Integrative analysis of the genome, transcriptome, and proteome identifies causal mechanisms of complex traits.
Okamoto, J.; Yin, X.; Ryan, B.; Chiou, J.; Luca, F.; Pique-Regi, R.; Im, H. K.; Morrison, J.; Burant, C.; Fauman, E.; Laakso, M.; Boehnke, M.; Wen, X.
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We present multi-integration of transcriptome-wide association studies and colocalization (Multi-INTACT), an algorithm that models multiple gene products (e.g. encoded RNA transcript and protein levels) to implicate causal genes and relevant gene products. In simulations, Multi-INTACT achieves higher power than existing methods, maintains calibrated false discovery rates, and detects the true causal gene product(s). We apply Multi-INTACT to GWAS on 1,408 metabolites, integrating the GTEx expression and UK Biobank protein QTL datasets. Multi-INTACT infers 52% to 109% more metabolite causal genes than protein-alone or expression-alone analyses and indicates both gene products are relevant for most gene nominations.
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