He Jie, Zhao Yunfeng, Zhao Jingli, Gao Jin, Xu Pao, Yang Runqing
Freshwater Fisheries Research Center, 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.
J Appl Genet. 2018 Feb;59(1):99-107. doi: 10.1007/s13353-018-0428-7. Epub 2018 Jan 20.
To genetically analyse growth traits in genetically improved farmed tilapia (GIFT), the body weight (BWE) and main morphological traits, including body length (BL), body depth (BD), body width (BWI), head length (HL) and length of the caudal peduncle (CPL), were measured six times in growth duration on 1451 fish from 45 mixed families of full and half sibs. A random regression model (RRM) was used to model genetic changes of the growth traits with days of age and estimate the heritability for any growth point and genetic correlations between pairwise growth points. Using the covariance function based on optimal RRMs, the heritabilities were estimated to be from 0.102 to 0.662 for BWE, 0.157 to 0.591 for BL, 0.047 to 0.621 for BD, 0.018 to 0.577 for BWI, 0.075 to 0.597 for HL and 0.032 to 0.610 for CPL between 60 and 140 days of age. All genetic correlations exceeded 0.5 between pairwise growth points. Moreover, the traits at initial days of age showed less correlation with those at later days of age. With phenotypes observed repeatedly, the model choice showed that the optimal RRMs could more precisely predict breeding values at a specific growth time than repeatability models or multiple trait animal models, which enhanced the efficiency of selection for the BWE and main morphological traits.
为了对遗传改良尼罗罗非鱼(GIFT)的生长性状进行遗传分析,对来自45个全同胞和半同胞混合家系的1451尾鱼在生长期间测量了6次体重(BWE)和主要形态性状,包括体长(BL)、体深(BD)、体宽(BWI)、头长(HL)和尾柄长(CPL)。使用随机回归模型(RRM)对生长性状随日龄的遗传变化进行建模,并估计任何生长点的遗传力以及成对生长点之间的遗传相关性。基于最优RRM的协方差函数,在60至140日龄期间,体重的遗传力估计值为0.102至0.662,体长为0.157至0.591,体深为0.047至0.621,体宽为0.018至0.577,头长为0.075至0.597,尾柄长为0.032至0.610。成对生长点之间的所有遗传相关性均超过0.5。此外,初始日龄的性状与后期日龄的性状相关性较小。通过重复观察表型发现,模型选择表明,最优RRM比重复性模型或多性状动物模型能够更精确地预测特定生长时间的育种值,从而提高了体重和主要形态性状的选择效率。