A large‐scale transcriptome‐wide association study (TWAS) of 10 blood cell phenotypes reveals complexities of TWAS fine‐mapping. Issue 1 (15th November 2021)

• Record Type: Journal Article
• Authors: Tapia, Amanda L.; Rowland, Bryce T.; Rosen, Jonathan D.; Preuss, Michael; Young, Kris; Graff, Misa; Choquet, Hélène; Couper, David J.; Buyske, Steve; Bien, Stephanie A.; Jorgenson, Eric; Kooperberg, Charles; Loos, Ruth J. F.; Morrison, Alanna C.; North, Kari E.; Yu, Bing; Reiner, Alexander P.; Li, Yun; Raffield, Laura M.
• Is Part Of: Genetic epidemiology. Volume 46:Issue 1(2022)
• Page Start: 3
• Page End: 16
• Publication Date: 2021-11-15
• Subjects: fine‐mapping -- hematological traits -- R Shiny -- TWAS; Genetic epidemiology -- Periodicals; Heredity -- Periodicals; Medical geography -- Periodicals; 614
• DOI: 10.1002/gepi.22436
• Languages: English
• ISSNs: 0741-0395

Abstract

Abstract: Hematological measures are important intermediate clinical phenotypes for many acute and chronic diseases and are highly heritable. Although genome‐wide association studies (GWAS) have identified thousands of loci containing trait‐associated variants, the causal genes underlying these associations are often uncertain. To better understand the underlying genetic regulatory mechanisms, we performed a transcriptome‐wide association study (TWAS) to systematically investigate the association between genetically predicted gene expression and hematological measures in 54, 542 Europeans from the Genetic Epidemiology Research on Aging cohort. We found 239 significant gene‐trait associations with hematological measures; we replicated 71 associations at p < 0.05 in a TWAS meta‐analysis consisting of up to 35, 900 Europeans from the Women's Health Initiative, Atherosclerosis Risk in Communities Study, and BioMe Biobank. Additionally, we attempted to refine this list of candidate genes by performing conditional analyses, adjusting for individual variants previously associated with hematological measures, and performed further fine‐mapping of TWAS loci. To facilitate interpretation of our findings, we designed an R Shiny application to interactively visualize our TWAS results by integrating them with additional genetic data sources (GWAS, TWAS from multiple reference panels, conditional analyses, known GWAS variants, etc.). Our results and application highlight frequently overlooked TWAS challenges and illustrate the complexity of TWAS fine‐mapping.