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Coronary heart disease (CHD) is a complex genetic disease involving gene-environment interaction. Many association studies between single nucleotide polymorphisms (SNPs) of candidate genes and CHD have been reported. We have applied a new method to analyze such relationships using support vector machines (SVMs), which is one of the methods for artificial neuronal network. We assumed that common haplotype implicit in genotypes will differ between cases and controls, and that this will allow SVM-derived patterns to be classifiable according to subject genotypes. Fourteen SNPs of ten candidate genes in 86 CHD patients and 119 controls were investigated. Genotypes were transformed to a numerical vector by giving scores based on difference between the genotypes of each subject and the reference genotypes, which represent the healthy normal population. Overall classification accuracy by SVMs was 64.4% with a receiver operating characteristic (ROC) area of 0.639. By conventional analysis using the χ 2  test, the association between CHD and the SNP of the scavenger receptor B1 gene was most significant in terms of allele frequencies in cases  vs . controls (p = 0.0001). In conclusion, we suggest that the application of SVMs for association studies of SNPs in candidate genes shows considerable promise and that further work could be usefully performed upon the estimation of CHD susceptibility in individuals of high risk.

clinical chemistry and laboratory medicine (cclm)

Analysis of Multiple Single Nucleotide Polymorphisms of Candidate Genes Related to Coronary Heart Disease Susceptibility by Using Support Vector Machines