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Mutations in SCN1A, SCN2A, and SCN8A, which code for neuronal voltage-gated sodium channel alpha-subunits, have been associated with certain early onset epilepsy syndromes. Despite this association, many clinicians are uncertain about the value of missense genetic variants in the management of epilepsy because many mutations are classified as having unknown significance. A recent database analysis identified gene variants that are pathogenic and benign, giving clinicians a better understanding of how to interpret SCN test results. Details of the investigation include the following:
- Investigators used 8 algorithms to evaluate the pathogenicity of various genetic variants. They also used logistic regression to help determine if combining algorithms might improve their ability to predict pathogenicity.
- 440 variants were considered pathogenic or likely pathogenic.
- Most computer algorithms that attempt to determine the value of SCN test results are very sensitive but also suffer from low specificity.
- The Mendelian Clinically Applicable Pathogenicity algorithm proved most valuable, with an accuracy of 0.90.
Holland KD, Bouley TM, Horn PS. Comparison and optimization of in silico algorithms for predicting the pathogenicity of sodium channel variants in epilepsy. Epilepsia. 2017;58(7):1190-1198.
Mutations in SCN1A, SCN2A, and SCN8A, which code for neuronal voltage-gated sodium channel alpha-subunits, have been associated with certain early onset epilepsy syndromes. Despite this association, many clinicians are uncertain about the value of missense genetic variants in the management of epilepsy because many mutations are classified as having unknown significance. A recent database analysis identified gene variants that are pathogenic and benign, giving clinicians a better understanding of how to interpret SCN test results. Details of the investigation include the following:
- Investigators used 8 algorithms to evaluate the pathogenicity of various genetic variants. They also used logistic regression to help determine if combining algorithms might improve their ability to predict pathogenicity.
- 440 variants were considered pathogenic or likely pathogenic.
- Most computer algorithms that attempt to determine the value of SCN test results are very sensitive but also suffer from low specificity.
- The Mendelian Clinically Applicable Pathogenicity algorithm proved most valuable, with an accuracy of 0.90.
Holland KD, Bouley TM, Horn PS. Comparison and optimization of in silico algorithms for predicting the pathogenicity of sodium channel variants in epilepsy. Epilepsia. 2017;58(7):1190-1198.
Mutations in SCN1A, SCN2A, and SCN8A, which code for neuronal voltage-gated sodium channel alpha-subunits, have been associated with certain early onset epilepsy syndromes. Despite this association, many clinicians are uncertain about the value of missense genetic variants in the management of epilepsy because many mutations are classified as having unknown significance. A recent database analysis identified gene variants that are pathogenic and benign, giving clinicians a better understanding of how to interpret SCN test results. Details of the investigation include the following:
- Investigators used 8 algorithms to evaluate the pathogenicity of various genetic variants. They also used logistic regression to help determine if combining algorithms might improve their ability to predict pathogenicity.
- 440 variants were considered pathogenic or likely pathogenic.
- Most computer algorithms that attempt to determine the value of SCN test results are very sensitive but also suffer from low specificity.
- The Mendelian Clinically Applicable Pathogenicity algorithm proved most valuable, with an accuracy of 0.90.
Holland KD, Bouley TM, Horn PS. Comparison and optimization of in silico algorithms for predicting the pathogenicity of sodium channel variants in epilepsy. Epilepsia. 2017;58(7):1190-1198.