Online Program

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Thursday, October 3
Thu, Oct 3, 2:45 PM - 3:47 PM
TBD
Speed Session 1

Imputed Transcriptome-Wide Association Studies of Blood Cell Traits in European Ancestry Cohorts (306569)

Eric Boerwinkle, University of Texas Health Science Center 
David Couper, University of North Carolina 
Santhi Ganesh, University of Michigan 
Misa Graff, University of North Carolina 
Megan Grove, University of Texas Health Science Center 
Eric Jorgenson, Kaiser Permanente Northern California 
Yun Li, University of North Carolina 
Alanna Morrison, University of Texas Health Science Center 
Kari North, University of North Carolina 
Laura Raffield, University of North Carolina 
Alexander P Reiner, University of Washington 
Jonathan Rosen, University of North Carolina 
*Amanda L Tapia, University of North Carolina 
Kristin Young, University of North Carolina 
Bing Yu, University of Texas Health Science Center 

Keywords: TWAS, blood cell traits, penalized regression, imputation

Blood cell traits (BCTs) are important intermediate clinical phenotypes for many diseases and are highly heritable. Although genome-wide association studies (GWAS) have identified >2700 variants for BCTs, the biological mechanisms underlying these associations remain largely unknown. To better understand these mechanisms, we aim to associate imputed transcript levels with BCTs in individuals of European ancestry in the Atherosclerosis Risk in Communities Study, Genetic Epidemiology Research on Adult Health and Aging, and UK Biobank. We follow a transcriptome-WAS (TWAS) approach using penalized regression to first build prediction models in a training set (e.g. GTEx whole blood). With the trained models, we then impute transcripts in each individual and associate imputed transcripts with BCTs. The TWAS approach reduces multiple testing burden by assessing variants’ aggregate effect on transcripts, allows for better identification of the target gene at known loci for BCTs, and can identify novel loci missed by single variant analyses. These ongoing TWAS analyses involve >550,000 individuals and are well-powered to explore the transcriptional impact of BCT-associated genetic variants.