We estimated the correlation (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hat r_b$$\end{document}r^b) of genetic effects at the top-associated cis-eQTLs/mQTLs between brain and blood. Because the rb method accounts for estimation errors, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hat r_b$$\end{document}r^b can be interpreted as an estimate of correlation of true cis-eQTL effects between brain and blood, as demonstrated by simulations (Supplementary Fig. 2). We applied the method to summary-level eQTL data from GTEx and found that genetic effects on gene expression at the top-associated cis-eQTLs were almost perfectly correlated between different brain regions (mean \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hat r_b = 0.94$$\end{document}r^b=0.94 for cis-eQTLs), especially between the non-cerebellar regions (mean \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hat r_b = 0.98$$\end{document}r^b=0.98 and s.e. = 0.003), in contrast to 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).
