Affiliation:
1. Department of Biology, University of Waterloo, Waterloo, Ontario, Canada
Abstract
Our work characterizes the influence of cohabitation as a factor influencing the composition of the skin microbiome. Although the body site and sampled individual were stronger influences than other factors collected as metadata in this study, we show that modeling of detected microbial taxa can help with correct identifications of cohabiting partners based on skin microbiome profiles using machine learning approaches. These results show that a cohabiting partner can significantly influence our microbiota. Follow-up studies will be important for investigating the implications of shared microbiome on dermatological health and the shared contributions of cohabiting parents to the microbiome profiles of their infants.
Funder
Gouvernement du Canada | Natural Sciences and Engineering Research Council of Canada
Publisher
American Society for Microbiology
Subject
Computer Science Applications,Genetics,Molecular Biology,Modelling and Simulation,Ecology, Evolution, Behavior and Systematics,Biochemistry,Physiology,Microbiology
Cited by
96 articles.
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