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Approaches have been developed to analyze large numbers of SNPs in relation to disease status. We compared the set association approach (SAA), multifactor dimensionality reduction (MDR) and random forest (RF) to select from 93 SNPs a subset of SNPs that are important in determining HDL-cholesterol levels. The study population consisted of a random sample from a Dutch monitoring project for cardiovascular disease risk factors and was dichotomized into cases (low HDLcholesterol, n=533) and controls (high HDL-cholesterol, n=545) based on gender-specific median values. All approaches prioritized three SNPs as important (CETP Taq1B, CETP -629 C/A and LPL ser447ter). SNPs with weaker main effects or only relevant in interaction are not prioritized by all methods. MTHFR 677 C/T was selected in combination with CETP Taq1B as best model by MDR. APOC3 3175 G/C, CCR2 val62ile and NOS2A asp346asp were additionally prioritized by RF. P-values were significant for both SAA (p=0.0007) and RF (po0.03), the best model obtained with MDR was not significant (po0.14). The application of different approaches provides information whether SNPs contribute by their main and/or interaction effects. SNPs with clear main effects are selected by all methods, strengthening our confidence that these SNPs are truly involved in determining HDL-cholesterol levels.
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