There are likely to be many susceptibility genes each with combinations of rare and common alleles and genotypes that impact disease susceptibility primarily through non-linear interactions with genetic and environmental factors
MDR is a strategy to tackle the dimensionality problem of interaction detection
MDR creates a one-dimensional multi-locus genotype variable (high and low risk), which is evaluated for its ability to classify and predict disease status through cross-validation and permutation testing.
MDR Steps A single model with minimum classification error is the best Model 9/10 training data 1/10 test data 10 runs 10 cross-validation 10 best models. The model with minimum PE is the best n -locus model .
Homogenous Populations The EIGENSTRAT algorithm ( a ) Principal components analysis to genotype data to infer continuous axes of genetic variation. ( b ) Genotype at a candidate SNP and Phenotype are adjusted by amounts attributable to ancestry along each axis removing all correlations to ancestry. ( c ) After ancestry adjustment, an association statistic is computed (Price et al, 2006)
Type of data (DV) Qualitative (categorical) 1 independent variable 2 independent variables Quantitative (measurement) Relationships Differences 2 groups Multiple groups Nonparametric Parametric 2 dependent variables Goodness of fit x 2 Independence test x 2 1 predictor Multiple predictors Continuous measurement Ranks Multiple regression Spearman r s Primary interest Degree of relationship Form of relationship Pearson r Regression independent dependent 2-sample t Mann-Whitney U Related sample t Wilcoxon T 1 IV Multiple IVs independent dependent One-way ANOVA Kruskal-Wallis H Factorial ANOVA Repeated measures ANOVA Friedman McNemar test Hypothesis Testing