Model selection and Bayesian methods in statistical genetics: summary of group 11 contributions to Genetic Analysis Workshop 15. Export

@article{citeulike:3025932, 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.}, address = {Department of Epidemiology, University of Texas M. D. Anderson Cancer Center, Houston, TX, USA. mdswartz@mdanderson.org}, author = {Swartz, M. D. and Thomas, D. C. and Daw, E. W. and Albers, K. and Charlesworth, J. C. and Dyer, T. C. and Fridley, B. L. and Govil, M. and Kraft, P. and Kwon, S. and Logue, M. W. and Oh, C. and Pique-Regi, R. and Saba, L. and Schumacher, F. R. and Uh, H. W.}, citeulike-article-id = {3025932}, citeulike-linkout-0 = {http://dx.doi.org/10.1002/gepi.20285}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/18046760}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=18046760}, doi = {10.1002/gepi.20285}, issn = {0741-0395}, journal = {Genetic epidemiology}, keywords = {assoc, humgen, mining, stat}, posted-at = {2009-07-01 15:45:38}, priority = {2}, title = {Model selection and Bayesian methods in statistical genetics: summary of group 11 contributions to Genetic Analysis Workshop 15.}, url = {http://dx.doi.org/10.1002/gepi.20285}, volume = {31 Suppl 1}, year = {2007} }

TY - JOUR ID - citeulike:3025932 L3 - citeulike-article-id:3025932 N2 - 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. JF - Genetic epidemiology SN - 0741-0395 AD - Department of Epidemiology, University of Texas M. D. Anderson Cancer Center, Houston, TX, USA. mdswartz@mdanderson.org TI - Model selection and Bayesian methods in statistical genetics: summary of group 11 contributions to Genetic Analysis Workshop 15. VL - 31 Suppl 1 KW - assoc KW - humgen KW - mining KW - stat AU - Swartz, MD AU - Thomas, DC AU - Daw, EW AU - Albers, K AU - Charlesworth, JC AU - Dyer, TC AU - Fridley, BL AU - Govil, M AU - Kraft, P AU - Kwon, S AU - Logue, MW AU - Oh, C AU - Pique-Regi, R AU - Saba, L AU - Schumacher, FR AU - Uh, HW PY - 2007/// UR - http://dx.doi.org/10.1002/gepi.20285 ER -