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Statistical methods for detecting genotype-phenotype association in the presence of environmental covariates
Norwegian University of Science and Technology, Faculty of Information Technology, Mathematics and Electrical Engineering, Department of Mathematical Sciences.
2013 (English)MasteroppgaveStudent thesis
Abstract [en]

This thesis shows how statistical methods based on logistic regression models can be used to analyze and interpret biological data. In genome-wide association stud- ies, the aim is to detect association between genetic markers and a given phenotype. This thesis considers a situation where the phenotype is the absence or presence of a common disease, the genetic marker is a biallelic single nucleotide polymorphism (SNP), and environmental covariates are available. The main goal is to study and compare four statistical methods (Score test, Likelihood ratio test, Wald test and Cochran-Armitage test for trend) which, by using different approaches, test the hypothesis about whether there is an association or not between the disease and the genetic marker. The methods are applied to simulated datasets in order to measure their test size and statistical power, and to compare them. Interaction between the genetic marker and the environmental effect is also considered, and strategies for simulating cohort and case-control data with genotype and environ- mental covariates are studied. The power simulations show that methods based on logistic regression models are appropriate for detecting genotype-phenotype association, but when the environ- mental effect is moderate, a simpler method (Cochran-Armitage test for trend) which does not require model fitting at all, is adequate. When an interaction effect is included in the model, the hypothesis testing becomes more complex. Several possible approaches to this problem are discussed.

Place, publisher, year, edition, pages
Institutt for matematiske fag , 2013. , 84 p.
URN: urn:nbn:no:ntnu:diva-22628Local ID: ntnudaim:8972OAI: diva2:650404
Available from: 2013-09-20 Created: 2013-09-20 Last updated: 2013-09-20Bibliographically approved

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