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Genetics Research International
Volume 2013, Article ID 546909, 7 pages
http://dx.doi.org/10.1155/2013/546909
Research Article

Regression Modeling and Meta-Analysis of Diagnostic Accuracy of SNP-Based Pathogenicity Detection Tools for UGT1A1 Gene Mutation

1Golestan Blv. Toxicology Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
2Genetic Department, Faculty of Science, Shahid Chamran University, Ahvaz, Iran
3Department of Medical Genetics, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
4Research Center of Thalassemia & Hemoglobinopathy, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

Received 11 May 2013; Revised 30 June 2013; Accepted 12 July 2013

Academic Editor: Kenta Nakai

Copyright © 2013 Fakher Rahim et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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 (). 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.