数量性状位点
遗传建筑学
联想(心理学)
统计能力
基因组学
遗传学
进化生物学
作者
Elizabeth G. Atkinson,Adam X. Maihofer,Masahiro Kanai,Alicia R. Martin,Konrad J. Karczewski,Marcos L. Santoro,Jacob C. Ulirsch,Yoichiro Kamatani,Yukinori Okada,Hilary K. Finucane,Karestan C. Koenen,Caroline M. Nievergelt,Mark J. Daly,Benjamin M. Neale
出处
期刊:Nature Genetics
[Springer Nature]
日期:2021-01-18
卷期号:53 (2): 195-204
被引量:23
标识
DOI:10.1038/s41588-020-00766-y
摘要
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. Tractor is a statistical framework that facilitates the inclusion of admixed individuals in association studies by leveraging local ancestry. Tractor generates accurate ancestry-specific effect-size estimates and improves the resolution of association signals.
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