MEB Seminar: An Atlas of Genetic Correlations across Human Diseases and Traits
Speaker: Brendan Bulik-Sullivan, staff scientist at the Broad Institute of MIT and Harvard.
Identifying genetic correlations between complex traits and diseases can provide useful etiological insights and help prioritize likely causal relationships. The major challenges preventing estimation of genetic correlation from genome-wide association study (GWAS) data with current methods are the lack of availability of individual genotype data and widespread sample overlap among meta-analyses. We have recently developed a technique – cross-trait LD Score regression – for estimating genetic correlation using only GWAS summary statistics. A key advantage of this method is that it is not biased by sample overlap, because it can infer the number of overlapping samples from the summary data. So far, we have used this method to estimate a few thousand genetic correlations from publicly available GWAS summary data. In addition, we have implemented these methods in an easy-to-use open source software package called ldsc, which has already been adopted by several GWAS consortia.
In this talk, I will discuss the LD Score regression family of methods, with a focus on cross-trait LD Score regression for estimating genetic correlation. I will describe some published results, as well as work in progress applying the method to new phenotypes and cross-sex genetic correlation.