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The impact of inherited genetic variation on gene expression in humans is well-established. The majority of known expression quantitative trait loci (eQTLs) impact expression of local genes ( cis -eQTLs). More research is needed to identify effects of genetic variation on distant genes ( trans -eQTLs) and understand their biological mechanisms. One common trans -eQTLs mechanism is "mediation" by a local ( cis ) transcript. Thus, mediation analysis can be applied to genome-wide SNP and expression data in order to identify transcripts that are " cis -mediators" of trans -eQTLs, including those " cis -hubs" involved in regulation of many trans -genes. Identifying such mediators helps us understand regulatory networks and suggests biological mechanisms underlying trans -eQTLs, both of which are relevant for understanding susceptibility to complex diseases. The multitissue expression data from the Genotype-Tissue Expression (GTEx) program provides a unique opportunity to study cis -mediation across human tissue types. However, the presence of complex hidden confounding effects in biological systems can make mediation analyses challenging and prone to confounding bias, particularly when conducted among diverse samples. To address this problem, we propose a new method: Genomic Mediation analysis with Adaptive Confounding adjustment (GMAC). It enables the search of a very large pool of variables, and adaptively selects potential confounding variables for each mediation test. Analyses of simulated data and GTEx data demonstrate that the adaptive selection of confounders by GMAC improves the power and precision of mediation analysis. Application of GMAC to GTEx data provides new insights into the observed patterns of cis -hubs and trans -eQTL regulation across tissue types.

Identifying cis-mediators for trans-eQTLs across many human tissues using genomic mediation analysis