Tuning hyperparameters for gene interaction models in genome-wide association studies

Abstract

In genetic epidemiology, epistasis has been the subject of several researchers to understand the underlying causes of complex diseases. Identifying gene-gene and/or gene-environmental interactions are becoming more challenging due to multiple genetic and environmental factors acting together or independently. The limitations of current computational approaches motivated the development of a deep learning method in our recent study. The approach trained a multilayered feedforward neural network to discover interacting genes associated with complex diseases. The models are evaluated under various simulated scenarios and compared with the previous methods. The results showed significant improvements in predicting gene interactions over the traditional machine learning techniques. This study is further extended to maximize the predictive performance of the method by tuning the hyperparameters using Cartesian grid and random grid searching. Several experiments are conducted on real datasets to identify higher-order interacting genes responsible for diseases. The findings demonstrated randomly chosen trials are more efficient than trials chosen by grid search for optimizing hyperparameters. The optimal configuration of hyperparameter values improved the model performance without overfitting. The results illustrate top 30 gene interactions responsible for sporadic breast cancer and hypertension.