Clustering high dimensional mixed data to uncover sub-phenotypes:joint analysis of phenotypic and genotypic data

TL;DR

This paper introduces a Bayesian mixture model for clustering high-dimensional mixed phenotypic and genotypic data to identify sub-phenotypes related to metabolic syndrome, demonstrating its effectiveness in uncovering meaningful groups and discriminatory variables.

Contribution

A novel latent variable Bayesian model with variable selection for joint analysis of phenotypic and genotypic data in high dimensions is proposed.

Findings

Identified two meaningful sub-phenotypes ('healthy' and 'at risk')

Discovered key phenotypic and genotypic variables for discrimination

Sub-phenotypes strongly correlate with disease classification after seven years

Abstract

The LIPGENE-SU.VI.MAX study, like many others, recorded high dimensional continuous phenotypic data and categorical genotypic data. LIPGENE-SU.VI.MAX focuses on the need to account for both phenotypic and genetic factors when studying the metabolic syndrome (MetS), a complex disorder that can lead to higher risk of type 2 diabetes and cardiovascular disease. Interest lies in clustering the LIPGENE-SU.VI.MAX participants into homogeneous groups or sub-phenotypes, by jointly considering their phenotypic and genotypic data, and in determining which variables are discriminatory. A novel latent variable model which elegantly accommodates high dimensional, mixed data is developed to cluster LIPGENE-SU.VI.MAX participants using a Bayesian finite mixture model. A computationally efficient variable selection algorithm is incorporated, estimation is via a Gibbs sampling algorithm and an approximate BIC-MCMC criterion is developed to select the optimal model. Two clusters or sub-phenotypes (`healthy' and `at risk') are uncovered. A small subset of variables is deemed discriminatory which notably includes phenotypic and genotypic variables, highlighting the need to jointly consider both factors. Further, seven years after the LIPGENE-SU.VI.MAX data were collected, participants underwent further analysis to diagnose presence or absence of the MetS. The two uncovered sub-phenotypes strongly correspond to the seven year follow up disease classification, highlighting the role of phenotypic and genotypic factors in the MetS, and emphasising the potential utility of the clustering approach in early screening. Additionally, the ability of the proposed approach to define the uncertainty in sub-phenotype membership at the participant level is synonymous with the concepts of precision medicine and nutrition.