Multifactor Dimensionality Reduction–Phenomics: A Novel Method to Capture Genetic Heterogeneity with Use of Phenotypic Variables

Complex human diseases do not have a clear inheritance pattern, and it is expected that risk involves multiple genes with modest effects acting independently or interacting. Major challenges for the identification of genetic effects are genetic heterogeneity and difficulty in analyzing high-order interactions. To address these challenges, we present MDR-Phenomics, a novel approach based on the multifactor dimensionality reduction (MDR) method, to detect genetic effects in pedigree data by integration of phenotypic covariates (PCs) that may reflect genetic heterogeneity. The P value of the test is calculated using a permutation test adjusted for multiple tests. To validate MDR-Phenomics, we compared it with two MDR-based methods: (1) traditional MDR pedigree disequilibrium test (PDT) without consideration of PCs (MDR-PDT) and (2) stratified phenotype (SP) analysis based on PCs, with use of MDR-PDT with a Bonferroni adjustment (SP-MDR). Using computer simulations, we examined the statistical power and type I error of the different approaches under several genetic models and sampling scenarios. We conclude that MDR-Phenomics is more powerful than MDR-PDT and SP-MDR when there is genetic heterogeneity, and the statistical power is affected by sample size and the number of PC levels. We further compared MDR-Phenomics with conditional logistic regression (CLR) for testing interactions across single or multiple loci with consideration of PC. The results show that CLR with PC has only slightly smaller power than does MDR-Phenomics for single-locus analysis but has considerably smaller power for multiple loci. Finally, by applying MDR-Phenomics to autism, a complex disease in which multiple genes are believed to confer risk, we attempted to identify multiple gene effects in two candidate genes of interest—the serotonin transporter gene (SLC6A4) and the integrin beta 3 gene (ITGB3) on chromosome 17. Analyzing four markers in SLC6A4 and four markers in ITGB3 in 117 white family triads with autism and using sex of the proband as a PC, we found significant interaction between two markers—rs1042173 in SLC6A4 and rs3809865 in ITGB3.