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

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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.

• Tipo de documento: Artigo Científico
• Fonte: Nature Genetics; v. 53, n. 2, p. 21-pg., 2021-01-18.

Resumo

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)

• Processo FAPESP: 18/09328-2 - Avaliação do desempenho do escore poligênico de risco e da interação gene ambiente em uma amostra brasileira miscigenada
• Beneficiário: Marcos Leite Santoro
• Modalidade de apoio: Bolsas no Exterior - Estágio de Pesquisa - Pós-Doutorado