KCRR: a nonlinear machine learning with a modified genomic similarity matrix improved the genomic prediction efficiency. Issue 6 (8th May 2021)

• Record Type: Journal Article
• Authors: An, Bingxing; Liang, Mang; Chang, Tianpeng; Duan, Xinghai; Du, Lili; Xu, Lingyang; Zhang, Lupei; Gao, Xue; Li, Junya; Gao, Huijiang
• Is Part Of: Briefings in bioinformatics. Volume 22:Issue 6(2021)
• Publication Date: 2021-05-08
• Subjects: kernel ridge regression -- Cosine kernel -- genomic prediction -- support vector regression; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbab132
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Nowadays, advances in high-throughput sequencing benefit the increasing application of genomic prediction (GP) in breeding programs. In this research, we designed a Cosine kernel–based KRR named KC RR to perform GP. This paper assessed the prediction accuracies of 12 traits with various heritability and genetic architectures from four populations using the genomic best linear unbiased prediction (GBLUP), BayesB, support vector regression (SVR), and KC RR. On the whole, KC RR performed stably for all traits of multiple species, indicating that the hypothesis of KC RR had the potential to be adapted to a wide range of genetic architectures. Moreover, we defined a modified genomic similarity matrix named Cosine similarity matrix (CS matrix). The results indicated that the accuracies between GBLUP_kinship and GBLUP_CS almost unanimously for all traits, but the computing efficiency has increased by an average of 20 times. Our research will be a significant promising strategy in future GP.