The availability of transcriptome-informative datasets for a wide range of human tissues and cells has led to the development of computational methods to integrate transcriptomic data with information related to genetic variation. These approaches can be used to calculate the overrepresentation of genes expressed in certain tissues and cell types164 and also to predict the component of the transcriptomic changes regulated by genetic variation and test its association with the phenotype of interest165, 166. Applying these methods, investigators were able to derive additional information regarding SUD pathophysiology from genome-wide association data34, 167. For instance, the integration of genetic and transcriptomic information can permit calculation of the genetically regulated component of tissue-specific transcriptomic changes associated with substance-use traits168 or to estimate the colocalization of risk alleles associated with alcohol consumption with expression quantitative trait loci169.
