eQTL data have been recognized for nearly a decade as potentially important for understanding complex genetic variation. Nicolae et al.1 showed that common variant-common disease associations are strongly enriched for genetic regulation of gene expression. Therefore, integrative approaches combining transcriptomic and genetic association data have great potential. Current transcriptomic imputation association analyses increase power for genetic discovery, with great potential for further development, including leveraging additional data types such as chromatin modifications79 (e.g. methylation, histone modification), imputing different tissues or different exposures (e.g. age, smoking, trauma) and modeling trans/coexpression effects. It remains critical to leverage transcriptomic impuation associations to provide insights into specific disease mechanisms. Here, the accelerated identification of disease-associated genes allows the detection of novel pathways and distinct spatiotemporal patterns of expression in schizophrenia risk.
