Computational protocol: A Novel Polymorphism in the Promoter of the CYP4A11 Gene Is Associated with Susceptibility to Coronary Artery Disease

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Protocol publication

[…] Six common SNPs such as rs3890011, rs1126742, rs9332978, and rs9333029 of the CYP4A11 gene and rs3093098 and rs1558139 of the CYP4F2 gene were selected for the study based on their known functional relevance, haplotype tagging properties, and previously reported associations with cardiovascular diseases [, , ]. The functionality of the selected SNPs and their haplotype properties were assessed in silico by the SNP Function Prediction tool developed by Xu and Taylor [] and available online at the SNPinfo Web Server ( SNP rs1126742 of CYP4A11 was excluded from the study because of insufficient genotyping call rate (<70%) for this polymorphism. [...] An association analysis between SNPs and disease risk could detect a difference of 2–6% in the genotype distributions between the cases and controls assuming 81–92% statistical power and a 5% type I error (α = 0.05) on the basis of the sample sizes of 637 CAD patients and 686 healthy controls. Allele frequencies were estimated by the gene counting method, and the chi-square test was used to assess significant departures from Hardy–Weinberg equilibrium (HWE). Categorical variables were also compared by using the chi-square test. Allele, genotype, and haplotype frequencies in the study groups were evaluated by the SNPassoc package for R [] and the SNPStats software []. The strength of the association of the SNPs with the occurrence of coronary artery disease was measured by multiple logistic regression analysis to calculate odds ratios (OR) with 95% confidence intervals (CI) and adjusted for confounding factors. Epistatic interactions between SNPs (log-likelihood ratio test (LRT)) were analyzed by the SNPassoc package for R [], assuming codominant, dominant, and recessive models, and adjusted for age, gender, and hypertension. Haplotypes of CYP4A11 and CYP4F2 were estimated in the entire groups of CAD patients and controls by the SNPStats software. P value ≤ 0.05 was set to be statistically significant. As an adjustment for multiple testing, false discovery rate- (FDR-) based Q value was calculated for each SNP using the method proposed by Benjamini and Hochberg [] and implemented in the FDR calculator available online at Significance of the associations was assessed by a 0.20 threshold of Q value, as previously suggested []. The regulatory potential of the studied SNPs was evaluated by the SNP Function Prediction tool [] using the TRANSFAC database on potential transcription factor recognition sites (BIOBASE Corporation, Wolfenbuettel, Germany) as well as by using the rSNPBase database of curated regulatory SNPs ( []. […]

Pipeline specifications

Software tools SNPinfo, SNPassoc, snpStats, Biobase, rSNPBase
Databases TRANSFAC
Applications Miscellaneous, GWAS
Organisms Homo sapiens