Construction of Synaptic Neural Network for Genetic Interaction Analysis

Contribution by a single gene to the association with trait may be either independent or through interactions with other genes. Examining all available genes for the main effect should be carried out without the time constraint. However the number of possible interacting combinations would soon become formidably large with the growing number of genes. Therefore it is often coerced to identify a group of candidate genes for the interaction and investigate only within it. Such an identification process should be able to select the group of genes having possibilities to interact with each other. Main effect of each gene should not necessarily be the criterion for the selection. We devised a neural network process that was quite sensitive to the interaction of a particular gene to the remaining ones. Contribution of each gene to the association by the genes as a whole was estimated. Selection was made based on the statistical significance for the existence of such contribution. It was demonstrated that this process might perform reliable candidate gene selection for the interaction even when the selected genes did not show significant main effect, through single scan of each individual gene.