Computational protocol: Detection of susceptibility loci on APOA5 and COLEC12 associated with metabolic syndrome using a genome-wide association study in a Taiwanese population

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Protocol publication

[…] Categorical data were evaluated using the chi-square test. We conducted the Student’s t-test to compare the difference in the means from two continuous variables. To estimate the association of the investigated SNP with MetS, we conducted a logistic regression analysis to evaluate the ORs and their 95% CIs, adjusting for covariates including age and sex []. Furthermore, we estimated the association of the investigated SNP with individual components of MetS by using logistic regression analysis, adjusting for age and sex []. The genotype frequencies were assessed for Hardy-Weinberg equilibrium using a χ2 goodness-of-fit test with 1 degree of freedom (i.e. the number of genotypes minus the number of alleles). Multiple testing was adjusted by the Bonferroni correction. The criterion for significance was set at P < 0.05 for all tests. Data are presented as the mean ± standard deviation.To investigate gene-gene interactions, we employed the GMDR method []. We tested two-way interactions using 10-fold cross-validation. The GMDR software provides some output parameters, including the testing accuracy and empirical P values, to assess each selected interaction. Moreover, we provided age and sex as covariates for gene-gene interaction models in our interaction analyses. Permutation testing obtains empirical P values of prediction accuracy as a benchmark based on 1,000 shuffles. In order to correct for multiple testing, we applied a conservative Bonferroni correction factor for the number of tests employed in the GMDR analysis.Based on the effect sizes in this study, the power to detect significant associations was evaluated by QUANTO software (http://biostats.usc.edu/Quanto.html). The Manhattan and Q-Q plots were drawn by using the R package ‘qqman’. […]

Pipeline specifications

Software tools GMDR, Quanto, qqman
Application GWAS
Chemicals Cholesterol, Glucose