A multi-objective program for quantitative subtyping of clinically relevant phenotypes

Identifying genetic variations that underlie human disease is very important to advance our understanding of the disease´s pathophysiology and promote its personalized treatment. However, many disease phenotypes have complex clinical manifestations and a complicated etiology. Gene finding efforts for complex diseases have had limited success to date. Research results suggest that one way to enhance these efforts is to differentiate subtypes of a complex multifactorial disease phenotype. Existing subtyping methods rely on cluster analysis using only clinical features of a disorder without guidance from genetic data, resulting in subtypes for which genotype association may be limited. In this work, we seek to derive a novel computational method based on multi-objective programming that is capable of clinically categorizing a disease phenotype so as to discover genetically different subtypes. Our approach optimizes two objectives: (1) the cluster-derived subtypes should differ significantly on clinical features; (2) these subtypes can be well separated using candidate genes. This work has been motivated by clinical studies of opioid dependence, a serious, prevalent disorder that is heterogeneous phenotypically. Analyses on a sample of 1,470 European American subjects aggregated from multiple genetic studies of opioid dependence show that the proposed algorithm is superior to existing subtyping methods.