Determination of high-confidence germline genetic variants in next-generation sequencing through machine learning models: an approach to reduce the burden of orthogonal confirmation - Muqing Yan; Qiandong Zeng; Zhenxi Zhang; Patricia Okamoto; Stanley Letovsky; Angela Kenyon; Natalia Leach; Jennifer Reiner (BMC Genomics)
Abstract Background Orthogonal confirmation of variants identified by next-generation sequencing (NGS) is routinely performed in many clinical laboratories to improve assay specificity. However, confirmatory testing of all clinically significant variants increases both turnaround time and operating costs for laboratories. Improvements to early NGS methods and bioinformatics algorithms have dramatically improved variant calling accuracy, particularly for single nucleotide variants (SNVs), thus calling into question the necessity of confirmatory testing for all variant types. The purpose of this study is to develop a new machine learning approach to capture false positive heterozygous variants (SNVs) from whole exome sequencing (WES) data. Results WES variant calls from Genome in a Bottle (GIAB) cell lines and their associated quality features were used to train five different machine learning models to predict whether a variant was a true positive or false positive based on quality metrics. Logistic regression and random forest models exhibited the highest false positive capture rates among the selected models, but GradientBoosting achieved the best balance between false positive capture rates and true positive flag rates. Further assessment using simulated false positive events as well as different combinations of quality features showed that model performance can be refined. Integration of the highest-performing models into a custom two-tiered confirmation bypass pipeline with additional guardrail metrics achieved 99.9% precision and 98% specificity in the identification of true positive heterozygous SNVs within the GIAB benchmark regions. Furthermore, testing on an independent set of heterozygous SNVs (n = 93) detected by exome sequencing of patient samples and cell lines demonstrated 100% accuracy. Conclusions Machine-learning models can be trained to classify SNVs into high or low-confidence categories with high precision, thus reducing the level of confirmatory testing required. Laboratories interested in deploying such models should consider incorporating additional quality criteria and thresholds to serve as guardrails in the assessment process.