We know from the rb analysis above that cis-eQTLs are almost perfectly correlated in different brain regions. We then sought to combine data from the brain regions to increase the power of detecting eQTLs for follow-up analysis (e.g., identification of putative functional genes for brain-related traits and diseases). However, if there is sample overlap between two tissues and the phenotypic correlation is nonzero, the estimation errors of the SNP effects from the two tissues will be correlated. We implemented in the SMR software package (URLs) a summary-data-based method, which only requires summary-level data in the cis-regions to account for sample overlaps, to meta-analyze cis-eQTL data in correlated samples (MeCS) (Methods). MeCS is very similar to existing meta-analysis approaches such as MTAG39 or the Han et al. method40 that account for sample overlaps. However, there is a small but important distinction. That is, MeCS uses “null” SNPs (e.g., PeQTL > 0.01) to quantify sampling correlation of the estimated SNP effects between two data sets (θ), similar to the strategy used in the latest version of METAL (method unpublished, URLs), whereas MTAG39
