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Int J Mol Epidemiol Genet 2013;4(2):77-85

Original Article
Meta-analysis diagnostic accuracy of SNP-based pathogenicity detec-tion
tools: a case of UTG1A1 gene mutations

Hamid Galehdari, Najmaldin Saki, Javad Mohammadi-asl, Fakher Rahim

Faculty of Science, Department of Genetic, Shahid Chamran Univerity, Ahvaz, Iran; Research Center of Thalassemia &
Hemoglobinopathy, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran; Petroleum and Environmental Pollutants
Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran; Department of medical Genetics, Ahvaz
Jundishapur University of Medical sciences, Ahvaz, Iran; Toxicology Research Center, Ahvaz Jundishapur University of Medical
sciences, Ahvaz, Iran

Received May 6, 2013; Accepted June 12, 2013; Epub June 25, 2013; Published June 30, 2013

Abstract: Crigler-Najjar syndrome (CNS) type I and type II are usually inherited as autosomal recessive conditions that result
from mutations in the UGT1A1 gene. The main objective of the present review is to summarize results of all available evidence
on the accuracy of SNP-based pathogenicity detection tools compared to published clinical result for the prediction of in
nsSNPs that leads to disease using prediction performance method. A comprehensive search was performed to find all
mutations related to CNS. Database searches included dbSNP, SNPdbe, HGMD, Swissvar, ensemble, and OMIM. All the
mutation related to CNS was extracted. The pathogenicity prediction was done using SNP-based pathogenicity detection tools
include SIFT, PHD-SNP, PolyPhen2, fathmm, Provean, and Mutpred. Overall, 59 different SNPs related to missense mutations
in the UGT1A1 gene, were reviewed. Comparing the diagnostic OR, PolyPhen2 and Mutpred have the highest detection 4.983
(95% CI: 1.24 – 20.02) in both, following by SIFT (diagnostic OR: 3.25, 95% CI: 1.07 – 9.83). The highest MCC of SNP-based
pathogenicity detection tools, was belong to SIFT (34.19%) followed by Provean, PolyPhen2, and Mutpred (29.99%, 29.89%,
and 29.89%, respectively). Hence the highest SNP-based pathogenicity detection tools ACC, was fit to SIFT (62.71%) followed
by PolyPhen2, and Mutpred (61.02%, in both). Our results suggest that some of the well-established SNP-based pathogenicity
detection tools can appropriately reflect the role of a disease-associated SNP in both local and global structures.
(IJMEG1305002).

Keywords: Crigler-Najjar syndrome (CNS), UGT1A1 gene, SIFT, PHD-SNP, PolyPhen2, fathmm, Provean, Mutpred

Address correspondence to: Dr. Fakher Rahim, Toxicology Research Center, Ahvaz Jundishapur University of Medical
sciences, Ahvaz, Iran. Tel: +986113367562; E-mail: Bioinfo2003@ajums.ac.ir; F-rahim@Razi.Tums.ac.ir