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Feature selection and causal analysis for microbiome studies in the presence of confounding using standardization

Emily Goren, Chong Wang, Zhulin He, Amy M Sheflin, Dawn Chiniquy, Jessica E Prenni, Susannah Tringe, Daniel P Schachtman, Peng Liu
doi: https://doi.org/10.1101/2020.08.09.243188
Emily Goren
1Department of Statistics, Iowa State University, 2438 Osborn Dr, 50011, Ames, IA, USA
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Chong Wang
1Department of Statistics, Iowa State University, 2438 Osborn Dr, 50011, Ames, IA, USA
2Department of Veterinary Diagnostic and Production Animal Medicine, Iowa State University, 2203 Lloyd Veterinary Medical Center, 50011, Ames, IA, USA
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Zhulin He
1Department of Statistics, Iowa State University, 2438 Osborn Dr, 50011, Ames, IA, USA
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Amy M Sheflin
3Department of Horticulture and Landscape Architecture, Colorado State University, 301 University Ave, 80523, Fort Collins, CO, USA
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Dawn Chiniquy
4Joint Genome Institute, Department of Energy, 2800 Mitchell Dr, 94598, Walnut Creek, CA, USA
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Jessica E Prenni
3Department of Horticulture and Landscape Architecture, Colorado State University, 301 University Ave, 80523, Fort Collins, CO, USA
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Susannah Tringe
4Joint Genome Institute, Department of Energy, 2800 Mitchell Dr, 94598, Walnut Creek, CA, USA
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Daniel P Schachtman
5Department of Agronomy and Horticulture, University of Nebraska, 1825 N 38th St, 68583, Lincoln, NE, USA
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Peng Liu
1Department of Statistics, Iowa State University, 2438 Osborn Dr, 50011, Ames, IA, USA
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  • For correspondence: pliu@iastate.edu
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Abstract

Background Microbiome studies have uncovered associations between microbes and human, animal, and plant health outcomes. This has led to an interest in developing microbial interventions for treatment of disease and optimization of crop yields which requires identification of microbiome features that impact the outcome in the population of interest. That task is challenging because of the high dimensionality of microbiome data and the confounding that results from the complex and dynamic interactions among host, environment, and microbiome. In the presence of such confounding, variable selection and estimation procedures may have unsatisfactory performance in identifying microbial features with an effect on the outcome.

Results In this manuscript, we aim to estimate population-level effects of individual microbiome features while controlling for confounding by a categorical variable. Due to the high dimensionality and confounding-induced correlation between features, we propose feature screening, selection, and estimation conditional on each stratum of the confounder followed by a standardization approach to estimation of population-level effects of individual features.

Comprehensive simulation studies demonstrate the advantages of our approach in recovering relevant features. Utilizing a potential-outcomes framework, we outline assumptions required to ascribe causal, rather than associational, interpretations to the identified microbiome effects. We conducted an agricultural study of the rhizosphere microbiome of sorghum in which nitrogen fertilizer application is a confounding variable. In this study, the proposed approach identified microbial taxa that are consistent with biological understanding of potential plant-microbe interactions.

Conclusions Standardization enables more accurate identification of individual microbiome features with an effect on the outcome of interest compared to other variable selection and estimation procedures when there is confounding by a categorical variable.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted August 10, 2020.
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Feature selection and causal analysis for microbiome studies in the presence of confounding using standardization
Emily Goren, Chong Wang, Zhulin He, Amy M Sheflin, Dawn Chiniquy, Jessica E Prenni, Susannah Tringe, Daniel P Schachtman, Peng Liu
bioRxiv 2020.08.09.243188; doi: https://doi.org/10.1101/2020.08.09.243188
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Feature selection and causal analysis for microbiome studies in the presence of confounding using standardization
Emily Goren, Chong Wang, Zhulin He, Amy M Sheflin, Dawn Chiniquy, Jessica E Prenni, Susannah Tringe, Daniel P Schachtman, Peng Liu
bioRxiv 2020.08.09.243188; doi: https://doi.org/10.1101/2020.08.09.243188

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