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

Author(s):Show less -

Atkinson, Elizabeth G. ; Maihofer, Adam X. ; Kanai, Masahiro ; Martin, Alicia R. ; Karczewski, Konrad J. ; Santoro, Marcos L. ; Ulirsch, Jacob C. ; Kamatani, Yoichiro ; Okada, Yukinori ; Finucane, Hilary K. ; Koenen, Karestan C. ; Nievergelt, Caroline M. ; Daly, Mark J. ; Neale, Benjamin M.

• Document type: Journal article
• Source: Nature Genetics; v. 53, n. 2, p. 21-pg., 2021-01-18.

Abstract

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 the inclusion of admixed individuals in association studies by leveraging local ancestry. We test Tractor with simulated and empirical two-way admixed African-European cohorts. Tractor generates accurate ancestry-specific effect-size estimates and p values, can boost genome-wide association study (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. (AU)

• FAPESP's process: 18/09328-2 - Evaluation of polygenic risk score performance and gene-environment interaction in an admixed Brazilian cohort
• Grantee: Marcos Leite Santoro
• Support Opportunities: Scholarships abroad - Research Internship - Post-doctor