Extension of GWAS results for lipid-related phenotypes to extreme obesity using electronic health record (EHR) data and the Metabochip - Parihar, Ankita; Wood, G. Craig; Chu, Xin; Jin, Qunjan; Argyropoulos, George; Still, Christopher D.; Shuldiner, Alan R.; Mitchell, Braxton D.; Gerhard, Glenn S. (Frontiers in Genetics, 2014)
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 a GWAS for lipid traits in a population with extreme obesity. Our genome wide analysis identified association of SNPs residing at previously lipid associated loci with all lipid phenotypes, as well as 14 of 24 previously identified lipid-associated SNPs, although for a number of known lipid SNPs and body weight SNPs no association was found. Association analysis using several approaches to adjust for use of lipid lowering medications 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 analyses for GWAS. ￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼