SAERMA: Stacked Autoencoder Rule Mining Algorithm for the Interpretation of Epistatic Interactions in GWAS for Extreme Obesity

by   Casimiro Aday Curbelo Montañez, et al.

One of the most important challenges in the analysis of high-throughput genetic data is the development of efficient computational methods to identify statistically significant Single Nucleotide Polymorphisms (SNPs). Genome-wide association studies (GWAS) use single-locus analysis where each SNP is independently tested for association with phenotypes. The limitation with this approach, however, is its inability to explain genetic variation in complex diseases. Alternative approaches are required to model the intricate relationships between SNPs. Our proposed approach extends GWAS by combining deep learning stacked autoencoders (SAEs) and association rule mining (ARM) to identify epistatic interactions between SNPs. Following traditional GWAS quality control and association analysis, the most significant SNPs are selected and used in the subsequent analysis to investigate epistasis. SAERMA controls the classification results produced in the final fully connected multi-layer feedforward artificial neural network (MLP) by manipulating the interestingness measures, support and confidence, in the rule generation process. The best classification results were achieved with 204 SNPs compressed to 100 units (77 although it was possible to achieve 73 logloss=0.62, and MSE=0.21) with 50 hidden units - both supported by close model interpretation.



There are no comments yet.


page 1

page 8


Analysis of Extremely Obese Individuals Using Deep Learning Stacked Autoencoders and Genome-Wide Genetic Data

The aetiology of polygenic obesity is multifactorial, which indicates th...

Extracting Epistatic Interactions in Type 2 Diabetes Genome-Wide Data Using Stacked Autoencoder

2 Diabetes is a leading worldwide public health concern, and its increas...

Deep Learning Classification of Polygenic Obesity using Genome Wide Association Study SNPs

In this paper, association results from genome-wide association studies ...

Utilising Deep Learning and Genome Wide Association Studies for Epistatic-Driven Preterm Birth Classification in African-American Women

Genome Wide Association Studies (GWAS) are used to identify statisticall...

Bayesian Neural Networks for Genetic Association Studies of Complex Disease

Discovering causal genetic variants from large genetic association studi...

Mining Feature Relationships in Data

When faced with a new dataset, most practitioners begin by performing ex...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.