Additional advantages include: Like other gene-based tests, it has much smaller multiple-testing burden (~20K tests maximum, ~10K genes with high quality prediction in most tissues) compared to single variant tests (~5–10M tests). Moving beyond the stringent Bonferroni correction, priors on genes can be less restrictive than for SNPs.Informative priors and groupings of functional units (based on known pathways, for example) are much more straightforward to construct for genes than SNPs.No actual transcriptome data are required since the predicted expression levels are a function of genetic variation alone. Thus, the method can be applied to any existing dataset with large-scale genome interrogation such as those in dbGaP or other repositories. Re-analyses of existing datasets, with a focus on mechanism using PrediXcan, address a gap that has largely characterized GWAS to date.Reverse causality is not a major concern since disease status or drug treatment does not alter germline genomic variation.Meta-analysis of gene-based results is simplified since less stringent harmonization between studies is required.Multiple tissues can be evaluated using a reference transcriptome dataset (such as GTEx). In general, the only limitation is the
