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Semi-Parametric Test Based on Spline Smoothing for Genetic Association Studies Under Stratified Populations

Zhang, Qi
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1172597104

Abstract Details

2007, PhD, University of Cincinnati, Medicine : Biostatistics (Environmental Health).
Several approaches are available to adjust for population stratification in genetic association studies. However, these methods either have low power and can not control for population stratification when population stratification becomes strong or require unrealistic assumptions. Here, I propose a semi-parametric test based on penalized spline smoothing to adjust for population stratification in genetic association studies. In my method, at first, a genetic background variable is derived for each individual by using principal component analysis at a set of independent markers; and then the relationship between trait values, genotypic scores at candidate markers, and the genetic background variable are modeled through a semi-parametric model. Penalized spline smoothing can be expressed in a linear mixed model framework, which allows such model to be fitted using standard mixed model software, and also allows the fitting of complex models. I conducted coalescence simulations as well as the simulation using HapMap data to compare the power and type I error of this test with two existing methods, Genomic Control (GC) and Structure Association (SA). My method not only has lower false positive rate but also has higher sensitivity to detect the true association in the presence of population stratification compared with the GC and SA methods. In most cases I considered, my method has higher power; and in the case of very high level of genetic heterogeneity and greater differential prevalence, my method has substantially higher power than that of the other two methods.
Dr. Ranjan Deka (Advisor)
126 p.
Biology, Biostatistics
population stratification; semi-parametric model; penalized spline smoothing; principal component

Recommended Citations

Citations

  • Zhang, Q. (2007). Semi-Parametric Test Based on Spline Smoothing for Genetic Association Studies Under Stratified Populations [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1172597104

    APA Style (7th edition)

  • Zhang, Qi. Semi-Parametric Test Based on Spline Smoothing for Genetic Association Studies Under Stratified Populations. 2007. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1172597104.

    MLA Style (8th edition)

  • Zhang, Qi. "Semi-Parametric Test Based on Spline Smoothing for Genetic Association Studies Under Stratified Populations." Doctoral dissertation, University of Cincinnati, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1172597104

    Chicago Manual of Style (17th edition)

ucin1172597104
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© 2007, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.