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Ph.D. Theses

A Computational Analysis of Human Genetic Variation

By Asif Javed
Advisor: Petros Drineas
November 5, 2008

The cost of assaying individuals for SNPs has decreased rapidly over the past few years. This has paved the way for a more statistical and computational analysis of human genetic variations. Fortunately the Linkage Disequilibrium (LD) structure of the genome, whereby neighboring SNPs exhibit varying degrees of correlation, facilitates the analysis. The thesis comprises of three such studies.

In the first study we exploit the LD structure to identify a smaller representative subset of SNPs (known as tagging SNPs) which can then be used to predict the remaining tagged SNPs. The skewed nature of modern genetic datasets, with hundreds of individuals genotyped for millions of SNPs, demands novel algorithms for genome-wide data. In the second study we describe a novel window definition, which divides long genomic datasets into contiguous non-overlapping windows of high linear structure which allows efficient extension of our tSNP selection method to genome-wide datasets.

Recombination rate plays a key role in determining the linkage structure within a region. In the third study, we use change in SNP pattern among extant haplotypes as evidence of recombinations. Using biological insight we infer the recombinational history of DNA segments.

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