A Comprehensive Strategy Combining Feature Selection and Local Optimization Algorithm to Optimize the Design of Low-Density Chip for Genomic Selection

Abstract

Design of low-density SNP chips provides an opportunity for wide application of genomic selection at lower cost. A novel strategy referred to as the “block-free” method is proposed in this study to select a subset of SNPs from a high-density chip to form a low-density panel. In this method, Feature Selection using a Feature Similarity (FSFS) algorithm was first performed to remove highly correlated SNPs, and then a Multiple-Objective, Local-Optimization (MOLO) algorithm was used to pick SNPs for the low-density panel. Two other commonly used methods called the “uniform” method and the “block-based” method were also implemented for comparison purposes. A real pig dataset with 7967 individuals from three breeds containing 43,832 SNPs was used for comparison of the methods. In terms of genotype imputation accuracy and genomic prediction accuracy, our strategy was superior in most cases when the densities were lower than 1K. The genotype imputation accuracy from the low-density chip compared to the original high-density chip was higher than 90% in all pig breeds as the density increased to 1K. In addition, the accuracies of predicted genomic breeding values (GEBV) calculated using the imputed panel were nearly 90% of estimates from the original chip for all traits and breeds. Our strategy is effective to design low-density chips by making full use of information of close relationships for genomic selection in animals and plants.