Dataset Information

A single nucleotide mutation in Nppc is associated with a long bone abnormality in lbab mice.

ABSTRACT: The long bone abnormality (lbab) mouse is a new autosomal recessive mutant characterized by overall smaller body size with proportionate dwarfing of all organs and shorter long bones. Previous linkage analysis has located the lbab mutation on chromosome 1 between the markers D1Mit9 and D1Mit488.A genome-based positional approach was used to identify a mutation associated with lbab disease. A total of 122 genes and expressed sequence tags at the lbab region were screened for possible mutation by using genomic DNA from lbabl/lbab, lbab/+, and +/+ B6 mice and high throughput temperature gradient capillary electrophoresis. A sequence difference was identified in one of the amplicons of gene Nppc between lbab/lbab and +/+ mice. One-step reverse transcriptase polymerase chain reaction was performed to validate the difference of Nppc in different types of mice at the mRNA level. The mutation of Nppc was unique in lbab/lbab mice among multiple mouse inbred strains. The mutation of Nppc is co-segregated with lbab disease in 200 progenies produced from heterozygous lbab/+ parents.A single nucleotide mutation of Nppc is associated with dwarfism in lbab/lbab mice. Current genome information and technology allow us to efficiently identify single nucleotide mutations from roughly mapped disease loci. The lbab mouse is a useful model for hereditary human achondroplasia.

SUBMITTER: Jiao Y

PROVIDER: S-EPMC1867825 | biostudies-literature | 2007 Apr

REPOSITORIES: biostudies-literature

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A single nucleotide mutation in Nppc is associated with a long bone abnormality in lbab mice.

Jiao Yan Y Yan Jian J Jiao Feng F Yang Hongbin H Donahue Leah Rae LR Li Xinmin X Roe Bruce A BA Stuart John J Gu Weikuan W

BMC genetics 20070417

<h4>Background</h4>The long bone abnormality (lbab) mouse is a new autosomal recessive mutant characterized by overall smaller body size with proportionate dwarfing of all organs and shorter long bones. Previous linkage analysis has located the lbab mutation on chromosome 1 between the markers D1Mit9 and D1Mit488.<h4>Results</h4>A genome-based positional approach was used to identify a mutation associated with lbab disease. A total of 122 genes and expressed sequence tags at the lbab region were ...[more]

PMID: 17439653

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Project description:BackgroundIn recent years the number of human genetic variants deposited into the publicly available databases has been increasing exponentially. The latest version of dbSNP, for example, contains ~50 million validated Single Nucleotide Variants (SNVs). SNVs make up most of human variation and are often the primary causes of disease. The non-synonymous SNVs (nsSNVs) result in single amino acid substitutions and may affect protein function, often causing disease. Although several methods for the detection of nsSNV effects have already been developed, the consistent increase in annotated data is offering the opportunity to improve prediction accuracy.ResultsHere we present a new approach for the detection of disease-associated nsSNVs (Meta-SNP) that integrates four existing methods: PANTHER, PhD-SNP, SIFT and SNAP. We first tested the accuracy of each method using a dataset of 35,766 disease-annotated mutations from 8,667 proteins extracted from the SwissVar database. The four methods reached overall accuracies of 64%-76% with a Matthew's correlation coefficient (MCC) of 0.38-0.53. We then used the outputs of these methods to develop a machine learning based approach that discriminates between disease-associated and polymorphic variants (Meta-SNP). In testing, the combined method reached 79% overall accuracy and 0.59 MCC, ~3% higher accuracy and ~0.05 higher correlation with respect to the best-performing method. Moreover, for the hardest-to-define subset of nsSNVs, i.e. variants for which half of the predictors disagreed with the other half, Meta-SNP attained 8% higher accuracy than the best predictor.ConclusionsHere we find that the Meta-SNP algorithm achieves better performance than the best single predictor. This result suggests that the methods used for the prediction of variant-disease associations are orthogonal, encoding different biologically relevant relationships. Careful combination of predictions from various resources is therefore a good strategy for the selection of high reliability predictions. Indeed, for the subset of nsSNVs where all predictors were in agreement (46% of all nsSNVs in the set), our method reached 87% overall accuracy and 0.73 MCC. Meta-SNP server is freely accessible at http://snps.biofold.org/meta-snp.

Dataset Information

A single nucleotide mutation in Nppc is associated with a long bone abnormality in lbab mice.

Publications

A single nucleotide mutation in Nppc is associated with a long bone abnormality in lbab mice.

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