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Identification of candidate regulatory SNPs by combination of transcription-factor-binding site prediction, SNP genotyping and haploChIP
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
2009 (English)In: Nucleic Acids Research, ISSN 0305-1048, E-ISSN 1362-4962, Vol. 37, no 12, e85- p.Article in journal (Refereed) Published
Abstract [en]

Disease-associated SNPs detected in large-scale association studies are   frequently located in non-coding genomic regions, suggesting that they may be involved in transcriptional regulation. Here we describe a new strategy for detecting regulatory SNPs (rSNPs), by combining   computational and experimental approaches. Whole genome ChIP-chip data   for USF1 was analyzed using a novel motif finding algorithm called   BCRANK. 1754 binding sites were identified and 140 candidate rSNPs were   found in the predicted sites. For validating their regulatory function,   seven SNPs found to be heterozygous in at least one of four human cell   samples were investigated by ChIP and sequence analysis (haploChIP). In   four of five cases where the SNP was predicted to affect binding, USF1   was preferentially bound to the allele containing the consensus motif.   Allelic differences in binding for other proteins and histone marks   further reinforced the SNPs regulatory potential. Moreover, for one of   these SNPs, H3K36me3 and POLR2A levels at neighboring heterozygous SNPs   indicated effects on transcription. Our strategy, which is entirely   based on in vivo data for both the prediction and validation steps, can   identify individual binding sites at base pair resolution and predict   rSNPs. Overall, this approach can help to pinpoint the causative SNPs   in complex disorders where the associated haplotypes are located in regulatory regions. Availability: BCRANK is available from Bioconductor  (http://www.bioconductor.org).

Place, publisher, year, edition, pages
2009. Vol. 37, no 12, e85- p.
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:uu:diva-119762DOI: 10.1093/nar/gkp381ISI: 000268115200033OAI: oai:DiVA.org:uu-119762DiVA: diva2:300877
Available from: 2010-03-01 Created: 2010-03-01 Last updated: 2011-03-16Bibliographically approved

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Wadelius, Claes

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