Identifying personal microbiomes using metagenomic codes

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 alg...

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Vydáno v:Proceedings of the National Academy of Sciences - PNAS Ročník 112; číslo 22; s. E2930
Hlavní autoři: Franzosa, Eric A, Huang, Katherine, Meadow, James F, Gevers, Dirk, Lemon, Katherine P, Bohannan, Brendan J M, Huttenhower, Curtis
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States 02.06.2015
Témata:
Confidentiality - standards
Confidentiality - trends
Genetic Markers - genetics
Genetic Variation
Humans
Metagenomics - methods
Microbiota - genetics
Models, Genetic
Precision Medicine - methods
strain variation
human microbiome
microbial ecology
metagenomics
forensic genetics
ISSN:1091-6490, 1091-6490
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Shrnutí: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, ∼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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ISSN:1091-6490
1091-6490
DOI:10.1073/pnas.1423854112