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

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, 7 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 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. Copyright © 2017 John Wiley & Sons, Ltd.

Symbiont community stability through severe coral bleaching in a thermally extreme lagoon.

Coral reefs are threatened by climate change as coral-algal symbioses are currently living close to their upper thermal limits. The resilience of the algal partner plays a key role in determining the thermal tolerance of the coral holobiont and therefore, understanding the acclimatory limits of present day coral-algal symbioses is fundamental to forecasting corals' responses to climate change. This study characterised the symbiont community in a highly variable and thermally extreme (Max = 37.5 °C, Min = 16.8 °C) lagoon located in the southern Persian/Arabian Gulf using next generation sequencing of ITS2 amplicons. Despite experiencing extreme temperatures, severe bleaching and many factors that would be expected to promote the presence of, or transition to clade D dominance, the symbiont communities of the lagoon remain dominated by the C3 variant, Symbiodinium thermophilum. The stability of this symbiosis across multiple genera with different means of symbiont transmission highlights the importance of Symbiodinium thermophilum for corals living at the acclimatory limits of modern day corals. Corals in this extreme environment did not undergo adaptive bleaching, suggesting they are living at the edge of their acclimatory potential and that this valuable source of thermally tolerant genotypes may be lost in the near future under climate change.