Abstract

The DrugBank database consists of ~800 genes that are well characterized drug targets. This list of genes is a useful resource for association testing. For example, loss of function (LOF) genetic variation has the potential to mimic the effect of drugs, and high impact variation in these genes can impact downstream traits. Identifying novel associations between genetic variation in these genes and a range of diseases can also uncover new uses for the drugs that target these genes. Phenome Wide Association Studies (PheWAS) have been successful in identifying genetic associations across hundreds of thousands of diseases. We have conducted a novel gene based PheWAS to test the effect of rare variants in DrugBank genes, evaluating associations between these genes and more than 500 quantitative and dichotomous phenotypes. We used whole exome sequencing data from 38,568 samples in Geisinger MyCode Community Health Initiative. We evaluated the results of this study when binning rare variants using various filters based on potential functional impact. We identified multiple novel associations, and the majority of the significant associations were driven by functionally annotated variation. Overall, this study provides a sweeping exploration of rare variant associations within functionally relevant genes across a wide range of diagnoses.

Details

Title
Rare variants in drug target genes contributing to complex diseases, phenome-wide
Author
Shefali Setia Verma 1 ; Josyula, Navya 2   VIAFID ORCID Logo  ; Verma, Anurag 1 ; Zhang, Xinyuan 1   VIAFID ORCID Logo  ; Veturi, Yogasudha 1 ; Dewey, Frederick E 3 ; Hartzel, Dustin N 4 ; Lavage, Daniel R 4 ; Leader, Joe 5 ; Ritchie, Marylyn D 1 ; Pendergrass, Sarah A 2 

 Perelman School of Medicine, Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA 
 Biomedical and Translational Informatics Institute, Geisinger, Danville, PA, USA 
 Regeneron Genetics Center, Tarrytown, NY, USA 
 Phenomic Analytics and Clinical Data Core, Geisinger, Danville, PA, USA 
 Biomedical and Translational Informatics Institute, Geisinger, Danville, PA, USA; Phenomic Analytics and Clinical Data Core, Geisinger, Danville, PA, USA 
Pages
1-16
Publication year
2018
Publication date
Mar 2018
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2014352701
Copyright
© 2018. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.