Modelling the lactation curve in Alpine × Beetal crossbred dairy goats using random regression models fitted with Legendre polynomial and B-spline functions

IF 1.9 3区 农林科学 Q2 AGRICULTURE, DAIRY & ANIMAL SCIENCE Journal of Animal Breeding and Genetics Pub Date : 2024-01-12 DOI:10.1111/jbg.12849
Amritanshu Upadhyay, Rani Alex, Mahesh Shivanand Dige, Shweta Sahoo, Kashif Dawood Khan, Pradyut Das, Vikas Vohra, Gopal Ramdasji Gowane
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Abstract

The current study sought to genetically assess the lactation curve of Alpine × Beetal crossbred goats through the application of random regression models (RRM). The objective was to estimate genetic parameters of the first lactation test-day milk yield (TDMY) for devising a practical breeding strategy within the nucleus breeding programme. In order to model variations in lactation curves, 25,998 TDMY records were used in this study. For the purpose of estimating genetic parameters, orthogonal Legendre polynomials (LEG) and B-splines (BS) were examined in order to generate suitable and parsimonious models. A single-trait RRM technique was used for the analysis. The average first lactation TDMY was 1.22 ± 0.03 kg and peak yield (1.35 ± 0.02 kg) was achieved around the 7th test day (TD). The present investigation has demonstrated the superiority of the B-spline model for the genetic evaluation of Alpine × Beetal dairy goats. The optimal random regression model was identified as a quadratic B-spline function, characterized by six knots to represent the central trend. This model effectively captured the patterns of additive genetic influences, animal-specific permanent environmental effects (c2) and 22 distinct classes of (heterogeneous) residual variance. Additive variances and heritability (h2) estimates were lower in the early lactation, however, moderate across most parts of the lactation studied, ranging from 0.09 ± 0.04 to 0.33 ± 0.06. The moderate heritability estimates indicate the potential for selection using favourable combinations of test days throughout the lactation period. It was also observed that a high proportion of total variance was attributed to the animal's permanent environment. Positive genetic correlations were observed for adjacent TDMY values, while the correlations became less pronounced for more distant TDMY values. Considering better fitting of the lactation curve, the use of B-spline functions for genetic evaluation of Alpine × Beetal goats using RRM is recommended.

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使用 Legendre 多项式和 B-spline 函数拟合的随机回归模型对阿尔卑斯 × Beetal 杂交奶山羊的泌乳曲线进行建模。
本研究试图通过应用随机回归模型(RRM),对阿尔卑斯×比塔尔杂交山羊的泌乳曲线进行遗传评估。目的是估算第一次泌乳测试日产奶量(TDMY)的遗传参数,以便在核心育种计划中制定切实可行的育种策略。为了模拟泌乳曲线的变化,本研究使用了 25,998 个 TDMY 记录。为了估算遗传参数,研究人员研究了正交 Legendre 多项式(LEG)和 B-样条曲线(BS),以生成合适的简约模型。分析中使用了单性状 RRM 技术。第一个泌乳期的平均TDMY为1.22 ± 0.03千克,产量峰值(1.35 ± 0.02千克)在第7个测试日(TD)左右达到。本研究表明,B-样条模型在阿尔卑斯×贝塔尔奶山羊的遗传评估中具有优越性。最佳随机回归模型被确定为二次 B-样条函数,其特点是有六个结代表中心趋势。该模型有效地捕捉了加性遗传影响、动物特异性永久环境效应(c2)和 22 个不同类别的(异质性)残差。泌乳早期的加性方差和遗传力(h2)估计值较低,但在所研究的泌乳期的大部分时间里都处于中等水平,从 0.09 ± 0.04 到 0.33 ± 0.06 不等。适度的遗传率估计值表明,在整个泌乳期使用有利的试验日数组合进行选择是有潜力的。研究还发现,动物的永久环境在总变异中所占比例较高。相邻的 TDMY 值之间存在正的遗传相关性,而距离较远的 TDMY 值之间的相关性则不太明显。考虑到更好地拟合泌乳曲线,建议使用 B-样条函数利用 RRM 对阿尔卑斯山羊×贝塔尔山羊进行遗传评估。
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来源期刊
Journal of Animal Breeding and Genetics
Journal of Animal Breeding and Genetics 农林科学-奶制品与动物科学
CiteScore
5.20
自引率
3.80%
发文量
58
审稿时长
12-24 weeks
期刊介绍: The Journal of Animal Breeding and Genetics publishes original articles by international scientists on genomic selection, and any other topic related to breeding programmes, selection, quantitative genetic, genomics, diversity and evolution of domestic animals. Researchers, teachers, and the animal breeding industry will find the reports of interest. Book reviews appear in many issues.
期刊最新文献
Issue Information Influence of variance component estimates on genomic predictions for growth and reproductive-related traits in Nellore cattle. Genomic selection strategies for the German Merino sheep breeding programme - A simulation study. Correction to: Rahbar et al., 2023. Defining desired genetic gains for Pacific white shrimp (Litopeneaus vannamei) breeding objectives using participatory approaches. Journal of Animal Breeding and Genetics. 2024;141:390-402. Combining genomics and semen microbiome increases the accuracy of predicting bull prolificacy.
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