Annotation of Human Exome Gene Variants with Consensus Pathogenicity
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Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, 79104 Freiburg im Breisgau, Germany
2
Institute of Medical Bioinformatics and Systems Medicine, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, 79110 Freiburg im Breisgau, Germany
*
Author to whom correspondence should be addressed.
Genes 2020, 11(9), 1076; https://doi.org/10.3390/genes11091076
Received: 29 July 2020
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Revised: 10 September 2020
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Accepted: 11 September 2020
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Published: 14 September 2020
(This article belongs to the Section Technologies and Resources for Genetics)
A novel approach is developed to address the challenge of annotating with phenotypic effects those exome variants for which relevant empirical data are lacking or minimal. The predictive annotation method is implemented as a stacked ensemble of supervised base-learners, including distributed random forest and gradient boosting machines. Ensemble models were trained and cross-validated on evidence-based categorical variant effect annotations from the ClinVar database, and were applied to 84 million non-synonymous single nucleotide variants (SNVs). The consensus model combined 39 functional mutation impacts, cross-species conservation score, and gene indispensability score. The indispensability score, accounting for differences in variant pathogenicities including in essential and mutation-tolerant genes, considerably improved the predictions. The consensus combination is consistent with as many input scores as possible while minimizing false predictions. The input scores are ranked based on their ability to predict effects. The score rankings and categorical phenotypic variant effect predictions are aimed for direct use in clinical and biological applications to prioritize human exome variants and mutations.
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Keywords:
variant of unknown significance (VUS); single-nucleotide variant (SNV); variant effect prediction (VEP); hit ratio (HR); stacked ensemble of supervised learners (SESL); next generation sequencing (NGS); alternative allele frequency (AAF)
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MDPI and ACS Style
Jaravine, V.; Balmford, J.; Metzger, P.; Boerries, M.; Binder, H.; Boeker, M. Annotation of Human Exome Gene Variants with Consensus Pathogenicity. Genes 2020, 11, 1076. https://doi.org/10.3390/genes11091076
AMA Style
Jaravine V, Balmford J, Metzger P, Boerries M, Binder H, Boeker M. Annotation of Human Exome Gene Variants with Consensus Pathogenicity. Genes. 2020; 11(9):1076. https://doi.org/10.3390/genes11091076
Chicago/Turabian StyleJaravine, Victor, James Balmford, Patrick Metzger, Melanie Boerries, Harald Binder, and Martin Boeker. 2020. "Annotation of Human Exome Gene Variants with Consensus Pathogenicity" Genes 11, no. 9: 1076. https://doi.org/10.3390/genes11091076
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