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PLoS One. 2014 Aug 19;9(8):e104452. doi: 10.1371/journal.pone.0104452. eCollection 2014.

Gene-specific function prediction for non-synonymous mutations in monogenic diabetes genes.

Author information

1
Endocrine Genetics Lab, The McGill University Health Center (Montreal Children's Hospital), Montréal, Québec, Canada.
2
Human Genetics Center, Division of Epidemiology, Human Genetics and Environmental Sciences, The University of Texas School of Public Health, Houston, Texas, United States of America.
3
Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas, United States of America; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America.
4
Human Genetics Center, Division of Epidemiology, Human Genetics and Environmental Sciences, The University of Texas School of Public Health, Houston, Texas, United States of America; Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas, United States of America.

Abstract

The rapid progress of genomic technologies has been providing new opportunities to address the need of maturity-onset diabetes of the young (MODY) molecular diagnosis. However, whether a new mutation causes MODY can be questionable. A number of in silico methods have been developed to predict functional effects of rare human mutations. The purpose of this study is to compare the performance of different bioinformatics methods in the functional prediction of nonsynonymous mutations in each MODY gene, and provides reference matrices to assist the molecular diagnosis of MODY. Our study showed that the prediction scores by different methods of the diabetes mutations were highly correlated, but were more complimentary than replacement to each other. The available in silico methods for the prediction of diabetes mutations had varied performances across different genes. Applying gene-specific thresholds defined by this study may be able to increase the performance of in silico prediction of disease-causing mutations.

PMID:
25136813
PMCID:
PMC4138110
DOI:
10.1371/journal.pone.0104452
[Indexed for MEDLINE]
Free PMC Article
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