Dataset Information

Prediction of functional regulatory SNPs in monogenic and complex disease.

ABSTRACT: Next-generation sequencing (NGS) technologies are yielding ever higher volumes of human genome sequence data. Given this large amount of data, it has become both a possibility and a priority to determine how disease-causing single nucleotide polymorphisms (SNPs) detected within gene regulatory regions (rSNPs) exert their effects on gene expression. Recently, several studies have explored whether disease-causing polymorphisms have attributes that can distinguish them from those that are neutral, attaining moderate success at discriminating between functional and putatively neutral regulatory SNPs. Here, we have extended this work by assessing the utility of both SNP-based features (those associated only with the polymorphism site and the surrounding DNA) and gene-based features (those derived from the associated gene in whose regulatory region the SNP lies) in the identification of functional regulatory polymorphisms involved in either monogenic or complex disease. Gene-based features were found to be capable of both augmenting and enhancing the utility of SNP-based features in the prediction of known regulatory mutations. Adopting this approach, we achieved an AUC of 0.903 for predicting regulatory SNPs. Finally, our tool predicted 225 new regulatory SNPs with a high degree of confidence, with 105 of the 225 falling into linkage disequilibrium blocks of reported disease-associated genome-wide association studies SNPs.

SUBMITTER: Zhao Y

PROVIDER: S-EPMC3957483 | biostudies-literature | 2011 Oct

REPOSITORIES: biostudies-literature

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Prediction of functional regulatory SNPs in monogenic and complex disease.

Zhao Yiqiang Y Clark Wyatt T WT Mort Matthew M Cooper David N DN Radivojac Predrag P Mooney Sean D SD

Human mutation 20110909 10

Next-generation sequencing (NGS) technologies are yielding ever higher volumes of human genome sequence data. Given this large amount of data, it has become both a possibility and a priority to determine how disease-causing single nucleotide polymorphisms (SNPs) detected within gene regulatory regions (rSNPs) exert their effects on gene expression. Recently, several studies have explored whether disease-causing polymorphisms have attributes that can distinguish them from those that are neutral, ...[more]

PMID: 21796725

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Project description:Background:From genome wide association studies on Alzheimer's disease (AD), it has been shown that many single nucleotide polymorphisms (SNPs) of genes of different pathways affect the disease risk. One of the pathways is endocytosis, and variants in these genes may affect their functions in amyloid precursor protein (APP) trafficking, amyloid-beta (A?) production as well as its clearance in the brain. This study uses computational methods to predict the effect of novel SNPs, including untranslated region (UTR) variants, splice site variants, synonymous SNPs (sSNPs) and non-synonymous SNPs (nsSNPs) in three endocytosis genes associated with AD, namely PICALM, SYNJ1 and SH3KBP1. Materials and Methods:All the variants' information was retrieved from the Ensembl genome database, and then different variation prediction analyses were performed. UTRScan was used to predict UTR variants while MaxEntScan was used to predict splice site variants. Meta-analysis by PredictSNP2 was used to predict sSNPs. Parallel prediction analyses by five different software packages including SIFT, PolyPhen-2, Mutation Assessor, I-Mutant2.0 and SNPs&GO were used to predict the effects of nsSNPs. The level of evolutionary conservation of deleterious nsSNPs was further analyzed using ConSurf server. Mutant protein structures of deleterious nsSNPs were modelled and refined using SPARKS-X and ModRefiner for structural comparison. Results:A total of 56 deleterious variants were identified in this study, including 12 UTR variants, 18 splice site variants, eight sSNPs and 18 nsSNPs. Among these 56 deleterious variants, seven variants were also identified in the Alzheimer's Disease Sequencing Project (ADSP), Alzheimer's Disease Neuroimaging Initiative (ADNI) and Mount Sinai Brain Bank (MSBB) studies. Discussion:The 56 deleterious variants were predicted to affect the regulation of gene expression, or have functional impacts on these three endocytosis genes and their gene products. The deleterious variants in these genes are expected to affect their cellular function in endocytosis and may be implicated in the pathogenesis of AD as well. The biological consequences of these deleterious variants and their potential impacts on the disease risks could be further validated experimentally and may be useful for gene-disease association study.

Dataset Information

Prediction of functional regulatory SNPs in monogenic and complex disease.

Publications

Prediction of functional regulatory SNPs in monogenic and complex disease.

Similar Datasets

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