Single Nucleotide Variant Detection Using Next Generation Sequencing

Single nucleotide variants (SNVs) occur when a single nucleotide (e.g., A, T, C, or G) is altered in the DNA sequence. SNVs are by far the most common type of sequence change, and there are a number of endogenous and exogenous sources of damage that lead to the single base pair substitution mutations that create SNVs. The biologic impact of SNVs in coding regions depends on their type (synonymous versus missense), and in noncoding regions depends on their impact on RNA processing or gene regulation. Nonetheless, selection pressure reduces the overall frequency of single base pair substitutions in coding DNA and in associated regulatory sequences, with the result that the overall SNV rate in coding DNA is much less than that of noncoding DNA. The utility of a clinical next generation sequencing (NGS) assay designed to detect SNVs depends on assay design features including an amplification-based versus hybrid capture-based targeted approach, DNA library complexity, depth of sequencing, tumor cellularity (in sequencing of cancer specimens), specimen fixation, and sequencing platform. From a bioinformatic perspective, many popular NGS analysis programs for SNV detection are designed for constitutional genome analysis where variants occur in either 50% (heterozygous) or 100% (homozygous) of the reads; these prior probabilities are often built-in to the algorithms, and consequently SNVs with variant allele frequencies (VAFs) falling too far outside the expected range for homozygous and heterozygous variants are often ignored as false positives. Thus, sensitive and specific bioinformatic approaches for acquired SNVs require either significant revision of the software packages designed for constitutional testing or new algorithms altogether. Some bioinformatic tools are optimized for very sensitive detection of SNVs in NGS data, but these tools require high coverage depth for acceptable performance and rely on spike-in control samples in order to calibrate run-dependent error models, features that must be accounted for in assay design. There are a number of online tools that can be used to predict the impact of an SNV and evaluate whether an SNV has a documented disease association. Guidelines for reporting SNVs detected in constitutional NGS testing have been developed; consensus guidelines for reporting somatic or acquired SNVs are under development.