Genomic Selection for Early Growth Traits in Inner Mongolian Cashmere Goats Using ABLUP, GBLUP, and ssGBLUP Methods

文献类型： 外文期刊

第一作者： Zhang, Tao

作者： Zhang, Tao;Gao, Linyu;Zhou, Bohan;Xu, Qi;Liu, Yifan;Lv, Qi;Zhang, Yanjun;Wang, Ruijun;Su, Rui;Wang, Zhiying;Zhang, Tao;Gao, Linyu;Zhou, Bohan;Xu, Qi;Liu, Yifan;Li, Jinquan;Lv, Qi;Zhang, Yanjun;Wang, Ruijun;Su, Rui;Wang, Zhiying;Gao, Linyu;Zhou, Bohan;Xu, Qi;Liu, Yifan;Li, Jinquan;Lv, Qi;Zhang, Yanjun;Wang, Ruijun;Su, Rui;Wang, Zhiying

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关键词： genomic selection; accuracy; GEBV; Inner Mongolian cashmere goats

期刊名称：ANIMALS （ 影响因子：2.7； 五年影响因子：3.2 ）

ISSN： 2076-2615

年卷期： 2025 年 15 卷 12 期

页码：

收录情况： SCI

摘要： Simple Summary: This study focused on improving the growth rates of Inner Mongolian cashmere goats (IMCGs) by identifying the most effective genomic selection methods for early growth traits. Faster growth rates are important for farmers because they can lead to increased meat production, ultimately enhancing the economic value of these goats. We analyzed data from 2256 cashmere goats, looking at traits such as birth weight, weaning weight, daily weight gain, and yearling weight. By using advanced statistical models, we aimed to determine which method would provide the most accurate predictions for breeding values based on genetic information. Our research found that specific factors like birth year, herd, sex, birth type, and the age of dams play important roles in growth traits. Among the models we tested, the one that included additive genetic effects from both the individual and its mother, as well as environmental effects, was best. Notably, the method known as ssGBLUP offered the highest accuracy in predicting breeding values, making it the preferred choice for breeding programs aimed at enhancing growth rates in IMCGs. In conclusion, we recommend using the ssGBLUP method for improving breeding efficiency in the growth traits of IMCGs. This study aimed to identify the best model and method for the genomic selection of early growth traits in Inner Mongolian cashmere goats (IMCGs). Using data from 50,728 SNPs, the phenotypes (birth weight, BW; weaning weight, WW; daily weight gain, DWG; and yearling weight, YW) of 2256 individuals, and pedigree information from 14,165 individuals, fixed effects were analyzed using a generalized linear model. Four single-trait animal models with varying combinations of individual and maternal effects were evaluated using the ABLUP, GBLUP, and ssGBLUP methods. The best model was selected based on a likelihood ratio test. Five-fold cross-validation was used to assess the accuracy and reliability of the genomic estimated breeding values (GEBVs). Birth year and herd significantly affected BW (p < 0.05) and WW, DWG, and YW (p < 0.01), while sex, birth type, and dam age had highly significant effects on all traits (p < 0.01). Model 4, incorporating direct and maternal additive genetic effects, maternal environmental effects, and their covariance, was optimal. Additionally, ssGBLUP achieved the highest GEBV accuracy (0.61-0.70), outperforming the GBLUP and ABLUP methods. Thus, ssGBLUP is recommended for enhancing the genetic progress in IMCGs. Under the best method, the heritability estimates for BW, WW, DGW, and YW were 0.11, 0.25, 0.15, and 0.23, respectively.

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