Multi-view biclustering for genotype-phenotype association studies of complex diseases

Abstract

Complex disorders exhibit great heterogeneity in both clinical manifestation and genetic etiology. This heterogeneity substantially limits the identification of geneotype-phenotype associations. Differentiating homogeneous subtypes of a complex phenotype will enable the detection of genetic variants contributing to the effect of subtypes that cannot be detected by the non-differentiated phenotype. However, the most sophisticated subtyping methods available so far perform unsupervised cluster analysis or latent class analysis on only phenotypic features. Without guidance from the genetic dimension, the resultant subtypes can be suboptimal and genetic associations may fail. We propose a multi-view biclustering approach that integrates phenotypic features and genetic markers to detect confirming evidence in the two views for a disease subtype. This approach groups subjects in clusters that are consistent between the phenotypic and genetic views, and simultaneously identifies the phenotypic features that are used to define a subtype and the genotypes that are associated with the subtype. Our simulation study validates this approach, and our extensive comparison with several biclustering and multi-view data analytics on real-life disease data demonstrates the superior performance of the proposed approach.