Abstract
Orthogonal confirmation of next-generation sequencing (NGS)-detected germline variants is standard practice, although published studies have suggested that confirmation of the highest-quality calls may not always be necessary. The key question is how laboratories can establish criteria that consistently identify those NGS calls that require confirmation. Most prior studies addressing this question have had limitations: they have been generally of small scale, omitted statistical justification, and explored limited aspects of underlying data. The rigorous definition of criteria that separate high-accuracy NGS calls from those that may or may not be true remains a crucial issue. We analyzed five reference samples and over 80,000 patient specimens from two laboratories. Quality metrics were examined for approximately 200,000 NGS calls with orthogonal data, including 1662 false positives. A classification algorithm used these data to identify a battery of criteria that flag 100% of false positives as requiring confirmation (CI lower bound, 98.5% to 99.8%, depending on variant type) while minimizing the number of flagged true positives. These criteria identify false positives that the previously published criteria miss. Sampling analysis showed that smaller data sets resulted in less effective criteria. Our methodology for determining test- and laboratory-specific criteria can be generalized into a practical approach that can be used by laboratories to reduce the cost and time burdens of confirmation without affecting clinical accuracy.