Common diseases with a genetic basis are likely to have a very complex etiology, in which the mapping between genotype and phenotype is far from straightforward. A new comprehensive statistical and computational strategy for identifying the missing link between genotype and phenotype is proposed, which emphasizes the need to address heterogeneity in the first stage of any analysis. A simulation study comparing three unsupervised clustering methods was conducted, and the best methodBayesian Classificationwas evaluated further for its performance and applicability to real data under a wide range of simulation conditions.
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The proposed two-stage analysis strategy was then applied to late-onset Alzheimer disease data. Bayesian Classification found statistically significant clusterings for independent family-based and case-control datasets, which used the same five markers in LRRTM3 as their most influential in determining cluster assignment. In subsequent analyses to detect main effects and gene-gene interactions, markers in four genesPLAU, IDE, CDC2 and ACEwere found to be associated with late-onset Alzheimer disease in particular subsets of the data based on their LRRTM3 haplotype. While each of these genes are viable candidates for LOAD based on their known biological function, further studies are needed to replicate these statistical findings and to elucidate possible biological interaction mechanisms between LRRTM3 and these genes.
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Going forward, genetic studies will increasingly focus time and resources to collecting phenotypic data that can refine definitions or subcategories of traits or diseases and can serve as endophenotypes, which are more likely to have simple etiologies and to directly map to specific genetic markers. In the case of neurological diseases, one collection of phenotyping technologies which has matured considerably over the past five to ten years is neuroimaging. In addition, an emphasis on possible biological mechanisms of disease has positively influenced the design of behavioral assessment tools, increasing their utility as phenotyping tools, which provide endophenotypes that can be mapped to genotypic data. Methodologies enabling the integration of disparate data sources (genotyping and neuroimaging or behavioral) must be investigated in order to harness the power inherit in their complexity.
| Identifer | oai:union.ndltd.org:VANDERBILT/oai:VANDERBILTETD:etd-09272006-120618 |
| Date | 01 November 2006 |
| Creators | Thornton-Wells, Tricia Ann |
| Contributors | Marylyn D. Ritchie, Jonathan L. Haines, Michael P. McDonald, Jason H. Moore |
| Publisher | VANDERBILT |
| Source Sets | Vanderbilt University Theses |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.library.vanderbilt.edu/available/etd-09272006-120618/ |
| Rights | unrestricted, I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Vanderbilt University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
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