The predicted impact of coding single nucleotide polymorphisms database.


Rudd, MF; Williams, RD; Webb, EL; Schmidt, S; Sellick, GS; Houlston, RS; (2005) The predicted impact of coding single nucleotide polymorphisms database. Cancer epidemiology, biomarkers & prevention, 14 (11 Pt 1). pp. 2598-604. ISSN 1055-9965 DOI: https://doi.org/10.1158/1055-9965.EPI-05-0469

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Abstract

Nonsynonymous single nucleotide polymorphisms (nsSNP) have the potential to affect the structure or function of expressed proteins and are, therefore, likely to represent modifiers of inherited susceptibility. We have classified and catalogued the predicted functionality of nsSNPs in genes relevant to the biology of cancer to facilitate sequence-based association studies. Candidate genes were identified using targeted search terms and pathways to interrogate the Gene Ontology Consortium database, Kyoto Encyclopedia of Genes and Genomes database, Iobion's Interaction Explorer PathwayAssist Program, National Center for Biotechnology Information Entrez Gene database, and CancerGene database. A total of 9,537 validated nsSNPs located within annotated genes were retrieved from National Center for Biotechnology Information dbSNP Build 123. Filtering this list and linking it to 7,080 candidate genes yielded 3,666 validated nsSNPs with minor allele frequencies > or =0.01 in Caucasian populations. The functional effect of nsSNPs in genes with a single mRNA transcript was predicted using three computational tools-Grantham matrix, Polymorphism Phenotyping, and Sorting Intolerant from Tolerant algorithms. The resultant pool of 3,009 fully annotated nsSNPs is accessible from the Predicted Impact of Coding SNPs database at http://www.icr.ac.uk/cancgen/molgen/MolPopGen_PICS_database.htm. Predicted Impact of Coding SNPs is an ongoing project that will continue to curate and release data on the putative functionality of coding SNPs.

Item Type: Article
Faculty and Department: Faculty of Epidemiology and Population Health > Dept of Infectious Disease Epidemiology
PubMed ID: 16284384
Web of Science ID: 233351100022
URI: http://researchonline.lshtm.ac.uk/id/eprint/2423

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