Large-scale genetic studies identify new associations between genotype and disease-phenotype (Benjamin et al. 2007;Lee et al. 2008;Mathew 2008;Glinskii et al. 2009). In many cases, and in particular when the associated genotype maps to a non-coding region of the genome, the genetic data alone does not reveal the molecular cause of the disease (Glinskii et al. 2009). Non-coding regions of the genome are in a majority of cases transcribed into RNA (Ribonucleic acid) (Weinstock 2007), and if a disease-associated mutation alters the structure of the transcript, this may have functional consequences (Halvorsen et al. 2010). We have recently identified disease-associated SNPs (Single Nucleotide Polymorphisms) in the regulatory regions of mRNA transcripts that significantly alter the folding of the transcript. Much like bacterial Riboswitches (Tucker and Breaker 2005), RiboSNitches adopt significantly altered conformations if a specific SNP is present (Halvorsen et al. 2010). Furthermore, we have shown that secondary mutations and binding of genotype specific locked nucleic acids (LNAs) can rescue the structure and regulatory function of the RNA. We hypothesize that specific haplotypes (combinations of SNPs in high linkage disequilibrium) will stabilize certain transcripts. We propose to use our predictive SNPfold (Halvorsen et al. 2010) algorithm (which models the ensemble of possible RNA conformations) to identify RNA structure-stabilizing haplotypes in the human genome and thus discover and experimentally validate novel posttranscriptional cellular regulatory mechanisms. We are fundamentally interested in understanding the structural consequences of common genetic variation on the function of the transcriptome.
Genetic mutations that cause human disease are encoded in our DNA (Deoxy riboNucleic Acid) but generally affect downstream molecular processes in our cells. This proposal aims to understand the effects of disease-associated mutations on the structure and function of RNA (RiboNucleic Acid). RNA is a genetic messenger molecule that is also a central component of the cell's regulatory machinery. Mutations are transcribed into RNA and in some cases will alter the structure of the RNA, causing it to misfold and this results in aberrant regulation and disease. We will develop and validate computer algorithms that are highly predictive of the effects of mutations on the structure of RNA to identify mutations that ar likely to disrupt its function.
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