Modeling Unobserved Heterogeneity in Susceptibility to Ambient Benzo[a]pyrene Concentration among Children with Allergic Asthma Using an Unsupervised Learning Algorithm.

Current studies of gene × air pollution interaction typically seek to identify unknown heritability of common complex illnesses arising from variability in the host's susceptibility to environmental pollutants of interest. Accordingly, a single component generalized linear models are often used to model the risk posed by an environmental exposure variable of interest in relation to a priori determined DNA variants. However, reducing the phenotypic heterogeneity may further optimize such approach, primarily represented by the modeled DNA variants. Here, we reduce phenotypic heterogeneity of asthma severity, and also identify single nucleotide polymorphisms (SNP) associated with phenotype subgroups. Specifically, we first apply an unsupervised learning algorithm method and a non-parametric regression to find a biclustering structure of children according to their allergy and asthma severity. We then identify a set of SNPs most closely correlated with each sub-group. We subsequently fit a logistic regression model for each group against the healthy controls using benzo[]pyrene (B[]P) as a representative airborne carcinogen. Application of such approach in a case-control data set shows that SNP clustering may help to partly explain heterogeneity in children's asthma susceptibility in relation to ambient B[]P concentration with greater efficiency.