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


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2021-01-18

AUTHORS

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

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. More... »

PAGES

195-204

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  • Journal

    TITLE

    Nature Genetics

    ISSUE

    2

    VOLUME

    53

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/s41588-020-00766-y

    DOI

    http://dx.doi.org/10.1038/s41588-020-00766-y

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1134668243

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/33462486


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