An algorithm for model construction and its applications to pharmacogenomic studies.

A model depicts the relationship between clinical phenotypes and genotypes on a set of genetic polymorphisms. After the model is constructed and validated, it may be used to predict clinical phenotypes such as traits of complex diseases. A pharmacogenomic model is used to predict the efficacies or adverse drug reactions of a medication. The construction of a model is a challenging task. This is because a single-locus polymorphism does not contain enough information to stratify patients in general, given the complex biological mechanisms involved. An exhaustive search for the correct combination of genotypes across multiple loci is, however, computationally infeasible. We are, thus, motivated to propose a novel algorithm for the construction of models using the multiple single-nucleotide polymorphism (SNP) information in diplotype forms. This algorithm utilizes the techniques of genetic algorithms and Boolean algebra (GABA). The proposed algorithm is tested on simulated data, as well as real genotype datasets of chronic hepatitis C patients treated with interferon-combined therapy. A model for predicting the treatment efficacy is constructed and validated. The results showed that the proposed algorithm is very effective in deriving models comprising multiple SNPs.