The objective of the present study was to investigate how variation in the faecal microbial composition is associated with variation in average daily gain (ADG), backfat thickness (BFT), daily feed intake (DFI), feed conversion ratio (FCR), and residual feed intake (RFI), using data from two experimental pig lines that were divergent for feed efficiency. Estimates of microbiability were obtained by a Bayesian approach using animal mixed models. Microbiome-wide association analyses (MWAS) were conducted by single-operational taxonomic units (OTU) regression and by back-solving solutions of best linear unbiased prediction using a microbiome covariance matrix. In addition, accuracy of microbiome predictions of phenotypes using the microbiome covariance matrix was evaluated. Estimates of heritability ranged from 0.31 ± 0.13 for FCR to 0.51 ± 0.10 for BFT. Estimates of microbiability were lower than those of heritability for all traits and were 0.11 ± 0.09 for RFI, 0.20 ± 0.11 for FCR, 0.04 ± 0.03 for DFI, 0.03 ± 0.03 for ADG, and 0.02 ± 0.03 for BFT. Bivariate analyses showed a high microbial correlation of 0.70 ± 0.34 between RFI and FCR. The two approaches used for MWAS showed similar results. Overall, eight OTU with significant or suggestive effects on the five traits were identified. They belonged to the genera and families that are mainly involved in producing short-chain fatty acids and digestive enzymes. Prediction accuracy of phenotypes using a full model including the genetic and microbiota components ranged from 0.60 ± 0.19 to 0.78 ± 0.05. Similar accuracies of predictions of the microbial component were observed using models that did or did not include an additive animal effect, suggesting no interaction with the genetic effect. Our results showed substantial associations of the faecal microbiome with feed efficiency related traits but negligible effects with growth traits. Microbiome data incorporated as a covariance matrix can be used to predict phenotypes of animals that do not (yet) have phenotypic information. Connecting breeding environment between training sets and predicted populations could be necessary to obtain reliable microbiome predictions.

中文翻译：

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猪饲料效率和生产性能特征的微生物学和微生物组关联分析

本研究的目的是研究粪便微生物组成的变化如何与平均日增重 (ADG)、背膘厚度 (BFT)、每日采食量 (DFI)、饲料转化率 (FCR) 和残留采食量 (RFI)，使用来自饲料效率不同的两个实验猪系的数据。使用动物混合模型通过贝叶斯方法获得微生物性的估计值。微生物组范围的关联分析 (MWAS) 通过单操作分类单元 (OTU) 回归和使用微生物组协方差矩阵的最佳线性无偏预测的反解解决方案进行。此外，还评估了使用微生物组协方差矩阵预测表型的微生物组预测的准确性。FCR 的遗传力估计值范围为 0.31 ± 0.13 到 0.51 ± 0。10 为 BFT。微生物性的估计值低于所有性状的遗传力，RFI 为 0.11 ± 0.09，FCR 为 0.20 ± 0.11，DFI 为 0.04 ± 0.03，ADG 为 0.03 ± 0.03，BFT 为 0.02 ± 0.03。双变量分析显示，RFI 和 FCR 之间的微生物相关性很高，为 0.70 ± 0.34。用于 MWAS 的两种方法显示出相似的结果。总体而言，确定了对五个特征具有显着或暗示影响的八个 OTU。它们属于主要参与生产短链脂肪酸和消化酶的属和科。使用包含遗传和微生物群成分的完整模型预测表型的准确度范围为 0.60 ± 0.19 至 0.78 ± 0.05。使用包含或不包含附加动物效应的模型观察到微生物成分预测的类似准确性，表明与遗传效应没有相互作用。我们的研究结果表明，粪便微生物组与饲料效率相关性状存在显着关联，但与生长性状的影响可以忽略不计。作为协方差矩阵合并的微生物组数据可用于预测（尚未）具有表型信息的动物的表型。为了获得可靠的微生物组预测，可能需要在训练集和预测种群之间连接繁殖环境。作为协方差矩阵合并的微生物组数据可用于预测（尚未）具有表型信息的动物的表型。为了获得可靠的微生物组预测，可能需要在训练集和预测种群之间连接繁殖环境。作为协方差矩阵合并的微生物组数据可用于预测（尚未）具有表型信息的动物的表型。为了获得可靠的微生物组预测，可能需要在训练集和预测种群之间连接繁殖环境。