title: Extension of GWAS results for lipid-related phenotypes to extreme obesity using electronic health record (EHR) data and the Metabochip.
abstract: A variety of health-related data are commonly deposited into electronic health records (EHRs), including laboratory, diagnostic, and medication information. The digital nature of EHR data facilitates efficient extraction of these data for research studies, including genome-wide association studies (GWAS). Previous GWAS have identified numerous SNPs associated with variation in total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG). These findings have led to the development of specialized genotyping platforms that can be used for fine-mapping and replication in other populations. We have combined the efficiency of EHR data and the economic advantages of the Illumina Metabochip, a custom designed SNP chip targeted to traits related to coronary artery disease, myocardial infarction, and type 2 diabetes, to conduct an array-wide analysis of lipid traits in a population with extreme obesity. Our analyses identified associations with 12 of 21 previously identified lipid-associated SNPs with effect sizes similar to prior results. Association analysis using several approaches to account for lipid-lowering medication use resulted in fewer and less strongly associated SNPs. The availability of phenotype data from the EHR and the economic efficiency of the specialized Metabochip can be exploited to conduct multi-faceted genetic association analyses.
authors.affiliation: Department of Medicine and Program for Personalized and Genomic Medicine, University of Maryland School of Medicine Baltimore, MD, USA.
authors.forename: Ankita
authors.initials: A
authors.lastname: Parihar
authors.affiliation: Geisinger Clinic, Geisinger Obesity Institute Danville, PA, USA.
authors.forename: G Craig
authors.initials: GC
authors.lastname: Wood
authors.affiliation: Geisinger Clinic, Geisinger Obesity Institute Danville, PA, USA.
authors.forename: Xin
authors.initials: X
authors.lastname: Chu
authors.affiliation: Department of Pathology and Laboratory Medicine, Department of Biochemistry and Molecular Biology, Institute for Personalized Medicine, Pennsylvania State University College of Medicine Hershey, PA, USA.
authors.forename: Qunjan
authors.initials: Q
authors.lastname: Jin
authors.affiliation: Geisinger Clinic, Geisinger Obesity Institute Danville, PA, USA.
authors.forename: George
authors.initials: G
authors.lastname: Argyropoulos
authors.affiliation: Geisinger Clinic, Geisinger Obesity Institute Danville, PA, USA.
authors.forename: Christopher D
authors.initials: CD
authors.lastname: Still
authors.affiliation: Department of Medicine and Program for Personalized and Genomic Medicine, University of Maryland School of Medicine Baltimore, MD, USA ; Geriatric Research and Education Clinical Center, Veterans Administration Medical Center Baltimore, MD, USA.
authors.forename: Alan R
authors.initials: AR
authors.lastname: Shuldiner
authors.affiliation: Department of Medicine and Program for Personalized and Genomic Medicine, University of Maryland School of Medicine Baltimore, MD, USA ; Geriatric Research and Education Clinical Center, Veterans Administration Medical Center Baltimore, MD, USA.
authors.forename: Braxton D
authors.initials: BD
authors.lastname: Mitchell
authors.affiliation: Department of Pathology and Laboratory Medicine, Department of Biochemistry and Molecular Biology, Institute for Personalized Medicine, Pennsylvania State University College of Medicine Hershey, PA, USA.
authors.forename: Glenn S
authors.initials: GS
authors.lastname: Gerhard
keywords: EHR; GWAS; lipids; obesity