Predicting functional effects of missense variants in voltage-gated sodium and calcium channels

• Verfasst von: Heyne, Henrike [VerfasserIn]
• Verfasst von: Baez-Nieto, David [VerfasserIn]
• Verfasst von: Iqbal, Sumaiya [VerfasserIn]
• Verfasst von: Palmer, Duncan S. [VerfasserIn]
• Verfasst von: Brunklaus, Andreas [VerfasserIn]
• Verfasst von: May, Patrick [VerfasserIn]
• Verfasst von: Johannesen, Katrine M. [VerfasserIn]
• Verfasst von: Lauxmann, Stephan [VerfasserIn]
• Verfasst von: Lemke, Johannes R. [VerfasserIn]
• Verfasst von: Møller, Rikke S. [VerfasserIn]
• Verfasst von: Pérez-Palma, Eduardo [VerfasserIn]
• Verfasst von: Scholl, Ute I. [VerfasserIn]
• Verfasst von: Syrbe, Steffen [VerfasserIn]
• Verfasst von: Lerche, Holger [VerfasserIn]
• Verfasst von: Lal, Dennis [VerfasserIn]
• Verfasst von: Campbell, Arthur J. [VerfasserIn]
• Verfasst von: Wang, Hao-Ran [VerfasserIn]
• Verfasst von: Pan, Jen [VerfasserIn]
• Verfasst von: Daly, Mark J. [VerfasserIn]
• Titel: Predicting functional effects of missense variants in voltage-gated sodium and calcium channels
• Verf.angabe: Henrike O. Heyne, David Baez-Nieto, Sumaiya Iqbal, Duncan S. Palmer, Andreas Brunklaus, Patrick May, Katrine M. Johannesen, Stephan Lauxmann, Johannes R. Lemke, Rikke S. Møller, Eduardo Pérez-Palma, Ute I. Scholl, Steffen Syrbe, Holger Lerche, Dennis Lal, Arthur J. Campbell, Hao-Ran Wang, Jen Pan, Mark J. Daly
• E-Jahr: 2020
• Jahr: 12 August 2020
• Umfang: 16 S.
• Fussnoten: Gesehen am 19.10.2020
• Titel Quelle: Enthalten in: Science translational medicine
• Ort Quelle: Washington, DC : AAAS, 2009
• Jahr Quelle: 2020
• Band/Heft Quelle: 12(2020,556) Artikel-Nummer eaay6848, 16 Seiten
• ISSN Quelle: 1946-6242
• DOI: doi:10.1126/scitranslmed.aay6848
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1735933252

Abstract

Predicting ion channel variant phenotypes - Ion channel variants have been associated with disease, predominantly neurological. Heyne et al. developed a tool to predict the functional effects of variants in disease-associated voltage-gated sodium and calcium ion channels using machine learning-based statistical models. Loss of function versus gain of function (LOF or GOF) was predicted separately from neutrality versus pathogenicity. Their model was trained to classify variant effects using protein sequences and structures containing missense variants with known or highly probable effects and validated against experimentally tested variants and in cohorts including individuals with epilepsy and autism. This work could have implications for ion channel and clinical genetics research. - Malfunctions of voltage-gated sodium and calcium channels (encoded by SCNxA and CACNA1x family genes, respectively) have been associated with severe neurologic, psychiatric, cardiac, and other diseases. Altered channel activity is frequently grouped into gain or loss of ion channel function (GOF or LOF, respectively) that often corresponds not only to clinical disease manifestations but also to differences in drug response. Experimental studies of channel function are therefore important, but laborious and usually focus only on a few variants at a time. On the basis of known gene-disease mechanisms of 19 different diseases, we inferred LOF (n = 518) and GOF (n = 309) likely pathogenic variants from the disease phenotypes of variant carriers. By training a machine learning model on sequence- and structure-based features, we predicted LOF or GOF effects [area under the receiver operating characteristics curve (ROC) = 0.85] of likely pathogenic missense variants. Our LOF versus GOF prediction corresponded to molecular LOF versus GOF effects for 87 functionally tested variants in SCN1/2/8A and CACNA1I (ROC = 0.73) and was validated in exome-wide data from 21,703 cases and 128,957 controls. We showed respective regional clustering of inferred LOF and GOF nucleotide variants across the alignment of the entire gene family, suggesting shared pathomechanisms in the SCNxA/CACNA1x family genes. - A machine learning method can predict loss- versus gain-of-function effects of human genetic variants in disease-associated ion channels. - A machine learning method can predict loss- versus gain-of-function effects of human genetic variants in disease-associated ion channels.