TY - JOUR
T1 - LD Score regression as an estimator of confounding and genetic correlations in genome-wide association studies
JF - bioRxiv
DO - 10.1101/234815
SP - 234815
AU - James J. Lee
AU - Carson C. Chow
Y1 - 2017/01/01
UR - http://biorxiv.org/content/early/2017/12/30/234815.abstract
N2 - In order to infer that a single-nucleotide polymorphism (SNP) either affects a phenotype or is linkage disequilibrium with a causal site, we must have some assurance that any SNP-phenotype correlation is not the result of confounding with some environmental variable that also affects the trait. Here we provide a mathematical analysis of LD Score regression, a recently developed method for using summary statistics from genome-wide association studies (GWAS) to ensure that confounding does not inflate the number of false positives. We do not treat the effects of genetic variation as a random variable and thus are able to obtain results about the unbiasedness of this method. We demonstrate that LD Score regression can produce estimates of confounding at null SNPs that are nearly unbiased or overly conservative under fairly general conditions. This robustness can hold even in cases now thought to be unfavorable, such as a correlation over SNPs between LD Scores and the degree of confounding. LD Score regression is thus an even stronger technique for causal inference than foreseen by its developers. Additionally, we demonstrate that LD Score regression can produce unbiased estimates of the genetic correlation, even when its estimates of the genetic covariance and the two univariate heritabilities are substantially biased.
ER -