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|Title: ||Linkage and association mapping for quantitative phenotypes in isolated populations|
|Authors: ||Franklin, Christopher Steven|
|Supervisor(s): ||Wilson, Jim|
|Issue Date: ||25-Nov-2011|
|Publisher: ||The University of Edinburgh|
|Abstract: ||Many complex diseases are known to have a substantial genetically heritable
component. Elucidation of these genetic risk factors provides increased
knowledge of the biological mechanisms that result in the diseases while also
presenting new potential targets for therapy. This thesis explores the
methodology of mapping genetic loci using isolated populations in the context
of quantitative trait analysis.
Chapter 1 explores the rational for the project, discussing the benefits of using
quantitative traits rather than binary disease status and the pros and cons of
using isolated populations. This is followed by a brief history of genetic mapping
with reference to type 2 diabetes mellitus (T2D) and related quantitative traits.
Chapter 2 introduces the methods used in this thesis. This includes strategies to
deal with medication, methods to determine kinship between individuals,
linkage analysis, association analysis and meta‐analysis of multiple studies.
Chapter 3 presents linkage analysis of T2D related traits carried out in 2 – 4
populations depending on availability of the traits and appropriate marker data. Chapter 4 presents the results of association analysis for T2D related traits in 3
– 5 populations using genome‐wide SNP data. The results using the alternate
methods described in chapter 2 are compared using fasting glucose as this was
the most widely measured phenotype.
Chapter 5 introduces additional traits derived by pulse wave analysis and
discusses their relevance to metabolic disease before presenting association
analysis using the preferred method from chapter 4.
An overall discussion of the strengths and weaknesses of the analysis is given in
|Sponsor(s): ||Economic and Social Research Council (ESRC)|
|Keywords: ||genetic association|
|Appears in Collections:||School of Clinical Sciences thesis and dissertation collection|
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