Exome chip‐driven association study of lipidemia in >14, 000 Koreans and evaluation of genetic effect on identified variants between different ethnic groups. Issue 6 (13th May 2019)

• Record Type: Journal Article
• Authors: Han, Sohee; Hwang, Mi Yeong; Yoon, Kyungheon; Kim, Yun Kyoung; Kim, Young‐Jin; Kim, Bong‐Jo; Moon, Sanghoon
• Is Part Of: Genetic epidemiology. Volume 43:Issue 6(2019)
• Page Start: 617
• Page End: 628
• Publication Date: 2019-05-13
• Subjects: dyslipidemia -- Exome chip -- genome‐wide association study -- Korea -- lipid‐trait; Genetic epidemiology -- Periodicals; Heredity -- Periodicals; Medical geography -- Periodicals; 614
• DOI: 10.1002/gepi.22208
• Languages: English
• ISSNs: 0741-0395

Abstract

Abstract: Lipid levels in blood are widely used to diagnose and monitor chronic diseases. It is essential to identify the genetic traits involved in lipid metabolism for understanding chronic diseases. However, the influence of genetic traits varies depending on race, sex, age, and ethnicity. Therefore, research focusing on populations of individual countries is required, and the results can be used as a basis for comparison of results of other studies at the cross‐racial and cross‐country levels. In the present study, we selected lipid‐related variants and evaluated their effects on lipid‐related diseases in more than 14, 000 subjects of three cohorts using the Illumina Human Exome Beadchip. A genome‐wide association study was conducted using EPACTs after adjusting for age, sex, and recruitment area. A genome‐wide significance cutoff was defined as p < 5E−08 in all the three cohorts. Sixteen variants represented the lipid traits and were classified as vulnerable to borderline hypertriglyceridemia, hyper‐LDL‐cholesterolemia, or hypo‐HDL‐cholesterolemia. Moreover, we compared the genetic effects of the 16 variants between ethnic groups and identified the missense variants in apolipoprotein A‐V, cholesterol ester transfer protein, and apolipoprotein E as Asian‐specific. Our study provides candidate genes as markers for chronic diseases through the evaluation of genetic effects.