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

Journal Article · · BMC Bioinformatics
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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.

Research Organization:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Univ. of Nebraska, Lincoln, NE (United States)
Sponsoring Organization:
USDOE; USDOE Office of Science (SC), Biological and Environmental Research (BER)
Grant/Contract Number:
AC02-05CH11231; SC0014395
OSTI ID:
1805307
Alternate ID(s):
OSTI ID: 1814837
OSTI ID: 1828346
Journal Information:
BMC Bioinformatics, Journal Name: BMC Bioinformatics Journal Issue: 1 Vol. 22; ISSN 1471-2105
Publisher:
Springer Science + Business MediaCopyright Statement
Country of Publication:
United Kingdom
Language:
English

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59 BASIC BIOLOGICAL SCIENCES
Causal inference
High-dimensional feature selection
Microbiome analysis
Next-generation sequencing
Standardization