Microbial and Genomic Information Synergistically Contribute to Predicting Swine Performance Across Production Systems.

IF 1.9 3区 农林科学 Q2 AGRICULTURE, DAIRY & ANIMAL SCIENCE
Christian Maltecca, Enrico Mancin, Jicai Jiang, Maria Chiara Fabbri, Riccardo Bozzi, Clint Schwab, Francesco Tiezzi
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Abstract

Microbiota composition represents a promising tool in precision farming, simultaneously serving as a benchmark of environmental challenge, a predictor of animal physiological status, and a direct target for host selection. In this paper, we compared the ability of microbiota composition and genomic information to predict swine performance in two production settings, namely a purebred nucleus (NU) and a terminal cross commercial population (TE). Microbiota consistently predicted all traits in both scenarios (NU-TE: training on NU to predict TE; TE-NU: training on TE to predict NU) and at two time points: mid-test and off-test. The highest correlation (i.e., prediction accuracy) was achieved for back fat, with values of 0.08 and 0.04, and 0.30 and 0.23 for mid and off-tests, predicting from nucleus to terminal, and vice versa. Similarly, daily gains correlations were 0.05 and 0.04, and 0.18 and 0.15 for the same time points and scenario combinations. Including genomic information yielded correlations ranging from low for loin area to moderate for back fat (0.19 nucleus to terminal, 0.16 for the opposite). Microbiota had higher prediction accuracies than genomic for back fat both from nucleus to terminal and vice versa (+0.11, +0.07) and daily gain (+0.08, +0.02) at off-test. Lower accuracies were obtained for the IMF. Including genomic and microbial information produced higher accuracies than microbiota or genomic alone for back fat (0.37 and 0.29 for nucleus to terminal and opposite) and daily gain (0.19 and 0.21 for nucleus to terminal and opposite). Results for other traits differed for different scenarios. Results show that microbiota composition effectively predicted most growth and carcass traits, particularly growth and fat deposition, across production systems, prediction scenarios (NU-TE and TE-NU), and time points (mid-test and off-test). These findings highlight the potential of microbiota profiles to predict phenotypes across production systems and support their use as a tool for selecting animals in environments they have not been exposed to.

微生物和基因组信息协同有助于预测猪生产系统的生产性能。
微生物群组成是精准农业中一个很有前途的工具,同时可以作为环境挑战的基准,动物生理状态的预测指标,以及宿主选择的直接目标。在本文中,我们比较了微生物群组成和基因组信息在两种生产环境下预测猪生产性能的能力,即纯种核心(NU)和终端杂交商业群体(TE)。微生物群在两种情况下(NU-TE:在NU上训练以预测TE; TE-NU:在TE上训练以预测NU)和两个时间点(测试中期和测试结束)一致地预测了所有性状。背部脂肪获得了最高的相关性(即预测精度),其值为0.08和0.04,中期和非测试为0.30和0.23,从核到末端预测,反之亦然。同样,日收益相关性为0.05和0.04,相同时间点和情景组合的日收益相关性为0.18和0.15。包括基因组信息得出的相关性范围从腰部面积低到背部脂肪中等(0.19核到末端,0.16相反)。在非试验条件下,微生物群对背脂肪(+0.11,+0.07)和日增重(+0.08,+0.02)的预测精度均高于基因组学。IMF的准确性较低。包含基因组和微生物信息比单独使用微生物群或基因组信息对背部脂肪和日增重(分别为0.37和0.29和0.19)和日增重(分别为0.19和0.21)的准确性更高。在不同的情况下,其他特征的结果也有所不同。结果表明,在不同的生产系统、不同的预测情景(NU-TE和TE-NU)和不同的时间点(测试中和测试结束),微生物群组成可以有效地预测大部分生长和胴体性状,尤其是生长和脂肪沉积。这些发现强调了微生物群谱在预测生产系统表型方面的潜力,并支持它们作为一种工具在动物未暴露的环境中选择动物。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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.
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