*What your riboSNitches may say about you*

Genome wide association studies are powerful for correlating human genotype to phenotype. These studies are designed to identify the polymorphisms in the genetic code that are most predictive of a phenotype. Rapid advances in genotyping technologies enable comprehensive coverage of the genome, including a majority of intergenic polymorphisms. Interestingly, when included in the association analysis, non-coding polymorphisms are often the most highly predictive of the phenotype. Furthermore, Single Nucleotide Polymorphisms (SNPs) are inherited together in Linkage Disequilibrium (LD) blocks. As a result, identifying the causative SNP in an LD block mapping to non-coding regions of the genome remains a contemporary computational and experimental challenge in the field of genomics. Although non-coding regions of the genome are not translated into protein, they are in a majority of cases transcribed in RiboNucleic Acid (RNA). Since RNA is a single stranded polymer, it will fold and the higher-order structures it adopts are integral to numerous RNA-mediated post-transcriptional regulatory functions in the cell. In detailed and focused studies of individual transcripts, we discovered that disruption of RNA structural features in non-coding regions of transcribed RNAs are causative in at least three human disease states – hyperferritinemia cataract syndrome, retinoblastoma and cartilage hair hypoplasia – and that altered RNA structure determines hepatitis C virus clearance efficiency. Computing the structural ensemble of RNA secondary structures improves our computational ability to predict RiboSNitches (structural features in RNA that are disrupted by a SNP). Combined with genome-wide structure probing experiments enabled by next generation sequencing, our data begins to reveal the intricate balance between genotype, phenotype and transcriptome structure. Understanding these relationships will likely be integral to personalized interpretation of genotyping data.

Alain Laederach, Univ. of North Carolina at Chapel Hill
http://bio.unc.edu/people/faculty/laederach/