Biology subjects

Mathieson, S.

Publications and source records attributed to Mathieson, S..

12 recordsLinked to original sources

bioRxiv2026

A reusable neural approach to recombination mapping for model and non-model species

Pedigree and crossing experiments can measure crossovers directly and provide the gold standard for recombination mapping, but their cost restricts fine-scale recombination mapping to only a few species. Patterns of linkage disequilibrium (LD) provide an alternative statistical approach for inferring variation in recombination along the genome. LD, however, is confounded by many evolutionary factors, such as demographic changes, life-history traits, and genomic structural variation. We present fastrho, a state-space neural-network estimator trained across a range of simulation-based priors. In simulated bottleneck and expansion scenarios, the fixed checkpoint recovered local map shape without target-specific retraining; comparisons with pyrho used lookup tables constructed under the simulation-generating history. We further evaluated generalizability across multiple species and, to account for additional confounders not represented in the initial training data, designed specialized models for inference in selfing plants, structured Arabis populations, and large- malaria-vector populations. A major biological application of the mosquito model was the construction of a five-arm recombination atlas spanning 13 Ag3 populations, providing a detailed view of recombination-rate variation across the dataset. Recombination maps inferred from Ag3 pedigrees provided independent, coarse-scale support for this atlas. Finally, analyses of resistance loci and redpoll bird supergenes demonstrate how selection and structural variation influence LD. Throughout our study, we use experimental maps for independent validation. Together, our results establish fastrho as a flexible framework for robust recombination mapping across diverse biological systems.

evolutionary biology↗

bioRxiv2026

Fast pairwise coalescence enables gene-resolution scans for recent selection in diverse human populations

Identifying the genetic changes that shaped recent human adaptation depends on our ability to detect selection from genomic data. Summary statistics from haplotype scans have been widely used for that purpose, aggregating genetic signal over windows, though resolution is limited by linkage and their power may diminish as sweeps approach fixation, as in the case of the integrated haplotype score (iHS). Ancient DNA based scans recover signal by analysing time-series trajectories, but the majority of human populations fall outside the geographic range of any existing ancient DNA dataset. Pairwise coalescence times provide a way to complement statistics and can be applied to any modern cohort, yet computing them densely enough at cohort scale poses a computational challenge due to the quadratic growth in the number of haplotype pairs. We introduce gamma_smc_cu, a GPU implementation of the Gamma-SMC algorithm (Schweiger and Durbin, 2023) for pairwise time-to-the-most-recent-common-ancestor (TMRCA) inference. Applied to the 1000 Genomes Project (3,202 phased samples, corresponding to 6,404 haplotypes; 829,638 within-population pairs across 26 populations and five different continental ancestries; [~]1012 per-site posterior evaluations), it yields a gene-level TMRCA landscape of 17,823 autosomal protein-coding genes after masking for segmental duplications. The scan recovers well-known sweeps (LCT, SLC24A5, EDAR, FADS1, HERC2, ABCC11) and, combined with a depleted-to-enriched variant-class profile, resolves haplotype-block signals down to the gene level. Of seven case studies, two are developed in the main text -- GRK2 /ADRBK1 (chr11q13.2; SAS+EUR) and TREML1 /TREM2 (chr6p21.1) -- and the remaining five (IFIH1 chr2q24/IBS, CCDC92 chr12q24/CDX, SLC6A15 chr12q21/CHS, BPIFA2 chr20q11/GIH, CLEC6A chr12p13/CDX) are presented in the Supplementary Information (SI). Notably, TREML1 /TREM2 is a shared out-of-Africa signal -- ranked below the within-population 1% tail in 16 of 19 non-African 1000 Genomes panels that PopHumanScan and five landmark haplotype-based scans miss. A previous 10 kb-windowed-mean iHS scan dilutes the cluster of extreme sites packed inside the [~]5 kb gene bodies, while our own gene-level iHS independently recovers the locus in three South Asian panels (BEB, STU, ITU; top 0.4% genome-wide). We cross-validate the seven cases against the 9.7 million per-variant selection posteriors from a recent West-Eurasian ancient DNA scan. BPIFA2 is detected concordantly (s {approx} 1.8% per generation). GRK2 and CCDC92 reach detection threshold in flanking variants but not within their own gene bodies, while the TREML1 /TREM2 cluster falls below it. To calibrate novelty, we review the candidate landscape against an expanded eight-catalog set spanning curated haplotype scans, the largest current West-Eurasian ancient-DNA leads, and a recent 26-population iHS refinement; the vast majority of our loci overlap at least one prior entry, and only a handful -- including TREML1 /TREM2 -- remain unflagged. The contributions of this work are gene-level resolution, systematic ancient DNA cross-validation, and a reusable TMRCA landscape that complements aDNA panels.

evolutionary biology↗

bioRxiv2026

Popformer: Learning general signatures of positive selection with a self-supervised transformer

Understanding natural selection can help shed light on the genetics underpinning adaptive evolution. The widespread availability of large-scale human genetic variation data has led to the development of data-driven methods for detecting signatures of selection, many of which are based on deep learning. However, these methods often fail to generalize well to the diversity of selection signatures across a broad range of evolutionary scenarios. We propose a novel transformer-based model, Popformer, for learning encodings of general patterns of genetic variation. Popformer includes site-wise and haplotype-wise attention, allowing us to capture variation among both genetic positions and individuals. It additionally learns relative positional embeddings for inter-SNP distances. The model is pre-trained with an analog of the masked language modeling objective across a range of real human genomic data, similar to the task of genetic imputation. Using dimensionality reduction, we show that the pre-trained model learns meaningful embeddings of genomic windows that correspond with population structure. We also show that the model can accurately perform genotype imputation. We further fine-tune the model on selection classification and demonstrate that our model is more accurate than other selection classification methods, on selection simulations of both well-specified and mis-specified demographic models. Using a novel real data validation approach, we apply Popformer to human data from the 1000 Genomes Project and reveal its ability to generalize from simulated data to the diversity of real data. Overall, our method provides a new direction for population genetic inference methods, with future fine-tuning applications including the inference of recombination rates, introgression, and local ancestry. Author summaryNatural selection lies at the heart of adaptation and evolution. Identifying genomic signals of natural selection can lead to insights about evolutionary pressures acting on populations, and provide an understanding of how populations differentiate. Detecting these signals using only present-day genomic data is difficult, and methods developed for this task often use specialized simulations, trading generalization for power. We show here that adapting deep learning techniques from modern language and image processing can be used to identify signals of selection with both high power and robustness to misspecification. By pre-training our transformer model, Popformer, on real data, we induce learned model representations that are representative of the variation in real data. We fine-tune our model on simulations inferred from European populations, and compare with other machine learning methods and summary statistics. Our results suggest that both our model architecture and our training regime are useful for strong performance in diverse simulation tests and in recovering known selection signatures in real data.

genomics↗

bioRxiv2025

Interpreting Convolutional Neural Networks in Population Genetics

AO_SCPLOWBSTRACTC_SCPLOWMachine learning approaches have become a powerful alternative to traditional methods in population genetics. Convolutional neural networks (CNNs) in particular have been successful in inferring natural selection, recombination rate estimation, introgression, dispersal distances, and effective population size changes. One limitation of CNNs and other deep learning methods is that they can be difficult to interpret. When they have been shown to be as or more successful than summary-statistic-based methods, what are they learning? Here we investigate CNNs from two different methods: the pg-gan discriminator for identifying real vs. simulated data, and two networks trained to detect selective sweeps. We first compute correlations between learned network features and traditional summary statistics, then assess whether summary statistics can be predicted from the learned features. To understand the learned features, we compute feature importance through SHAP values and feature groupings through dimensionality reduction. Finally we use decision trees and random forests to build an interpretable "model-of-the-model". Our results reveal that some CNN architectures can implicitly compute summary statistics such as pairwise heterozygosity, while statistics such as the site frequency spectrum are less similar to the networks learned features. We find that long-range linkage disequilibrium is readily approximated by the networks and may be more efficiently computed by CNNs than traditional methods (which are quadratic in the number of sites). Overall, this work contributes to the interpretability of deep learning methods in population genetics by clarifying the relationships between model architecture, learned network features, established summary statistics, and predicted evolutionary parameters.

genomics↗

bioRxiv2025

On the use of generative models for evolutionary inference of malaria vectors from genomic data

Malaria in sub-Saharan Africa is transmitted by mosquitoes, in particular the Anopheles gambiae complex. Efforts to control the spread of malaria have often focused on these vectors, but less is known about the demographic history of populations and species in the Anopheles complex. Here, we first quantify the genetic structure of mosquito populations in sub-Saharan Africa using unsupervised machine learning. We then adapt and apply an innovative generative deep learning algorithm to infer the joint evolutionary history of populations sampled in Guinea and Burkina Faso, West Africa. We further develop a novel model selection approach and discover that an evolutionary model with migration fits this pair of populations better than a model without post-split migration. For the migration model, we find that our method accurately captures population genetic differentiation. These findings demonstrate that machine learning and generative models are a valuable direction for future understanding of the evolution of malaria vectors, including the joint inference of demography and natural selection. Understanding changes in population size, migration patterns, and adaptation in hosts, vectors, and pathogens will assist malaria control interventions, with the ultimate goal of predicting nuanced outcomes from insecticide resistance to population collapse.

genomics↗

bioRxiv2024

Identity-by-descent (IBD) segment outlier detection in endogamous populations using pedigree cohorts

Genomic segments that are inherited from a common ancestor are referred to as identical-by-descent (IBD). Because these segments are inherited, they not only allow us to study diseases, population characteristics, and the sharing of rare variants, but also understand hidden familial relationships within populations. Over the past two decades, various IBD finding algorithms have been developed using hidden Markov models (HMMs), hashing and extension, and Burrows-Wheeler Transform (BWT) approaches. In this study, we investigate the utility of pedigree information in IBD outlier detection methods for endogamous populations. With the increasing prevalence of computationally efficient sequencing technology and proper documentation of pedigree structures, we expect complete pedigree information to become readily available for more populations. While IBD segments have been used to reconstruct pedigrees, because we now have access to the pedigree, it is a natural question to ask if including pedigree information would substantially improve IBD segment finding for the purpose of studying inheritance. We propose an IBD pruning algorithm for reducing the number of false positives in IBD segments detected by existing software. While existing software already identify IBD segments with high success rates, our algorithm analyzes the familial relationships between cohorts of individuals who are initially hypothesized to share IBD segments to remove outliers. Our algorithm is inspired by a k-Nearest Neighbors (kNN) approach with a novel distance metric for pedigrees with loops. We apply our method to simulated genomic data under an Amish pedigree, but it could be applied to pedigrees from other human populations as well as domesticated animals such as dogs and cattle.

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↗

bioRxiv2022

The genomic footprint of social stratification in admixing American populations

Cultural and socioeconomic differences stratify human societies and shape their genetic structure beyond the sole effect of geography. Despite mating being limited by the permeability of sociocultural stratification, most demographic models in population genetics often assume random mating. Taking advantage of the correlation between sociocultural stratification and the proportion of genetic ancestry in admixed populations, we sought to infer the former process in the Americas. To this aim, we define a mating model where the individual proportions of the genome inherited from Native American, European and sub-Saharan African ancestral populations constrain the mating probabilities through ancestry-related assortative mating and sex bias parameters. We simulate a wide range of admixture scenarios under this model. Then, we train a deep neural network and retrieve good performance in predicting mating parameters from genomic data. Our results show how population stratification shaped by racial and gender hierarchies have constrained the admixture processes in the Americas since the European colonisation and the subsequent Atlantic slave trade.

genetics↗

bioRxiv2020

Automatic inference of demographic parameters using Generative Adversarial Networks

AO_SCPLOWBSTRACTC_SCPLOWPopulation genetics relies heavily on simulated data for validation, inference, and intuition. In particular, since the evolutionary "ground truth" for real data is always limited, simulated data is crucial for training supervised machine learning methods. Simulation software can accurately model evolutionary processes, but requires many hand-selected input parameters. As a result, simulated data often fails to mirror the properties of real genetic data, which limits the scope of methods that rely on it. Here, we develop a novel approach to estimating parameters in population genetic models that automatically adapts to data from any population. Our method, pg-gan, is based on a generative adversarial network that gradually learns to generate realistic synthetic data. We demonstrate that our method is able to recover input parameters in a simulated isolation-with-migration model. We then apply our method to human data from the 1000 Genomes Project, and show that we can accurately recapitulate the features of real data.

genomics↗

bioRxiv2020

Ancestral Haplotype Reconstruction in Endogamous Populations using Identity-By-Descent

In this work we develop a novel algorithm for reconstructing the genomes of ancestral individuals, given genotype or sequence data from contemporary individuals and an extended pedigree of family relationships. A pedigree with complete genomes for every individual enables the study of allele frequency dynamics and haplotype diversity across generations, including deviations from neutrality such as transmission distortion. When studying heritable diseases, ancestral haplotypes can be used to augment genome-wide association studies and track disease inheritance patterns. The building blocks of our reconstruction algorithm are segments of Identity-By-Descent (IBD) shared between two or more genotyped individuals. The method alternates between identifying a source for each IBD segment and assembling IBD segments placed within each ancestral individual. Unlike previous approaches, our method is able to accommodate complex pedigree structures with hundreds of individuals genotyped at millions of SNPs. We apply our method to an Old Order Amish pedigree from Lancaster, Pennsylvania, whose founders came to the United States from Europe during the early 18th century. The pedigree includes 1338 individuals from the past 10 generations, 394 with genotype data. The motivation for reconstruction is to understand the genetic basis of diseases segregating in the family through tracking haplotype transmission over time. Using our algorithm thread, we are able to reconstruct an average of 224 ancestral individuals per chromosome. For these ancestral individuals, on average we reconstruct 79% of their haplotypes. We also identify a region on chromosome 16 that is difficult to reconstruct - we find that this region harbors a short Amish-specific copy number variation and the gene HYDIN. thread was developed for endogamous populations, but can be applied to any extensive pedigree with the recent generations genotyped. We anticipate that this type of practical ancestral reconstruction will become more common and necessary to understand rare and complex heritable diseases in extended families. Author summaryWhen analyzing complex heritable traits, it is often useful to have genomic data from many generations of an extended family, to increase the amount of information available for statistical inference. However, we typically only have genomic data from the recent generations of a pedigree, as ancestral individuals are deceased. In this work we present an algorithm, called thread, for reconstructing the genomes of ancestral individuals, given a complex pedigree and genomic data from the recent generations. Previous approaches have not been able to accommodate large datasets (both in terms of sites and individuals), made simplifying assumptions about pedigree structure, or did not tie reconstructed sequences back to specific individuals. We apply thread to a complex Old Order Amish pedigree of 1338 individuals, 394 with genotype data.

bioinformatics↗

bioRxiv2018

FADS1 and the timing of human adaptation to agriculture

Variation at the FADS1/FADS2 gene cluster is functionally associated with differences in lipid metabolism and is often hypothesized to reflect adaptation to an agricultural diet. Here, we test the evidence for this relationship using both modern and ancient DNA data. We show that almost all the inhabitants of Europe carried the ancestral allele until the derived allele was introduced approximately 8,500 years ago by Early Neolithic farming populations. However, we also show that it was not under strong selection in these populations. We find that this allele, and other proposed agricultural adaptations at LCT/MCM6 and SLC22A4, were not strongly selected until much later, perhaps as late as the Bronze Age. Similarly, increased copy number variation at the salivary amylase gene AMY1 is not linked to the development of agriculture although, in this case, the putative adaptation precedes the agricultural transition. Our analysis shows that selection at the FADS locus was not tightly linked to the initial introduction of agriculture and the Neolithic transition. Further, it suggests that the strongest signals of recent human adaptation in Europe did not coincide with the Neolithic transition but with more recent changes in environment, diet or efficiency of selection due to increases in effective population size.

evolutionary biology↗

bioRxiv2018

A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks

An explosion of high-throughput DNA sequencing in the past decade has led to a surge of interest in population-scale inference with whole-genome data. Recent work in population genetics has centered on designing inference methods for relatively simple model classes, and few scalable general-purpose inference techniques exist for more realistic, complex models. To achieve this, two inferential challenges need to be addressed: (1) population data are exchangeable, calling for methods that efficiently exploit the symmetries of the data, and (2) computing likelihoods is intractable as it requires integrating over a set of correlated, extremely high-dimensional latent variables. These challenges are traditionally tackled by likelihood-free methods that use scientific simulators to generate datasets and reduce them to hand-designed, permutation-invariant summary statistics, often leading to inaccurate inference. In this work, we develop an exchangeable neural network that performs summary statistic-free, likelihood-free inference. Our frame-work can be applied in a black-box fashion across a variety of simulation-based tasks, both within and outside biology. We demonstrate the power of our approach on the recombination hotspot testing problem, outperforming the state-of-the-art.

evolutionary biology↗