利用单步基因组最佳线性无偏预测提高汉宇肉牛线性体型测量性状基因组评估的准确性。

IF 2.9 Q2 Biochemistry, Genetics and Molecular Biology
Masoumeh Naserkheil, Deuk Hwan Lee, Hossein Mehrban
{"title":"利用单步基因组最佳线性无偏预测提高汉宇肉牛线性体型测量性状基因组评估的准确性。","authors":"Masoumeh Naserkheil, Deuk Hwan Lee, Hossein Mehrban","doi":"10.1186/s12863-020-00928-1","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Recently, there has been a growing interest in the genetic improvement of body measurement traits in farm animals. They are widely used as predictors of performance, longevity, and production traits, and it is worthwhile to investigate the prediction accuracies of genomic selection for these traits. In genomic prediction, the single-step genomic best linear unbiased prediction (ssGBLUP) method allows the inclusion of information from genotyped and non-genotyped relatives in the analysis. Hence, we aimed to compare the prediction accuracy obtained from a pedigree-based BLUP only on genotyped animals (PBLUP-G), a traditional pedigree-based BLUP (PBLUP), a genomic BLUP (GBLUP), and a single-step genomic BLUP (ssGBLUP) method for the following 10 body measurement traits at yearling age of Hanwoo cattle: body height (BH), body length (BL), chest depth (CD), chest girth (CG), chest width (CW), hip height (HH), hip width (HW), rump length (RL), rump width (RW), and thurl width (TW). The data set comprised 13,067 phenotypic records for body measurement traits and 1523 genotyped animals with 34,460 single-nucleotide polymorphisms. The accuracy for each trait and model was estimated only for genotyped animals using five-fold cross-validations.</p><p><strong>Results: </strong>The accuracies ranged from 0.02 to 0.19, 0.22 to 0.42, 0.21 to 0.44, and from 0.36 to 0.55 as assessed using the PBLUP-G, PBLUP, GBLUP, and ssGBLUP methods, respectively. The average predictive accuracies across traits were 0.13 for PBLUP-G, 0.34 for PBLUP, 0.33 for GBLUP, and 0.45 for ssGBLUP methods. Our results demonstrated that averaged across all traits, ssGBLUP outperformed PBLUP and GBLUP by 33 and 43%, respectively, in terms of prediction accuracy. Moreover, the least root of mean square error was obtained by ssGBLUP method.</p><p><strong>Conclusions: </strong>Our findings suggest that considering the ssGBLUP model may be a promising way to ensure acceptable accuracy of predictions for body measurement traits, especially for improving the prediction accuracy of selection candidates in ongoing Hanwoo breeding programs.</p>","PeriodicalId":9197,"journal":{"name":"BMC Genetics","volume":" ","pages":"144"},"PeriodicalIF":2.9000,"publicationDate":"2020-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7709290/pdf/","citationCount":"0","resultStr":"{\"title\":\"Improving the accuracy of genomic evaluation for linear body measurement traits using single-step genomic best linear unbiased prediction in Hanwoo beef cattle.\",\"authors\":\"Masoumeh Naserkheil, Deuk Hwan Lee, Hossein Mehrban\",\"doi\":\"10.1186/s12863-020-00928-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Recently, there has been a growing interest in the genetic improvement of body measurement traits in farm animals. They are widely used as predictors of performance, longevity, and production traits, and it is worthwhile to investigate the prediction accuracies of genomic selection for these traits. In genomic prediction, the single-step genomic best linear unbiased prediction (ssGBLUP) method allows the inclusion of information from genotyped and non-genotyped relatives in the analysis. Hence, we aimed to compare the prediction accuracy obtained from a pedigree-based BLUP only on genotyped animals (PBLUP-G), a traditional pedigree-based BLUP (PBLUP), a genomic BLUP (GBLUP), and a single-step genomic BLUP (ssGBLUP) method for the following 10 body measurement traits at yearling age of Hanwoo cattle: body height (BH), body length (BL), chest depth (CD), chest girth (CG), chest width (CW), hip height (HH), hip width (HW), rump length (RL), rump width (RW), and thurl width (TW). The data set comprised 13,067 phenotypic records for body measurement traits and 1523 genotyped animals with 34,460 single-nucleotide polymorphisms. The accuracy for each trait and model was estimated only for genotyped animals using five-fold cross-validations.</p><p><strong>Results: </strong>The accuracies ranged from 0.02 to 0.19, 0.22 to 0.42, 0.21 to 0.44, and from 0.36 to 0.55 as assessed using the PBLUP-G, PBLUP, GBLUP, and ssGBLUP methods, respectively. The average predictive accuracies across traits were 0.13 for PBLUP-G, 0.34 for PBLUP, 0.33 for GBLUP, and 0.45 for ssGBLUP methods. Our results demonstrated that averaged across all traits, ssGBLUP outperformed PBLUP and GBLUP by 33 and 43%, respectively, in terms of prediction accuracy. Moreover, the least root of mean square error was obtained by ssGBLUP method.</p><p><strong>Conclusions: </strong>Our findings suggest that considering the ssGBLUP model may be a promising way to ensure acceptable accuracy of predictions for body measurement traits, especially for improving the prediction accuracy of selection candidates in ongoing Hanwoo breeding programs.</p>\",\"PeriodicalId\":9197,\"journal\":{\"name\":\"BMC Genetics\",\"volume\":\" \",\"pages\":\"144\"},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2020-12-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7709290/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"BMC Genetics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1186/s12863-020-00928-1\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Biochemistry, Genetics and Molecular Biology\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Genetics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/s12863-020-00928-1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Biochemistry, Genetics and Molecular Biology","Score":null,"Total":0}
引用次数: 0

摘要

背景:最近,人们对农场动物体尺性状的遗传改良越来越感兴趣。它们被广泛用作性能、寿命和生产性状的预测因子,因此值得研究基因组选择对这些性状的预测准确性。在基因组预测中,单步基因组最佳线性无偏预测(ssGBLUP)方法允许在分析中纳入基因分型和非基因分型亲本的信息。因此,我们的目的是比较仅基于基因分型动物的血统最佳线性无偏预测法(PBLUP-G)、传统的血统最佳线性无偏预测法(PBLUP)、基因组最佳线性无偏预测法(GBLUP)和单步基因组最佳线性无偏预测法(ssGBLUP)对汉和牛一岁时以下 10 个体型性状的预测准确性:体高 (BH)、体长 (BL)、胸深 (CD)、胸围 (CG)、胸宽 (CW)、臀高 (HH)、臀宽 (HW)、臀长 (RL)、臀宽 (RW) 和臀宽 (TW)。数据集包括 13,067 份体型测量性状的表型记录和 1523 头基因分型动物的 34,460 个单核苷酸多态性。通过五倍交叉验证,仅对基因分型动物的每个性状和模型的准确性进行了估计:使用 PBLUP-G、PBLUP、GBLUP 和 ssGBLUP 方法评估的准确度分别为 0.02 至 0.19、0.22 至 0.42、0.21 至 0.44 和 0.36 至 0.55。各性状的平均预测准确度分别为:PBLUP-G 为 0.13,PBLUP 为 0.34,GBLUP 为 0.33,ssGBLUP 为 0.45。我们的结果表明,从所有性状的平均值来看,ssGBLUP 的预测准确率分别比 PBLUP 和 GBLUP 高 33% 和 43%。此外,ssGBLUP 方法的均方根误差最小:我们的研究结果表明,考虑使用 ssGBLUP 模型可能是确保体型特征预测准确性的一种有前途的方法,特别是在正在进行的汉和育种项目中,它可以提高候选体型特征的预测准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Improving the accuracy of genomic evaluation for linear body measurement traits using single-step genomic best linear unbiased prediction in Hanwoo beef cattle.

Improving the accuracy of genomic evaluation for linear body measurement traits using single-step genomic best linear unbiased prediction in Hanwoo beef cattle.

Background: Recently, there has been a growing interest in the genetic improvement of body measurement traits in farm animals. They are widely used as predictors of performance, longevity, and production traits, and it is worthwhile to investigate the prediction accuracies of genomic selection for these traits. In genomic prediction, the single-step genomic best linear unbiased prediction (ssGBLUP) method allows the inclusion of information from genotyped and non-genotyped relatives in the analysis. Hence, we aimed to compare the prediction accuracy obtained from a pedigree-based BLUP only on genotyped animals (PBLUP-G), a traditional pedigree-based BLUP (PBLUP), a genomic BLUP (GBLUP), and a single-step genomic BLUP (ssGBLUP) method for the following 10 body measurement traits at yearling age of Hanwoo cattle: body height (BH), body length (BL), chest depth (CD), chest girth (CG), chest width (CW), hip height (HH), hip width (HW), rump length (RL), rump width (RW), and thurl width (TW). The data set comprised 13,067 phenotypic records for body measurement traits and 1523 genotyped animals with 34,460 single-nucleotide polymorphisms. The accuracy for each trait and model was estimated only for genotyped animals using five-fold cross-validations.

Results: The accuracies ranged from 0.02 to 0.19, 0.22 to 0.42, 0.21 to 0.44, and from 0.36 to 0.55 as assessed using the PBLUP-G, PBLUP, GBLUP, and ssGBLUP methods, respectively. The average predictive accuracies across traits were 0.13 for PBLUP-G, 0.34 for PBLUP, 0.33 for GBLUP, and 0.45 for ssGBLUP methods. Our results demonstrated that averaged across all traits, ssGBLUP outperformed PBLUP and GBLUP by 33 and 43%, respectively, in terms of prediction accuracy. Moreover, the least root of mean square error was obtained by ssGBLUP method.

Conclusions: Our findings suggest that considering the ssGBLUP model may be a promising way to ensure acceptable accuracy of predictions for body measurement traits, especially for improving the prediction accuracy of selection candidates in ongoing Hanwoo breeding programs.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
BMC Genetics
BMC Genetics 生物-遗传学
CiteScore
4.30
自引率
0.00%
发文量
77
审稿时长
4-8 weeks
期刊介绍: BMC Genetics is an open access, peer-reviewed journal that considers articles on all aspects of inheritance and variation in individuals and among populations.
×
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:481959085
Book学术官方微信