Influence of Gene Effect on Predicting Animal Phenotype Using Back-Propagation Artificial Neural Networks

Abstract

Recently, largescale, high-density single-nucleotide polymorphism (SNP) marker information has become available. However, the simple relation was not enough for describing the relation between markers and genotype value, and the genetic diversity should be carefully monitored as genomic selection for quantitative traits as a routine technology for animal genetic improvement. In this paper, back-propagation neural network is used to simulate and predict the genotype values, and the different gene effects were used to discuss the influences on estimating the polygenic genotype value. The results showed that after phenotype value being normalized, optimization network could be established for predicting the phenotype value without fearing that the gene effect is too large. If the number of hidden neurons is large enough, the stability of back-propagation artificial neural network established for predicting phenotype value is very well. the gene effect could not affected the precise of optimum neural network for estimating the animal phenotype, the optimum neural network could be selected for predicting the phenotype values of quantitative traits controlled by genes with small or large effects.