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 circumvent these difficulties by introducing a technique – cross-trait LD Score regression – for estimating genetic correlation that requires only GWAS summary statistics and is not biased by sample overlap. We use this method to estimate 276 genetic correlations among 24 traits. The results include genetic correlations between anorexia nervosa and schizophrenia, anorexia and obesity and associations between educational attainment and several diseases. These results highlight the power of genome-wide analyses, since there currently are no significantly associated SNPs for anorexia nervosa and only three for educational attainment.

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 metaanalyses. We circumvent these difficulties by introducing a technique -cross-trait LD Score regression -for estimating genetic correlation that requires only GWAS summary statistics and is not biased by sample overlap. We use this method to estimate 276 genetic correlations among 24 traits. The results include genetic correlations between anorexia nervosa and schizophrenia, anorexia and obesity and associations between educational attainment and several diseases. These results highlight the power of genome-wide analyses, since there currently are no significantly associated SNPs for anorexia nervosa and only three for educational attainment.Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use

We introduce an approach for identifying disease-relevant tissues and cell types by analyzing gene expression data together with genome-wide association study (GWAS) summary statistics. Our approach uses stratified LD score regression to test whether disease heritability is enriched in regions surrounding genes with the highest specific expression in a given tissue. We apply our approach to gene expression data from several sources together with GWAS summary statistics for 48 diseases and traits (average N=169K), detecting significant tissue-specific enrichments (FDR<5%) for 34 traits. In our analysis of multiple tissues, we detect a broad range of enrichments that recapitulate known biology. In our brain-specific and immune-specific analyses, significant enrichments include an enrichment of inhibitory over excitatory neurons for bipolar disorder but excitatory over inhibitory neurons for schizophrenia and body mass index. Our results demonstrate that our polygenic approach is a powerful way to leverage gene expression data for interpreting GWAS signal.