  The aetiology of polygenic obesity is multifactorial, which indicates that
life-style and environmental factors may influence multiples genes to aggravate
this disorder. Several low-risk single nucleotide polymorphisms (SNPs) have
been associated with BMI. However, identified loci only explain a small
proportion of the variation ob-served for this phenotype. The linear nature of
genome wide association studies (GWAS) used to identify associations between
genetic variants and the phenotype have had limited success in explaining the
heritability variation of BMI and shown low predictive capacity in
classification studies. GWAS ignores the epistatic interactions that less
significant variants have on the phenotypic outcome. In this paper we utilise a
novel deep learning-based methodology to reduce the high dimensional space in
GWAS and find epistatic interactions between SNPs for classification purposes.
SNPs were filtered based on the effects associations have with BMI. Since
Bonferroni adjustment for multiple testing is highly conservative, an important
proportion of SNPs involved in SNP-SNP interactions are ignored. Therefore,
only SNPs with p-values < 1x10-2 were considered for subsequent epistasis
analysis using stacked auto encoders (SAE). This allows the nonlinearity
present in SNP-SNP interactions to be discovered through progressively smaller
hidden layer units and to initialise a multi-layer feedforward artificial
neural network (ANN) classifier. The classifier is fine-tuned to classify
extremely obese and non-obese individuals. The best results were obtained with
2000 compressed units (SE=0.949153, SP=0.933014, Gini=0.949936,
Lo-gloss=0.1956, AUC=0.97497 and MSE=0.054057). Using 50 compressed units it
was possible to achieve (SE=0.785311, SP=0.799043, Gini=0.703566,
Logloss=0.476864, AUC=0.85178 and MSE=0.156315).
