A multi-tissue transcriptome analysis of human metabolites guides interpretability of associations based on multi-SNP models for gene expression

There is particular interest in transcriptome-wide association studies (TWAS) - gene-level tests based on multi-SNP predictive models of gene expression - for identifying causal genes at loci associated with complex traits. However, interpretation of TWAS associations may be complicated by divergent effects of model SNPs on phenotype and gene expression. We developed an iterative modelling scheme for obtaining multi-SNP models of gene expression and applied this framework to generate expression models for 43 human tissues from the Genotype-Tissues Expression (GTEx) Project. We characterized the performance of single- and multi-SNP models for identifying causal genes in GWAS data for 46 circulating metabolites. We show that: (a) multi-SNP models captured more variation in expression than the top cis-eQTL (median 2-fold improvement); (b) predicted expression based on multi-SNP models was associated (FDR