Regression Modeling and Meta-Analysis of Diagnostic Accuracy of SNP-Based Pathogenicity Detection Tools for UGT1A1 Gene Mutation
Joint Authors
Galehdari, Hamid
Rahim, Fakher
Saki, Najmaldin
Mohammadi-asl, Javad
Source
Genetics Research International
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-08-13
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
Aims.
This review summarized all available evidence on the accuracy of SNP-based pathogenicity detection tools and introduced regression model based on functional scores, mutation score, and genomic variation degree.
Materials and Methods.
A comprehensive search was performed to find all mutations related to Crigler-Najjar syndrome.
The pathogenicity prediction was done using SNP-based pathogenicity detection tools including SIFT, PHD-SNP, PolyPhen2, fathmm, Provean, and Mutpred.
Overall, 59 different SNPs related to missense mutations in the UGT1A1 gene, were reviewed.
Results.
Comparing the diagnostic OR, our model showed high detection potential (diagnostic OR: 16.71, 95% CI: 3.38–82.69).
The highest MCC and ACC belonged to our suggested model (46.8% and 73.3%), followed by SIFT (34.19% and 62.71%).
The AUC analysis showed a significance overall performance of our suggested model compared to the selected SNP-based pathogenicity detection tool (P=0.046).
Conclusion.
Our suggested model is comparable to the well-established SNP-based pathogenicity detection tools that can appropriately reflect the role of a disease-associated SNP in both local and global structures.
Although the accuracy of our suggested model is not relatively high, the functional impact of the pathogenic mutations is highlighted at the protein level, which improves the understanding of the molecular basis of mutation pathogenesis.
American Psychological Association (APA)
Rahim, Fakher& Galehdari, Hamid& Mohammadi-asl, Javad& Saki, Najmaldin. 2013. Regression Modeling and Meta-Analysis of Diagnostic Accuracy of SNP-Based Pathogenicity Detection Tools for UGT1A1 Gene Mutation. Genetics Research International،Vol. 2013, no. 2013, pp.1-7.
https://search.emarefa.net/detail/BIM-480410
Modern Language Association (MLA)
Rahim, Fakher…[et al.]. Regression Modeling and Meta-Analysis of Diagnostic Accuracy of SNP-Based Pathogenicity Detection Tools for UGT1A1 Gene Mutation. Genetics Research International No. 2013 (2013), pp.1-7.
https://search.emarefa.net/detail/BIM-480410
American Medical Association (AMA)
Rahim, Fakher& Galehdari, Hamid& Mohammadi-asl, Javad& Saki, Najmaldin. Regression Modeling and Meta-Analysis of Diagnostic Accuracy of SNP-Based Pathogenicity Detection Tools for UGT1A1 Gene Mutation. Genetics Research International. 2013. Vol. 2013, no. 2013, pp.1-7.
https://search.emarefa.net/detail/BIM-480410
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-480410