Abstract
Measurement and characterization of the human microbiome in large population-based human studies has recently become a reality secondary to technological advances in high-throughput DNA sequencing. These advances bring new challenges and knowledge gaps for study planning, data analysis, and interpretation that are novel to large-scale epidemiologic studies. In this issue of the Journal, Sinha et al. (Am J Epidemiol. 2018;187(6):1282-1290) have provided data with which to inform statistical power and sample size requirements for microbiome studies in population-based settings. This work serves as a helpful starting point for study planning while also serving as a springboard for discussion regarding additional considerations for improving microbiome research. This commentary emphasizes the importance of selecting microbiome metrics appropriate for the biological hypothesis under investigation, as well as the need for new analytical tools that can better capitalize on the unique yet rich information contained in microbiome data sets.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1291-1294 |
| Number of pages | 4 |
| Journal | American journal of epidemiology |
| Volume | 187 |
| Issue number | 6 |
| DOIs | |
| State | Published - Jun 1 2018 |
Bibliographical note
Funding Information:Author affiliations: Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota (Ryan T. Demmer); Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York (Ryan T. Demmer). This commentary was supported by a grant from the National Institute of Diabetes and Digestive and Kidney Diseases (grant R01 DK102932) to R.T.D. Conflict of interest: none declared.
Publisher Copyright:
© The Author(s) 2018. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved.
Keywords
- microbiome
- microbiota