Analysis of multiple diverse phenotypes via semiparametric canonical correlation analysis.

ABSTRACT: Studying multiple outcomes simultaneously allows researchers to begin to identify underlying factors that affect all of a set of diseases (i.e., shared etiology) and what may give rise to differences in disorders between patients (i.e., disease subtypes). In this work, our goal is to build risk scores that are predictive of multiple phenotypes simultaneously and identify subpopulations at high risk of multiple phenotypes. Such analyses could yield insight into etiology or point to treatment and prevention strategies. The standard canonical correlation analysis (CCA) can be used to relate multiple continuous outcomes to multiple predictors. However, in order to capture the full complexity of a disorder, phenotypes may include a diverse range of data types, including binary, continuous, ordi

SUBMITTER: Agniel D 

PROVIDER: S-EPMC5640493 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature