Computational and experimental approaches to regulatory genetic variation
Abstract: 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.
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