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

Bulik-Sullivan, Brendan K, Loh, Po-Ru, Finucane, Hilary K, Ripke, Stephan, Yang, Jian, Schizophrenia Working Group of the Psychiatric Genetics Consorti, , Patterson, Nick, Daly, Mark J., Price, Alkes L., Neale, Benjamin M., Holmans, Peter Alan, Escott-Price, Valentina, Kirov, George and O'Donovan, Michael Conlon 2015. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nature Genetics 47 (3) , pp. 291-295. 10.1038/ng.3211

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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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Medicine Advanced Research Computing @ Cardiff (ARCCA) MRC Centre for Neuropsychiatric Genetics and Genomics (CNGG)
Subjects:	R Medicine > R Medicine (General)
Additional Information:	Peter Holmans, Valentina Escott-Price, George Kirov and Michael O'Donovan are collaborators on this article.
Publisher:	Nature
ISSN:	1061-4036
Date of Acceptance:	7 January 2015
Last Modified:	18 Dec 2020 13:15
URI:	http://orca-mwe.cf.ac.uk/id/eprint/85639

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