  Genome Wide Association Studies (GWAS) are used to identify statistically
significant genetic variants in case-control studies. GWAS typically use a
p-value threshold of 5 x 10-8 to identify highly ranked single nucleotide
polymorphisms (SNPs). However, evidence has shown that many of these are, in
fact, false positives. Using lower p-values it is possible to to investigate
the joint epistatic interactions between SNPs and provide better insights into
phenotype expression. However, computational complexity is increased
exponentially as a function of higher-order combinations. In this paper, we
propose a novel framework, based on nonlinear transformations of
combinatorically large SNP data, using stacked autoencoders, to identify
higher-order SNP interactions. We focus on the challenging problem of
classifying preterm births. Evidence suggests that this complex condition has a
strong genetic component with unexplained heritability reportedly between
20%-40%. This claim is substantiated using a GWAS data set, obtained from
dbGap, which contains predominantly urban low-income African-American women who
had normal deliveries (between 37 and 42 weeks of gestation) and preterm
deliveries (less than 37 weeks of gestation). Latent representations from
original SNP sequences are used to initialize a deep learning classifier before
it is fine-tuned for classification tasks (term and preterm births). The
complete network models the epistatic effects of major and minor SNP
perturbations. All models are evaluated using standard binary classifier
performance metrics. The findings show that important information pertaining to
SNPs and epistasis can be extracted from 4666 raw SNPs generated using logistic
regression (p-value=5 x 10-3) and used to fit a deep learning model and obtain
results (Sen=0.9289, Spec=0.9591, Gini=0.9651, Logloss=0.3080, AUC=0.9825,
MSE=0.0942) using 500 hidden nodes.
