Principal components analysis corrects for stratification in genome-wide association studies View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2006-07-23

AUTHORS

Alkes L Price, Nick J Patterson, Robert M Plenge, Michael E Weinblatt, Nancy A Shadick, David Reich

ABSTRACT

Population stratification—allele frequency differences between cases and controls due to systematic ancestry differences—can cause spurious associations in disease studies. We describe a method that enables explicit detection and correction of population stratification on a genome-wide scale. Our method uses principal components analysis to explicitly model ancestry differences between cases and controls. The resulting correction is specific to a candidate marker's variation in frequency across ancestral populations, minimizing spurious associations while maximizing power to detect true associations. Our simple, efficient approach can easily be applied to disease studies with hundreds of thousands of markers. More... »

PAGES

904-909

Journal

TITLE

Nature Genetics

ISSUE

8

VOLUME

38

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/ng1847

    DOI

    http://dx.doi.org/10.1038/ng1847

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1031429813

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/16862161


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