Institut de l'Elevage, Castanet-Tolosan, France; GenPhySE, Université de Toulouse, INRAE, ENVT, F-31326 Castanet Tolosan, France.
GenPhySE, Université de Toulouse, INRAE, ENVT, F-31326 Castanet Tolosan, France.
Animal. 2021 Feb;15(2):100040. doi: 10.1016/j.animal.2020.100040. Epub 2021 Jan 11.
Numerous meat sheep breeding programs in developed and developing countries are characterized by incomplete sire information and a predominant use of natural matings. These two parameters potentially affect the benefit of genomic selection (GS), especially for the selection of a late-in-life trait. Using stochastic simulations, the genetic gains obtained using genomic and conventional strategies for a maternal trait were evaluated in meat sheep population. Natural mating and artificial insemination (AI)-based designs, inspired by the current diversity of designs used for French meat sheep breeds, were modeled and three genomic strategies were tested and compared with a conventional selection strategy: parentage assignment, GS based on a male or a male and female reference population. Genomic selection based on a male reference population did not always outperform conventional selection. Its benefit depended on the design, the level of missing information on dam sires, and the level of AI. Genomic selection based on a male and female reference population always outperformed the conventional selection strategy, even if only 25 % of the females in the nucleus were genotyped.
许多发达国家和发展中国家的肉用绵羊繁殖计划的特点是不完全的父本信息和主要使用自然交配。这两个参数可能会影响基因组选择(GS)的效益,特别是对生命后期性状的选择。利用随机模拟,评估了在肉用绵羊群体中使用基因组和常规策略对母性性状获得的遗传增益。受法国肉用绵羊品种目前设计多样性的启发,对自然交配和人工授精(AI)设计进行了建模,并测试了三种基因组策略,并与常规选择策略进行了比较:亲子关系鉴定、基于雄性或雄性和雌性参考群体的 GS。基于雄性参考群体的基因组选择并不总是优于常规选择。其效益取决于设计、母本父本信息缺失的程度和 AI 的水平。基于雄性和雌性参考群体的基因组选择总是优于常规选择策略,即使核内只有 25%的雌性被基因分型。