The potential of microbiota information to better predict efficiency traits in growing pigs fed a conventional and a high-fiber diet

IF 3.6 1区 农林科学 Q1 AGRICULTURE, DAIRY & ANIMAL SCIENCE Genetics Selection Evolution Pub Date : 2024-01-19 DOI:10.1186/s12711-023-00865-4
Vanille Déru, Francesco Tiezzi, Céline Carillier-Jacquin, Benoit Blanchet, Laurent Cauquil, Olivier Zemb, Alban Bouquet, Christian Maltecca, Hélène Gilbert
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Abstract

Improving pigs’ ability to digest diets with an increased dietary fiber content is a lever to improve feed efficiency and limit feed costs in pig production. The aim of this study was to determine whether information on the gut microbiota and host genetics can contribute to predict digestive efficiency (DE, i.e. digestibility coefficients of energy, organic matter, and nitrogen), feed efficiency (FE, i.e. feed conversion ratio and residual feed intake), average daily gain, and daily feed intake phenotypes. Data were available for 1082 pigs fed a conventional or high-fiber diet. Fecal samples were collected at 16 weeks, and DE was estimated using near‑infrared spectrometry. A cross-validation approach was used to predict traits within the same diet, for the opposite diet, and for a combination of both diets, by implementing three models, i.e. with only genomic (Gen), only microbiota (Micro), and both genomic and microbiota information (Micro+Gen). The predictive ability with and without sharing common sires and breeding environment was also evaluated. Prediction accuracy of the phenotypes was calculated as the correlation between model prediction and phenotype adjusted for fixed effects. Prediction accuracies of the three models were low to moderate (< 0.47) for growth and FE traits and not significantly different between models. In contrast, for DE traits, prediction accuracies of model Gen were low (< 0.30) and those of models Micro and Micro+Gen were moderate to high (> 0.52). Prediction accuracies were not affected by the stratification of diets in the reference and validation sets and were in the same order of magnitude within the same diet, for the opposite diet, and for the combination of both diets. Prediction accuracies of the three models were significantly higher when pigs in the reference and validation populations shared common sires and breeding environment than when they did not (P < 0.001). The microbiota is a relevant source of information to predict DE regardless of the diet, but not to predict growth and FE traits for which prediction accuracies were similar to those obtained with genomic information only. Further analyses on larger datasets and more diverse diets should be carried out to complement and consolidate these results.
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微生物群信息在更好地预测饲喂常规日粮和高纤维日粮的生长猪的效率特征方面的潜力
在养猪生产中,提高猪对膳食纤维含量增加的日粮的消化能力是提高饲料效率和限制饲料成本的一个杠杆。本研究旨在确定肠道微生物群和宿主遗传学方面的信息是否有助于预测消化效率(DE,即能量、有机物和氮的消化系数)、饲料效率(FE,即饲料转化率和剩余饲料摄入量)、平均日增重和日采食量表型。1082 头饲喂常规或高纤维日粮的猪的数据可用。在 16 周时收集粪便样本,并使用近红外光谱法估算 DE。采用交叉验证的方法,通过实施三种模型,即只有基因组信息(Gen)、只有微生物群信息(Micro)以及基因组和微生物群信息(Micro+Gen)的模型,预测相同日粮、相反日粮以及两种日粮组合的性状。同时还评估了有无共享共同父本和育种环境的预测能力。表型的预测准确度是根据固定效应调整后的模型预测与表型之间的相关性计算得出的。三个模型的预测准确率均为中低水平(0.52)。预测准确度不受参考集和验证集中日粮分层的影响,在相同日粮、相反日粮和两种日粮组合中,预测准确度的数量级相同。当参考群和验证群中的猪具有共同的父系和繁殖环境时,三种模型的预测准确率明显高于不具有共同父系和繁殖环境时(P < 0.001)。无论日粮如何,微生物群都是预测仔猪死亡率的相关信息来源,但在预测生长和FE性状方面却不是,其预测准确率与仅用基因组信息获得的预测准确率相似。应该对更大的数据集和更多样化的日粮进行进一步分析,以补充和巩固这些结果。
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来源期刊
Genetics Selection Evolution
Genetics Selection Evolution 生物-奶制品与动物科学
CiteScore
6.50
自引率
9.80%
发文量
74
审稿时长
1 months
期刊介绍: Genetics Selection Evolution invites basic, applied and methodological content that will aid the current understanding and the utilization of genetic variability in domestic animal species. Although the focus is on domestic animal species, research on other species is invited if it contributes to the understanding of the use of genetic variability in domestic animals. Genetics Selection Evolution publishes results from all levels of study, from the gene to the quantitative trait, from the individual to the population, the breed or the species. Contributions concerning both the biological approach, from molecular genetics to quantitative genetics, as well as the mathematical approach, from population genetics to statistics, are welcome. Specific areas of interest include but are not limited to: gene and QTL identification, mapping and characterization, analysis of new phenotypes, high-throughput SNP data analysis, functional genomics, cytogenetics, genetic diversity of populations and breeds, genetic evaluation, applied and experimental selection, genomic selection, selection efficiency, and statistical methodology for the genetic analysis of phenotypes with quantitative and mixed inheritance.
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