Variance Components based models are commonly used for linkage and association mapping of quantitative traits. Score Tests based on these models are generally more robust to various modeling assumptions than the corresponding likelihood ratio tests. They are also computationally much simpler than the likelihood ratio tests, making them the natural choice for whole genome scans, which have become increasingly common with the emergence of high-throughput genotyping technologies. However the popularity of score statistics have
been limited, due to several practical issues, such as lack of availability of software and guidelines for choice of score statistic variants. In this dissertation, we develop novel score statistics for both linkage and association mapping, elucidate the theoretical properties of these and of the existing variants, and also compare some of the existing and proposed score variants using simulation. Analytical arguments and simulation results are used to develop guidelines for choice of appropriate score variants under different practical situations.
In this dissertation, we are primarily concerned with identifying robust and powerful score statistics for detecting genetic susceptibility loci for complex diseases by mapping underlying quantitative phenotypes. Unlike Mendelian disorders, complex diseases in humans typically have a large number of modest genetic effects, which cumulatively have a significant impact on the disease. The work in this dissertation is aimed at maximizing the power of genome scans to detect more of these small genetic effects. This is of considerable public health significance, as the identified genetic variants can be followed up to gain important insights into the etiology of the disease, which can further lead to development of screening tests and preventive and therapeutic interventions for complex diseases.
| Identifer | oai:union.ndltd.org:PITT/oai:PITTETD:etd-07292008-115432 |
| Date | 28 September 2008 |
| Creators | Bhattacharjee, Samsiddhi |
| Contributors | Dr. Daniel E. Weeks, Dr. Eleanor Feingold, Dr. Bernard J. Devlin, Dr. Michael M. Barmada |
| 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-07292008-115432/ |
| 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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