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

Schizophrenia Working Group of the Psychiatric Genomics Consortium, Brendan Bulik-Sullivan, Po Ru Loh, Hilary K. Finucane, Stephan Ripke, Jian Yang, Nick Patterson, Mark J. Daly, Alkes L. Price, Benjamin M. Neale, Aiden Corvin, James T.R. Walters, Kai How Farh, Peter A. Holmans, Phil Lee, David A. Collier, Hailiang Huang, Tune H. Pers, Ingrid Agartz, Esben Agerbo & 31 others Margot Albus, Madeline Alexander, Farooq Amin, Silviu A. Bacanu, Martin Begemann, Richard A. Belliveau, Judit Bene, Sarah E. Bergen, Elizabeth Bevilacqua, Tim B. Bigdeli, Donald W. Black, Richard Bruggeman, Nancy G. Buccola, Randy L. Buckner, William Byerley, Wiepke Cahn, Guiqing Cai, Murray J. Cairns, Dominique Campion, Rita M. Cantor, Vaughan J. Carr, Noa Carrera, Stanley V. Catts, Kimberly D. Chambert, Raymond C.K. Chan, Ronald Y.L. Chen, Eric Y.H. Chen, Wei Cheng, Eric F.C. Cheung, Siow Ann Chong, Hong Lee

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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.

Number of pages5
JournalNature Genetics
Issue number3
Publication statusPublished - 25 Feb 2015

ASJC Scopus subject areas

  • Genetics

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