Holobiont effect accounts for more methane emission variance than the additive and microbiome effects on dairy cattle

Saborío-Montero, Alejandro; Gutiérrez-Rivas, Mónica; López-García, Adrián; García-Rodriguez, A.; Atxaerandio, Raquel; Goiri, Idoia; Jiménez-Montero, J. A.; González-Recio, Óscar

doi:10.1016/j.livsci.2021.104538

Cited by 17 publications

(8 citation statements)

References 31 publications

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“…In contrast, Tang et al [ 6 ] obtained a lower estimate of than that of for body weight (BW), average daily gain (ADG), backfat thickness (BFT), and intramuscular fatness, using samples from five different parts of the GIT. Overall, these studies highlight the relevance of variability in the GIT microbiome composition associated with variability in performance traits, which suggests the possibility of predicting future phenotypes based on predicted microbial values ( ) [ 7 ]. However, in livestock, only a few studies have evaluated the accuracy of phenotype predictions by including microbiota effects in linear mixed models [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

“…Overall, these studies highlight the relevance of variability in the GIT microbiome composition associated with variability in performance traits, which suggests the possibility of predicting future phenotypes based on predicted microbial values ( ) [ 7 ]. However, in livestock, only a few studies have evaluated the accuracy of phenotype predictions by including microbiota effects in linear mixed models [ 7 , 8 ]. In addition, similar to genome-wide association studies, microbiome components can be considered as potential markers of the selected complex traits, and their associations can be identified through microbiome-wide association studies (MWAS) [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

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Microbiability and microbiome-wide association analyses of feed efficiency and performance traits in pigs

et al. 2022

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Background 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. Results 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. Conclusions 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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Microbiability and microbiome-wide association analyses of feed efficiency and performance traits in pigs

et al. 2022

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“…This clearly shows that fitting both random effects simultaneous is beneficial but that assuming a zero covariance between the two random effects is too simplistic. How to model both effects simultaneously and how to interpret the results from such models biologically is an ongoing research topic [69][70][71] but this is outside the scope of our study.…”

Section: Discussionmentioning

confidence: 99%

Composition of the ileum microbiota is a mediator between the host genome and phosphorus utilization and other efficiency traits in Japanese quail (Coturnix japonica)

et al. 2022

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Background Phosphorus is an essential nutrient in all living organisms and, currently, it is the focus of much attention due to its global scarcity, the environmental impact of phosphorus from excreta, and its low digestibility due to its storage in the form of phytates in plants. In poultry, phosphorus utilization is influenced by composition of the ileum microbiota and host genetics. In our study, we analyzed the impact of host genetics on composition of the ileum microbiota and the relationship of the relative abundance of ileal bacterial genera with phosphorus utilization and related quantitative traits in Japanese quail. An F2 cross of 758 quails was genotyped with 4k genome-wide single nucleotide polymorphisms (SNPs) and composition of the ileum microbiota was characterized using target amplicon sequencing. Heritabilities of the relative abundance of bacterial genera were estimated and quantitative trait locus (QTL) linkage mapping for the host was conducted for the heritable genera. Phenotypic and genetic correlations and recursive relationships between bacterial genera and quantitative traits were estimated using structural equation models. A genomic best linear unbiased prediction (GBLUP) and microbial (M)BLUP hologenomic selection approach was applied to assess the feasibility of breeding for improved phosphorus utilization based on the host genome and the heritable part of composition of the ileum microbiota. Results Among the 59 bacterial genera examined, 24 showed a significant heritability (nominal p ≤ 0.05), ranging from 0.04 to 0.17. For these genera, six genome-wide significant QTL were mapped. Significant recursive effects were found, which support the indirect host genetic effects on the host’s quantitative traits via microbiota composition in the ileum of quail. Cross-validated microbial and genomic prediction accuracies confirmed the strong impact of microbial composition and host genetics on the host’s quantitative traits, as the GBLUP accuracies based on the heritable microbiota-mediated components of the traits were similar to the accuracies of conventional GBLUP based on genome-wide SNPs. Conclusions Our results revealed a significant effect of host genetics on composition of the ileal microbiota and confirmed that host genetics and composition of the ileum microbiota have an impact on the host’s quantitative traits. This offers the possibility to breed for improved phosphorus utilization based on the host genome and the heritable part of composition of the ileum microbiota.

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“…While not used for metagenomic predictions, Difford et al (2018) found that the variance explained by both the genomic and metagenomic data for dairy cows was greater than when either one was examined alone, as did Zhang et al (2020) on the same dataset using a Bayesian method. Saborío-Montero et al (2021) concluded that not only are both the genome and metagenome important for explaining the phenotypic variation, but that the interaction between genome and metagenome is also important. Recently, Ross et al (2020) combined metagenomic and genomic predictions for studying enteric methane production in sheep.…”

Section: Combining Metagenomic and Other Prediction Systemsmentioning

confidence: 99%

“…They revealed two things: that there is a substantial difference in the microbiability of the same dataset based on whether the data was derived from 16S or reduced representation sequencing; and also that the restriction enzyme chosen for reduced representation sequencing had a large impact on the microbiability. Saborío-Montero et al (2021) found that the method used to calculate the microbiome relationships also affects the microbiability. This would suggest that the microbiability is strongly reflective of the method used and thus any comparison of microbiability between studies should be done with extreme caution.…”

Section: Measuring Accuracy and Microbiabilitymentioning

confidence: 99%

Metagenomic Predictions: A Review 10 years on

Ross

Hayes

2022

Front. Genet.

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Metagenomic predictions use variation in the metagenome (microbiome profile) to predict the unknown phenotype of the associated host. Metagenomic predictions were first developed 10 years ago, where they were used to predict which cattle would produce high or low levels of enteric methane. Since then, the approach has been applied to several traits and species including residual feed intake in cattle, and carcass traits, body mass index and disease state in pigs. Additionally, the method has been extended to include predictions based on other multi-dimensional data such as the metabolome, as well to combine genomic and metagenomic information. While there is still substantial optimisation required, the use of metagenomic predictions is expanding as DNA sequencing costs continue to fall and shows great promise particularly for traits heavily influenced by the microbiome such as feed efficiency and methane emissions.

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Holobiont effect accounts for more methane emission variance than the additive and microbiome effects on dairy cattle

Cited by 17 publications

References 31 publications

Microbiability and microbiome-wide association analyses of feed efficiency and performance traits in pigs

Microbiability and microbiome-wide association analyses of feed efficiency and performance traits in pigs

Composition of the ileum microbiota is a mediator between the host genome and phosphorus utilization and other efficiency traits in Japanese quail (Coturnix japonica)

Metagenomic Predictions: A Review 10 years on

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