TY - JOUR TI - Targeted surveillance strategies for efficient detection of novel antibiotic resistance variants AU - Hicks, Allison L AU - Kissler, Stephen M AU - Mortimer, Tatum D AU - Ma, Kevin C AU - Taiaroa, George AU - Ashcroft, Melinda AU - Williamson, Deborah A AU - Lipsitch, Marc AU - Grad, Yonatan H A2 - Franco, Eduardo A2 - Davenport, Miles P A2 - Feldgarden, Michael A2 - Nicholas, Medland VL - 9 PY - 2020 DA - 2020/06/30 SP - e56367 C1 - eLife 2020;9:e56367 DO - 10.7554/eLife.56367 UR - https://doi.org/10.7554/eLife.56367 AB - Genotype-based diagnostics for antibiotic resistance represent a promising alternative to empiric therapy, reducing inappropriate antibiotic use. However, because such assays infer resistance based on known genetic markers, their utility will wane with the emergence of novel resistance. Maintenance of these diagnostics will therefore require surveillance to ensure early detection of novel resistance variants, but efficient strategies to do so remain undefined. We evaluate the efficiency of targeted sampling approaches informed by patient and pathogen characteristics in detecting antibiotic resistance and diagnostic escape variants in Neisseria gonorrhoeae, a pathogen associated with a high burden of disease and antibiotic resistance and the development of genotype-based diagnostics. We show that patient characteristic-informed sampling is not a reliable strategy for efficient variant detection. In contrast, sampling informed by pathogen characteristics, such as genomic diversity and genomic background, is significantly more efficient than random sampling in identifying genetic variants associated with resistance and diagnostic escape. KW - antibiotic resistance KW - diagnostic KW - surveillance KW - Neisseria gonorrhoeae JF - eLife SN - 2050-084X PB - eLife Sciences Publications, Ltd ER -