Selected model for each trait.

The optimal strategy for genetic selection is a selection index based on economic weight; however, in developing countries where economic weight estimation is not always evident and easy for breeders due to a lack of economic data. Thus, this study aimed to construct selection indices for crossbred goats, which could be used as an alternative to economic selection index and to explore the relationship among economically important traits. The data set contained records of birth weight (BW), weaning weight (WW), pre-weaning weight gain (ADG), pre-weaning Kleiber ratio (KR), pre-weaning relative growth rate (RGR), pre-weaning growth efficiency (GE), and pre-weaning survival (RR) of crossbred goats. Genetic parameter estimates were obtained using a single-trait animal model. General linear model, principal component analysis, and cluster procedures of SAS were also used for data analysis. Kid survival was negatively correlated with all investigated traits except BW. Traits such as KR, GE, RGR, WW, and ADG were highly and positively correlated. According to the Kaiser method, two principal components were selected from seven investigated traits. The first principal component (PC1) explained 57.71%, and the second principal component (PC2) explained 14.57% of the estimated breeding value variance, totaling 72.28% of the total genetic additive variance. PC1 explained most of the direct additive genetic variation and correlated with the estimated breeding value of WW, ADG, KR, GE, and RGR, whereas PC2 was correlated with the estimated breeding value of BW and RR. Besides, the cluster analysis categorized seven traits into two major groups. The first group includes BW and RR, whereas traits such as WW, ADG, KR, GE, and RGR were included in the second group. Therefore, two based selection indices, or principal component scores (PCS) were derived. Animals with higher PCS1 could be used to improve WW, ADG, KR, GE, and RGR, whereas animals with higher PCS2 scores could be used to improve BW and pre-weaning survival of crossbred kids. The selection of the most appropriate and specific selection index regarding the two groups of traits is determined by the breeding objectives defined for specific genetic improvement program. These selection indices could be used as an alternative approach when economic weights for traits of interests are not available to construct the economic selection index. However, further works should be done on refining the selection indices and validating them in independent datasets.遗传选育的最优策略为基于经济权重的选择指数；然而在发展中国家，由于经济数据匮乏，育种者往往难以准确估算并明确经济权重。为此，本研究旨在构建杂交山羊的选择指数，作为经济选择指数的替代方案，并探究各经济重要性性状间的关联。本数据集包含杂交山羊的初生重（birth weight, BW）、断奶重（weaning weight, WW）、断奶前增重（pre-weaning weight gain, ADG）、断奶前克莱伯比率（pre-weaning Kleiber ratio, KR）、断奶前相对生长率（pre-weaning relative growth rate, RGR）、断奶前生长效率（pre-weaning growth efficiency, GE）及断奶前存活率（pre-weaning survival, RR）的记录数据。遗传参数估计采用单性状动物模型（single-trait animal model）完成；数据分析同时运用了SAS的一般线性模型（general linear model）、主成分分析（principal component analysis）及聚类分析流程。研究发现，除初生重外，断奶前存活率与其余所有调研性状均呈负相关；克莱伯比率、生长效率、相对生长率、断奶重及断奶前增重等性状间呈高度正相关。依据凯泽法（Kaiser method），从7个调研性状中提取2个主成分：第一主成分（PC1）可解释57.71%的育种值方差，第二主成分（PC2）可解释14.57%，二者累计可解释总加性遗传方差的72.28%。PC1涵盖了绝大多数直接加性遗传变异，与断奶重、断奶前增重、克莱伯比率、生长效率及相对生长率的育种值显著相关；而PC2则与初生重及断奶前存活率的育种值相关。此外，聚类分析将7个性状划分为两大组别：第一组包含初生重与断奶前存活率，第二组则涵盖断奶重、断奶前增重、克莱伯比率、生长效率及相对生长率。据此得到2个基础选择指数，即主成分得分（principal component scores, PCS）。具有更高PCS1得分的个体可用于改良断奶重、断奶前增重、克莱伯比率、生长效率及相对生长率，而具有更高PCS2得分的个体则可用于提升杂交山羊羔羊的初生重与断奶前存活率。针对这两类性状选择最为适配的专用选择指数，需依据特定遗传改良计划所设定的育种目标而定。当无法获取目标性状的经济权重以构建经济选择指数时，本研究构建的选择指数可作为替代方案。不过，后续仍需对该选择指数进行优化，并在独立数据集上开展验证工作。