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

Bulik-Sullivan, Brendan K., Loh, Po-Ru, Finucane, Hilary K., Ripke, Stephen, Yang, Jian, Schizophrenia Working Group of the Psychiatric Genomics Consortium, Patterson, Nick, Daly, Mark J., Price, Alkes L., Neale, Benjamin M., Nertney, Deborah A., Mowry, Bryan J. and Catts, Stanley V. (2015) LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nature Genetics, 47 3: 291-295. doi:10.1038/ng.3211

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Author Bulik-Sullivan, Brendan K.
Loh, Po-Ru
Finucane, Hilary K.
Ripke, Stephen
Yang, Jian
Schizophrenia Working Group of the Psychiatric Genomics Consortium
Patterson, Nick
Daly, Mark J.
Price, Alkes L.
Neale, Benjamin M.
Nertney, Deborah A.
Mowry, Bryan J.
Catts, Stanley V.
Title LD Score regression distinguishes confounding from polygenicity in genome-wide association studies
Journal name Nature Genetics   Check publisher's open access policy
ISSN 1061-4036
1546-1718
Publication date 2015-02-25
Year available 2015
Sub-type Article (original research)
DOI 10.1038/ng.3211
Volume 47
Issue 3
Start page 291
End page 295
Total pages 5
Place of publication New York, NY United States
Publisher Nature Publishing Group
Collection year 2016
Language eng
Formatted 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.
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Queensland Brain Institute Publications
Official 2016 Collection
School of Medicine Publications
 
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Citation counts: TR Web of Science Citation Count  Cited 80 times in Thomson Reuters Web of Science Article | Citations
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