Tractor uses local ancestry to enable inclusion of admixed individuals into GWAS and boost power

Admixed populations are routinely excluded from genomic studies due to concerns over population structure. Here, we present a statistical framework and software package, Tractor, to facilitate inclusion of admixed individuals in association studies by leveraging local ancestry. We test Tractor with simulated and empirical 2-way admixed African-European cohorts. Tractor generates accurate ancestry-specific effect size estimates and p values, can boost GWAS power, and improves the resolution of association signals. Using a local ancestry aware regression model, we replicate known hits for blood lipids, discover novel hits missed by standard GWAS, and localize signals closer to putative causal variants.