最佳遗传贡献理论及其在水产动物选择育种中的应用前景

水产动物多性状复合育种技术已发展成为国内水产选择育种的重要技术体系。在限定的近交水平下,如何选种和配种实现遗传进展最大化是当前该体系亟待解决的一个突出问题。在动植物选择育种中,最佳遗传贡献理论(Optimum Contribution,OC)已成为平衡育种核心群长期遗传进展与近交水平的有效工具。本文论述了OC理论的提出背景和发展过程、不同优化算法的特点和该理论在动植物选择育种中的应用进展,并进一步综述了基于基因组信息的OC理论研究新进展。遗传贡献目标函数的优化算法主要包括拉格朗日乘数法、半正定规划法和差分进化算法等。基于拉格朗日乘数法,执行OC选择10代后获得的遗传进展要比最佳线性无偏预测法(Best Linear Unbiased Prediction,BLUP)育种值直接选择高21%-60%。针对水产动物等高繁殖力大群体,育种学家进一步改进了算法,利用候选亲本父母本群体的加性遗传相关矩阵来计算候选亲本群体的加性遗传相关矩阵和逆矩阵,降低了逆矩阵的维数,提高了最佳遗传贡献值的计算效率。但是拉格朗日乘数法并不能保证求解出的遗传贡献值为全局最大值,而半正定规划方法利用内点算法可以获得候选亲本的最佳遗传贡献值,与前者相比遗传进展可进一步提高1.5%-9%。差分进化算法可将遗传进展、遗传多样性、后代近交、场间遗传联系、多阶段选择、分子标记利用和成本等多种因素纳入目标函数进行优化,同时完成个体选择和交配方案制定两个关键任务。复合系谱和基因组信息,在限定的近交水平下,可以获得更为准确的遗传贡献值,遗传进展可进一步提高。OC选择已经应用在畜牧、林木育种研究中,育种群体的近交水平得到了有效控制,与BLUP直接选择相比,目标性状的遗传进展进一步提高(17%-30%)。针对水产动物多性状复合育种技术体系的特点,本文分析了OC理论应用的紧迫性和可行性,提出了亟待解决的关键技术问题和解决方案,为下一步在水产动物选择育种中应用OC理论提供借鉴和指导。

Optimum Contribution Theory and the Prospect of Its Application in Selective Breeding in Aquaculture

Aquatic multi-trait integrated breeding system is an important selective breeding technology to improve economic traits of aquatic animals in China. It has been a vital issue how to select and mate the broodstock candidates to maximize the genetic gain at a defined rate of inbreeding in the breeding system. The optimum contribution theory （OC） has become an effective tool to establish equilibrium between the genetic gain and the inbreeding in the nucleus population. In this review we introduced the establishment and development of optimum contribution theory, the characteristics of different optimization algorithms, and its application in selective breeding of plants and animals. Three algorithms, Lagrange multipliers, Semidefinite programming and Differential evolution, have been used in the calculation of optimum genetic contribution. At equal rates of inbreeding, genetic gains calculated with Lagrange multipliers were 21%–60%greater than that with selection for BLUP-EBV. An improved algorithm based on Lagrange multipliers was invented for the calculation of optimal genetic contributions in the case of large number of candidates in the aquatic animal population. The additive relationship matrix between the selection candidates and the inverse of this matrix was replaced with the relationship matrix between the parents of the selection candidates and its inverse in the calculation of the optimal genetic contribution of the selection candidates to the next generation. Lagrange multipliers did not guarantee that the final solution is the global maximum;on the contrast the SDP method could always find the optimum solution that maximized the genetic gain using the interior point algorithms. The expected gains obtained from the Semidifinite programming were 1.5%–9% greater than that from Lagrange multipliers. Individual selection and mate allocation could be performed using Differential evolution algorithm. Many issues including genetic gain, diversity, progeny inbreeding, connections among farms, multi-stage