remains with published genome-wide ancient DNA data (n=167, dating from 33,000-850 BP). We also recorded genetic sex, genetic ancestry, date and paleoclimate data for each individual, and{delta} 13C and{delta} 15N stable isotope values where available (n=67). ResultsA polygenic score (PRS) for height predicts 6.8% of the variance in femur length in our data (n=117, SD=0.0068%, p<0.001), controlling for sex, ancestry, and date. This is consistent with the predictive power of height PRS in present-day populations and the low coverage of ancient samples. Comparatively, sex explains about 15% of the variance in femur length in our sample. Environmental effects also likely play a role in variation, independent of genetics, though with considerable uncertainty (longitude: R2=0.0317, SD=0.009, p=0.019). DiscussionPolygenic scores explain a small but significant proportion of the variance in height in ancient individuals, though not enough to make useful predictions of individual phenotypes. However, environmental variables also contribute to phenotypic outcomes and understanding their interaction with direct genetic predictions will provide a framework with which to model how plasticity and genetic changes ultimately combine to drive adaptation and evolution.

evolutionary biology↗

bioRxiv2021

Long runs of homozygosity are correlated with marriage preferences across global population samples

Children of consanguineous unions carry long runs of homozygosity (ROH) in their genomes, due to their parents recent shared ancestry. This increases the burden of recessive disease in populations with high levels of consanguinity and has been heavily studied in some groups. However, there has been little investigation of the broader effect of consanguinity on patterns of genetic variation on a global scale. Here, we collect published genetic data and information about marriage practices from 396 worldwide populations and show that preference for cousin marriage has a detectable effect on the distribution of long ROH in these samples, increasing the expected number of ROH longer than 10Mb by a factor of 1.5 (P=2.3 x 10-4). Variation in marriage practice and consequent rates of consanguinity is therefore an important aspect of demographic history for the purposes of modeling human genetic variation. However, marriage practices explain a relatively small proportion of the variation in ROH distribution and consequently the ability to predict marriage practices from population genetic samples (for example of ancient populations) is limited.

genetics↗

bioRxiv2020

Estimating time-varying selection coefficients from time series data of allele frequencies

Time series data of allele frequencies are a powerful resource for detecting and classifying natural and artificial selection. Ancient DNA now allows us to observe these trajectories in natural populations of long-lived species such as humans. Here, we develop a hidden Markov model to infer selection coefficients that vary over time. We show through simulations that our approach can accurately estimate both selection coefficients and the timing of changes in selection. Finally, we analyze some of the strongest signals of selection in the human genome using ancient DNA. We show that the European lactase persistence mutation was selected over the past 5,000 years with a selection coefficient of 2-2.5% in Britain, Central Europe and Iberia, but not Italy. In northern East Asia, selection at the ADH1B locus associated with alcohol metabolism intensified around 4,000 years ago, approximately coinciding with the introduction of rice-based agriculture. Finally, a derived allele at the FADS locus was selected in parallel in both Europe and East Asia, as previously hypothesized. Our approach is broadly applicable to both natural and experimental evolution data and shows how time series data can be used to resolve fine-scale details of selection.

genetics↗