TWAS: Transcriptome-Wide Association Study through expression imputation

Many genetic variants influence complex traits by modulating gene expression, thus altering the abundance levels of one or multiple proteins. We developed an approach to identify genes whose expression is significantly associated to complex traits in individuals without directly measured expression levels. We leverage a relatively small set of reference individuals for whom both gene expression and genetic variation (single nucleotide polymorphisms, SNPs) have been measured to impute the cis-genetic component of expression into a much larger set of phenotyped individuals from their SNP genotype data. We then correlate the imputed gene expression to the trait to perform a transcriptome-wide association study (TWAS) and identify significant expression-trait associations. Our approach requires only the summary statistic level GWAS data.

News:

• Tutorial for performing your own TWAS is available here.
• TWAS code and pre-computed expression weights are available here.

Manuscripts describing TWAS:

• Integrative approaches for large-scale transcriptome-wide association studies.
  Gusev A, Ko A, Shi H, Bhatia G, Chung W, Penninx B, Jansen R, de Geus E, Boomsma DI, Wright FA, Sullivan PF, Nikkola E, Alvarez M, Civelek M, Lusis AJ, Lehtimäki T, Raitoharju E, Kähönen M, Seppälä I, Raitakari OT, Kuusisto J, Laakso M, Price AL, Pajukanta P, Pasaniuc B. Nature Genetics 2016 [preprint]

Please contact Sasha (agusev@hsph.harvard.edu) or Bogdan (pasaniuc@ucla.edu) for any comments or suggestions related to the software.