Extending genome-wide association studies to admixed cohorts with high degrees of relatedness.

Admixed populations comprise a large portion of the human population worldwide, but are often excluded from genome-wide association studies (GWASs) due to analytic challenges. Our group developed Tractor, a local-ancestry-informed GWAS tool designed for admixed samples that produces accurate ancestry-specific effect sizes and boosts the discovery power to identify ancestry-enriched loci. However, Tractor operates under an assumption of unrelated samples. Here, to address this gap, we propose Tractor-Mix, which allows for well-calibrated association studies in datasets containing admixed samples with relatedness. Extensive simulations show that this method is competitive with other state-of-the-art approaches that do not produce ancestry-specific results. Empirical testing of Tractor-Mix on admixed samples from the UK Biobank, Yale-Penn cohort and Mexico City Prospective Study highlight the value of this method, identifying ancestry-specific associations. In summary, Tractor-Mix extends the capabilities of current models and enables well-calibrated GWASs for related samples with admixture.