Biology subjects

Comajoan Cara, M.

Publications and source records attributed to Comajoan Cara, M..

3 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

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↗

bioRxiv2023

PopGenAdapt: Semi-Supervised Domain Adaptation for Genotype-to-Phenotype Prediction in Underrepresented Populations

The lack of diversity in genomic datasets, currently skewed towards individuals of European ancestry, presents a challenge in developing inclusive biomedical models. The scarcity of such data is particularly evident in labeled datasets that include genomic data linked to electronic health records. To address this gap, this paper presents PopGenAdapt, a genotype-to-phenotype prediction model which adopts semi-supervised domain adaptation (SSDA) techniques originally proposed for computer vision. PopGenAdapt is designed to leverage the substantial labeled data available from individuals of European ancestry, as well as the limited labeled and the larger amount of unlabeled data from currently underrepresented populations. The method is evaluated in underrepresented populations from Nigeria, Sri Lanka, and Hawaii for the prediction of several disease outcomes. The results suggest a significant improvement in the performance of genotype-to-phenotype models for these populations over state-of-the-art supervised learning methods, setting SSDA as a promising strategy for creating more inclusive machine learning models in biomedical research. Our code is available at https://github.com/AI-sandbox/PopGenAdapt.

bioinformatics↗