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

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

Posted 19 May 2020
bioRxiv DOI: 10.1101/2020.05.17.100727

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 ancestry-specific 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. ### Competing Interest Statement M.J.D. is a founder of Maze Therapeutics. A.R.M. serves as a consultant for 23andMe and is a member of the Precise.ly Scientific Advisory Board. B.M.N. is a member of the Deep Genomics Scientific Advisory Board and serves as a consultant for the Camp4 Therapeutics Corporation, Takeda Pharmaceutical and Biogen. The remaining authors declare no competing interests.

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