IV. Challenges and Opportunities of WGS for Illness Detection and Response

2019 FSMA Annual Report

The implementation of WGS as the new standard subtyping method in PulseNet began in spring 2019. CDC has worked for several years to increase laboratory and epidemiological capacity for the transition from PFGE to WGS. As of April 26, 2019, 50 states including 64 labs have converted to BioNumerics 7.6, and 48 states including 55 labs are WGS Analysis Certified for Listeria, Salmonella, Escherichia, and Campylobacter. In addition, there have been numerous trainings, webinars, and meetings to prepare for the transition to WGS including

  • 50-State OutbreakNet calls
  • 50-State Laboratory calls
  • PulseNet Webinars
  • PulseNet/OutbreakNet Regional meetings
  • CSTE’s Advanced Molecular Detection (AMD) Molecular Epidemiology training
  • MiSeq/BioNumerics training
  • Integrated Food Safety Centers of Excellence WGS training (BioNumerics and WGS interpretation) webinars
  • CoE “Office hours” (live consultations for WGS facilitated by CoE staff)



The Working Group’s discussion included the following observations:

  • Concerns exist regarding the future need to triage cluster investigations as states transition to WGS due to the potential increased epidemiological workload and due to reductions in federal funding awarded to states for testing.
  • Efforts are underway to create a hierarchical nomenclature consisting of a sequence of five or six numbers (allele codes) that will indicate the relatedness of isolates. Outbreaks will still have outbreak codes, but allele codes will be the WGS equivalent of PFGE patterns. In addition, epidemiologists and laboratorians will still be able to use WGS trees to examine data, but allele codes will be used more often.
  • WGS predicts resistance of all sequenced isolates, not only those submitted to CDC for phenotypic resistance testing. WGS will help to rapidly identify known antimicrobial resistance (AR) genes, which will enable public health response in near real time and enable isolates and resistance determinants to be compared globally. In addition, AR surveillance for multistate outbreaks will become timelier and more representative, especially for single-state outbreaks. NARMS will continue to receive 1 in 20 surveillance pathogens to detect novel resistance.
  • Although Campylobacter is not routinely sequenced, it has been sequenced to help with AR patterns in the past. Campylobacter isolate sequencing will also be impacted by the increased use of CIDTs by clinical laboratories.
  • Concerns were expressed regarding BioNumerics’ global usability and upkeep. Efforts are underway to create a software that is open-source, as well as prepare for metagenomics.
  • A concerted effort by federal partners is needed to create a single/joint pipeline for states to submit human, animal, and environmental data. Data currently go through the National Center for Biotechnology Information (NCBI) and then to the PulseNet database.
  • WGS helps define “clades of concern”—groups of closely related strains that persist for years. These clades of concern have caused repeated outbreaks from similar sources, more sporadic cases over time, are often multidrug resistant, and may not appear as classic time-place-person outbreaks. Defining clades of concern can provide focus for intensive investigation, traceback, and environmental assessment and new prevention measures by industry and regulatory partners.


Based on these questions, the Working Group highlighted the following possible responses:

What do you see as CDC’s role in helping to prevent, or limit, the emergence of multidrug-resistant (MDR) strains in food animals and subsequent transmission to humans?

CDC could

  • Address ongoing and recurring clades (strains of concern)
  • Improve epidemiological tools for rapid identification of clusters with limited WGS of isolates and increase in CIDT-diagnosed patients
  • Examine resistant agents in produce, not just resistance from agents in humans and animals
  • Look at AR in sporadic illnesses, not just in outbreaks
  • Evaluate information about environmental sources of resistance to enable CDC to identify resistance genes before human illness occurs
  • Strengthen its capability to handle large, complex data sources (i.e., WGS)

How can CDC engage regulatory, animal, and agriculture partners with public health problems, where there is unclear, or lack of, regulatory oversight?

CDC could

  • Use an interdisciplinary, interagency, One Health approach as was demonstrated with enteric zoonotic issues and backyard flocks
  • Engage the USDA Animal and Plant Health Inspection Service on farm-related issues with a focus on the current gap in access
  • Explore solutions with the new DFWED Prevention Coordination Unit

What do your organizations or agencies see as opportunities for WGS in the next year?


  • Increased identification of clusters and outbreaks
  • Help regulators/policy-makers focus on priority areas


  • Triaging to address the potential increase in cluster volume
  • Increased resources (human and IT) needed
  • Effectively communicating what data mean to public/stakeholders
Page last reviewed: April 7, 2020