For studies that integrate GWAS results with eQTL or DNA methylation QTL (mQTL) data to identify putative functional genes and regulatory elements for brain-related phenotypes and diseases16,17, the statistical power is limited by the small sample sizes of the brain eQTL or mQTL data (often in the order of 100s). On the other hand, there are blood eQTL and mQTL data available from thousands of individuals8,9 and the sample sizes of some of the ongoing projects have reached 10,000s (e.g., the GoDMC and eQTLGen consortia). The questions are to what extent the cis-genetic effects on gene expression and DNA methylation (DNAm) in blood differ from those in brain and whether we can gain power to detect associations of genes (or DNAm sites) with brain-related traits by using the cis-eQTL (or cis-mQTL) effects estimated from a large blood sample as proxies for those in brain. Liu et al.14 extended the stratified linkage disequilibrium (LD) score regression method to estimate genetic correlation (rg) of gene expression between tissues at all SNPs in local or distal regions and showed that the mean estimate
