Genome-wide association study of coronary artery disease in the Japanese.
ABSTRACT A new understanding of the genetic basis of coronary artery disease (CAD) has recently emerged from genome-wide association (GWA) studies of common single-nucleotide polymorphisms (SNPs), thus far performed mostly in European-descent populations. To identify novel susceptibility gene variants for CAD and confirm those previously identified mostly in populations of European descent, a multistage GWA study was performed in the Japanese. In the discovery phase, we first genotyped 806 cases and 1337 controls with 451 382 SNP markers and subsequently assessed 34 selected SNPs with direct genotyping (541 additional cases) and in silico comparison (964 healthy controls). In the replication phase, involving 3052 cases and 6335 controls, 12 SNPs were tested; CAD association was replicated and/or verified for 4 (of 12) SNPs from 3 loci: near BRAP and ALDH2 on 12q24 (P=1.6 × 10(-34)), HLA-DQB1 on 6p21 (P=4.7 × 10(-7)), and CDKN2A/B on 9p21 (P=6.1 × 10(-16)). On 12q24, we identified the strongest association signal with the strength of association substantially pronounced for a subgroup of myocardial infarction cases (P=1.4 × 10(-40)). On 6p21, an HLA allele, DQB1(*)0604, could show one of the most prominent association signals in an ∼8-Mb interval that encompasses the LTA gene, where an association with myocardial infarction had been reported in another Japanese study. CAD association was also identified at CDKN2A/B, as previously reported in different populations of European descent and Asians. Thus, three loci confirmed in the Japanese GWA study highlight the likely presence of risk alleles with two types of genetic effects - population specific and common - on susceptibility to CAD.
Article: Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease.[show abstract] [hide abstract]
ABSTRACT: We performed a meta-analysis of 14 genome-wide association studies of coronary artery disease (CAD) comprising 22,233 individuals with CAD (cases) and 64,762 controls of European descent followed by genotyping of top association signals in 56,682 additional individuals. This analysis identified 13 loci newly associated with CAD at P < 5 × 10⁻⁸ and confirmed the association of 10 of 12 previously reported CAD loci. The 13 new loci showed risk allele frequencies ranging from 0.13 to 0.91 and were associated with a 6% to 17% increase in the risk of CAD per allele. Notably, only three of the new loci showed significant association with traditional CAD risk factors and the majority lie in gene regions not previously implicated in the pathogenesis of CAD. Finally, five of the new CAD risk loci appear to have pleiotropic effects, showing strong association with various other human diseases or traits.Nature Genetics 03/2011; 43(4):333-8. · 35.53 Impact Factor
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ABSTRACT: Genomewide association studies are now a widely used approach in the search for loci that affect complex traits. After detection of significant association, estimates of penetrance and allele-frequency parameters for the associated variant indicate the importance of that variant and facilitate the planning of replication studies. However, when these estimates are based on the original data used to detect the variant, the results are affected by an ascertainment bias known as the "winner's curse." The actual genetic effect is typically smaller than its estimate. This overestimation of the genetic effect may cause replication studies to fail because the necessary sample size is underestimated. Here, we present an approach that corrects for the ascertainment bias and generates an estimate of the frequency of a variant and its penetrance parameters. The method produces a point estimate and confidence region for the parameter estimates. We study the performance of this method using simulated data sets and show that it is possible to greatly reduce the bias in the parameter estimates, even when the original association study had low power. The uncertainty of the estimate decreases with increasing sample size, independent of the power of the original test for association. Finally, we show that application of the method to case-control data can improve the design of replication studies considerably.The American Journal of Human Genetics 05/2007; 80(4):605-15. · 10.60 Impact Factor