Many genetic variants influence complex traits by modulating gene expression, thus altering the abundance levels of one or multiple proteins. Here, we introduce a powerful strategy that integrates gene expression measurements with summary association statistics from large-scale genome-wide association studies (GWAS) to identify genes whose cis-regulated expression is associated to complex traits. We leverage expression imputation to perform a transcriptome wide association scan (TWAS) to identify significant expression-trait associations. We applied our approaches to expression data from blood and adipose tissue measured in ~3,000 individuals overall. We imputed gene expression into GWAS data from over 900,000 phenotype measurements to identify 69 novel genes significantly associated to obesity-related traits (BMI, lipids, and height). Many of the novel genes are associated with relevant phenotypes in the Hybrid Mouse Diversity Panel. Our results showcase the power of integrating genotype, gene expression and phenotype to gain insights into the genetic basis of complex traits.

Many genetic variants influence complex traits by modulating gene expression, thus altering the abundance levels of one or multiple proteins. In this work we introduce a powerful strategy that integrates gene expression measurements with large-scale genome-wide association data to identify genes whose cis-regulated expression is associated to complex traits. We use a relatively small reference panel of individuals for which both genetic variation and gene expression have been measured to impute gene expression into large cohorts of individuals and identify expression-trait associations. We extend our methods to allow for indirect imputation of the expression-trait association from summary association statistics of large-scale GWAS 1-3 . We applied our approaches to expression data from blood and adipose tissue measured in ~3,000 individuals overall. We then imputed gene expression into GWAS data from over 900,000 phenotype measurements 4-6 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.

We assessed gene expression profiles in 2,752 twins, using a classic twin design to quantify expression heritability and quantitative trait loci (eQTL) in peripheral blood. The most highly heritable genes (~777) were grouped into distinct expression clusters, enriched in gene-poor regions, associated with specific gene function/ontology classes, and strongly associated with disease designation. The design enabled a comparison of twin-based heritability to estimates based on dizygotic IBD sharing and distant genetic relatedness. Consideration of sampling variation suggests that previous heritability estimates have been upwardly biased. Genotyping of 2,494 twins enabled powerful identification of eQTLs, which were further examined in a replication set of 1,895 unrelated subjects. A large number of local eQTLs (6,988) met replication criteria, while a relatively small number of distant eQTLs (165) met quality control and replication standards. Our results provide an important new resource toward understanding the genetic control of transcription.

Key messagesd This large-scale cross-trait GWAS provides strong evidence for shared genetic components between obesityrelated metabolic traits and asthma subtypes.d The strongest positive genetic correlation was observed between obesity and later-onset asthma.d Mendelian randomization analysis provided strong evidence in support of BMI causally increasing the risk of asthma.