Xiaoya  Zhang , Jianquan  Li

Hainan Key Laboratory of Crop Molecular Breeding, Sanya, 572025, China
Author    Correspondence author
Computational Molecular Biology, 2024, Vol. 14, No. 2   
Received: 03 Feb., 2024    Accepted: 14 Mar., 2024    Published: 01 Apr., 2024

This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

With the advancement of genomics technology, whole genome prediction (GWP) and genome selection (GS) have become important tools in plant and animal breeding. Genomic selection utilizes whole genome marker information to select target traits through predictive models, improving breeding efficiency and accuracy. This study comprehensively reviews the application of whole genome prediction technology in plant and animal breeding, with a focus on exploring its role in improving breeding efficiency. Analyzing current genome selection models and methods, exploring the potential application of GS in improving important agronomic and economic traits, as well as its prospects in different fields. Research has shown that GS technology has greatly improved selection efficiency in multiple breeding projects, particularly in enhancing plant disease resistance and increasing crop yield. In animal breeding, genome selection has been widely applied to improve the reproductive traits, health, and productivity of livestock.

Genomic selection; Plant breeding; Animal breeding; Machine learning; Genotype-environment interaction