l e t t e r sTo identify the genetic bases for nine metabolic traits, we conducted a meta-analysis combining Korean genome-wide association results from the KARE project (n = 8,842) and the HEXA shared control study (n = 3,703). We verified the associations of the loci selected from the discovery metaanalysis in the replication stage (30,395 individuals from the BioBank Japan genome-wide association study and individuals comprising the Health2 and Shanghai Jiao Tong University Diabetes cohorts). We identified ten genome-wide significant signals newly associated with traits from an overall metaanalysis. The most compelling associations involved 12q24.11 (near MYL2) and 12q24.13 (in C12orf51) for high-density lipoprotein cholesterol, 2p21 (near SIX2-SIX3) for fasting plasma glucose, 19q13.33 (in RPS11) and 6q22.33 (in RSPO3) for renal traits, and 12q24.11 (near MYL2), 12q24.13 (in C12orf51 and near OAS1), 4q31.22 (in ZNF827) and 7q11.23 (near TBL2-BCL7B) for hepatic traits. These findings highlight previously unknown biological pathways for metabolic traits investigated in this study.

Although over 30 common genetic susceptibility loci have been identified to be independently associated with coronary artery disease (CAD) risk through genome-wide association studies (GWAS), genetic risk variants reported to date explain only a small fraction of heritability. To identify novel susceptibility variants for CAD and confirm those previously identified in European population, GWAS and a replication study were performed in the Koreans and Japanese. In the discovery stage, we genotyped 2123 cases and 3591 controls with 521 786 SNPs using the Affymetrix SNP Array 6.0 chips in Korean. In the replication, direct genotyping was performed using 3052 cases and 4976 controls from the KItaNagoya Genome study of Japan with 14 selected SNPs. To maximize the coverage of the genome, imputation was performed based on 1000 Genome JPT+CHB and 5.1 million SNPs were retained. CAD association was replicated for three GWAS-identified loci (1p13.3/SORT1 (rs599839), 9p21.3/CDKN2A/2B (rs4977574), and 11q22.3/ PDGFD (rs974819)) in Koreans. From GWAS and a replication, SNP rs3782889 showed a strong association (combined P=3.95 × 10(-14)), although the association of SNP rs3782889 doesn't remain statistically significant after adjusting for SNP rs11066015 (proxy SNP with BRAP (r(2)=1)). But new possible CAD-associated variant was observed for rs9508025 (FLT1), even though its statistical significance did marginally reach at the genome-wide a significance level (combined P=6.07 × 10(-7)). This study shows that three CAD susceptibility loci, which were previously identified in European can be directly replicated in Koreans and also provides additional evidences implicating suggestive loci as risk variants for CAD in East Asian.

ObjectivesRecent genetic association studies have provided convincing evidence that several novel loci and single nucleotide polymorphisms (SNPs) are associated with the risk of developing type 2 diabetes mellitus (T2DM). The aims of this study were: 1) to develop a predictive model of T2DM using genetic and clinical data; and 2) to compare misclassification rates of different models.MethodsWe selected 212 individuals with newly diagnosed T2DM and 472 controls aged in their 60s from the Korean Genome and Epidemiology Study. A total of 499 known SNPs from 87 T2DM-related genes were genotyped using germline DNA. SNPs were analyzed for significant association with T2DM using various classification algorithms including Quest (Quick, Unbiased, Efficient, Statistical tree), Support Vector Machine, C4.5, logistic regression, and K-nearest neighbor.ResultsWe tested these models using the complete Korean Genome and Epidemiology Study cohort (n = 10,038) and computed the T2DM misclassification rates for each model. Average misclassification rates ranged at 28.2–52.7%. The misclassification rates for the logistic and machine-learning algorithms were lower than the statistical tree algorithms. Using 1-to-1 matched data, the misclassification rate of the statistical tree QUEST algorithm using body mass index and SNP variables was the lowest, but overall the logistic regression performed best.ConclusionsThe K-nearest neighbor method exhibited more robust results than other algorithms. For clinical and genetic data, our “multistage adjustment” model outperformed other models in yielding lower rates of misclassification. To improve the performance of these models, further studies using warranted, strategies to estimate better classifiers for the quantification of SNPs need to be developed.

Myocardial infarction (MI) is a complex disease caused by combination of genetic and environmental factors. Although genome-wide association studies (GWAS) identified more than 46 risk loci which are associated with coronary artery disease and MI, most of the genetic variability inMI still remains undefined. Here, we screened the susceptibility loci for MI using exome sequencing and validated candidate variants in replication sets. We identified that three genes (GYG1, DIS3L and DDRGK1) were associated with MI at the discovery and replication stages. Further research will be required to determine the functional association of these genes with MI risk, and these associations have to be confirmed in other ethnic populations.