Incorporating heterosis effects enhances genetic evaluations for milk production and functional traits in Chilean crossbred dairy cows.

IF 4.4 1区 农林科学 Q1 AGRICULTURE, DAIRY & ANIMAL SCIENCE
Álvaro G Morales, Sabrina T Amorim, Carlos Lizana, Rubén G Pulido, Mark D Hanigan, Rebecca R Cockrum, Gota Morota
{"title":"Incorporating heterosis effects enhances genetic evaluations for milk production and functional traits in Chilean crossbred dairy cows.","authors":"Álvaro G Morales, Sabrina T Amorim, Carlos Lizana, Rubén G Pulido, Mark D Hanigan, Rebecca R Cockrum, Gota Morota","doi":"10.3168/jds.2025-26445","DOIUrl":null,"url":null,"abstract":"<p><p>To accurately predict breeding values, genetic evaluations must define an appropriate model that considers the main factors influencing the traits of interest. The challenge increases in crossbreeding populations, such as those found in Chile, where multiple breeds, including those from different countries, are used. In particular, the country of origin is not currently incorporated into genetic models used in Chile. The objectives of this study were 2-fold: (1) to evaluate different genetic models of increasing complexity to determine if the inclusion of a specific strain classification, strain proportions in crossbred animals, and heterosis effects could enhance genetic evaluations compared with the current model used in Chile; and (2) to determine the presence of heterosis effects among specific dairy genetic strains when crossed with Chilean Friesian cows, focusing on annual milk, fat, and protein production; SCS; and calving interval. A dataset of 1,429,132 records from 586,624 cows that calved between 1998 and 2018 was provided by the Chilean Agricultural and Services Cooperative (COOPRINSEM; Osorno, Chile). Using pedigree information, the proportion of each cattle breed according to its different countries of origin (genetic strain) was calculated for each animal. Subsequently, strains concerning Chilean Friesian cows were selected with a wide range of proportions. The final dataset included 8 strains: Chilean Friesian, French Holstein-Friesian, US-Holstein, US-Jersey, French Montbéliarde, New Zealand Holstein-Friesian, Swedish Red and White, and British Friesian, representing 369,755 observations collected between 2009 and 2018. Production (milk, fat, and protein production per lactation) and functional traits (SCS and calving interval) were also provided by COOPRINSEM. Four different models were tested: the current model using only 5 breed categories based on physical appearance (M1), a more precise classification considering 8 dairy strains categories (M2), M2 plus the inclusion of a cross-classified effect of crossbreeding proportion with strain categories (M3), and M3 plus the cross-classified effect of heterosis level with strain categories (M4). Our results show that M4 was the best model for analyzing information on crossbred dairy cows from Chile. Our study also suggests that there are specific effects for some strains, as well as heterosis effects between several strains and the Chilean Friesian.</p>","PeriodicalId":354,"journal":{"name":"Journal of Dairy Science","volume":" ","pages":""},"PeriodicalIF":4.4000,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Dairy Science","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.3168/jds.2025-26445","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, DAIRY & ANIMAL SCIENCE","Score":null,"Total":0}
引用次数: 0

Abstract

To accurately predict breeding values, genetic evaluations must define an appropriate model that considers the main factors influencing the traits of interest. The challenge increases in crossbreeding populations, such as those found in Chile, where multiple breeds, including those from different countries, are used. In particular, the country of origin is not currently incorporated into genetic models used in Chile. The objectives of this study were 2-fold: (1) to evaluate different genetic models of increasing complexity to determine if the inclusion of a specific strain classification, strain proportions in crossbred animals, and heterosis effects could enhance genetic evaluations compared with the current model used in Chile; and (2) to determine the presence of heterosis effects among specific dairy genetic strains when crossed with Chilean Friesian cows, focusing on annual milk, fat, and protein production; SCS; and calving interval. A dataset of 1,429,132 records from 586,624 cows that calved between 1998 and 2018 was provided by the Chilean Agricultural and Services Cooperative (COOPRINSEM; Osorno, Chile). Using pedigree information, the proportion of each cattle breed according to its different countries of origin (genetic strain) was calculated for each animal. Subsequently, strains concerning Chilean Friesian cows were selected with a wide range of proportions. The final dataset included 8 strains: Chilean Friesian, French Holstein-Friesian, US-Holstein, US-Jersey, French Montbéliarde, New Zealand Holstein-Friesian, Swedish Red and White, and British Friesian, representing 369,755 observations collected between 2009 and 2018. Production (milk, fat, and protein production per lactation) and functional traits (SCS and calving interval) were also provided by COOPRINSEM. Four different models were tested: the current model using only 5 breed categories based on physical appearance (M1), a more precise classification considering 8 dairy strains categories (M2), M2 plus the inclusion of a cross-classified effect of crossbreeding proportion with strain categories (M3), and M3 plus the cross-classified effect of heterosis level with strain categories (M4). Our results show that M4 was the best model for analyzing information on crossbred dairy cows from Chile. Our study also suggests that there are specific effects for some strains, as well as heterosis effects between several strains and the Chilean Friesian.

结合杂种优势效应可以提高智利杂交奶牛产奶量和功能性状的遗传评价。
为了准确地预测育种价值,遗传评估必须定义一个适当的模型,该模型考虑了影响感兴趣性状的主要因素。杂交种群面临的挑战增加了,比如在智利发现的杂交种群,在那里使用多个品种,包括来自不同国家的品种。特别是,原产国目前没有纳入智利使用的遗传模型。本研究的目的有两个方面:(1)评估日益复杂的不同遗传模型,以确定与智利目前使用的模型相比,是否包含特定的品系分类、杂交动物的品系比例和杂种优势效应可以提高遗传评估;(2)确定特定奶牛遗传品系与智利弗里西奶牛杂交时存在的杂种优势效应,重点关注年产奶量、脂肪和蛋白质产量;SCS;和产犊间隔。智利农业和服务合作社(COOPRINSEM;智利奥索尔诺)提供了1998年至2018年间产犊的586624头奶牛的1,429,132条记录的数据集。利用家谱信息,根据不同的原产国(遗传品系)计算出每种牛品种的比例。随后,以广泛的比例选择了与智利弗里西亚奶牛有关的品系。最终的数据集包括8个品系:智利弗里西亚、法国荷尔斯泰因-弗里西亚、美国-荷尔斯泰因、美国-泽西、法国蒙巴梅里亚德、新西兰荷尔斯泰因-弗里西亚、瑞典红白和英国弗里西亚,代表了2009年至2018年收集的369755项观察结果。产量(每次泌乳的产奶量、脂肪量和蛋白质量)和功能性状(SCS和产犊间隔)也由COOPRINSEM提供。试验了四种不同的模型:当前模型仅采用基于外貌的5个品种类别(M1),更精确的考虑8个乳品系类别的分类(M2), M2加上杂交比例与品系类别的交叉分类效应(M3), M3加上杂种优势水平与品系类别的交叉分类效应(M4)。结果表明,M4是分析智利杂交奶牛信息的最佳模型。我们的研究还表明,对某些菌株有特定的影响,以及一些菌株与智利弗里伊斯之间的杂种优势效应。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Journal of Dairy Science
Journal of Dairy Science 农林科学-奶制品与动物科学
CiteScore
7.90
自引率
17.10%
发文量
784
审稿时长
4.2 months
期刊介绍: The official journal of the American Dairy Science Association®, Journal of Dairy Science® (JDS) is the leading peer-reviewed general dairy research journal in the world. JDS readers represent education, industry, and government agencies in more than 70 countries with interests in biochemistry, breeding, economics, engineering, environment, food science, genetics, microbiology, nutrition, pathology, physiology, processing, public health, quality assurance, and sanitation.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信