Subpopulations in clinical samples of

Objectives: WGS has become a key tool for diagnosing Methods: Based on a dataset of 35 538 clinical Results: By lowering the fraction of reads required to identify a resistance-associated variant in a sample from 0.90 to 0.05, the sensitivity increased significantly from 94.3% to 96.4% without a significant impact on specificity. Allowing compensatory mutations to predict resistance further lowered the false negative rate. Finally, we found that samples with resistant subpopulations were less likely to be compensated than homogeneous resistant samples. Further analysis of these samples revealed distinct clusters with differing amounts of within-sample diversity, pointing towards different mechanisms of resistance acquisition, such as within-host evolution and secondary infections. Conclusions: Our results indicate that a substantial fraction of false negative calls in WGS-based rifampicin resistance prediction can be explained by masked resistant subpopulations. The genetic diversity within the heterogeneous samples is consistent with at least 28% of the rifampicin resistance arising from secondary infections.