Semi-Parametric Testing of Single-Nucleotide Polymorphism Effects On Continuous Outcome

<p> To detect association between Single Nucleotide Polymorphism (SNP) and disease, extensive investigations have been conducted. Most work has focused on testing SNP effect one by one due to the difficulty of including a large number of SNPs in a statistical model. In this thesis, we explored a semi-parametric method built upon kernel machines to include multiple SNPs in a model. The information of SNPs similarity calculated based on identity-by-state (IBS) algorithm was included in a kernel matrix. We evaluated the method via various scenarios based on simulation studies. The method is efficient to estimate and test SNP effects. The testing power increases with the increase of sample size and the strength of SNP effect. The testing power also increases with the increase of SNPs similarity level and decrease of sparsity level of kernel matrix. We applied this method to a SNPs-Lung function data to test the SNP effects on lung function measured by Forced Vital Capacity.</p>

Name thesis:degree_name

M.S.P.H.

Level thesis:degree_level

Campus Access Thesis

Discipline thesis:degree_discipline

Epidemiology and Biostatistics

Year

2012

Author dc:creator

• He, Hong

Contributors dc:contributor

• Hongmei Zhang

• Biostatistics
• Physical Sciences and Mathematics
• Statistics and Probability

Statement dc:rights

• © 2012, Hong He

Repository record dc:identifier

https://scholarcommons.sc.edu/etd/549

OAI identifier oai:identifier

oai:scholarcommons.sc.edu:etd-1550