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Publication Detail

Title: A regression framework to uncover pleiotropy in large-scale electronic health record data.

Authors: Li, Ruowang; Duan, Rui; Kember, Rachel L; Rader, Daniel J; Damrauer, Scott M; Moore, Jason H; Chen, Yong

Published In J Am Med Inform Assoc, (2019 10 01)

Abstract: Pleiotropy, where 1 genetic locus affects multiple phenotypes, can offer significant insights in understanding the complex genotype-phenotype relationship. Although individual genotype-phenotype associations have been thoroughly explored, seemingly unrelated phenotypes can be connected genetically through common pleiotropic loci or genes. However, current analyses of pleiotropy have been challenged by both methodologic limitations and a lack of available suitable data sources.In this study, we propose to utilize a new regression framework, reduced rank regression, to simultaneously analyze multiple phenotypes and genotypes to detect pleiotropic effects. We used a large-scale biobank linked electronic health record data from the Penn Medicine BioBank to select 5 cardiovascular diseases (hypertension, cardiac dysrhythmias, ischemic heart disease, congestive heart failure, and heart valve disorders) and 5 mental disorders (mood disorders; anxiety, phobic and dissociative disorders; alcohol-related disorders; neurological disorders; and delirium dementia) to validate our framework.Compared with existing methods, reduced rank regression showed a higher power to distinguish known associated single-nucleotide polymorphisms from random single-nucleotide polymorphisms. In addition, genome-wide gene-based investigation of pleiotropy showed that reduced rank regression was able to identify candidate genetic variants with novel pleiotropic effects compared to existing methods.The proposed regression framework offers a new approach to account for the phenotype and genotype correlations when identifying pleiotropic effects. By jointly modeling multiple phenotypes and genotypes together, the method has the potential to distinguish confounding from causal genotype and phenotype associations.

PubMed ID: 31529123 Exiting the NIEHS site

MeSH Terms: Cardiovascular Diseases/genetics*; Electronic Health Records*; Genetic Association Studies; Genetic Pleiotropy*; Genetic Predisposition to Disease; Genotype; Humans; Mental Disorders/genetics*; Phenotype; Polymorphism, Single Nucleotide

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