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In silico detection of sequence variations modifying transcriptional regulation
KTH, School of Biotechnology (BIO), Gene Technology.
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2008 (English)In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 4, no 1, e5- p.Article in journal (Refereed) Published
Abstract [en]

Identification of functional genetic variation associated with increased susceptibility to complex diseases can elucidate genes and underlying biochemical mechanisms linked to disease onset and progression. For genes linked to genetic diseases, most identified causal mutations alter an encoded protein sequence. Technological advances for measuring RNA abundance suggest that a significant number of undiscovered causal mutations may alter the regulation of gene transcription. However, it remains a challenge to separate causal genetic variations from linked neutral variations. Here we present an in silico driven approach to identify possible genetic variation in regulatory sequences. The approach combines phylogenetic footprinting and transcription factor binding site prediction to identify variation in candidate cis-regulatory elements. The bioinformatics approach has been tested on a set of SNPs that are reported to have a regulatory function, as well as background SNPs. In the absence of additional information about an analyzed gene, the poor specificity of binding site prediction is prohibitive to its application. However, when additional data is available that can give guidance on which transcription factor is involved in the regulation of the gene, the in silico binding site prediction improves the selection of candidate regulatory polymorphisms for further analyses. The bioinformatics software generated for the analysis has been implemented as a Web-based application system entitled RAVEN ( regulatory analysis of variation in enhancers). The RAVEN system is available at http://www.cisreg.ca for all researchers interested in the detection and characterization of regulatory sequence variation.

Place, publisher, year, edition, pages
2008. Vol. 4, no 1, e5- p.
National Category
Biochemistry and Molecular Biology
Identifiers
URN: urn:nbn:se:kth:diva-7864DOI: 10.1371/journal.pcbi.0040005ISI: 000255407500008Scopus ID: 2-s2.0-38949195057OAI: oai:DiVA.org:kth-7864DiVA: diva2:13015
Note
QC 20100621Available from: 2007-12-19 Created: 2007-12-19 Last updated: 2012-03-20Bibliographically approved
In thesis
1. Computational and experimental approaches to regulatory genetic variation
Open this publication in new window or tab >>Computational and experimental approaches to regulatory genetic variation
2007 (English)Doctoral thesis, comprehensive summary (Other scientific)
Abstract [en]

Genetic variation is a strong risk factor for many human diseases, including diabetes, cancer, cardiovascular disease, depression, autoimmunity and asthma. Most of the disease genes identified so far alter the amino acid sequences of encoded proteins. However, a significant number of genetic variants affecting complex diseases may alter the regulation of gene transcription. The map of the regulatory elements in the human genome is still to a large extent unknown, and it remains a challenge to separate the functional regulatory genetic variations from linked neutral variations.

The objective of this thesis was to develop methods for the identification of genetic variation with a potential to affect the transcriptional regulation of human genes, and to analyze potential regulatory polymorphisms in the CD36 glycoprotein, a candidate gene for cardiovascular disease.

An in silico tool for the prediction of regulatory polymorphisms in human genes was implemented and is available at www.cisreg.ca/RAVEN. The tool was evaluated using experimentally verified regulatory single nucleotide polymorphisms (SNPs) collected from the scientific literature, and tested in combination with experimental detection of allele specific expression of target genes (allelic imbalance). Regulatory SNPs were shown to be located in evolutionary conserved regions more often than background SNPs, but predicted transcription factor binding sites were unable to enrich for regulatory SNPs unless additional information linking transcription factors with the target genes were available.

The in silico tool was applied to the CD36 glycoprotein, a candidate gene for cardiovascular disease. Potential regulatory SNPs in the alternative promoters of this gene were identified and evaluated in vitro and in vivo using a clinical study for coronary artery disease. We observed association to the plasma concentrations of inflammation markers (serum amyloid A protein and C-reactive protein) in myocardial infarction patients, which highlights the need for further analyses of potential regulatory polymorphisms in this gene.

Taken together, this thesis describes an in silico approach to identify putative regulatory polymorphisms which can be useful for directing limited laboratory resources to the polymorphisms most likely to have a phenotypic effect.

Place, publisher, year, edition, pages
Stockholm: Bioteknologi, 2007
Series
Trita-BIO-Report, ISSN 1654-2312 ; 2007:12
Keyword
Molecular biology, Genetics, single nucleotide polymprhism (SNP), regulatory SNP, transcription factor binding site, phylogenetic footprinting, allelic imbalance, EMSA, CD36, cardiovascular disease.
National Category
Other Industrial Biotechnology
Identifiers
urn:nbn:se:kth:diva-4593 (URN)978-91-7178-827-6 (ISBN)
Public defence
2008-01-18, FD5, AlbaNova Universitetscentrum, Roslagstullsbacken 21, Stockholm, 10:00
Opponent
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Available from: 2007-12-19 Created: 2007-12-19 Last updated: 2012-03-20Bibliographically approved

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