Míriam Barrabés

This paper introduces Gnomix, a high-resolution ancestry deconvolution method for next-generation genomic data, achieving higher accuracy and faster performance than existing methods.

This paper introduces non-parametric methods to adjust polygenic scores for ancestry bias, improving accuracy for non-European populations using the UK Biobank.

snputils provides a high-performance Python library for handling genetic variation and population structure data, improving efficiency and integration in genomic research.

The paper analyzes a Mexican FH cohort to show rare and common alleles in FH genes and a novel locus contribute to disease risk, with replication in UK Biobank.

This study evaluates polygenic risk score (PRS) performance across four ancestry groups for 14 diseases, finding that ancestry-specific PRS models improve accuracy and identifying cross-trait interactions.

This paper predicts dog phenotypes (breed, height, weight) from genotypes using linear and non-linear methods, showing that linear methods outperform non-linear for breed classification but not for height and weight regression.

This paper uses Generative Moment Matching Networks to simulate SNP sequences and introduces training improvements and evaluation metrics.