Ultrasensitive detection of rare mutations using next-generation targeted resequencing

With next-generation DNA sequencing tech-nologies, one can interrogate a specific genomic region of interest at very high depth of coverage and identify less prevalent, rare mutations in hetero-geneous clinical samples. However, the mutation detection levels are limited by the error rate of the sequencing technology as well as by the availability of variant-calling algorithms with high statistical power and low false positive rates. We demonstrate that we can robustly detect mutations at 0.1 % fractional representation. This represents accurate detection of one mutant per every 1000 wild-type alleles. To achieve this sensitive level of mutation detection, we inte-grate a high accuracy indexing strategy and refer-ence replication for estimating sequencing error variance. We employ a statistical model to estimate the error rate at each position of the refer-ence and to quantify the fraction of variant base in the sample. Our method is highly specific (99%) and sensitive (100%) when applied to a known 0.1% sample fraction admixture of two synthetic DNA samples to validate our method. As a clinical appli-cation of this method, we analyzed nine clinical samples of H1N1 influenza A and detected an oseltamivir (antiviral therapy) resistance mutation in the H1N1 neuraminidase gene at a sample fraction of 0.18%