Influenza viruses exhibit considerable diversity between hosts. Additionally, different quasispecies can be found within the same host. High-throughput sequencing technologies can be used to sequence a patient-derived virus population at sufficient depths to identify low-frequency variants (LFV) present in a quasispecies, but many challenges remain for reliable LFV detection because of experimental errors introduced during sample preparation and sequencing. High genomic copy numbers and extensive sequencing depths are required to differentiate false positive from real LFV, especially at low allelic frequencies (AFs). This study proposes a general approach for identifying LFV in patient-derived samples obtained during routine surveillance. Firstly, validated thresholds were determined for LFV detection, whilst balancing both the cost and feasibility of reliable LFV detection in clinical samples. Using a genetically well-defined population of influenza A viruses, thresholds of at least 104 genomes per microlitre and AF of ≥5 % were established as detection limits. Secondly, a subset of 59 retained influenza A (H3N2) samples from the 2016-2017 Belgian influenza season was composed. Thirdly, as a proof of concept for the added value of LFV for routine influenza monitoring, potential associations between patient data and whole genome sequencing data were investigated. A significant association was found between a high prevalence of LFV and disease severity. This study provides a general methodology for influenza LFV detection, which can also be adopted by other national influenza reference centres and for other viruses such as SARS-CoV-2. Additionally, this study suggests that the current relevance of LFV for routine influenza surveillance programmes might be undervalued.
Keywords: Influenza; low-frequency variants; next-generation sequencing; patient data; surveillance.