Title:
Functional assessments of amino acid variation in human genomes

dc.contributor.advisor Gibson, Greg
dc.contributor.author Preeprem, Thanawadee
dc.contributor.committeeMember Harvey, Stephen C.
dc.contributor.committeeMember Jordan, I. King
dc.contributor.committeeMember Vannberg, Fredrik O.
dc.contributor.committeeMember Wang, May Dongmei
dc.contributor.department Biology
dc.date.accessioned 2014-05-22T15:31:44Z
dc.date.available 2014-05-22T15:31:44Z
dc.date.created 2014-05
dc.date.issued 2014-04-03
dc.date.submitted May 2014
dc.date.updated 2014-05-22T15:31:44Z
dc.description.abstract The Human Genome Project, initiated in 1990, creates an enormous amount of excitement in human genetics—a field of study that seeks answers to the understanding of human evolution, diseases and development, gene therapy, and preventive medicine. The first completion of a human genome in 2003 and the breakthroughs of sequencing technologies in the past few years deliver the promised benefits of genome studies, especially in the roles of genomic variability and human health. However, intensive resource requirements and the associated costs make it infeasible to experimentally verify the effect of every genetic variation. At this stage of genome studies, in silico predictions play an important role in identifying putative functional variants. The most common practice for genome variant evaluation is based on the evolutionary conservation at the mutation site. Nonetheless, sequence conservation is not the absolute predictor for deleteriousness since phylogenetic diversity of aligned sequences used to construct the prediction algorithm has substantial effects on the analysis. This dissertation aims at overcoming the weaknesses of the conservation-based assumption for predicting the variant effects. The dissertation describes three different integrative computational approaches to identify a subset of high-priority amino acid mutations, derived from human genome data. The methods investigate variant-function relationships in three aspects of genome studies—personal genomics, genomics of epilepsy disorders, and genomics of variable drug responses. For genetic variants found in genomes of healthy individuals, an eight-level variant classification scheme is implemented to rank variants that are important towards individualized health profiles. For candidate genetic variants of epilepsy disorders, a novel 3-dimensional structure-based assessment protocol for amino acid mutations is established to improve discrimination between neutral and causal variants at less conserved sites, and to facilitate variant prioritization for experimental validations. For genomic variants that may affect inter-individual variability in drug responses, an explicit structure-based predictor for structural disturbances is developed to efficiently evaluate unknown variants in pharmacogenes. Overall, the three integrative approaches provide an opportunity for examining the effects of genomic variants from multiple perspectives of genome studies. They also introduce an efficient way to catalog amino acid variants on a large scale genome data.
dc.description.degree Ph.D.
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/51869
dc.language.iso en_US
dc.publisher Georgia Institute of Technology
dc.subject Human genome variations
dc.subject Single nucleotide polymorphisms
dc.subject Missense mutations
dc.subject Amino acid mutations
dc.subject Integrative analysis
dc.subject Functional assessment
dc.subject.lcsh Functional genomics
dc.subject.lcsh Computer simulation
dc.subject.lcsh Mutation (Biology)
dc.subject.lcsh Amino acids
dc.title Functional assessments of amino acid variation in human genomes
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.advisor Gibson, Greg
local.contributor.corporatename College of Sciences
local.contributor.corporatename School of Biological Sciences
relation.isAdvisorOfPublication 5606ef18-bd5a-4b7b-b3fc-05821bf66602
relation.isOrgUnitOfPublication 85042be6-2d68-4e07-b384-e1f908fae48a
relation.isOrgUnitOfPublication c8b3bd08-9989-40d3-afe3-e0ad8d5c72b5
thesis.degree.level Doctoral
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