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Nat Genet DOI:10.1038/ng.3211

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

Publication TypeJournal Article
Year of Publication2015
AuthorsBulik-Sullivan, BK, Loh, P-R, Finucane, HK, Ripke, S, Yang, J, Patterson, N, Daly, MJ, Price, AL, Neale, BM
Corporate AuthorsSchizophrenia Working Group of the Psychiatric Genomics Consortium
JournalNat Genet
Date Published2015 Mar
KeywordsComputer Simulation, Genome, Human, Genome-Wide Association Study, Humans, Linkage Disequilibrium, Polymorphism, Single Nucleotide, Regression Analysis, Sample Size

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.


Alternate JournalNat. Genet.
PubMed ID25642630
PubMed Central IDPMC4495769
Grant ListR03 CA173785 / CA / NCI NIH HHS / United States
R01 HG006399 / HG / NHGRI NIH HHS / United States
F32 HG007805 / HG / NHGRI NIH HHS / United States
U01 MH094421 / MH / NIMH NIH HHS / United States
R01 MH101244 / MH / NIMH NIH HHS / United States
U01 MH094432 / MH / NIMH NIH HHS / United States
MR/L010305/1 / / Medical Research Council / United Kingdom
R01 MH094421 / MH / NIMH NIH HHS / United States