Abstract
AbstractProteome-wide association study (PWAS) integrating proteomics data with GWAS data is a powerful tool to identify risk genes for complex diseases, which can inform disease mechanisms with genetic effects mediated through protein abundance. We propose a novel omnibus method to improve PWAS power by modeling unknown genetic architectures with multiple statistical models. We applied TIGAR, PrediXcan, and FUSION to train protein abundance imputation models for 8,430 proteins from dorsolateral prefrontal cortex with whole genome sequencing data (n=355). Next, the trained models were integrated with GWAS summary data of Alzheimer’s disease (AD) dementia (n=762,917) to conduct PWAS. Last, we employed the Aggregated Cauchy Association Test to obtain omnibus PWAS (PWAS-O) p-values from these three models. PWAS-O identified 43 risk genes of AD dementia including 5 novel risk genes that were interconnected through a protein-protein interaction network includingTOMM40,APOC1, andAPOC2. PWAS-O can be easily applied to study complex diseases.
Publisher
Cold Spring Harbor Laboratory
Cited by
3 articles.
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