Abstract
To develop a model synthesis method for parsimoniously modeling gene-environmental interactions (GEI) associated with clinical outcomes and phenotypes. The AMBROSIA model synthesis approach utilizes the k-way interaction information (KWII), an information-theoretic metric capable of identifying variable combinations associated with GEI. For model synthesis, AMBROSIA considers relevance of combinations to the phenotype, it precludes entry of combinations with redundant information, and penalizes for unjustifiable complexity; each step is KWII based. The performance and power of AMBROSIA were evaluated with simulations and Genetic Association Workshop 15 (GAW15) data sets of rheumatoid arthritis (RA). AMBROSIA identified parsimonious models in data sets containing multiple interactions with linkage disequilibrium present. For the GAW15 data set containing 9187 single-nucleotide polymorphisms, the parsimonious AMBROSIA model identified nine RA-associated combinations with power >90%. AMBROSIA was compared with multifactor dimensionality reduction across several diverse models and had satisfactory power. Software source code is available from http://www.cse.buffalo.edu/DBGROUP/bioinformatics/resources.html. AMBROSIA is a promising method for GEI model synthesis.