Benchmarking local genetic correlation estimation methods using summary statistics from genome-wide association studies

Abstract

Local genetic correlation evaluates the correlation of genetic effects between different traits across genetic variants in a local region. It has been proven informative for understanding the genetic similarities of complex traits beyond that captured by global genetic correlation calculated across the whole genome. Several summary-statistics-based approaches have been developed for estimating local genetic correlation, including{rho} -hess, SUPERGNOVA, and LAVA. However, there has not been a comprehensive evaluation of these methods to offer practical guidelines on the choices of these methods. In this study, we conduct benchmark comparisons of the performance of these three methods through extensive simulation and real data analyses. We focus on two technical difficulties in estimating local genetic correlation: sample overlaps across traits and local linkage disequilibrium (LD) estimates when only the external reference panels are available. Our simulations suggest that the type-I error and estimation accuracy are highly dependent on the estimation of the local LD matrix. These observations are corroborated by real data analyses of 31 complex traits. Overall, our results offer insights into post-GWAS local correlation studies and highlight issues that demand future methodology developments.