Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.558459
Title: Imputation aided analysis of the association between autoimmune diseases and the MHC
Author: Moutsianas, Loukas
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2011
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Abstract:
The Major Histocompatibility Complex (MHC) is a genomic region in chromosome 6 which has been consistently found to be associated with the risk of developing virtually all common autoimmune diseases. Although its importance in disease pathogenesis has been known for decades, efforts to disentangle the roles of the classical human leukocyte antigens (HLA) and other variants responsible for the susceptibility to disease have often met with limited success, owing to the complex structure and extreme heterogeneity of the region. In this thesis, I interrogate the MHC for association with three common autoimmune diseases, ankylosing spondylitis, psoriasis and multiple sclerosis, with the aim of confirming the previously-reported associations and of identifying novel ones. To do so, I employ a systematic, joint analysis of single nucleotide polymorphism (SNP) and HLA allele data, in a logistic regression framework, using a recently developed algorithm to predict the HLA alleles for samples where such information is unavailable. To ensure the reliability of the analysis, I apply stringent quality control procedures and integrate over the uncertainty of the HLA allele predictions. Moreover, I resolve the haplotype phase of individuals from the HapMap project to create reliable reference panels, used in both HLA prediction and in quality control procedures. By directly testing HLA subtypes for association with the disease, the power to detect such associations is increased. I present the results of the analysis on the three disease phenotypes and discuss the evidence for important novel findings amongst both SNPs and HLA alleles in two of the diseases. In the final part of this thesis, I introduce a novel, model-based approach to detect inconsistencies in the data and show how it can be used to flag problematic SNPs which conventional quality control procedures may fail to identify.
Supervisor: McVean, Gilean Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.558459  DOI: Not available
Keywords: Multiple Sclerosis ; Mathematical genetics and bioinformatics (statistics) ; Genetics (life sciences) ; statistical genetics ; association studies ; autoimmune diseases ; multiple sclerosis ; ankylosing spondylitis ; psoriasis ; Li & Stephens model
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