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简短通讯:利用单核苷酸多态性基因型和健康史预测奶牛产奶量、干物质采食量、体重和剩余采食量的未来表型

Short communication: Use of single nucleotide polymorphism genotypes and health history to predict future phenotypes for milk production, dry matter intake, body weight, and residual feed intake in dairy cattle.

作者信息

Yao C, Armentano L E, VandeHaar M J, Weigel K A

机构信息

Department of Dairy Science, University of Wisconsin, Madison 53706.

Department of Dairy Science, University of Wisconsin, Madison 53706.

出版信息

J Dairy Sci. 2015 Mar;98(3):2027-32. doi: 10.3168/jds.2014-8707. Epub 2014 Dec 18.

Abstract

As feed prices have increased, the efficiency of feed utilization in dairy cattle has attracted increasing attention. In this study, we used residual feed intake (RFI) as a measurement of feed efficiency along with its component traits, adjusted milk energy (aMilkE), adjusted dry matter intake (aDMI), and adjusted metabolic body weight (aMBW), where the adjustment was for environmental factors. These traits may also be affected by prior health problems. Therefore, the carryover effects of 3 health traits from the rearing period and 10 health traits from the lactating period (in the same lactation before phenotype measurements) on RFI, aMilkE, aDMI, and aMBW were evaluated. Cows with heavier birth weight and greater body weight at calving of this lactation had significant increases in aMilkE, aDMI, and aMBW. The only trait associated with RFI was the incidence of diarrhea early in the lactation. Mastitis and reproductive problems had negative carryover effects on aMilkE. The aMBW of cows with metabolic disorders early in the lactation was lower than that of unaffected cows. The incidence of respiratory disease during lactating period was associated with greater aMBW and higher aDMI. To examine the contribution of health traits to the accuracy of predicted phenotype, genomic predictions were computed with or without information regarding 13 health trait phenotypes using random forests (RF) and support vector machine algorithms. Adding health trait phenotypes increased prediction accuracies slightly, except for prediction of RFI using RF. In general, the accuracies were greater for support vector machine than RF, especially for RFI. The methods described herein can be used to predict future phenotypes for dairy replacement heifers, thereby facilitating culling decisions that can lead to decreased feed costs during the rearing period. For these decisions, prediction of the animal's own phenotype is of greater importance than prediction of the genetic superiority or inferiority that will transmit to its offspring.

摘要

随着饲料价格上涨,奶牛的饲料利用效率越来越受到关注。在本研究中,我们使用剩余采食量(RFI)作为饲料效率的衡量指标,以及其组成性状,即校正乳能量(aMilkE)、校正干物质采食量(aDMI)和校正代谢体重(aMBW),其中校正是针对环境因素。这些性状也可能受到既往健康问题的影响。因此,评估了育成期的3个健康性状和泌乳期(在表型测量前的同一泌乳期)的10个健康性状对RFI、aMilkE、aDMI和aMBW的残留效应。本泌乳期出生体重较重且产犊时体重较大的奶牛,其aMilkE、aDMI和aMBW显著增加。与RFI相关的唯一性状是泌乳早期腹泻的发生率。乳腺炎和繁殖问题对aMilkE有负面残留效应。泌乳早期患有代谢紊乱的奶牛的aMBW低于未受影响的奶牛。泌乳期呼吸系统疾病的发生率与较高的aMBW和较高的aDMI相关。为了检验健康性状对预测表型准确性的贡献,使用随机森林(RF)和支持向量机算法,在有或无13种健康性状表型信息的情况下计算基因组预测。除了使用RF预测RFI外,添加健康性状表型会略微提高预测准确性。一般来说,支持向量机的准确性高于RF,尤其是对于RFI。本文所述方法可用于预测奶牛后备小母牛的未来表型,从而促进淘汰决策,这可能会降低育成期的饲料成本。对于这些决策,预测动物自身的表型比预测其后代的遗传优劣更为重要。

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