Type
Text
Type
Dissertation
Advisor
Stephen J. Finch | Mendell, Nancy R. | Wei Zhu | Derek Gordon.
Date
2010-12-01
Keywords
Genetics -- Statistics | Double Sampling Method, Genome-wide Association Studies, Genotype Imputation, Likelihood Ratio Test Allowing for Errors
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/11401/72722
Publisher
The Graduate School, Stony Brook University: Stony Brook, NY.
Format
application/pdf
Abstract
Genotype imputation provides an essential technique for genome-wide association studies (GWAS) with hundreds of thousands of SNPs. Understanding the connection between imputation inconsistencies and the power to detect association at imputed markers or the disease genes close to them is important for the optimal design of imputation-based GWAS since genotype misclassification can significantly decrease statistical power to detect association. Double sampling of genotypes is a statistical procedure in which a portion of subjects receive a second and more precise genotyping. This paper applies the likelihood ratio test allowing for errors (LRT-AE), which incorporates double sample information for genotypes on a sub-sample of cases/controls, to correct for imputation inconsistencies. Parameters used to determine the log likelihoods are determined using the Expectation-Maximization (EM) algorithm. To compare the performance of the LRT-AE with the performance of the likelihood ratio test (LRT), which makes no adjustment for imputation inconsistencies, I perform simulation studies using a factorial design with high and low settings of: disease minor allele frequency (MAF), heterozygote relative risk, mode of inheritance (MOI), disease prevalence, and proportion of double sampled subjects. The LRT-AE method maintains correct type I error rates for all null simulations and all significance level thresholds (5%, 1%). Power improvement, however, is not significant unless more than 50% of subjects are in the double sampled group. Unbiased estimates of imputation inconsistency rates are also obtained from the LRT-AE method.
Recommended Citation
Yuan, Qilong, "Application of Double Sampling to Combine Measured and Imputed Genotype Data in Genetic Association Studies" (2010). Stony Brook Theses and Dissertations Collection, 2006-2020 (closed to submissions). 1925.
https://commons.library.stonybrook.edu/stony-brook-theses-and-dissertations-collection/1925