Jamie D Kapplinger1, John R Giudicessi1, Dan Ye1, David J Tester1, Thomas E Callis1, Carmen R Valdivia1, Jonathan C Makielski1, Arthur A Wilde1, Michael J Ackerman2. 1. From the Departments of Medicine (Division of Cardiovascular Diseases), Pediatrics (Division of Pediatric Cardiology), and Molecular Pharmacology and Experimental Therapeutics, Windland Smith Rice Sudden Death Genomics Laboratory, Mayo Clinic, Rochester, MN (J.D.K., J.R.G., D.Y., D.J.T., M.J.A.); Transgenomic Inc., New Haven, CT (T.E.C.); Division of Cardiovascular Medicine, Department of Medicine, University of Wisconsin, Madison (C.R.V., J.C.M.); Department of Cardiology, Heart Center, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands (A.A.W.); and Princess Al-Jawhara Al-Brahim Centre of Excellence in Research of Hereditary Disorders, Jeddah, Kingdom of Saudi Arabia (A.A.W.). 2. From the Departments of Medicine (Division of Cardiovascular Diseases), Pediatrics (Division of Pediatric Cardiology), and Molecular Pharmacology and Experimental Therapeutics, Windland Smith Rice Sudden Death Genomics Laboratory, Mayo Clinic, Rochester, MN (J.D.K., J.R.G., D.Y., D.J.T., M.J.A.); Transgenomic Inc., New Haven, CT (T.E.C.); Division of Cardiovascular Medicine, Department of Medicine, University of Wisconsin, Madison (C.R.V., J.C.M.); Department of Cardiology, Heart Center, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands (A.A.W.); and Princess Al-Jawhara Al-Brahim Centre of Excellence in Research of Hereditary Disorders, Jeddah, Kingdom of Saudi Arabia (A.A.W.). ackerman.michael@mayo.edu.
Abstract
BACKGROUND: A 2% to 5% background rate of rare SCN5A nonsynonymous single nucleotide variants (nsSNVs) among healthy individuals confounds clinical genetic testing. Therefore, the purpose of this study was to enhance interpretation of SCN5A nsSNVs for clinical genetic testing using estimated predictive values derived from protein-topology and 7 in silico tools. METHODS AND RESULTS: Seven in silico tools were used to assign pathogenic/benign status to nsSNVs from 2888 long-QT syndrome cases, 2111 Brugada syndrome cases, and 8975 controls. Estimated predictive values were determined for each tool across the entire SCN5A-encoded Na(v)1.5 channel as well as for specific topographical regions. In addition, the in silico tools were assessed for their ability to correlate with cellular electrophysiology studies. In long-QT syndrome, transmembrane segments S3-S5+S6 and the DIII/DIV linker region were associated with high probability of pathogenicity. For Brugada syndrome, only the transmembrane spanning domains had a high probability of pathogenicity. Although individual tools distinguished case- and control-derived SCN5A nsSNVs, the composite use of multiple tools resulted in the greatest enhancement of interpretation. The use of the composite score allowed for enhanced interpretation for nsSNVs outside of the topological regions that intrinsically had a high probability of pathogenicity, as well as within the transmembrane spanning domains for Brugada syndrome nsSNVs. CONCLUSIONS: We have used a large case/control study to identify regions of Na(v)1.5 associated with a high probability of pathogenicity. Although topology alone would leave the variants outside these identified regions in genetic purgatory, the synergistic use of multiple in silico tools may help promote or demote a variant's pathogenic status.
BACKGROUND: A 2% to 5% background rate of rare SCN5A nonsynonymous single nucleotide variants (nsSNVs) among healthy individuals confounds clinical genetic testing. Therefore, the purpose of this study was to enhance interpretation of SCN5A nsSNVs for clinical genetic testing using estimated predictive values derived from protein-topology and 7 in silico tools. METHODS AND RESULTS: Seven in silico tools were used to assign pathogenic/benign status to nsSNVs from 2888 long-QT syndrome cases, 2111 Brugada syndrome cases, and 8975 controls. Estimated predictive values were determined for each tool across the entire SCN5A-encoded Na(v)1.5 channel as well as for specific topographical regions. In addition, the in silico tools were assessed for their ability to correlate with cellular electrophysiology studies. In long-QT syndrome, transmembrane segments S3-S5+S6 and the DIII/DIV linker region were associated with high probability of pathogenicity. For Brugada syndrome, only the transmembrane spanning domains had a high probability of pathogenicity. Although individual tools distinguished case- and control-derived SCN5A nsSNVs, the composite use of multiple tools resulted in the greatest enhancement of interpretation. The use of the composite score allowed for enhanced interpretation for nsSNVs outside of the topological regions that intrinsically had a high probability of pathogenicity, as well as within the transmembrane spanning domains for Brugada syndrome nsSNVs. CONCLUSIONS: We have used a large case/control study to identify regions of Na(v)1.5 associated with a high probability of pathogenicity. Although topology alone would leave the variants outside these identified regions in genetic purgatory, the synergistic use of multiple in silico tools may help promote or demote a variant's pathogenic status.
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