- author = "Carla Chia-Ming Chen",
- title = "Bayesian methodology for genetics of complex diseases",
- school = "Past, QUT Faculties \& Divisions, Faculty of Science and Technology, Queensland University of Technology",
- year = "2010",
- address = "Australia",
- keywords = "genetic algorithms, genetic programming, gene expression programming, 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, logic tree, logicFS, Monte Carlo logic regression, genetic programming for association study, random forest, GENICA",
-
URL = "
http://eprints.qut.edu.au/43357/",
-
URL = "
http://eprints.qut.edu.au/43357/1/Carla_Chen_Thesis.pdf",
- size = "291 pages",
-
abstract = "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.",
- notes = "ID Code: 43357 Supervisors: Mengersen, Kerrie and Keith, Jonathan",
