Methods of Genotype Feature Extraction Affecting the Prediction Accuracy of Genomic Selection
The purpose of this study was to explore and evaluate 6 different methods for extrac-ting genotype feature of single nucleotide polymorphisms(SNP).Six methods were analyzed and compared:principal component analysis(PCA),gene-principal component analysis(gene-PCA),SNP-Pearson correlation coefficient(SNP-PCC),linkage disequilibrium(LD),and genome-wide association study(GWAS)and random sampling(RS).The prediction accuracy of GEBV in 2 sets of data(Beijing duck,542 samples,SNP loci 39 932;Duroc pig,2 549 samples,SNP loci 230 884)and 3 sets of phenotypes(Beijing duck body length,Duroc pig backfat thickness and teat number)was evaluated.Results showed that SNP-PCC combined with 5 GS methods(GB-LUP,BayesA,BayesB,BayesC,and Bayesian Lasso)achieved relatively reliable prediction accu-racy for the Pecking duck body length phenotype and achieved the highest average prediction ac-curacy in pig backfat thickness and teat number phenotypes(increased by 5％,reaching 32.3％),and significantly improved computational efficiency(on average 5-7 times faster).In summary,this study found that selecting appropriate feature extraction methods can effectively improve the accuracy and computational efficiency of GS prediction,laying the foundation for in-depth re-search on the impact of different feature extraction methods on GS prediction accuracy,and pro-viding reference for their application in breeding practice.
万方数据
维普
