Biology subjects

Bonet, D.

Publications and source records attributed to Bonet, D..

5 recordsLinked to original sources

bioRxiv2026

Graph transformer for ancient ancestry inference

Local ancestry inference classifies segments of DNA in admixed individuals by their originating population. However, as the date of admixture becomes older, these segments become shorter and determining their ancestry becomes increasingly difficult. This limits many existing segment-based methods to relatively recent historical admixture events and more highly diverged populations. The rapidly expanding availability of ancient DNA offers a promising opportunity to use these ancient samples as references for local ancestry inference. A recent approach integrates ancient samples into the ancestral recombination graph (ARG) for local ancestry inference. Here, we introduce recent advances in deep learning for graphs into this ARG framework to create ARGMix, a graph transformer that infers local ancestry using the coalescent trees of the inferred ARG. Our approach employs ancient samples as references in the marginal trees to predict local ancestry. We train ARGMix on data reflecting the well-understood ancient European demography and demonstrate improved accuracy and robustness even under demographic misspecification. We then apply ARGMix to an ARG of ancient and present-day European samples for ancestry-specific analyses, finding evidence of continuity between Otzi the Iceman and present-day individuals from nearby regions.

genetics↗

bioRxiv2026

Biobank-Scale Polygenic Prediction in Admixed Populations Using Local Ancestry via the Group Lasso

Polygenic risk models trained in one ancestry often fail to perform well in others, in part due to linkage disequilibrium and allele frequency differences across ancestries. In response, separate models trained for specific ancestries have been introduced. However, this single ancestry approach is untenable in admixed groups, which have ancestry from multiple sources varying across the genome and between individuals. Here we present Combine, a biobank-scale sparse regression framework that augments per-variant genotype features with local ancestry dosages and fits all effects jointly using a variant-level group lasso penalty. In 99,298 admixed participants from the All of Us Research Program, Combine substantially outper-forms state-of-the-art multi-ancestry summary-statistic approaches (e.g., 144% relative improvement over PRS-CSx for white blood cell count). Furthermore, it matches or improves upon the predictive performance of highly optimized individual-level models (iPGS/snpnet) across seven of nine evaluated phenotypes, while uniquely providing locus-level interpretability to disentangle shared allelic effects from ancestry-linked tagging. An ancestry-specific extension, Combine-S, estimates haplotypic ancestry-associated SNP effects together with local-ancestry terms, enabling systematic identification of ancestry-dependent effect magnitudes and sign differences at established and plausible loci. Finally, we show that incorporating external GWAS evidence through group-specific penalty weights improves LDL cholesterol prediction without pre-filtering variants. Together, Combine provides a scalable framework for polygenic modeling that prioritizes efficient local ancestry-aware modeling and interpretation in admixed biobanks.

genomics↗

bioRxiv2026

snputils: A High-Performance Python Library for Genetic Variation and Population Structure

The increasing size and resolution of genomic and population genetic datasets offer unprecedented opportunities to study population structure and uncover the genetic basis of complex traits and diseases. The collection of existing analytical tools, however, is characterized by format incompatibilities, limited functionality, and computational inefficiencies, forcing researchers to construct fragile pipelines that chain together fragmented command-line utilities and ad hoc scripts. These are difficult to maintain, scale, and reproduce. To address such limitations, we present snputils, a Python library that unifies high-performance I/O, transformation, and analysis of genotype, ancestry, and phenotypic information within a single framework suitable for biobank-scale research. The library provides efficient tools for essential operations, including querying, cleaning, merging, and statistical analysis. In addition, it offers classical population genetic statistics with optional ancestry-specific masking. An identity-by-descent module supports reading of multiple formats, filtering and ancestry-restricted segment trimming for relatedness and demographic inference. snputils also incorporates ancestry-masking and multi-array functionalities for dimensionality reduction methods, as well as efficient implementations of admixture simulation, admixture mapping, and advanced visualization capabilities. With support for the most commonly used file formats, snputils integrates smoothly with existing tools and clinical databases. At the same time, its modular and optimized design reduces technical overhead, facilitating reproducible workflows that accelerate discoveries in population genetics, genomic research, and precision medicine. Benchmarking demonstrates a significant reduction in genotype data loading speed compared to existing Python libraries. The open-source library is available at https://github.com/AI-sandbox/snputils, with full documentation and tutorials at snputils.org.

bioinformatics↗

bioRxiv2026

ARGformer: learning on ancestral recombination graphs with transformers

Recent advances in inference of the ancestral recombination graph (ARG), which describes how segments of chromosomes trace back through recombination and shared lineages, have made it possible to reconstruct genome-wide genealogies for large cohorts, but it remains difficult to summarize and use this information for population genetic analyses. We present ARGformer, an encoder-only transformer that learns context-dependent embeddings with a self-supervised masked objective finetuned with contrastive learning for downstream retrieval tasks. We train ARGformer on genealogies from coalescent simulations and on genealogies inferred from ancient and present-day Homo sapiens genomes. Using only these learned embeddings, without access to genotype matrices, ARGformer captures patterns of global population structure and supports ancestry inference through clustering and nearest-neighbor retrieval. On genealogies that include archaic hominins, ARGformer can highlight Denisovan-derived segments in Oceanian genomes and reveals Oceanian-like ancestry in South American Indigenous populations. ARGformer is available at https://github.com/AI-sandbox/ARGformer.

genomics↗

bioRxiv2023

Machine Learning Strategies for Improved Phenotype Prediction in Underrepresented Populations

Precision medicine models often perform better for populations of European ancestry due to the over-representation of this group in the genomic datasets and large-scale biobanks from which the models are constructed. As a result, prediction models may misrepresent or provide less accurate treatment recommendations for underrepresented populations, contributing to health disparities. This study introduces an adaptable machine learning toolkit that integrates multiple existing methodologies and novel techniques to enhance the prediction accuracy for underrepresented populations in genomic datasets. By leveraging machine learning techniques, including gradient boosting and automated methods, coupled with novel population-conditional re-sampling techniques, our method significantly improves the phenotypic prediction from single nucleotide polymorphism (SNP) data for diverse populations. We evaluate our approach using the UK Biobank, which is composed primarily of British individuals with European ancestry, and a minority representation of groups with Asian and African ancestry. Performance metrics demonstrate substantial improvements in phenotype prediction for underrepresented groups, achieving prediction accuracy comparable to that of the majority group. This approach represents a significant step towards improving prediction accuracy amidst current dataset diversity challenges. By integrating a tailored pipeline, our approach fosters more equitable validity and utility of statistical genetics methods, paving the way for more inclusive models and outcomes.

bioinformatics↗