J Anim Sci. 2017 Nov;95(11):4787-4795. doi: 10.2527/jas2017.1944.
Reproduction efficiency is a major factor in the profitability of the beef cattle industry. Genomic selection (GS) is a promising tool that may improve the predictive accuracy and genetic gain of fertility traits. There is a wide range of traits used to measure fertility in dairy and beef cattle including continuous (days open), discrete (pregnancy status), and count (number of inseminations) responses. In this study, a joint analysis of age of puberty (AOP), age at first calving (AOC), and the heifer pregnancy status (HPS) was performed. Data used in this study consisted of records from 1,365 Composite Gene Combination (CGC; 50% Red Angus, 25% Charolais, 25% Tarentaise) first parity females born between 2002 and 2011. The pedigree file included 5,374 animals. A total of 3,902 animals were genotyped with different density SNP chips (3K to 50K SNP). Animals genotyped with low-density arrays were imputed to higher density (BovineSNP50 BeadChip) using FImpute. Data were analyzed using univariate and multivariate classical quantitative models (pedigree based) and univariate genomic approaches. For the latter, 3 different Bayesian methods (BayesA, BayesB, and BayesCπ) were implemented and compared. Estimates of heritabilities using univariate and multivariate analyses based on pedigree relationships ranged between 0.03 (for AOC) to 0.2 (AOP). Heritability of pregnancy status was 0.15 and 0.09 using the univariate and multivariate analyses, respectively. Genetic correlation between pregnancy status and the other 2 traits was low being 0.08 with age at puberty and -0.10 with age at first calving. Heritability estimates were slightly higher using genomic rather than average additive relationships. The accuracy of genomic prediction was similar across the 3 Bayesian methods with higher accuracies for age of puberty than the age at first calving likely due to the higher heritability of the former. The prediction of the binary pregnancy status measured using the area under the curve increased by 27% to 29% compared to a random classifier. Due to the small size of the data, all estimates have large posterior standard deviations and results should be interpreted with caution.
繁殖效率是肉牛产业盈利能力的一个主要因素。基因组选择（GS）是一种很有前途的工具，可以提高生育力性状的预测准确性和遗传增益。有一系列的性状用于衡量奶牛和肉牛的繁殖力，包括连续（开放天数）、离散（妊娠状况）和计数（授精次数）反应。在这项研究中，对青春期年龄（AOP）、首次配种年龄（AOC）和小母牛妊娠状况（HPS）进行了联合分析。本研究使用的数据来自于 2002 年至 2011 年间出生的 1365 头复合基因组合（CGC；50%红安格斯牛、25%夏洛莱牛、25%塔伦蒂亚牛）第一胎小母牛的记录。系谱文件包括 5374 头动物。共有 3902 头动物用不同密度的 SNP 芯片（3K 至 50K SNP）进行了基因分型。用低密度阵列进行基因分型的动物使用 FImpute 被估算到更高的密度（BovineSNP50 BeadChip）。数据使用单变量和多变量经典数量模型（基于系谱）和单变量基因组方法进行分析。对于后者，实施了 3 种不同的贝叶斯方法（BayesA、BayesB 和 BayesCπ）并进行了比较。使用单变量和多变量分析基于系谱关系估计的遗传力在 0.03（AOC）至 0.2（AOP）之间。使用单变量和多变量分析，妊娠状况的遗传力分别为 0.15 和 0.09。妊娠状况与其他 2 个性状之间的遗传相关性较低，与青春期年龄的遗传相关性为 0.08，与首次配种年龄的遗传相关性为-0.10。使用基因组而不是平均加性关系，遗传力估计值略高。3 种贝叶斯方法的基因组预测准确性相似，青春期年龄的预测准确性高于首次配种年龄，可能是因为前者的遗传力较高。使用曲线下面积测量的二元妊娠状况的预测准确性提高了 27%至 29%，与随机分类器相比。由于数据规模较小，所有估计值的后验标准差都很大，结果应谨慎解释。
