We applied our approaches to expression data from blood and adipose tissue measured in ~3,000 individuals overall. Through extensive simulations and real data analyses we show that our proposed approach increases performance over standard GWAS or eQTL-guided GWAS. Furthermore, we reanalyzed a 2010 lipid GWAS17 to find 25 new expression-trait associations in that data. 19 out of 25 contained genome-wide significant SNPs in the more recent and expanded lipids study5 thus showcasing the power of our approach to find robust associations. We imputed gene expression into GWAS data from over 900,000 phenotype measurements5–7 to identify 69 novel genes significantly associated to obesity-related traits (BMI, lipids, and height). Many of the novel genes were associated with relevant phenotypes in the Hybrid Mouse Diversity Panel. Overall our results showcase the power of integrating genotype, gene expression and phenotype to gain insights into the genetic basis of complex traits.
