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BACKGROUND: Methicillin-resistant Staphylococcus aureus (MRSA) is one of the most common healthcare-associated pathogens. To examine the role of inter-hospital patient sharing on MRSA transmission, a previous study collected 2,214 samples from 30 hospitals in Orange County, California and showed by spa typing that genetic differentiation decreased significantly with increased patient sharing. In the current study, we focused on the 986 samples with spa type t008 from the same population. METHODS: We used genome sequencing to determine the effect of patient sharing on genetic differentiation between hospitals. Genetic differentiation was measured by between-hospital genetic diversity, F ST , and the proportion of nearly identical isolates between hospitals. RESULTS: Surprisingly, we found very similar genetic diversity within and between hospitals, and no significant association between patient sharing and genetic differentiation measured by F ST . However, in contrast to F ST , there was a significant association between patient sharing and the proportion of nearly identical isolates between hospitals. We propose that the proportion of nearly identical isolates is more powerful at determining transmission dynamics than traditional estimators of genetic differentiation (F ST ) when gene flow between populations is high, since it is more responsive to recent transmission events. Our hypothesis was supported by the results from coalescent simulations. CONCLUSIONS: Our results suggested that there was a high level of gene flow between hospitals facilitated by patient sharing, and that the proportion of nearly identical isolates is more sensitive to population structure than F ST when gene flow is high.

Original publication

DOI

10.1186/s13073-016-0274-3

Type

Journal article

Journal

Genome Med

Publication Date

13/02/2016

Volume

8

Keywords

California, Computer Simulation, Cross Infection, Gene Flow, Genetic Variation, Genome, Bacterial, Humans, Methicillin-Resistant Staphylococcus aureus, Phylogeny, Sequence Analysis, DNA, Staphylococcal Infections