Real-world clinical applicability of pathogenicity predictors assessed on SERPINA1 mutations in alpha-1-antitrypsin deficiency
| Autor: | Romina Berardelli, Annamaria Fra, Ilaria Ferrarotti, Viola Ravasio, Mattia Laffranchi, Bibek Gooptu, Giuseppe Borsani, Edoardo Giacopuzzi |
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| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Male pathogenicity prediction Mutation Missense ExAC database Disease Computational biology Biology Endoplasmic Reticulum serpinopathies 03 medical and health sciences 0302 clinical medicine alpha-1-antitrypsin deficiency alpha-1-antitrypsin polymers serpins Genetics Genetics (clinical) alpha 1-Antitrypsin Deficiency Genetic variation Databases Genetic Exome Sequencing medicine Humans Exome Gene Alleles Alpha 1-antitrypsin deficiency business.industry Genetic variants Structural context Computational Biology Pathogenicity medicine.disease 030104 developmental biology Genetics Population 030220 oncology & carcinogenesis alpha 1-Antitrypsin Female Personalized medicine business Leukocyte Elastase |
| Zdroj: | Human mutation. 39(9) |
| ISSN: | 1098-1004 |
| Popis: | The growth of publicly available data informing upon genetic variations, mechanisms of disease, and disease subphenotypes offers great potential for personalized medicine. Computational approaches are likely required to assess a large number of novel genetic variants. However, the integration of genetic, structural, and pathophysiological data still represents a challenge for computational predictions and their clinical use. We addressed these issues for alpha-1-antitrypsin deficiency, a disease mediated by mutations in the SERPINA1 gene encoding alpha-1-antitrypsin. We compiled a comprehensive database of SERPINA1 coding mutations and assigned them apparent pathological relevance based upon available data. "Benign" and "pathogenic" variations were used to assess performance of 31 pathogenicity predictors. Well-performing algorithms clustered the subset of variants known to be severely pathogenic with high scores. Eight new mutations identified in the ExAC database and achieving high scores were selected for characterization in cell models and showed secretory deficiency and polymer formation, supporting the predictive power of our computational approach. The behavior of the pathogenic new variants and consistent outliers were rationalized by considering the protein structural context and residue conservation. These findings highlight the potential of computational methods to provide meaningful predictions of the pathogenic significance of novel mutations and identify areas for further investigation. |
| Databáze: | OpenAIRE |
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