Statistical evaluation of diet-microbe associations

Xiang Zhang,Albert K Groen,Aeiko H Zwinderman,Max Nieuwdorp

doi:10.1186/s12866-019-1464-0

Xiang Zhang, Albert K Groen + Show 2 more

Open Access

https://doi.org/10.1186/s12866-019-1464-0

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Abstract

BackgroundStatistical evaluation of the association between microbial abundance and dietary variables can be done in various ways. Currently, there is no consensus on which methods are to be preferred in which circumstances. Application of particular methods seems to be based on the tradition of a particular research group, availability of experience with particular software, or depending on the outcomes of the analysis.ResultsWe applied four popular methods including edgeR, limma, metagenomeSeq and shotgunFunctionalizeR, to evaluate the association between dietary variables and abundance of microbes. We found large difference in results between the methods. Our simulation studies revealed that no single method was optimal.ConclusionsWe advise researchers to run multiple analyses and focus on the significant findings identified by multiple methods in order to achieve a better control of false discovery rate, although the false discovery rate can still be substantial.

Highlights

Statistical evaluation of the association between microbial abundance and dietary variables can be done in various ways
Large difference in results between statistical analyses To evaluate effect of choosing different methods on outcomes in association studies, we performed association analyses between 67 dietary variables and 2073 Operational taxonomic unit (OTU) derived from 1090 HEalthy Life in an Urban Setting (HELIUS) participants with four methods
To find out which method we should choose for association studies, we developed a hierarchical model to simulate 16S ribosomal RNA (rRNA) data based on dietary variables with spiked-in associations

Summary

Introduction

Statistical evaluation of the association between microbial abundance and dietary variables can be done in various ways. There is no consensus on which methods are to be preferred in which circumstances. Application of particular methods seems to be based on the tradition of a particular research group, availability of experience with particular software, or depending on the outcomes of the analysis. Since diet shapes the composition of human microbiota and influences human health, linking abundance of microbes to dietary variables is a common practice in human microbiome studies [2, 3]. These association studies can improve our understanding of the relationships between the human microbiome and nutrient intake, and may help development of new therapeutic interventions. These zero counts can be due to either biological absence of a microbe, or insufficient sequencing. 3) microbiome data are compositional

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