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Title: Regression-based analytical methods in the localisation of disease related genetic variants
Author: Barber, M. J.
Awarding Body: University of Cambridge
Current Institution: University of Cambridge
Date of Award: 2007
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I examine the use of a regression approach, in particular a generalised estimating equation (GEE), which allows for the estimation of a working correlation matrix as well as non-normality in the residual distribution, in three different genetic applications. In all applications, the statistical aim is to detect and localise disease related genetic variants (DRGVs), should they exist. The first application is to quantitative trait linkage analysis with unselected sib-pairs, in which localisation of a locus governing a quantitative trait phenotype is of interest, given genetic marker data. A generalised linear model approach, which is equivalent to a GEE with an independence correlation matrix, is shown to extend previously proposed approaches based on ordinary least squares regression, by improving the specification of the residual error distribution. The second application is in the localisation of additional DRGVs on a chromosome from linkage analysis of affected sib-pair data, when a disease locus is already known to exist on the chromosome. I propose a multimarker regression approach that models the identity-by-descent states for affected sib-pairs at a series of linked markers in terms of the identity-by-descent state at the known disease locus. The third application is in the localisation of DRGVs given evidence of extreme genetic differentiation between two different populations. The populations considered could either be cases and controls or two geographically distinct populations, and, hence, might have been under differential selection pressure since common ancestry. A phase insensitive multi-single nucleotide polymorphism (SNP) strategy is implemented using a GEE to control for the non-independence between the SNPs. The relative significance of regions identified is assessed by comparison of the entire genome and the currently available SNP maps.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available