Modifying the Rhizosphere of Agricultural Crops to Improve Yield and Sustainability: Azospirillum as a Model Rhizotroph

Pereyra, María Alejandra; Creus, Cecilia M.

doi:10.1007/978-981-10-4862-3_2

Cited by 4 publications

(2 citation statements)

References 109 publications

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“…Various studies have shown the power of microbiome data to predict methane emission and feed efficiency in cattle [ 4 , 13 – 15 ], feed efficiency and carcass traits in pigs [ 16 , 17 ] and in poultry [ 18 ]. In addition, microbiota data from the rhizosphere has been used to predict various plant phenotypes (e.g., crop yield and diseases) [ 19 ]. Simultaneously, since the groundbreaking study of Meuwissen et al [ 20 ], prediction of complex traits using genomic information has been embraced in both plant [ 21 ] and animal breeding [ 22 ], as well as in human genetics [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

Opportunities and limits of combining microbiome and genome data for complex trait prediction

et al. 2021

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Background Analysis and prediction of complex traits using microbiome data combined with host genomic information is a topic of utmost interest. However, numerous questions remain to be answered: how useful can the microbiome be for complex trait prediction? Are estimates of microbiability reliable? Can the underlying biological links between the host’s genome, microbiome, and phenome be recovered? Methods Here, we address these issues by (i) developing a novel simulation strategy that uses real microbiome and genotype data as inputs, and (ii) using variance-component approaches (Bayesian Reproducing Kernel Hilbert Space (RKHS) and Bayesian variable selection methods (Bayes C)) to quantify the proportion of phenotypic variance explained by the genome and the microbiome. The proposed simulation approach can mimic genetic links between the microbiome and genotype data by a permutation procedure that retains the distributional properties of the data. Results Using real genotype and rumen microbiota abundances from dairy cattle, simulation results suggest that microbiome data can significantly improve the accuracy of phenotype predictions, regardless of whether some microbiota abundances are under direct genetic control by the host or not. This improvement depends logically on the microbiome being stable over time. Overall, random-effects linear methods appear robust for variance components estimation, in spite of the typically highly leptokurtic distribution of microbiota abundances. The predictive performance of Bayes C was higher but more sensitive to the number of causative effects than RKHS. Accuracy with Bayes C depended, in part, on the number of microorganisms’ taxa that influence the phenotype. Conclusions While we conclude that, overall, genome-microbiome-links can be characterized using variance component estimates, we are less optimistic about the possibility of identifying the causative host genetic effects that affect microbiota abundances, which would require much larger sample sizes than are typically available for genome-microbiome-phenome studies. The R code to replicate the analyses is in https://github.com/miguelperezenciso/simubiome.

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Section: Introductionmentioning

confidence: 99%

Opportunities and limits of combining microbiome and genome data for complex trait prediction

et al. 2021

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“…Likewise, Lloyd-Price et al showed that microbiome-data was predicted if future outbursts of bowel disease [10]. In cattle, various studies have shown the predictive power of microbiome for methane emission from rumen microbiome [4,11], feed efficiency and carcass traits in pigs [12,13], and various plant phenotypes (e.g., crop yield and diseases predicted from the microbiota data from the rhizosphere, [14]). On the other hand, since the groundbreaking study of Meuwissen, Hayes and Goddard [15], the prediction of complex traits using genome information has been embraced in both plant [16] and animal breeding [17] as well as in human genetics [18].…”

Section: Introductionmentioning

confidence: 99%

Opportunities and limits of combining microbiome and genome data for complex trait prediction

Pérez‐Enciso

Zingaretti

Ramayo-Caldas

et al. 2020

Preprint

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The analysis and prediction of complex traits using microbiome data combined with host genomic information is a topic of utmost interest. However, numerous questions remain to be answered: How useful can the microbiome be for complex trait prediction? Are microbiability estimates reliable? Can the underlying biological links between the host’s genome, microbiome, and the phenome be recovered? Here, we address these issues by (i) developing a novel simulation strategy that uses real microbiome and genotype data as input, and (ii) proposing a variance-component approach which, in the spirit of mediation analyses, quantifies the proportion of phenotypic variance explained by genome and microbiome, and dissects it into direct and indirect effects. The proposed simulation approach can mimic a genetic link between the microbiome and SNP data via a permutation procedure that retains the distributional properties of the data. Results suggest that microbiome data could significantly improve phenotype prediction accuracy, irrespective of whether some abundances are under direct genetic control by the host or not. Overall, random-effects linear methods appear robust for variance components estimation, despite the highly leptokurtic distribution of microbiota abundances. Nevertheless, we observed that accuracy depends in part on the number of microorganisms’ taxa influencing the trait of interest. While we conclude that overall genome-microbiome-links can be characterized via variance components, we are less optimistic about the possibility of identifying the causative effects, i.e., individual SNPs affecting abundances; power at this level would require much larger sample sizes than the ones typically available for genome-microbiome-phenome data.Author summaryThe microbiome consists of the microorganisms that live in a particular environment, including those in our organism. There is consistent evidence that these communities play an important role in numerous traits of relevance, including disease susceptibility or feed efficiency. Moreover, it has been shown that the microbiome can be relatively stable throughout an individual’s life and that is affected by the host genome. These reasons have prompted numerous studies to determine whether and how the microbiome can be used for prediction of complex phenotypes, either using microbiome alone or in combination with host’s genome data. However, numerous questions remain to be answered such as the reliability of parameter estimates, or which is the underlying relationship between microbiome, genome, and phenotype. The few available empirical studies do not provide a clear answer to these problems. Here we address these issues by developing a novel simulation strategy and we show that, although the microbiome can significantly help in prediction, it will be difficult to retrieve the actual biological basis of interactions between the microbiome and the trait.

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Biofertilizers as a Greener Alternative for Increasing Soil Fertility and Improving Food Security Under Climate Change Condition

Sreethu S,

Chhabra,

Kaur

et al. 2023

Communications in Soil Science and Plant Analysis

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Modifying the Rhizosphere of Agricultural Crops to Improve Yield and Sustainability: Azospirillum as a Model Rhizotroph

Cited by 4 publications

References 109 publications

Opportunities and limits of combining microbiome and genome data for complex trait prediction

Opportunities and limits of combining microbiome and genome data for complex trait prediction

Opportunities and limits of combining microbiome and genome data for complex trait prediction

Biofertilizers as a Greener Alternative for Increasing Soil Fertility and Improving Food Security Under Climate Change Condition

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