Application of Genomic Selection Techniques in Livestock Breeding
YAN Cheng-Qi, ZHAO Yuan, TIAN Hui-Bin, ZHANG Yu-Kun, LI Xiao-Long, ZHANG Qi, PU Meng-Ru, GAO Lei, XIAO Zi-Yue, FENG Lian-Jun, LI Fa-Di, WANG Wei-Min*
College of Pastoral Agriculture Science and Technology/State Key Laboratory of Grassland Seed Innovation and Grassland Agricultural Ecosystem, Lanzhou University, Lanzhou 730020, China
Abstract：With the deep integration of genomics and quantitative genetics, the practical application of genomic selection techniques in livestock breeding continues to expand. Particularly since the turn of the 21st century, iterative advancements in sequencing technology have reduced the cost of whole-genome genotyping, accelerating the adoption of genomic selection across a broader range of livestock species. This approach demonstrates significant potential in enhancing the production performance of selected populations, strengthening disease resistance within herds, and optimising breeding strategies.This paper provided a systematic review of the research progress and application of genomic selection technology in livestock breeding. The full paper revolves around: (1) the principles and development history of the technology, elucidating the methodological framework for genome-wide molecular marker screening and genetic effect assessment; (2) the methods and models used in the technology, involving traditional genomic estimated breeding value (GEBV) assessment methods such as best linear unbiased prediction (BLUP) and Bayesian method, as well as new methods of machine learning, such as support vector machines and random forests; (3) the methods to improve the accuracy of genome selection, considerations of across breeds, combining new methods and approaches such as genome-wide association analysis to increase the reliability and usability of the technology; (4) analysis of the current status of application, focusing on the great potential of the technology in improving the production performance of livestock, breeding for disease resistance and optimising breeding strategies. This paper constructed a three-dimensional analysis system of technology principle-application practice-trend development to provide theoretical support for the development of livestock breeding technology in the context of smart agriculture.
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