Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 112, Issue 22, Pages E2930-E2938Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1423854112
Keywords
forensic genetics; microbial ecology; metagenomics; human microbiome; strain variation
Categories
Funding
- National Institutes of Health (NIH) National Institute of Allergy and Infectious Diseases (NIAID) [HHSN272200900018C]
- NIH National Human Genome Research Institute (NHGRI) Grant [U54HG004969]
- NIH National Institute of General Medical Sciences Grant [P50GM098911]
- NIH NIAID Grant [R01 AI101018]
- Danone Research Grant [PLF-5972-GD]
- NIH NHGRI Grant [R01HG005969]
- Army Research Office Grant [W911NF-11-1-0473]
- National Science Foundation Faculty Early Career Development Grant [DBI-1053486]
- Div Of Biological Infrastructure
- Direct For Biological Sciences [1053486] Funding Source: National Science Foundation
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Community composition within the human microbiome varies across individuals, but it remains unknown if this variation is sufficient to uniquely identify individuals within large populations or stable enough to identify them over time. We investigated this by developing a hitting set-based coding algorithm and applying it to the Human Microbiome Project population. Our approach defined body site-specific metagenomic codes: sets of microbial taxa or genes prioritized to uniquely and stably identify individuals. Codes capturing strain variation in clade-specific marker genes were able to distinguish among 100s of individuals at an initial sampling time point. In comparisons with follow-up samples collected 30-300 d later, similar to 30% of individuals could still be uniquely pinpointed using metagenomic codes from a typical body site; coincidental (false positive) matches were rare. Codes based on the gut microbiome were exceptionally stable and pinpointed >80% of individuals. The failure of a code to match its owner at a later time point was largely explained by the loss of specific microbial strains (at current limits of detection) and was only weakly associated with the length of the sampling interval. In addition to highlighting patterns of temporal variation in the ecology of the human microbiome, this work demonstrates the feasibility of microbiome-based identifiability-a result with important ethical implications for microbiome study design. The datasets and code used in this work are available for download from huttenhower.sph.harvard.edu/idability.
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