Work

Model selection and Bayesian methods in statistical genetics: Summary of Group 11 contributions to Genetic Analysis Workshop 15

Year: 2007

Type: article

Abstract: The research presented in group 11 of the Genetic Analysis Workshop 15 (GAW15) falls into two major themes: Model selection approaches for gene mapping (both Bayesian and Frequentist); and other Bayesian methods. These methods either allow relaxation of some of the common assumptions, such as mode of inheritance, for studying complicated genetic systems, or allow incorporation of additional information into the model. Over half of the groups applied model selection methods on all three data sets, using models in which genetic markers were used as predictors for linkage, phenotype expression, or transmission to an affected offspring. Most groups employed variations of Stochastic Search Variable Selection as the model selection method of choice. A brief review of this class of methods is given in this summary paper, followed by highlights of other methods and overall summaries of each contribution to the GAW15 presentation group 11. These group contributions exhibit the value of framing genetic problems in terms of model selection, and highlight the impact of variable selection for gene mapping.

Source: Genetic Epidemiology

DOI: 10.1002/gepi.20285

Authors: Michael D. Swartz, Duncan C. Thomas, E. Warwick Daw, on behalf of Group 11

Institutions: The University of Texas MD Anderson Cancer Center, University of Southern California, Washington University in St. Louis

Language: English

Topic: Genetic Associations and Epidemiology

Subfield: Genetics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funders: National Institutes of Health, National Cancer Institute

Awards: Bayesian Hierarchical Risk Models: Nutrition, Genes, & Environment Interactions, G7892654806, M.D. Anderson Education Program in Cancer Prevention