Biologically relevant transcriptomic information can be extracted through detailed RNA-sequencing, as recently described by the CommonMind Consortium14 (CMC) in a large cohort of genotyped individuals with schizophrenia and bipolar disorder14. These analyses however are underpowered to detect statistically significant differential expression of genes mapping at schizophrenia (SCZ) risk loci, due to the small effects predicted by GWAS, combined with the difficulty of obtaining adequate sample sizes of neurological tissues14, and do not necessarily identify all risk variation in GWAS loci. Transcriptomic imputation is an alternative approach that leverages large eQTL reference panels to bridge the gap between large-scale genotyping studies and biologically useful transcriptome studies15,16. Transcriptomic imputation approaches codify the relationships between genotype and gene expression in matched panels of individuals, then impute the genetic component of the transcriptome into large-scale genotype-only datasets, such as case-control GWAS cohorts, enabling investigation of disease-associated gene expression changes. This will allow us to study genes with modest effect sizes, likely representing a large proportion of genomic risk for psychiatric disorders14,17.
