Tractor: A framework allowing for improved inclusion of admixed individuals in large-scale association studies.

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

doi:10.1038/s41588-020-00766-y

GenomicsNature GeneticsJan 18, 2021

Peer-reviewed journal article

Tractor: A framework allowing for improved inclusion of admixed individuals in large-scale association studies.

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

Abstract

Admixed populations are routinely excluded from medical 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 simulations and empirical data focused on admixed African-European individuals. Tractor generates ancestryspecific effect size estimates, 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 in admixed populations, discover novel hits missed by standard GWAS procedures, and localize signals closer to putative causal variants.

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