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

Brendan 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

Research output: Contribution to journalArticleResearchpeer-review

1180 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)291-295
Number of pages5
JournalNature Genetics
Volume47
Issue number3
DOIs
Publication statusPublished - 1 Mar 2015
Externally publishedYes

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