Bayesian methodology for genetics of complex diseases

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  author =       "Carla Chia-Ming Chen",
  title =        "Bayesian methodology for genetics of complex
  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 =          "",
  URL =          "",
  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",

Genetic Programming entries for Carla Chia-Ming Chen