不同试验日模型对白马里察羊产奶量的遗传参数估计

IF 1.6 3区 农林科学 Q2 AGRICULTURE, DAIRY & ANIMAL SCIENCE
Petya Zhelyazkova, Doytcho Dimov, Sreten Andonov
{"title":"不同试验日模型对白马里察羊产奶量的遗传参数估计","authors":"Petya Zhelyazkova, Doytcho Dimov, Sreten Andonov","doi":"10.5194/aab-66-253-2023","DOIUrl":null,"url":null,"abstract":"Abstract. The aims of this study were to estimate the genetic parameters of the test day milk yield (TDMY) of the White Maritza sheep breed population and to choose the most appropriate linear models for genetic-parameter estimation of test day milk yield. The White Maritza sheep breed is a multipurpose native sheep breed in Bulgaria. Test day milk yield data were collected from 1992 to 2015 (24 years). Milk yield recordings were made in 18 flocks according to the AC method (official milk recording by ICAR regulations). The database includes 8768 test day milk yield records belonging to 987 ewes. The pedigree file includes 1937 animals. Nine test day models (TDMs) were formulated and tested for the estimation of the genetic parameters of milk yield. The first three models were repeatability models (REP models), the second three were random regression models (RRMs), and the last three models were also random regression models with an added Ali and Schaeffer regression to describe the lactation curve using first-, second- and third-order polynomials. The average TDMY was 764.47 mL. There were no significant differences in the values of heritability (h2) calculated by the three REP models: REP1 0.355 ± 0.060, REP2 0.344 ± 0.047 and REP3 0.347 ± 0.060. The same applied to the repeatability coefficients, which, for the three REP models, were 0.384 ± 0.065, 0.376 ± 0.051 and 0.378 ± 0.065, respectively. Based on REP model 1, three models with random regression RRM1, RRM2 and RRM3 were constructed, which is associated with the use of first-, second- and third-order polynomials (for the random effects of both the animal and the permanent environment). The trajectories of h2 calculated by the three RRMs were not similar and demonstrated some differences, both at the beginning and in the middle of the milking period. The RRM with third-order polynomials demonstrated more genetic diversity until the 165th day of lactation, but Akaike information criterion (AIC), Bayesian information criterion (BIC) and log-likelihood (LogL) estimates were higher. The regression models with first- and second-degree polynomials were insufficient to reveal genetic diversity to a higher degree than REP model 1. The trend in the trajectories of h2 calculated by the three random regression models with Ali and Schaeffer regression models (ASRMs) was similar to that of random regression models without the Ali and Schaeffer regression incorporated. Although the noted advantages of the random regression models revealed, to a greater extent, the genetic diversity of test day milk yield, AIC, BIC and LogL estimates indicated that repeatability models achieved a better balance between complexity and fitness and a smaller prediction error compared to random regression models.","PeriodicalId":55481,"journal":{"name":"Archiv Fur Tierzucht-Archives of Animal Breeding","volume":"40 1","pages":"0"},"PeriodicalIF":1.6000,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Genetic-parameter estimation of milk yield in White Maritza sheep breed using different test day models\",\"authors\":\"Petya Zhelyazkova, Doytcho Dimov, Sreten Andonov\",\"doi\":\"10.5194/aab-66-253-2023\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract. The aims of this study were to estimate the genetic parameters of the test day milk yield (TDMY) of the White Maritza sheep breed population and to choose the most appropriate linear models for genetic-parameter estimation of test day milk yield. The White Maritza sheep breed is a multipurpose native sheep breed in Bulgaria. Test day milk yield data were collected from 1992 to 2015 (24 years). Milk yield recordings were made in 18 flocks according to the AC method (official milk recording by ICAR regulations). The database includes 8768 test day milk yield records belonging to 987 ewes. The pedigree file includes 1937 animals. Nine test day models (TDMs) were formulated and tested for the estimation of the genetic parameters of milk yield. The first three models were repeatability models (REP models), the second three were random regression models (RRMs), and the last three models were also random regression models with an added Ali and Schaeffer regression to describe the lactation curve using first-, second- and third-order polynomials. The average TDMY was 764.47 mL. There were no significant differences in the values of heritability (h2) calculated by the three REP models: REP1 0.355 ± 0.060, REP2 0.344 ± 0.047 and REP3 0.347 ± 0.060. The same applied to the repeatability coefficients, which, for the three REP models, were 0.384 ± 0.065, 0.376 ± 0.051 and 0.378 ± 0.065, respectively. Based on REP model 1, three models with random regression RRM1, RRM2 and RRM3 were constructed, which is associated with the use of first-, second- and third-order polynomials (for the random effects of both the animal and the permanent environment). The trajectories of h2 calculated by the three RRMs were not similar and demonstrated some differences, both at the beginning and in the middle of the milking period. The RRM with third-order polynomials demonstrated more genetic diversity until the 165th day of lactation, but Akaike information criterion (AIC), Bayesian information criterion (BIC) and log-likelihood (LogL) estimates were higher. The regression models with first- and second-degree polynomials were insufficient to reveal genetic diversity to a higher degree than REP model 1. The trend in the trajectories of h2 calculated by the three random regression models with Ali and Schaeffer regression models (ASRMs) was similar to that of random regression models without the Ali and Schaeffer regression incorporated. Although the noted advantages of the random regression models revealed, to a greater extent, the genetic diversity of test day milk yield, AIC, BIC and LogL estimates indicated that repeatability models achieved a better balance between complexity and fitness and a smaller prediction error compared to random regression models.\",\"PeriodicalId\":55481,\"journal\":{\"name\":\"Archiv Fur Tierzucht-Archives of Animal Breeding\",\"volume\":\"40 1\",\"pages\":\"0\"},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2023-09-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Archiv Fur Tierzucht-Archives of Animal Breeding\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5194/aab-66-253-2023\",\"RegionNum\":3,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"AGRICULTURE, DAIRY & ANIMAL SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Archiv Fur Tierzucht-Archives of Animal Breeding","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5194/aab-66-253-2023","RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AGRICULTURE, DAIRY & ANIMAL SCIENCE","Score":null,"Total":0}
引用次数: 1

摘要

摘要本研究的目的是估计白马里察羊品种群体试验日产奶量的遗传参数,并选择最合适的线性模型进行试验日产奶量的遗传参数估计。白马里察羊品种是保加利亚多用途的本地羊品种。试验日产奶量数据采集于1992 - 2015年(24年)。采用AC法(ICAR规定的正式产奶量记录)对18个鸡群进行产奶量记录。该数据库包括987只母羊的8768条试验日产奶量记录。家谱文件包括1937只动物。建立了9个试验日模型(tdm),并进行了试验,以估计产奶量的遗传参数。前3个模型为重复性模型(REP),后3个模型为随机回归模型(RRMs),后3个模型为随机回归模型,加入Ali和Schaeffer回归,采用一阶、二阶和三阶多项式描述泌乳曲线。平均TDMY为764.47 mL。REP1 0.355±0.060、REP2 0.344±0.047、REP3 0.347±0.060 3种REP模型计算的遗传力(h2)值差异无统计学意义。3种REP模型的重复性系数分别为0.384±0.065、0.376±0.051和0.378±0.065。在REP模型1的基础上,构建了随机回归模型RRM1、RRM2和RRM3,分别使用一阶、二阶和三阶多项式(动物和永久环境的随机效应)。3种RRMs计算的h2轨迹并不相似,在挤奶初期和中期都表现出一定的差异。在哺乳第165天之前,具有三阶多项式的RRM表现出较高的遗传多样性,但赤池信息准则(AIC)、贝叶斯信息准则(BIC)和对数似然(LogL)估计较高。与REP模型1相比,一阶和二阶多项式的回归模型不足以更好地揭示遗传多样性。采用Ali和Schaeffer回归模型(asrm)的3种随机回归模型计算的h2轨迹趋势与不采用Ali和Schaeffer回归的随机回归模型计算的h2轨迹趋势相似。尽管随机回归模型的显著优势在更大程度上揭示了试验日产奶量、AIC、BIC和LogL估计的遗传多样性,表明重复性模型比随机回归模型在复杂度和适应度之间取得了更好的平衡,预测误差更小。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Genetic-parameter estimation of milk yield in White Maritza sheep breed using different test day models
Abstract. The aims of this study were to estimate the genetic parameters of the test day milk yield (TDMY) of the White Maritza sheep breed population and to choose the most appropriate linear models for genetic-parameter estimation of test day milk yield. The White Maritza sheep breed is a multipurpose native sheep breed in Bulgaria. Test day milk yield data were collected from 1992 to 2015 (24 years). Milk yield recordings were made in 18 flocks according to the AC method (official milk recording by ICAR regulations). The database includes 8768 test day milk yield records belonging to 987 ewes. The pedigree file includes 1937 animals. Nine test day models (TDMs) were formulated and tested for the estimation of the genetic parameters of milk yield. The first three models were repeatability models (REP models), the second three were random regression models (RRMs), and the last three models were also random regression models with an added Ali and Schaeffer regression to describe the lactation curve using first-, second- and third-order polynomials. The average TDMY was 764.47 mL. There were no significant differences in the values of heritability (h2) calculated by the three REP models: REP1 0.355 ± 0.060, REP2 0.344 ± 0.047 and REP3 0.347 ± 0.060. The same applied to the repeatability coefficients, which, for the three REP models, were 0.384 ± 0.065, 0.376 ± 0.051 and 0.378 ± 0.065, respectively. Based on REP model 1, three models with random regression RRM1, RRM2 and RRM3 were constructed, which is associated with the use of first-, second- and third-order polynomials (for the random effects of both the animal and the permanent environment). The trajectories of h2 calculated by the three RRMs were not similar and demonstrated some differences, both at the beginning and in the middle of the milking period. The RRM with third-order polynomials demonstrated more genetic diversity until the 165th day of lactation, but Akaike information criterion (AIC), Bayesian information criterion (BIC) and log-likelihood (LogL) estimates were higher. The regression models with first- and second-degree polynomials were insufficient to reveal genetic diversity to a higher degree than REP model 1. The trend in the trajectories of h2 calculated by the three random regression models with Ali and Schaeffer regression models (ASRMs) was similar to that of random regression models without the Ali and Schaeffer regression incorporated. Although the noted advantages of the random regression models revealed, to a greater extent, the genetic diversity of test day milk yield, AIC, BIC and LogL estimates indicated that repeatability models achieved a better balance between complexity and fitness and a smaller prediction error compared to random regression models.
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
Archiv Fur Tierzucht-Archives of Animal Breeding
Archiv Fur Tierzucht-Archives of Animal Breeding 农林科学-奶制品与动物科学
CiteScore
3.20
自引率
0.00%
发文量
41
审稿时长
18-36 weeks
期刊介绍: Archives Animal Breeding is an open-access journal publishing original research papers, short communications, brief reports, and reviews by international researchers on scientific progress in farm-animal biology. The journal includes publications in quantitative and molecular genetics, genetic diversity, animal husbandry and welfare, physiology, and reproduction of livestock. It addresses researchers, teachers, stakeholders of academic and educational institutions, as well as industrial and governmental organizations in the field of animal production.
×
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