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Background The 9p21.3 locus is strongly associated with the risk of coronary artery disease (CAD) and with type 2 diabetes (T2D). We investigated the association of 9p21.3 variants with severity of CAD (defined by the number of vessel diseased [VD]) in the presence and absence of T2D. Methods We tested 11 9p21.3-variants for association in a white Italian study (N = 2,908), and carried out replication in 2 independent white populations, a German study (N = 2,028) and a Canadian Study (N=950). SNP association and permutation analyses were conducted. Results We identified two 9p21.3-variants, rs4977574 ( P < 4×10 -4 ) and rs2383207 ( P < 1.5×10 -3 ) that were associated with severity of CAD in subjects without T2D. Association of rs4977574 with severity of CAD was confirmed in the Canadian Study. Results from subgroup analysis among patients with T2D showed an interaction between rs10738610 and T2D with P = 4.82×10 -2 . Further investigation showed that rs10738610 ( P < 1.99×10 -2 ) was found to be significantly associated with severity of CAD in subjects with T2D. Conclusions The 9p21.3 locus is significantly associated with severity of CAD. The number of associations of 9p21.3 variants with severity of CAD is variable to the presence and absence of T2D. In a CAD-susceptible region of 115 kb, there is only one variant associated with the severity of coronary vessel disease in the presence of type 2 diabetes.

Left-sided congenital heart disease (CHD) encompasses a spectrum of malformations that range from bicuspid aortic valve to hypoplastic left heart syndrome. It contributes significantly to infant mortality and has serious implications in adult cardiology. Although left-sided CHD is known to be highly heritable, the underlying genetic determinants are largely unidentified. In this study, we sought to determine the impact of structural genomic variation on left-sided CHD and compared multiplex families (464 individuals with 174 affecteds (37.5%) in 59 multiplex families and 8 trios) to 1,582 well-phenotyped controls. 73 unique inherited or de novo CNVs in 54 individuals were identified in the left-sided CHD cohort. After stringent filtering, our gene inventory reveals 25 new candidates for LS-CHD pathogenesis, such as SMC1A, MFAP4, and CTHRC1, and overlaps with several known syndromic loci. Conservative estimation examining the overlap of the prioritized gene content with CNVs present only in affected individuals in our cohort implies a strong effect for unique CNVs in at least 10% of left-sided CHD cases. Enrichment testing of gene content in all identified CNVs showed a significant association with angiogenesis. In this first family-based CNV study of left-sided CHD, we found that both co-segregating and de novo events associate with disease in a complex fashion at structural genomic level. Often viewed as an anatomically circumscript disease, a subset of left-sided CHD may in fact reflect more general genetic perturbations of angiogenesis and/or vascular biology.

We tested whether genetic factors distinctly contribute to either development of coronary atherosclerosis or, specifically, to myocardial infarction in existing coronary atherosclerosis. We did two genome-wide association studies (GWAS) with coronary angiographic phenotyping in participants of European ancestry. To identify loci that predispose to angiographic coronary artery disease (CAD), we compared individuals who had this disorder (n=12 393) with those who did not (controls, n=7383). To identify loci that predispose to myocardial infarction, we compared patients who had angiographic CAD and myocardial infarction (n=5783) with those who had angiographic CAD but no myocardial infarction (n=3644). In the comparison of patients with angiographic CAD versus controls, we identified a novel locus, ADAMTS7 (p=4·98×10 −13). In the comparison of patients with angiographic CAD who had myocardial infarction versus those with angiographic CAD but no myocardial infarction, we identified a novel association at the ABO locus (p=7·62×10 −9). The ABO association was attributable to the glycotransferase-deficient enzyme that encodes the ABO blood group O phenotype previously proposed to protect against myocardial infarction. Our findings indicate that specific genetic predispositions promote the development of coronary atherosclerosis whereas others lead to myocardial infarction in the presence of coronary atherosclerosis. The relation to specific CAD phenotypes might modify how novel loci are applied in personalised risk assessment and used in the development of novel therapies for CAD. The PennCath and MedStar studies were supported by the Cardiovascular Institute of the University of Pennsylvania, by the MedStar Health Research Institute at Washington Hospital Center and by a research grant from GlaxoSmithKline. The funding and support for the other cohorts contributing to the paper are described in the webappendix.

It has long been recognized that 50% of the susceptibility for coronary artery disease (CAD) is due to predisposing genetic factors. Comprehensive prevention is likely to require knowledge of these genetic factors. Using a genomewide association study (GWAS), the Ottawa Heart Genomic Study and the deCODE group simultaneously identified the first genetic risk variant, at chromosome 9p21. The 9p21 variant became the first risk factor to be identified since 1964. 9p21 occurs in 75% of the population except for African Americans and is associated with a 25% increased risk for CAD with 1 copy and a 50% increased risk with 2 copies. Perhaps the most remarkable finding is that 9p21 is independent of all known risk factors, indicating there are factors contributing to the pathogenesis of CAD that are yet unknown. 9p21 in individuals with premature CAD is associated with a 2-fold increase in risk, similar to that of smoking and cholesterol. Routine genetic testing will probably remain controversial until a specific treatment is developed. Over a period of 5 years, however, GWASs have identified 30 genetic variants for CAD risk, of which only 6 act through the known risk factors. The 9p21 variant has now been established as an independent risk factor for CAD and, along with the additional 29 risk genetic variants recently identified, is likely to provide the thrust for genetic testing and personalized medicine in the near future.

In 4 of 15 patients with idiopathic atrial fibrillation, four novel, heterozygous mutations in GJA5 — the gene for the gap-junction protein connexin 40 — were identified. These supplement the list of mutations that cause atrial fibrillation and will improve our understanding of the molecular basis of atrial fibrillation. In 4 of 15 patients with idiopathic atrial fibrillation, four novel, heterozygous mutations in GJA5 — the gene for the gap-junction protein connexin 40 — were identified. Atrial fibrillation is characterized by rapid, erratic electrical activation of the atrial myocardium, resulting in the loss of effective contractility, an increased likelihood of clot formation, and an increased risk of stroke. 1 The rapid atrial activity may be conducted to the ventricles, resulting in the deterioration of heart function. In addition to causing substantial morbidity, atrial fibrillation confers an increased risk of mortality that is independent of coexisting risk factors. 2 In the United States, more than 2 million adults have atrial fibrillation, with the prevalence increasing with age (5.9 percent among those older than 65 years). 3 Thus, the socioeconomic burden . . .

There is ongoing controversy as to whether obesity confers risk for CAD independently of associated risk factors including diabetes mellitus. We have carried out a Mendelian randomization study using a genetic risk score (GRS) for body mass index (BMI) based on 35 risk alleles to investigate this question in a population of 5831 early onset CAD cases without diabetes mellitus and 3832 elderly healthy control subjects, all of strictly European ancestry, with adjustment for traditional risk factors (TRFs). We then estimated the genetic correlation between these BMI and CAD (rg) by relating the pairwise genetic similarity matrix to a phenotypic covariance matrix between these two traits. GRSBMI significantly (P=2.12 × 10(-12)) associated with CAD status in a multivariate model adjusted for TRFs, with a per allele odds ratio (OR) of 1.06 (95% CI 1.042-1.076). The addition of GRSBMI to TRFs explained 0.75% of CAD variance and yielded a continuous net recombination index of 16.54% (95% CI=11.82-21.26%, P<0.0001). To test whether GRSBMI explained CAD status when adjusted for measured BMI, separate models were constructed in which the score and BMI were either included as covariates or not. The addition of BMI explained ~1.9% of CAD variance and GRSBMI plus BMI explained 2.65% of CAD variance. Finally, using bivariate restricted maximum likelihood analysis, we provide strong evidence of genome-wide pleiotropy between obesity and CAD. This analysis supports the hypothesis that obesity is a causal risk factor for CAD.

The adipocyte-derived protein adiponectin is highly heritable and inversely associated with risk of type 2 diabetes mellitus (T2D) and coronary heart disease (CHD). We meta-analyzed 3 genome-wide association studies for circulating adiponectin levels (n = 8,531) and sought validation of the lead single nucleotide polymorphisms (SNPs) in 5 additional cohorts (n = 6,202). Five SNPs were genome-wide significant in their relationship with adiponectin (P< or =5x10(-8)). We then tested whether these 5 SNPs were associated with risk of T2D and CHD using a Bonferroni-corrected threshold of P< or =0.011 to declare statistical significance for these disease associations. SNPs at the adiponectin-encoding ADIPOQ locus demonstrated the strongest associations with adiponectin levels (P-combined = 9.2x10(-19) for lead SNP, rs266717, n = 14,733). A novel variant in the ARL15 (ADP-ribosylation factor-like 15) gene was associated with lower circulating levels of adiponectin (rs4311394-G, P-combined = 2.9x10(-8), n = 14,733). This same risk allele at ARL15 was also associated with a higher risk of CHD (odds ratio [OR] = 1.12, P = 8.5x10(-6), n = 22,421) more nominally, an increased risk of T2D (OR = 1.11, P = 3.2x10(-3), n = 10,128), and several metabolic traits. Expression studies in humans indicated that ARL15 is well-expressed in skeletal muscle. These findings identify a novel protein, ARL15, which influences circulating adiponectin levels and may impact upon CHD risk.

The transcription factor GATA2 was reported to associate with coronary artery disease (CAD) in the family-based Genecard sample (Connelly et al. in PLoS Genet 2:e139, 2006). We asked whether GATA2 associates with sporadic cases of CAD in the Ottawa Heart Genomics Study (OHGS) and Cleveland Clinic (CC) populations. We genotyped the lead single nucleotide polymorphism (SNP) from Genecard, rs2713604 which is located in intron 5-6 of GATA2 in 600 CAD cases and 625 controls, as well as a tag SNP rs1573949 (r ² = 0.87 in Caucasians of European ancestry in Utah from HapMap) in 1,136 cases and 1,162 controls in the OHGS1 population. A further 1,838 CAD cases and 913 controls derived from an independent sample combining genotypes from CC and OHGS2 populations were genotyped for rs1573949. Neither of the genotyped SNPs associates with CAD in the OHGS1 or CC/OHGS2 populations. Our data suggest that GATA2 does not contribute to the development of angiographic CAD among sporadic cases.

The majority of the heritability of coronary artery disease (CAD) remains unexplained, despite recent successes of genome-wide association studies (GWAS) in identifying novel susceptibility loci. Integrating functional genomic data from a variety of sources with a large-scale meta-analysis of CAD GWAS may facilitate the identification of novel biological processes and genes involved in CAD, as well as clarify the causal relationships of established processes. Towards this end, we integrated 14 GWAS from the CARDIoGRAM Consortium and two additional GWAS from the Ottawa Heart Institute (25,491 cases and 66,819 controls) with 1) genetics of gene expression studies of CAD-relevant tissues in humans, 2) metabolic and signaling pathways from public databases, and 3) data-driven, tissue-specific gene networks from a multitude of human and mouse experiments. We not only detected CAD-associated gene networks of lipid metabolism, coagulation, immunity, and additional networks with no clear functional annotation, but also revealed key driver genes for each CAD network based on the topology of the gene regulatory networks. In particular, we found a gene network involved in antigen processing to be strongly associated with CAD. The key driver genes of this network included glyoxalase I (GLO1) and peptidylprolyl isomerase I (PPIL1), which we verified as regulatory by siRNA experiments in human aortic endothelial cells. Our results suggest genetic influences on a diverse set of both known and novel biological processes that contribute to CAD risk. The key driver genes for these networks highlight potential novel targets for further mechanistic studies and therapeutic interventions.