between brain and blood in one data set, and between brain and blood in two data sets using summary-level data from GTEx v6 (whole blood and 10 brain regions)11, the CommonMind Consortium (CMC; dorsolateral prefrontal cortex)18, the Religious Orders Study and Memory and Aging Project (ROSMAP)19, and the Brain eQTL Almanac project (Braineac; 10 brain regions)20 (Methods and Supplementary Table 1). All eQTL effects were re-scaled based on the expression level per gene in standard deviation (SD) units. For the GTEx, CMC and ROSMAP data, which are based on RNA sequencing (RNA-Seq), we matched the data sets by Ensembl Gene IDs. For the Braineac data that are based on gene expression microarray, we matched the data sets by gene symbols and removed genes tagged by multiple gene expression probes to ensure a one-to-one match for genes between data sets. The main aim of this study is to quantify the extent to which cis-eQTL data in blood can be used in the SMR analysis21 (see below) to identify genes associated with brain-related phenotypes and disorders. If we had selected the top-associated cis-eQTLs in blood and compared their effects with those in brain, we would likely suffer a form of winner’s curse.
