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Genome-wide discovery of epistatic loci affecting antibiotic resistance in Neisseria gonorrhoeae using evolutionary couplings

Nature Microbiologyvolume 4pages328338 (2019) | Download Citation


Genome analysis should allow the discovery of interdependent loci that together cause antibiotic resistance. In practice, however, the vast number of possible epistatic interactions erodes statistical power. Here, we extend an approach that has been successfully used to identify epistatic residues in proteins to infer genomic loci that are strongly coupled. This approach reduces the number of tests required for an epistatic genome-wide association study of antibiotic resistance and increases the likelihood of identifying causal epistasis. We discovered 38 loci and 240 epistatic pairs that influence the minimum inhibitory concentrations of 5 different antibiotics in 1,102 isolates of Neisseria gonorrhoeae that were confirmed in a second dataset of 495 isolates. Many known resistance-affecting loci were recovered; however, the majority of associations occurred in unreported genes, such as murE. About half of the discovered epistasis involved at least one locus previously associated with antibiotic resistance, including interactions between gyrA and parC. Still, many combinations involved unreported loci and genes. While most variation in minimum inhibitory concentrations could be explained by identified loci, epistasis substantially increased explained phenotypic variance. Our work provides a systematic identification of epistasis affecting antibiotic resistance in N. gonorrhoeae and a generalizable approach for epistatic genome-wide association studies.

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Measured MICs and NCBI SRA identifiers for the raw sequencing data can be found in Supplementary Table 1.

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We thank members of the Marks laboratory and C. Sander for his support during this research project.

Author information

Author notes

  1. These authors contributed equally: B. Schubert, R. Maddamsetti.


  1. Department of Systems Biology, Harvard Medical School, Boston, MA, USA

    • Benjamin Schubert
    • , Rohan Maddamsetti
    • , Jackson Nyman
    •  & Debora S. Marks
  2. Department of Cell Biology, Harvard Medical School, Boston, MA, USA

    • Benjamin Schubert
  3. cBio Center, Dana-Farber Cancer Institute, Boston, MA, USA

    • Benjamin Schubert
  4. Department of Biological Sciences, Old Dominion University, Norfolk, VA, USA

    • Rohan Maddamsetti
  5. Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

    • Maha R. Farhat
  6. Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, MA, USA

    • Maha R. Farhat
  7. Broad Institute of Harvard and MIT, Cambridge, MA, USA

    • Debora S. Marks


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D.S.M. conceived the project and supervised the research. D.S.M, B.S and R.M designed and planned the research. B.S. implemented the analysis methods. B.S. and R.M. analysed the data with the help of J.N., M.R.F. and D.S.M.. B.S., R.M and D.S.M. wrote the paper. All authors reviewed and edited the manuscript.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Debora S. Marks.

Supplementary information

  1. Supplementary Information

    Supplementary Figs. 1–4.

  2. Reporting Summary

  3. Supplementary Table 1

    Minimum inhibitory concentration of penicillin (PEN), tetracycline (TET), cefixime (CFX), and ciprofloxacin (CIPRO) measured in the exploratory (n = 1,102) and confirmatory dataset (n = 495) and their NCBI SRA identifiers.

  4. Supplementary Table 2

    Significant evolutionarily coupled loci of exploratory dataset (n = 1,102).

  5. Supplementary Table 3

    Evolutionarily coupled gene–gene interactions of the exploratory dataset (n = 1,102).

  6. Supplementary Table 4

    Entire single-locus GWAS analysis of the exploratory and confirmatory dataset.

  7. Supplementary Table 5

    Confirmed and Bonferroni-corrected epistatic associations.

  8. Supplementary Table 6

    Entire epistatic GWAS analysis of the exploratory and confirmatory datasets.

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