Genome-wide linkage and association analysis of cardiometabolic phenotypes in Hispanic Americans

Risk of sudden coronary death based on genetic background in Chinese Han population

Associations between gene variations and sudden cardiac arrest or coronary artery disease have been reported by genome-wide association studies. However, the implication of the genetic status in cases of sudden coronary death (SCD) from the Chinese Han population has remained to be investigated. The present study established a mini-sequencing system to examine putative death-causing single nucleotide polymorphisms (SNPs) using multiplex PCR, single base extension reaction and capillary electrophoresis techniques. A total of 198 samples from the Chinese Han population (age range, 34-71 years; mean age, 53.86 years) were examined using this method. Samples were classified into three groups: Coronary heart disease (CHD, n=70), SCD (n=53) and control (n=75) group. Significant associations were identified for 10, 4 and 6 SNPs in CHD, SCD and sudden death from CHD, respectively, using the χ² test. The SNPs obtained by binary logistic regression may be used to assess and predict the risk of disease. The predictive accuracy of the SNPs in each prediction model and their area under the receiver operating characteristic curve (AUC) values were determined. The AUC of the four SNPs (rs12429889, rs10829156, rs16942421 and rs12155623) to predict CHD was 0.928, the AUC of the six SNPs (rs2389202, rs2982694, rs10183640, rs597503, rs16942421 and rs12155623) to predict SCD was 0.922 and the AUC of the four SNPs (rs16866933, rs4621553, rs10829156 and rs12155623) to predict sudden death from CHD was 0.912. The multifactor dimensionality reduction values were as follows: 0.8690 (prediction model of CHD), 0.7601 (prediction model of SCD) and 0.7628 (prediction model of sudden death from CHD). Taken together, the results of the present study suggested that these SNPs have considerable potential for application in genetic tests to predict CHD or SCD. However, further studies are required to investigate the putative functions of these SNPs.

Genome-wide interaction with the insulin secretion locus MTNR1B reveals CMIP as a novel type 2 diabetes susceptibility gene in African Americans

Although type 2 diabetes (T2D) results from metabolic defects in insulin secretion and insulin sensitivity, most of the genetic risk loci identified to date relates to insulin secretion. We reported that T2D loci influencing insulin sensitivity may be identified through interactions with insulin secretion loci, thereby leading to T2D. Here, we hypothesize that joint testing of variant main effects and interaction effects with an insulin secretion locus increases power to identify genetic interactions leading to T2D. We tested this hypothesis with an intronic MTNR1B SNP, rs10830963, which is associated with acute insulin response to glucose, a dynamic measure of insulin secretion. rs10830963 was tested for interaction and joint (main + interaction) effects with genome-wide data in African Americans (2,452 cases and 3,772 controls) from five cohorts. Genome-wide genotype data (Affymetrix Human Genome 6.0 array) was imputed to a 1000 Genomes Project reference panel. T2D risk was modeled using logistic regression with rs10830963 dosage, age, sex, and principal component as predictors. Joint effects were captured using the Kraft two degrees of freedom test. Genome-wide significant (P < 5 × 10-8 ) interaction with MTNR1B and joint effects were detected for CMIP intronic SNP rs17197883 (Pinteraction  = 1.43 × 10-8 ; Pjoint  = 4.70 × 10-8 ). CMIP variants have been nominally associated with T2D, fasting glucose, and adiponectin in individuals of East Asian ancestry, with high-density lipoprotein, and with waist-to-hip ratio adjusted for body mass index in Europeans. These data support the hypothesis that additional genetic factors contributing to T2D risk, including insulin sensitivity loci, can be identified through interactions with insulin secretion loci.

Analysis of Whole Exome Sequencing with Cardiometabolic Traits Using Family-Based Linkage and Association in the IRAS Family Study

Family-based methods are a potentially powerful tool to identify trait-defining genetic variants in extended families, particularly when used to complement conventional association analysis. We utilized two-point linkage analysis and single variant association analysis to evaluate whole exome sequencing (WES) data from 1205 Hispanic Americans (78 families) from the Insulin Resistance Atherosclerosis Family Study. WES identified 211,612 variants above the minor allele frequency threshold of ≥0.005. These variants were tested for linkage and/or association with 50 cardiometabolic traits after quality control checks. Two-point linkage analysis yielded 10,580,600 logarithm of the odds (LOD) scores with 1148 LOD scores ≥3, 183 LOD scores ≥4, and 29 LOD scores ≥5. The maximal novel LOD score was 5.50 for rs2289043:T>C, in UNC5C with subcutaneous adipose tissue volume. Association analysis identified 13 variants attaining genome-wide significance (P < 5 × 10^-08 ), with the strongest association between rs651821:C>T in APOA5 and triglyceride levels (P  =  3.67 × 10^-10 ). Overall, there was a 5.2-fold increase in the number of informative variants detected by WES compared to exome chip analysis in this population, nearly 30% of which were novel variants relative to the Database of Single Nucleotide Polymorphisms (dbSNP) build 138. Thus, integration of results from two-point linkage and single-variant association analysis from WES data enabled identification of novel signals potentially contributing to cardiometabolic traits.