Comparison of Methods for Transcriptome Imputation Through Application to Two Common Complex Diseases

Transcriptome imputation has become a popular method for integrating genotype data with publicly available expression data to investigate the potentially causal role of genes in complex traits. Here, we compare three approaches (PrediXcan, MetaXcan and FUSION) via application to genome-wide association study (GWAS) data for Crohn's disease and type 1 diabetes from the Wellcome Trust Case Control Consortium. We investigate: (i) how the results of each approach compare with each other and with those of standard GWAS analysis; and (ii) how variants in the models used by the prediction tools compare with variants previously reported as eQTLs. We find that all approaches produce highly correlated results when applied to the same GWAS data, although for a subset of genes, mostly in the major histocompatibility complex, the approaches strongly disagree. We also observe that most associations detected by these methods occur near known GWAS risk loci. PrediXcan and MetaXcan's models for predicting expression more consistently recapitulate known effects of genotype on expression, suggesting they are more robust than FUSION. Application of these transcriptome imputation approaches to summary statistics from meta-analyses in Crohn's disease and type 1 diabetes detects 53 significant expression-Crohn's disease associations and 154 significant expression-type 1 diabetes associations, providing insight into biology underlying these diseases. We conclude that while current implementations of transcriptome imputation typically detect fewer associations than GWAS, they nonetheless provide an interesting way of interpreting association signals to identify potentially causal genes, and that PrediXcan and MetaXcan generally produce more reliable results than FUSION.