The paper of Nick Mancuso et al. titled “Integrating Gene Expression with Summary Association Statistics to Identify Genes Associated with 30 Complex Traits”, in collaboration with Bogdan Lab, is now out in the American Journal of Human Genetics. This work applies the TWAS methodology for predicting cis genetic effects of gene expression on disease to summary data from 30 publicly available GWAS studies to identify 1,196 putative susceptibility genes. This included several hundred genes that were not near GWAS hits or were more significantly associated than the top hit. The work also develops new methods for using TWAS associations to estimate the genetic correlation (via expression) between traits, as well as to perform gene-based causal inference of one trait on another. Overall, these findings motivate us to think about how working in the space of predicted gene expression can yield new insights into the interplay between genes, tissues, and diseases.

See also:

  • repository for all study results.
  • repository for genetic correlation methods.
  • FUSION for software and documentation to perform TWAS analyses.
  • The recent paper of Liu et al. AJHG on partitioning heritability of gene expression; and pre-print of O’Connor et al. biorxiv proposing a novel estimator of the total effect of cis expression on disease.