trans-eQTLs may also have an important role in regulating gene expression especially for tissue-specific effects14. The methods developed in this study can be applied to trans-eQTL/mQTL data with minimal modification. Because the variance explained by individual trans-eQTL/mQTL is small on average9,38, very large sample sizes (e.g., 10,000s) are required to detect trans-eQTLs to be useful for the SMR analysis21. Third, the rb analysis was focused on the correlation at the top-associated cis-eQTLs/mQTLs with relatively large effects (i.e., P < 5 × 10−8 in a reference tissue) because the SMR test only uses cis-eQTLs/mQTLs at P < 5 × 10−8. The estimate of rb was slightly lower for cis-eQTLs/mQTLs selected at a less stringent threshold (Supplementary Fig. 26), consistent with the observation in simulation (Supplementary Fig. 27). However, this does not change our conclusion about the use of the top-associated cis-eQTLs/mQTLs identified in a large blood sample to identify putative target genes for brain-related traits. Last but not least, the MeCS method requires the correlation of errors in the estimated SNP effects between two samples (θ), which is estimated by a simple correlation approach at the null SNPs in the cis-region. This approach, however, is not applicable to eQTL or mQTL
