the modest phenotypic correlation in gene expression levels (mean rp = 0.33). It is therefore sensible to run a meta-analysis of the cis-eQTL effects across brain regions to gain power of detecting eQTLs for the whole brain (Supplementary Fig. 19). This can be done even if the brain regions are from different samples. We also found that the cis-eQTL effects were highly correlated between brain and blood in GTEx (mean \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hat r_b = 0.77$$\end{document}r^b=0.77 for cis-eQTLs), and the estimate only slightly decreased using data from different samples (mean \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hat r_b = 0.70$$\end{document}r^b=0.70). These estimates were significantly different from 1, suggesting there are real genetic differences between tissues. The genetic differences are partly due to cell-type-specific genetic effects regardless whether cell composition covariates have been included in the eQTL analysis or not. This is because adjusting for cell composition only removes the mean differences in gene expression level among cell types rather than cell-type-specific genetic effects. On the other hand, however, the strong between-tissue correlation in cis-eQTL effects does not contradict the result that many genes showed differential expression between brain and blood
