Genome-Wide Association Studies of the Korea Association REsource (KARE) Consortium

Abstract

During the last decade, large community cohorts have been established by the Korea National Institutes of Health (KNIH), and enormous epidemiological and clinical data have been accumulated. Using these information and samples in the cohorts, KNIH set out to do a large-scale genome-wide association study (GWAS) in 2007, and the Korea Association REsource (KARE) consortium was launched to analyze the data to identify the underlying genetic risk factors of diseases and diverse health indexes, such as blood pressure, obesity, bone density, and blood biochemical traits. The consortium consisted of 6 research divisions, formed by 25 principal investigators in 19 organizations, including 18 universities, 2 institutes, and 1 company. Each division focused on one of the following subjects: the identification of genetic factors, the statistical analysis of gene-gene interactions, the genetic epidemiology of gene-environment interactions, copy number variation, the bioinformatics related to a GWAS, and a GWAS of nutrigenomics. In this special issue, the study results of the KARE consortium are provided as 9 articles. We hope that this special issue might encourage the genomics community to share data and scientists, including clinicians, to analyze the valuable Korean data of KARE.

Association of coffee consumption with type 2 diabetes and glycemic traits: a Mendelian randomization study

“…Participants for this study were drawn from the Korea Association REsource (KARE) study [ 19 ], a sub-cohort of the Korean Genome and Epidemiology Study (KoGES) in Korea [ 20 ]. The KARE study was designed to distinguish genetic variants linked with several diseases among Asians.…”

“…The KARE study was designed to distinguish genetic variants linked with several diseases among Asians. The study is a population-based cohort of > 10,000 Koreans that assessed > one million genetic variants [ 19 21 ]. In this study, our primary outcome was T2D, and the secondary outcomes were glycemic traits; fasting blood glucose (FBG), 2h-postprandial glucose (2h-PG) and haemoglobin A1c (HbA1c) profiles.…”

A Visualization Tool for Computational Analysis of DNA Methylation Level Using Bisulfite Sequencing Data

“…The second type contains the total number of sequencing reads and the number of methylated reads at each position for each condition, which is a generalized format to accommodate diverse outputs of mapping programs. It reads multiple input files for different conditions to support a genome association study which is widely used in polymorphism studies (Hong et al, 2010), then creates HTML outputs which are easily accessible using a web browser. The HTML files generated by MethylDraw consist of four different categories: average view, gene view, condition view and full view (Fig.…”