Description
- Abstract:
- nheritable differences in DNA sequence are a major focus of the genetic community, as such differences are known to play a role in phenotypic variation, disease risk and environmental response. Single nucleotide polymorphisms account for a majority of human genetic variation and are advantageous to study due to their binary allelic nature, density in the genome and low rate of mutation. The study of the non-random association of the genotypes between two SNP loci, known as Linkage Disequilibrium, is of great interest as it is reflective of a wide range of population history, genetic subdivision, natural selection, epistasis and mutation and is integral in the study of disease association. Research on the block-like patterns of LD and the existence of LD over long genomic distance has produced a variety of conflicting results. Recent evidence has suggested the block-like patterns of LD are much for complex and that LD extends significantly further that previously believed. Current methods aimed at understanding and identifying patterns and long range LD and disease association fall short and suffer from computational limitations and complexity. This dissertation focuses on statistical inference in high dimensional space as it relates to epistatic interactions in the human genome. A stochastic context free grammar model is introduced as a novel method to infer associations in genome, yielding inference on jointly occurring pairs of pairs of associations both in close and distant proximity. PCA is used to access the existence of significant allele frequency differences between various ethnic groups. In the presence of such population stratification, the ancestry groups correlated to the top principal components are modeled by independent grammars, permitting the identification of population specific interactions. This statistical model allows for a complete search of the high dimension space of genomic data in a tractable amount of time, projecting the complex association landscape onto the constrained computable space of the context free model.
- Notes:
- Thesis (Ph.D. -- Brown University (2014)
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Citation
Schuster, Jessica S.,
"A Stochastic Context Free Model of Epistatic Interaction in the Human Genome"
(2014).
Applied Mathematics Theses and Dissertations.
Brown Digital Repository. Brown University Library.
https://doi.org/10.7301/Z0KP80H0
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Applied Mathematics Theses and Dissertations
Theses and Dissertations for the Applied Mathematics department....