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Clustering high-dimensional mixed data to uncover sub-phenotypes: joint analysis of phenotypic and genotypic data
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Date
2017-06-30
Authors
McParland, D.
Phillips, Catherine M.
Brennan, L.
Roche, H. M.
Gormley, I. C.
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Publisher
John Wiley & Sons, Inc.
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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 that 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, 7years 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 7-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.
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Keywords
Clustering , Mixed data , Phenotypic data , SNP data , Metabolic syndrome
Citation
McParland, D., Phillips, C. M., Brennan, L., Roche, H. M. and Gormley, I. C. (2017) 'Clustering high-dimensional mixed data to uncover sub-phenotypes: joint analysis of phenotypic and genotypic data', Statistics in Medicine, 36(28), pp. 4548-4569. doi:10.1002/sim.7371
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© 2017, John Wiley & Sons, Ltd. This is the peer reviewed version of the following article: McParland, D., Phillips, C. M., Brennan, L., Roche, H. M. and Gormley, I. C. (2017) 'Clustering high-dimensional mixed data to uncover sub-phenotypes: joint analysis of phenotypic and genotypic data', Statistics in Medicine, 36(28), pp. 4548-4569. doi:10.1002/sim.7371, which has been published in final form at https://doi.org/10.1002/sim.7371. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.