RT Journal Article
SR Electronic
T1 LD Score Regression Distinguishes Confounding from Polygenicity in Genome-Wide Association Studies
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 002931
DO 10.1101/002931
A1 Brendan K. Bulik-Sullivan
A1 Po-Ru Loh
A1 Hilary Finucane
A1 Stephan Ripke
A1 Jian Yang
A1 Schizophrenia Working Group of the Psychiatric Genomics Consortium
A1 Nick Patterson
A1 Mark J. Daly
A1 Alkes L. Price
A1 Benjamin M. Neale
YR 2014
UL http://biorxiv.org/content/early/2014/02/21/002931.abstract
AB Both polygenicity1,2 (i.e. many small genetic effects) and confounding biases, such as cryptic relatedness and population stratification3, can yield inflated distributions of test statistics in genome-wide association studies (GWAS). However, current methods cannot distinguish between inflation from bias and true signal from polygenicity. We have developed an approach that quantifies the contributions of each by examining the relationship between test statistics and linkage disequilibrium (LD). We term this approach LD Score regression. LD Score regression provides an upper bound on the contribution of confounding bias to the observed inflation in test statistics and can be used to estimate a more powerful correction factor than genomic control4–14. We find strong evidence that polygenicity accounts for the majority of test statistic inflation in many GWAS of large sample size.