Home > Research > Publications & Outputs > LD Score regression distinguishes confounding f...


Text available via DOI:

View graph of relations

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

Research output: Contribution to Journal/MagazineJournal articlepeer-review

  • Schizophrenia Working Group of the Psychiatric Genomics Consortium
<mark>Journal publication date</mark>03/2015
<mark>Journal</mark>Nature Genetics
Issue number3
Number of pages5
Pages (from-to)291-295
Publication StatusPublished
Early online date2/02/15
<mark>Original language</mark>English


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.