Within recent years there has been a move towards development of regression inspired methods for analysis of genomewide association studies of complex diseases. This is because multiple testing methods, such as the Bonferroni correction, tend to impose stringent significance thresholds and consequently, unless the study is very large, can reliably identify only those genomic regions with very strong signals of disease-gene association. However many complex diseases are suspected to be the result of the cumulative action of many loci, each having a small effect and there is a high probability the association signals in such studies will in fact be moderate and, furthermore, that extremely strong signals will be very rare. Although methods with higher power than the Bonferroni correction have been proposed, these tend to produce more false positive findings. This challenging problem of analysis methodology for genomewide association studies that is more efficient than existing approaches, but with false positive findings comparable with Bonferroni, is addressed in this thesis. A novel method based on nonparametric regression, capable of reliably identifying significant regions of disease-gene association in data from high dimensional genomewide studies, is developed and evaluated. The method is model-free and establishes significance thresholds that inherently account for the correlation (linkage disequilibrium) structure in the data through a tuning (bandwidth) parameter and assigned weights. A theoretically supported, computationally efficient method for obtaining the optimal tuning parameter is proposed and evaluated using simulations. Results of extensive evaluations and comparisons with existing methods show that the nonparametric approach is not only powerful but also leads to substantial reduction in false positive findings. The method is illustrated using data from the Wellcome Trust Case Control Consortium study (2007) of Crohn's disease.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:625495 |
| Date | January 2014 |
| Creators | Kirdwichai, Pianpool |
| Publisher | University of Reading |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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