Emily Goren1, Chong Wang1,2, Zhulin He1, Amy M Sheflin3, Dawn Chiniquy4, Jessica E Prenni3, Susannah Tringe4, Daniel P Schachtman5, Peng Liu6. 1. Department of Statistics, Iowa State University, 2438 Osborn Dr, Ames, IA, 50011, USA. 2. Department of Veterinary Diagnostic and Production Animal Medicine, Iowa State University, 2203 Lloyd Veterinary Medical Center, Ames, IA, 50011, USA. 3. Department of Horticulture and Landscape Architecture, Colorado State University, 301 University Ave, Fort Collins, CO, 80523, USA. 4. Department of Energy, Joint Genome Institute, 2800 Mitchell Dr, Walnut Creek, CA, 94598, USA. 5. Department of Agronomy and Horticulture, University of Nebraska, 1825 N 38th St, Lincoln, NE, 68583, USA. 6. Department of Statistics, Iowa State University, 2438 Osborn Dr, Ames, IA, 50011, USA. pliu@iastate.edu.
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.
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.
Authors: Rob Knight; Alison Vrbanac; Bryn C Taylor; Alexander Aksenov; Chris Callewaert; Justine Debelius; Antonio Gonzalez; Tomasz Kosciolek; Laura-Isobel McCall; Daniel McDonald; Alexey V Melnik; James T Morton; Jose Navas; Robert A Quinn; Jon G Sanders; Austin D Swafford; Luke R Thompson; Anupriya Tripathi; Zhenjiang Z Xu; Jesse R Zaneveld; Qiyun Zhu; J Gregory Caporaso; Pieter C Dorrestein Journal: Nat Rev Microbiol Date: 2018-07 Impact factor: 60.633