A Genome-Wide Prediction Method of Biological Multiple Traits Using Feedback Stacking Deep Networks

Genome-wide prediction methods establish a genetic statistical model using molecular marker genotypes distributed throughout the whole genome and biological trait data to predict phenotype values, in order to providing a guide for individual selection breeding. Most of the existing genome-wide selection methods focus on the prediction of a single trait, which can only examine each trait one by one when predicting multiple traits that would translate into neglect of the correlation among traits. In this paper, we proposed a joint prediction method for multiple biological traits based on feedback stacking deep networks, which stack orderly the output of the model to the non-shared layer so as to introduce the correlation among traits into the learning network. The method selected the single nucleotide polymorphism (SNP) loci obtained from whole genome sequencing as molecular markers, extracted the genotype of each SNP allele to constitute sample features, and adopted a two-stage feature selection method to pick up the SNP loci highly correlated with target, and then constructed a deep network model with feedback stacking for joint prediction of multiple traits. The proposed approach was compared with other models by using oyster and corn datasets, and the results showed that the method on this work could achieve better prediction effect than the others.