Power Studies of Regression-Based Linkage Methods for Selected Sibpairs in the Presence of Epistasis
Type
Text
Type
Dissertation
Advisor
Mendell, Nancy R. | Stephen J. Finch | Wei Zhu | Tao Wang.
Date
2010-08-01
Keywords
Complex Disease, Endophenotype, Disease Related Trait, Epistasis, Gene-Gene Interation, Quantitative Trait Locus, Regression-Based Linkage, Selected Sampling | Statistics -- Biology, Genetics -- Health Sciences, Epidemiology
Department
Department of Applied Mathematics and Statistics
Language
en_US
Source
This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree.
Identifier
http://hdl.handle.net/11401/72540
Publisher
The Graduate School, Stony Brook University: Stony Brook, NY.
Format
application/pdf
Abstract
Although the ubiquitousness of epistasis, or gene-gene interactions, is widely acknowledged, many commonly used quantitative-trait-locus (QTL) linkage analysis methods have been developed without explicitly modeling any dominance or epistasis effects. The power of regression-based linkage methods was investigated in this paper under a range of two-locus models of various degrees and types of epistasis. A quantitative trait is studied usually because of its association with some complex disease of interest. Therefore we introduced selection through disease affected probands, which has commonly been used in qualitative trait studies, into our QTL analysis, and compared it to random selection and selection based on individuals having abnormal quantitative trait values.
Recommended Citation
Huang, Chengrui, "Power Studies of Regression-Based Linkage Methods for Selected Sibpairs in the Presence of Epistasis" (2010). Stony Brook Theses and Dissertations Collection, 2006-2020 (closed to submissions). 1744.
https://commons.library.stonybrook.edu/stony-brook-theses-and-dissertations-collection/1744