  Quantitatively predicting phenotype variables by the expression changes in a
set of candidate genes is of great interest in molecular biology but it is also
a challenging task for several reasons. First, the collected biological
observations might be heterogeneous and correspond to different biological
mechanisms. Secondly, the gene expression variables used to predict the
phenotype are potentially highly correlated since genes interact though unknown
regulatory networks. In this paper, we present a novel approach designed to
predict quantitative trait from transcriptomic data, taking into account the
heterogeneity in biological samples and the hidden gene regulatory networks
underlying different biological mechanisms. The proposed model performs well on
prediction but it is also fully parametric, which facilitates the downstream
biological interpretation. The model provides clusters of individuals based on
the relation between gene expression data and the phenotype, and also leads to
infer a gene regulatory network specific for each cluster of individuals. We
perform numerical simulations to demonstrate that our model is competitive with
other prediction models, and we demonstrate the predictive performance and the
interpretability of our model to predict alcohol sensitivity from
transcriptomic data on real data from Drosophila Melanogaster Genetic Reference
Panel (DGRP).
