Journal
NATURE GENETICS
Volume 47, Issue 3, Pages 291-+Publisher
NATURE PORTFOLIO
DOI: 10.1038/ng.3211
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Funding
- US National Institutes of Health [F32 HG007805, R01 HG006399, R03 CA173785, R01 MH094421]
- Fannie and John Hertz Foundation
- Div Of Biological Infrastructure
- Direct For Biological Sciences [1349449] Funding Source: National Science Foundation
- Lundbeck Foundation [R155-2014-1724] Funding Source: researchfish
- Medical Research Council [MR/L023784/2, MR/L010305/1, G0801418B, G0801418] Funding Source: researchfish
- MRC [MR/L023784/2, G0801418] Funding Source: UKRI
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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.
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