Genomewide association analysis of coronary artery disease

Background - Modern genotyping platforms permit a systematic search for inherited components of complex diseases. We performed a joint analysis of two genomewide association studies of coronary artery disease. Methods - We first identified chromosomal loci that were strongly associated with coronary artery disease in the Wellcome Trust Case Control Consortium (WTCCC) study (which involved 1926 case subjects with coronary artery disease and 2938 controls) and looked for replication in the German MI [Myocardial Infarction] Family Study (which involved 875 case subjects with myocardial infarction and 1644 controls). Data on other single-nucleotide polymorphisms (SNPs) that were significantly associated with coronary artery disease in either study (P<0.001) were then combined to identify additional loci with a high probability of true association. Genotyping in both studies was performed with the use of the GeneChip Human Mapping 500K Array Set (Affymetrix). Results - Of thousands of chromosomal loci studied, the same locus had the strongest association with coronary artery disease in both the WTCCC and the German studies: chromosome 9p21.3 (SNP, rs1333049) (P=1.80x10–14 and P=3.40x10–6, respectively). Overall, the WTCCC study revealed nine loci that were strongly associated with coronary artery disease (P80%) of a true association: chromosomes 1p13.3 (rs599839), 1q41 (rs17465637), 10q11.21 (rs501120), and 15q22.33 (rs17228212). Conclusions - We identified several genetic loci that, individually and in aggregate, substantially affect the risk of development of coronary artery disease

Association analysis results in female gout case-control sample.

<p>Numbers of genotypes (11, 12, 22) according to alleles from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0007729#pone-0007729-t003" target="_blank">Table 3</a>.</p>a<p>Model including medication with diuretics, lipid lowering and antihypertensive therapy, HDL-C, type 2 diabetes, smoking, and BMI.</p

SNP marker used in analysis.

a<p>on human genome build 18.</p>b<p>in total sample (<i>n</i> = 4,960).</p

Characteristics of gout case and control study sample.

<p>Values denote means±standard deviations unless indicated otherwise. n. s., not significant; CAD, coronary artery disease; MI, myocardial infarction; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; BMI, body mass index.</p>a<p>At inclusion to study.</p>b<p>Defined as LDL-C ≥160 mg/dL or intake of lipid lowering medication.</p>c<p>Defined as blood pressure ≥140/90 mmHg or ongoing antihypertensive therapy.</p>d<p>Defined as history of diabetes mellitus or intake of antidiabetic medication.</p>e<p>Former or current smoking habit.</p

Association analysis results in male gout case-control sample.

<p>Numbers of genotypes (11, 12, 22) according to alleles from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0007729#pone-0007729-t003" target="_blank">Table 3</a>.</p>a<p>Model including medication with diuretics, lipid lowering and antihypertensive therapy, HDL-C, type 2 diabetes, smoking, and BMI.</p

Observed and expected relative risks.

a<p>Genotypic model (three-categorical), adjusted for age and sex.</p>b<p>Additive genotype model, adjusted for age and sex.</p><p>Observed associations of individual SNPs with secondary cardiovascular events (hazard ratios [HR] from Cox regression), and effects expected by Mendelian randomization if the fully adjusted <i>ln</i>[sPLA₂-IIa] association of HR (95% CI)  = 1.33 (1.09–1.63) is not due to confounding.</p

Genetic determination of sPLA₂-IIa concentrations.

a<p>Based on non-parametric Kruskal-Wallis test on untransformed sPLA₂-IIa concentrations.</p>b<p>Percent change in geometric mean sPLA₂-IIa concentration in heterozygotes in reference to common homozygotes.</p>c<p>Percent change in geometric mean sPLA₂-IIa concentration in rare homozygotes in reference to common homozygotes.</p>d<p>Percent change in geometric mean sPLA₂-IIa concentration per minor allele present.</p><p>Genotype distributions, proportion of variance in <i>ln</i>-transformed sPLA₂-IIa serum concentration explained by the SNPs studied (R²; one-way ANOVA), and results from age- and sex-adjusted regression models predicting <i>ln</i>-transformed sPLA₂-IIa concentrations from genotypes.</p

Baseline characteristics and sPLA2-IIa concentrations and activities in patients with stable coronary heart disease.

a<p>N and percentages refer to the subjects with concentration measurements available. Activity measurements were missing for 22 of these.</p

Location of the single nucleotide polymorphisms genotyped in this study in relation to the <i>PLA2G2A</i> gene (Panel A) and linkage disequilibrium patterns between the markers considered for haplotype estimation (Panels B and C).

<p>Panel A: depicted is chromosome 1, 20174.5–20182.2 kb; not shown are rs10799599 at position 20167087 and rs818678 at position 20238917. Panels B and C: D' values in Haploview standard colouring scheme are shown in B, r² values in Haploview r² colouring scheme are shown in C. Haplotype blocks were identified by Haploview's confidence interval option.</p

Genetic determination of sPLA₂-IIa activities.

a<p>Based on non-parametric Kruskal-Wallis test on untransformed sPLA2-II activities.</p>b<p>Percent change in geometric mean sPLA2-II activity in heterozygotes in reference to common homozygotes.</p>c<p>Percent change in geometric mean sPLA2-II activity in rare homozygotes in reference to common homozygotes.</p>d<p>Percent change in geometric mean sPLA2-II activity per minor allele present.</p><p>Proportion of variance in <i>ln</i>-transformed sPLA₂-IIa serum activity explained by the SNPs studied (R²; one-way ANOVA), and results from age- and sex-adjusted regression models predicting <i>ln</i>-transformed sPLA₂-IIa activities from genotypes.</p