2.2 How can WGS surveillance supplement phenotypic surveillance?

Phenotypic surveillance is widely used to monitor AMR but often has practical limitations, especially in low-resource settings. Time, cost and infrastructure constraints mean that routine surveillance is often limited to identifying the species, serotype or multi-locus sequence type (MLST type), and performing antimicrobial-susceptibility testing (AST). In higher-income countries, this may be supplemented with other methods, but such tools are often not feasible or scalable for routine use elsewhere.

In most routine surveillance systems, particularly in hospital and veterinary contexts, phenotypic AST continues to serve as the primary method for detecting resistance trends. The data forms the foundation of national and international AMR reporting systems, and is indispensable for tracking resistance prevalence over time.

While WGS offers powerful capabilities, it is not a replacement for phenotypic testing. WGS provides predictions of resistance based on known genes and mutations, but it cannot confirm whether those genetic determinants are expressed or functional. In this sense, WGS is best viewed as a complement to phenotypic surveillance. Phenotypic AST continues to play a critical role in validating genotypic predictions, revealing discrepancies and capturing resistance mechanisms that may not yet be well characterised in genetic databases. That said, phenotypic AST is not without its own limitations: it can miss low-level resistance and may be affected by test conditions.

As sequencing technologies advance, a genomic era is being entered where WGS is increasingly taking the lead in AMR surveillance. It offers the ability to detect a broad range of resistance genes, uncover transmission dynamics and generate detailed data from a single assay. While WGS is not yet ready to replace phenotypic testing for decisions about clinical treatment, it is already transforming how AMR is detected, understood and monitored. In the future, phenotypic testing may take on a more selective role, serving primarily as a confirmatory tool to support increasingly comprehensive genomic approaches.

Here’s how WGS adds value.

• Addressing limitations of routine testing: As described above, WGS provides more detailed information than standard tests, making it easier to spot new threats and understand how resistance spreads – saving time and enabling consistent, scalable surveillance across settings.
• Improving the accuracy of resistance detection: A concordance study has shown that WGS can be more accurate than phenotypic AST – especially where testing quality is inconsistent or performance varies (Rebelo et al., 2025).
• Detecting clinically relevant resistant genes: One of the biggest concerns in AMR surveillance is that most resistance genes affect how bacteria respond to treatment; by detecting these genes, WGS can indicate real clinical risk, making it a powerful tool for early warning systems and timely public health action.
• Explaining unusual phenotypic results: WGS can reveal mutations or resistance mechanisms that explain unexpected AST results, such as resistance without an obvious gene or vice versa.
• Enabling tracking and prediction: WGS can spot signs of antibiotic resistance before they show up in standard laboratory tests, helping track how resistance spreads between people, animals and the environment, and catching new threats early.

Having seen a summary of how WGS adds value to surveillance systems, Case Study 2 highlights some of the real-life benefits.