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

Brendan K Bulik-Sullivan, Po-Ru Loh, Hilary K Finucane, Stephan Ripke, Jian Yang, Nick Patterson, Mark J Daly, Alkes L Price, Benjamin M Neale, Schizophrenia Working Group of the Psychiatric Genomics Consortium, Francis O'Neill

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951 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)291-5
Number of pages5
JournalNature Genetics
Volume47
Issue number3
Early online date02 Feb 2015
DOIs
Publication statusPublished - Mar 2015

Keywords

  • Computer Simulation
  • Genome, Human
  • Genome-Wide Association Study
  • Humans
  • Linkage Disequilibrium
  • Polymorphism, Single Nucleotide
  • Regression Analysis
  • Sample Size

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    Bulik-Sullivan, B. K., Loh, P-R., Finucane, H. K., Ripke, S., Yang, J., Patterson, N., Daly, M. J., Price, A. L., Neale, B. M., Schizophrenia Working Group of the Psychiatric Genomics Consortium, & O'Neill, F. (2015). LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nature Genetics, 47(3), 291-5. https://doi.org/10.1038/ng.3211