Phenome-Wide Association Study of Polygenic Risk Score for Alzheimer’s Disease in Electronic Health Records - Fu, Mingzhou; , UCLA Precision Health Data Discovery Repository Working Group; , UCLA Precision Health ATLAS Working Group; Chang, Timothy S.; Antonio, Anna L.; Ariannejad, Maryam; Badillo, Angela M.; Balliu, Brunilda; Berkovich, Yael; Broudy, Michael; Dang, Tony; Denny, Chris; et al. (Frontiers in Aging Neuroscience, 2022)
Alzheimer’s disease (AD) is the most common form of dementia and a growing public health burden in the United States. Significant progress has been made in identifying genetic risk for AD, but limited studies have investigated how AD genetic risk may be associated with other disease conditions in an unbiased fashion. In this study, we conducted a phenome-wide association study (PheWAS) by genetic ancestry groups within a large academic health system using the polygenic risk score (PRS) for Alzheimer’s disease (AD). PRS was calculated using LDpred2 with genome-wide association study summary statistics. Phenotypes were extracted from electronic health record (EHR) diagnosis codes and mapped to more clinically meaningful PheCodes. Logistic regression with Firth’s bias correction was used for PRS-phenotype analyses. Mendelian randomization was used to examine causality in significant PheWAS associations. Our results showed a strong association between AD PRS and AD phenotype in European ancestry (OR=1.26, 95%CI: 1.13, 1.40). Among a total of 1515 PheWAS tests within the European sample, we observed strong associations of AD PRS with AD and related phenotypes, including mild cognitive impairment, memory loss, and dementias. We observed a phenome-wide significant association between AD PRS and gouty arthropathy (OR=0.90, adjusted p-value=0.05). Further causal inference tests with Mendelian randomization showed gout was not causally associated with AD. We concluded that genetic predisposition of AD was negatively associated with gout, but gout was not a causal risk factor for AD. Our study evaluated AD PRS in a real world EHR setting and provided evidence that AD PRS may help identify individuals who are genetically at-risk of AD and other related phenotypes. We identified non-neurodegenerative diseases associated with AD PRS, which is essential to understanding the genetic architecture of AD and potential side effects of drugs targeting genetic risk factors of AD. Together these findings expand our understanding of AD genetic and clinical risk factors, providing a framework for continued research in aging with the growing number of real world EHR linked with genetic data.