He Jie, Zhao Yunfeng, Zhao Jingli, Gao Jin, Han Dandan, Xu Pao, Yang Runqing
Freshwater Fisheries Research Centre of Chinese Academy of Fishery Sciences, Wuxi, 214081, China.
Key Laboratory of Aquatic Genomics, Ministry of Agriculture; Research Centre for Aquatic Biotechnology, Chinese Academy of Fishery Sciences, Beijing, 100141, China.
Genet Sel Evol. 2017 Nov 2;49(1):80. doi: 10.1186/s12711-017-0357-7.
Because of their high economic importance, growth traits in fish are under continuous improvement. For growth traits that are recorded at multiple time-points in life, the use of univariate and multivariate animal models is limited because of the variable and irregular timing of these measures. Thus, the univariate random regression model (RRM) was introduced for the genetic analysis of dynamic growth traits in fish breeding.
We used a multivariate random regression model (MRRM) to analyze genetic changes in growth traits recorded at multiple time-point of genetically-improved farmed tilapia. Legendre polynomials of different orders were applied to characterize the influences of fixed and random effects on growth trajectories. The final MRRM was determined by optimizing the univariate RRM for the analyzed traits separately via penalizing adaptively the likelihood statistical criterion, which is superior to both the Akaike information criterion and the Bayesian information criterion.
In the selected MRRM, the additive genetic effects were modeled by Legendre polynomials of three orders for body weight (BWE) and body length (BL) and of two orders for body depth (BD). By using the covariance functions of the MRRM, estimated heritabilities were between 0.086 and 0.628 for BWE, 0.155 and 0.556 for BL, and 0.056 and 0.607 for BD. Only heritabilities for BD measured from 60 to 140 days of age were consistently higher than those estimated by the univariate RRM. All genetic correlations between growth time-points exceeded 0.5 for either single or pairwise time-points. Moreover, correlations between early and late growth time-points were lower. Thus, for phenotypes that are measured repeatedly in aquaculture, an MRRM can enhance the efficiency of the comprehensive selection for BWE and the main morphological traits.
由于鱼类具有很高的经济重要性，其生长性状一直在持续改良。对于在生命过程中多个时间点记录的生长性状，由于这些测量的时间可变且不规律，单变量和多变量动物模型的应用受到限制。因此，单变量随机回归模型（RRM）被引入用于鱼类育种中动态生长性状的遗传分析。
我们使用多变量随机回归模型（MRRM）来分析遗传改良养殖罗非鱼在多个时间点记录的生长性状的遗传变化。应用不同阶数的勒让德多项式来表征固定效应和随机效应对生长轨迹的影响。最终的MRRM是通过对分析性状分别自适应惩罚似然统计准则来优化单变量RRM确定的，该准则优于赤池信息准则和贝叶斯信息准则。
在选定的MRRM中，体重（BWE）和体长（BL）的加性遗传效应由三阶勒让德多项式建模，体深（BD）的加性遗传效应由二阶勒让德多项式建模。通过使用MRRM的协方差函数，BWE的估计遗传力在0.086至0.628之间，BL的估计遗传力在0.155至0.556之间，BD的估计遗传力在0.056至0.607之间。仅60至140日龄测量的BD遗传力始终高于单变量RRM估计的遗传力。生长时间点之间的所有遗传相关性对于单个或成对时间点均超过0.5。此外，早期和晚期生长时间点之间的相关性较低。因此，对于水产养殖中重复测量的表型，MRRM可以提高对BWE和主要形态性状的综合选择效率。
