Extending Genome-Wide Association Studies to admixed cohorts with high degrees of relatedness

Abstract

44 Recently admixed populations comprise a large portion of the human population 45 worldwide, but are often excluded from Genome-Wide Association Studies (GWAS) due 46 to analytic challenges. Our group has previously developed a local ancestry informed 47 generalized linear model based method, Tractor, for GWAS in admixed samples, which 48 produces accurate ancestry-specific effect sizes and boosts discovery power to identify 49 ancestry-enriched loci. Tractor has been instrumental for elucidating the genetic 50 architecture of complex traits across admixed cohorts, however it operates under an 51 assumption of unrelated samples. As biobanks and other large-scale data sources 52 continue to grow, increasing numbers of closely or cryptically related admixed samples 53 are included. This brings new statistical challenges in conducting GWAS and motivates 54 the timely development of novel tools that can model admixture in various cohort settings. 55 Here, we propose a novel mixed model method, Tractor-Mix, that allows for well- 56 calibrated association studies in datasets containing admixed samples with high degrees 57 of relatedness. Similar to Tractor, our method conducts genetic association tests by 58 leveraging local ancestry to produce more accurate effect sizes and boost power under 59 heterogeneity while effectively controlling false positives. Extensive simulations show this 60 enhanced method is competitive with other state-of-the-art approaches that do not 61 produce ancestry-specific results. Empirical testing of Tractor-Mix on multiple cohorts, 62 including admixed samples from the UK Biobank and Mexico City Prospective Study, 63 highlight the value of the method, identifying ancestry-specific associations. In summary, 64 Tractor-Mix is a powerful association framework that extends the capabilities of current 65 models and will facilitate the inclusion of admixed samples in large-scale GWAS. 3 medRxiv preprint doi: https://doi.org/10.1101/2025.05.27.25328444; this version posted June 9, 2025. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license . 66