There are some inherent limitations to this approach. The accuracy of transcriptomic imputation is reliant on access to large eQTL reference panels, and it is therefore vital that efforts to collect and analyze these samples continue. Transcriptomic inputation has exciting advantages for gene discovery as well as downstream applications15,56,57; however, the relative merits of existing methodologies are as yet under-explored. Here, sparser elastic net models better captured gene expression regulation than BSLMM; at the same time, the improved performance of elastic net over max-eQTL models suggests that a single eQTL model is over-simplified2,15. Fundamentally, transcriptomic imputation methods model only the genetically regulated portion of gene expression, and so cannot capture or interpret variance of expression induced by environment or lifestyle factors, which may be of particular importance in psychiatric disorders. Given the right study design, analyzing genetic components of expression together with observed expression could open doors to better study the role of gene expression in disease.
