Age-related maculopathy (ARM) is a common cause of irreparable vision loss in industrialized countries. The disease is characterized by progressive loss of central vision making everyday tasks challenging. The etiology is complex and has both an environmental and a strong genetic components. The public health relevance of the work is to improve the understanding genetic causes in the disease etiology and ultimately to lead to better disease management and prevention. From my ARM work, I present four papers covering range of statistical approaches. The first paper presents fine-mapping efforts, using both linkage and association methods, under previously identified linkage peaks on chromosomes 1q31 and 10q26. We replicate the discovery of the complement factor H (CFH) gene on 1q31 and identify a novel locus, harboring three closely linked genes (PLEKHA1, LOC387715, and HTRA1), on 10q26. Both discoveries have been widely replicated. In the next paper I present meta-analysis of 11 CFH and 5 LOC387715 data sets. We also replicate these findings in two independent case-control cohorts, including one cohort, where ARM status was not a factor in the ascertainment. In the third paper we replicate discoveries of new complement related loci (C2 and CFB) on chromosome 19p13 as well as developing classification models based on SNPs from CFH, LOC387715, and C2. The last paper focuses on applying statistical techniques from the diagnostic medicine literature to ARM. We comment on the importance of understanding the difference and similarities between different goals of genetic studies: improving etiological understanding or finding variants that discriminate well between cases and controls. This work is particularly relevant today when there has been explosion in the availability of direct-to-consumer DNA tests.
In addition to carrying out linkage and association analysis, I also have extended the statistical theory behind score-based linkage analyses for X chromosomal markers. This work has public health relevance because many complex common diseases have sex-specific differences, such as prevalence and age of onset. Modeling those appropriately with powerful and robust methods will bring an improved understanding of their genetic basis.
| Identifer | oai:union.ndltd.org:PITT/oai:PITTETD:etd-04072009-094825 |
| Date | 29 June 2009 |
| Creators | Jakobsdóttir, Jóhanna |
| Contributors | Daniel E. Weeks, Eleanor Feingold, Sati Mazumdar, Lisa A. Weissfeld |
| Publisher | University of Pittsburgh |
| Source Sets | University of Pittsburgh |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.library.pitt.edu/ETD/available/etd-04072009-094825/ |
| Rights | restricted, 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 University of Pittsburgh 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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