Bayesian methodology for genetics of complex diseases
Chen, Carla Chia-Ming (2010) Bayesian methodology for genetics of complex diseases. PhD by Publication, Queensland University of Technology.
Genetic research of complex diseases is a challenging, but exciting, area of research. The early development of the research was limited, however, until the completion of the Human Genome and HapMap projects, along with the reduction in the cost of genotyping, which paves the way for understanding the genetic composition of complex diseases. In this thesis, we focus on the statistical methods for two aspects of genetic research: phenotype definition for diseases with complex etiology and methods for identifying potentially associated Single Nucleotide Polymorphisms (SNPs) and SNP-SNP interactions. With regard to phenotype definition for diseases with complex etiology, we firstly investigated the effects of different statistical phenotyping approaches on the subsequent analysis. In light of the findings, and the difficulties in validating the estimated phenotype, we proposed two different methods for reconciling phenotypes of different models using Bayesian model averaging as a coherent mechanism for accounting for model uncertainty. In the second part of the thesis, the focus is turned to the methods for identifying associated SNPs and SNP interactions. We review the use of Bayesian logistic regression with variable selection for SNP identification and extended the model for detecting the interaction effects for population based case-control studies. In this part of study, we also develop a machine learning algorithm to cope with the large scale data analysis, namely modified Logic Regression with Genetic Program (MLR-GEP), which is then compared with the Bayesian model, Random Forests and other variants of logic regression.
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|Item Type:||QUT Thesis (PhD by Publication)|
|Supervisor:||Mengersen, Kerrie & Keith, Jonathan|
|Keywords:||Bayesian, statistics, genetics, phenotype analysis, complex diseases, complex etiology, model comparison, latent class analysis, grade of membership, fuzzy clustering, item response theory, migraine, twin study, heritability, genome-wide linkage analysis, deviance information criteria, model averaging, MCMC, genomewide association studies, epistasis, logistic regression, stochastic search algorithm, case-control studies, Type I diabetes, single nucleotide polymorphism, gene expression programming, logic tree, logicFS, Monte Carlo logic regression, genetic programming for association study, random forest, GENICA|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Science and Technology|
|Institution:||Queensland University of Technology|
|Deposited On:||18 Jul 2011 05:18|
|Last Modified:||18 Jul 2011 05:18|
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