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Statistical Methods For Detecting Genetic Risk Factors of a Disease with Applications to Genome-Wide Association Studies

Ali, Fadhaa (2015) Statistical Methods For Detecting Genetic Risk Factors of a Disease with Applications to Genome-Wide Association Studies. Doctor of Philosophy (PhD) thesis, University of Kent,. (KAR id:47963)

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Abstract

This thesis aims to develop various statistical methods for analysing the data derived from genome wide association studies (GWAS).

Although GWAS have identified many potential genetic factors in the genome that affect the risks to complex

detecting new genetic risk variants can be improved by considering multiple genetic variants simultaneously with novel statistical methods.

We applied our methods to two GWAS datasets on coronary artery disease (CAD) and hypertension (HT), detecting several new risk haplotypes and recovering a number of the existing disease-associated genetic variants in the literature.

Item Type: Thesis (Doctor of Philosophy (PhD))
Thesis advisor: Zhang, Jian
Thesis advisor: Wang, Xue
Uncontrolled keywords: EM algorithm, mixture model, permutation test, logistic regression, clustering, risk haplotypes, risk genes, coronary artery disease, hypertension, genome wide association, disease-risk haplotypes, WTCCC
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Users 1 not found.
Date Deposited: 13 Apr 2015 10:14 UTC
Last Modified: 15 Apr 2020 03:06 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/47963 (The current URI for this page, for reference purposes)
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