Using human genetic variation to predict functional elements in non-coding genomic regions

The annotation of the human genome has been a daunting task requiring the creation of innovative methods to characterize its diverse elements. Given that previous studies have successfully used human polymorphism data to characterize functional elements within coding regions, the objective of this thesis is to use human polymorphism data to improve the identification of functional elements in both coding and non-coding regions. This study relies on using the combination of genetic variation from ethnically diverse human populations and several bioinformatics approaches to discriminate and identify several elements of functional importance within genomic regions.Human polymorphism data within genes was acquired from three different publicly available datasets. We then demonstrated that positions within introns that correspond to known functional elements involved in pre-mRNA splicing, including the branch site, splice sites, and polypyrimidine tract showed reduced levels of genetic variation. These precise sites of reduced polymorphism levels also coincide with the positions known for base pairing and interacting with their corresponding ligand. Furthermore, we observed regions of reduced genetic variation that were candidates for distance dependent localization sites of functional elements. Using several computational approaches, we provided additional evidence that suggests these regions correspond to intronic splicing enhancers in both the 5' and 3' splice site regions.We conclude that studies of genetic variation can successfully discriminate and identify functional elements in non-coding regions. Although current polymorphism data is only available for small gene subsets, as more non-coding sequence data becomes available, the methods employed here can be utilized to identify additional functional elements in the human genome and provide possible explanations for phenotypic associations.