Genome-wide association studies (GWAS) often identify disease-associated mutations in intergenic and non-coding regions of the genome. Given the high percentage of the human genome that is transcribed, we postulate that for some observed associations the disease phenotype is caused by a structural rearrangement in a regulatory region of the
RNA transcript. To identify such mutations, we have performed a genome-wide analysis of all known disease-associated Single Nucleotide Polymorphisms (SNPs) from the Human Gene Mutation Database (HGMD) that map to the
untranslated regions (
UTRs) of a gene. Rather than using minimum free energy approaches (e.g. mFold), we use a partition function calculation that takes into consideration the ensemble of possible RNA conformations for a given sequence. We identified in the human genome disease-associated SNPs that significantly alter the global conformation of the UTR to which they map. For six disease-states (
Hyperferritinemia Cataract Syndrome,
beta-Thalassemia,
Cartilage-Hair Hypoplasia,
Retinoblastoma,
Chronic Obstructive Pulmonary Disease (
COPD), and
Hypertension), we identified multiple SNPs in
UTRs that alter the
mRNA structural ensemble of the associated genes. Using a Boltzmann sampling procedure for sub-optimal
RNA structures, we are able to characterize and visualize the nature of the conformational changes induced by the disease-associated mutations in the structural ensemble. We observe in several cases (specifically the
5' UTRs of FTL and RB1) SNP-induced conformational changes analogous to those observed in bacterial regulatory
Riboswitches when specific
ligands bind. We propose that the UTR and SNP combinations we identify constitute a "RiboSNitch," that is a regulatory
RNA in which a specific SNP has a structural consequence that results in a disease phenotype. Our SNPfold algorithm can help identify RiboSNitches by leveraging GWAS data and an analysis of the
mRNA structural ensemble.