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SNP2RFLP: a computational tool to facilitate genetic mapping using benchtop analysis of SNPs.

ABSTRACT: Genome-wide analysis of single nucleotide polymorphism (SNP) markers is an extremely efficient means for genetic mapping of mutations or traits in mice. However, this approach often defines a relatively large recombinant interval. To facilitate the refinement of this interval, we developed the program SNP2RFLP. This program can be used to identify region-specific SNPs in which the polymorphic nucleotide creates a restriction fragment length polymorphism (RFLP) that can be readily assayed at the benchtop using restriction enzyme digestion of SNP-containing PCR products. The program permits user-defined queries that maximize the informative markers for a particular application. This facilitates fine-mapping in a region containing a mutation of interest, which should prove valuable to the mouse genetics community. SNP2RFLP and further details are publicly available at http://genetics.bwh.harvard.edu/snp2rflp/ .

SUBMITTER: Beckstead WA

PROVIDER: S-EPMC3001109 | biostudies-other | 2008 Oct-Dec

REPOSITORIES: biostudies-other

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SNP2RFLP: a computational tool to facilitate genetic mapping using benchtop analysis of SNPs.

Beckstead Wesley A WA Bjork Bryan C BC Stottmann Rolf W RW Sunyaev Shamil S Beier David R DR

Mammalian genome : official journal of the International Mammalian Genome Society 20081029 10-12

Genome-wide analysis of single nucleotide polymorphism (SNP) markers is an extremely efficient means for genetic mapping of mutations or traits in mice. However, this approach often defines a relatively large recombinant interval. To facilitate the refinement of this interval, we developed the program SNP2RFLP. This program can be used to identify region-specific SNPs in which the polymorphic nucleotide creates a restriction fragment length polymorphism (RFLP) that can be readily assayed at the ...[more]

PMID: 18958524

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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.

Project description:BACKGROUND:In the human genome, the transcription factors (TFs) and transcription factor-binding sites (TFBSs) network has a great regulatory function in the biological pathways. Such crosstalk might be affected by the single-nucleotide polymorphisms (SNPs), which could create or disrupt a TFBS, leading to either a disease or a phenotypic defect. Many computational resources have been introduced to predict the TFs binding variations due to SNPs inside TFBSs, sTRAP being one of them. METHODS:A literature review was performed and the experimental data for 18 TFBSs located in 12 genes was provided. The sequences of TFBS motifs were extracted using two different strategies; in the size similar with synthetic target sites used in the experimental techniques, and with 60 bp upstream and downstream of the SNPs. The sTRAP (http://trap.molgen.mpg.de/cgi-bin/trap_two_seq_form.cgi) was applied to compute the binding affinity scores of their cognate TFs in the context of reference and mutant sequences of TFBSs. The alternative bioinformatics model used in this study was regulatory analysis of variation in enhancers (RAVEN; http://www.cisreg.ca/cgi-bin/RAVEN/a). The bioinformatics outputs of our study were compared with experimental data, electrophoretic mobility shift assay (EMSA). RESULTS:In 6 out of 18 TFBSs in the following genes COL1A1, Hb ??, TF, FIX, MBL2, NOS2A, the outputs of sTRAP were inconsistent with the results of EMSA. Furthermore, no p value of the difference between the two scores of binding affinity under the wild and mutant conditions of TFBSs was presented. Nor, were any criteria for preference or selection of any of the measurements of different matrices used for the same analysis. CONCLUSION:Our preliminary study indicated some paradoxical results between sTRAP and experimental data. However, to link the data of sTRAP to the biological functions, its optimization via experimental procedures with the integration of expanded data and applying several other bioinformatics tools might be required.

Dataset Information

SNP2RFLP: a computational tool to facilitate genetic mapping using benchtop analysis of SNPs.

Publications

SNP2RFLP: a computational tool to facilitate genetic mapping using benchtop analysis of SNPs.

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