Title: Extracting Epistatic Interactions in Type 2 Diabetes Genome-Wide Data Using Stacked Autoencoder,
Abstract: 2 Diabetes is a leading worldwide public health concern, and its increasing
prevalence has significant health and economic importance in all nations. The
condition is a multifactorial disorder with a complex aetiology. The genetic
determinants remain largely elusive, with only a handful of identified
candidate genes. Genome wide association studies (GWAS) promised to
significantly enhance our understanding of genetic based determinants of common
complex diseases. To date, 83 single nucleotide polymorphisms (SNPs) for type 2
diabetes have been identified using GWAS. Standard statistical tests for single
and multi-locus analysis such as logistic regression, have demonstrated little
effect in understanding the genetic architecture of complex human diseases.
Logistic regression is modelled to capture linear interactions but neglects the
non-linear epistatic interactions present within genetic data. There is an
urgent need to detect epistatic interactions in complex diseases as this may
explain the remaining missing heritability in such diseases. In this paper, we
present a novel framework based on deep learning algorithms that deal with
non-linear epistatic interactions that exist in genome wide association data.
Logistic association analysis under an additive genetic model, adjusted for
genomic control inflation factor, is conducted to remove statistically
improbable SNPs to minimize computational overheads.