LD Score regression distinguishes confounding from polygenicity in genome-wide association studies View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2015-03

AUTHORS

Brendan K Bulik-Sullivan, Po-Ru Loh, Hilary K Finucane, Stephan Ripke, Jian Yang, Schizophrenia Working Group of the Psychiatric Genomics Consortium, Nick Patterson, Mark J Daly, Alkes L Price, Benjamin M Neale

ABSTRACT

Both polygenicity (many small genetic effects) and confounding biases, such as cryptic relatedness and population stratification, can yield an inflated distribution of test statistics in genome-wide association studies (GWAS). However, current methods cannot distinguish between inflation from a true polygenic signal and bias. We have developed an approach, LD Score regression, that quantifies the contribution of each by examining the relationship between test statistics and linkage disequilibrium (LD). The LD Score regression intercept can be used to estimate a more powerful and accurate correction factor than genomic control. We find strong evidence that polygenicity accounts for the majority of the inflation in test statistics in many GWAS of large sample size. More... »

PAGES

291-295

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/ng.3211

    DOI

    http://dx.doi.org/10.1038/ng.3211

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1044444076

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/25642630


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