Pivotal Role of High Sensitivity Variant Calls and Confirmation Methods for Next-Generation Sequencing Findings: A Case Report

Aims: The aim of this case report is to demonstrate the importance of prioritization of sensitivity over specificity, coupled with additional confirmation by using standard methods. Next-generation sequencing has revolutionized genetic research as it has allowed sequencing of human genomes within days. Generated raw sequencing data are manipulated using bioinformatic approaches for variant detection. Variant discovery should be performed on appropriately pre-processed data with the aforementioned prioritization of sensitivity over specificity. Presentation of the Case: Here, we report a case of a low quality variant call, emitted due to prioritization of sensitivity over specificity. This call was found to be a causative variant for the patient’s phenotype. DNA extracted from peripheral venous blood of a young female with encephalopathy was sequenced on a MiSeq apparatus. The obtained and analyzed call set emitted a low quality heterozygous insertion with a high probability of a false negative call. Annotation revealed a known pathogenic insertion rs758946412 with a frameshift consequence flagged with “Early infantile epileptic encephalopathy type 9” in ClinVar. The emitted insertion was validated and confirmed by using SANGER sequencing and RFLP. Conclusion: In the presented case, the variant could have easily been missed without the prioritization of sensitivity over specificity. Furthermore, the presented case also demonstrates the importance of additional methods for confirmation of NGS calls that do not meet the thresholds.

Integration of a Large-Scale Genetic Analysis Workbench Increases the Accessibility of a High-Performance Pathway-Based Analysis Method

The rapid increase in genetic dataset volume has demanded extensive adoption of biological knowledge to reduce the computational complexity, and the biological pathway is one well-known source of such knowledge. In this regard, we have introduced a novel statistical method that enables the pathway-based association study of large-scale genetic dataset—namely, PHARAOH. However, researcher-level application of the PHARAOH method has been limited by a lack of generally used file formats and the absence of various quality control options that are essential to practical analysis. In order to overcome these limitations, we introduce our integration of the PHARAOH method into our recently developed all-in-one workbench. The proposed new PHARAOH program not only supports various de facto standard genetic data formats but also provides many quality control measures and filters based on those measures. We expect that our updated PHARAOH provides advanced accessibility of the pathway-level analysis of large-scale genetic datasets to researchers.