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在不同个体亲缘关系上限下评估的日本黑牛胴体性状之间的基因组遗传力和相关性。

Genomic heritability and correlation between carcass traits in Japanese Black cattle evaluated under different ceilings of relatedness among individuals.

作者信息

Rostamzadeh Mahdabi Elaheh, Tian Rugang, Li Yuan, Wang Xiao, Zhao Meng, Li Hui, Yang Ding, Zhang Hao, Li SuFan, Esmailizadeh Ali

机构信息

Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran.

Institute of Animal Husbandry, Inner Mongolia Academy of Agricultural and Animal Husbandry Sciences, Hohhot, China.

出版信息

Front Genet. 2023 Feb 1;14:1053291. doi: 10.3389/fgene.2023.1053291. eCollection 2023.

DOI:10.3389/fgene.2023.1053291
PMID:36816045
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9928846/
Abstract

The investigation of carcass traits to produce meat with high efficiency has been in focus on Japanese Black cattle since 1972. To implement a successful breeding program in carcass production, a comprehensive understanding of genetic characteristics and relationships between the traits is of paramount importance. In this study, genomic heritability and genomic correlation between carcass traits, including carcass weight (CW), rib eye area (REA), rib thickness (RT), subcutaneous fat thickness (SFT), yield rate (YI), and beef marbling score (BMS) were estimated using the genomic data of 9,850 Japanese Black cattle (4,142 heifers and 5,708 steers). In addition, we investigated the effect of genetic relatedness degree on the estimation of genetic parameters of carcass traits in sub-populations created based on different GRM-cutoff values. Genome-based restricted maximum likelihood (GREML) analysis was applied to estimate genetic parameters. Using all animal data, the heritability values for carcass traits were estimated as moderate to relatively high magnitude, ranging from 0.338 to 0.509 with standard errors, ranging from 0.014 to 0.015. The genetic correlations were obtained low and negative between SFT and REA [-0.198 (0.034)] and between SFT and BMS [-0.096 (0.033)] traits, and high and negative between SFT and YI [-0.634 (0.022)]. REA trait was genetically highly correlated with YI and BMS [0.811 (0.012) and 0.625 (0.022), respectively]. In sub-populations created based on the genetic-relatedness ceiling, the heritability estimates ranged from 0.212 (0.131) to 0.647 (0.066). At the genetic-relatedness ceiling of 0.15, the correlation values between most traits with low genomic correlation were overestimated while the correlations between the traits with relatively moderate to high correlations, ranging from 0.380 to 0.811, were underestimated. The values were steady at the ceilings of 0.30-0.95 (sample size of 5,443-9,850) for most of the highly correlated traits. The results demonstrated that there is considerable genetic variation and also favorable genomic correlations between carcass traits. Therefore, the genetic improvement for the traits can be simultaneously attained through genomic selection. In addition, we observed that depending on the degree of relationship between individuals and sample size, the genomic heritability and correlation estimates for carcass traits may be different.

摘要

自1972年以来,日本黑牛一直是高效生产肉类的胴体性状研究的重点。为了在胴体生产中实施成功的育种计划,全面了解遗传特征以及性状之间的关系至关重要。在本研究中,利用9850头日本黑牛(4142头小母牛和5708头公牛)的基因组数据,估计了包括胴体重(CW)、眼肌面积(REA)、肋骨厚度(RT)、皮下脂肪厚度(SFT)、产肉率(YI)和牛肉大理石花纹评分(BMS)在内的胴体性状之间的基因组遗传力和基因组相关性。此外,我们还研究了基于不同基因组关系矩阵(GRM)截止值创建的亚群体中,遗传相关程度对胴体性状遗传参数估计的影响。采用基于基因组的限制最大似然法(GREML)分析来估计遗传参数。利用所有动物数据,胴体性状的遗传力估计值为中等至高,范围从0.338至0.509,标准误差范围从0.014至0.015。SFT与REA [-0.198(0.034)]以及SFT与BMS [-0.096(0.033)]性状之间的遗传相关性较低且为负,而SFT与YI [-0.634(0.022)]之间的遗传相关性较高且为负。REA性状与YI和BMS的遗传相关性分别为0.811(0.012)和0.625(0.022),呈高度相关。在基于遗传相关性上限创建的亚群体中,遗传力估计值范围从0.212(0.131)至0.647(0.066)。在遗传相关性上限为0.15时,大多数基因组相关性较低的性状之间的相关值被高估,而相关性相对中等至高(范围从0.380至0.811)的性状之间的相关性被低估。对于大多数高度相关的性状,在0.30 - 0.95的上限(样本量为5443 - 9850)时,这些值较为稳定。结果表明,胴体性状之间存在相当大的遗传变异以及良好的基因组相关性。因此,通过基因组选择可以同时实现这些性状的遗传改良。此外,我们观察到,根据个体之间的关系程度和样本量,胴体性状的基因组遗传力和相关性估计可能会有所不同。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d31a/9928846/3085c3a3b07b/fgene-14-1053291-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d31a/9928846/c763547eab19/fgene-14-1053291-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d31a/9928846/4424dfaa9219/fgene-14-1053291-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d31a/9928846/61e32b317e6a/fgene-14-1053291-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d31a/9928846/3085c3a3b07b/fgene-14-1053291-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d31a/9928846/c763547eab19/fgene-14-1053291-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d31a/9928846/4424dfaa9219/fgene-14-1053291-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d31a/9928846/61e32b317e6a/fgene-14-1053291-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d31a/9928846/3085c3a3b07b/fgene-14-1053291-g004.jpg

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