title: A method for evaluating the results of Bayesian model selection: application to linkage analyses of attributes determined by two or more genes.
abstract: OBJECTIVE
  We apply and evaluate the intrinsic Bayes factor (IBF) of Berger and Pericchi [J Am Stat Assoc 1996;91:109-122; Bayesian Statistics, Oxford University Press, vol 5, 1996] to linkage analyses done using the stochastic search variable selection (SSVS) method of George and McCulloch [J Am Stat Assoc 1993;88:881-889] as proposed by Suh et al. [Genet Epidemiol 2001;21(suppl 1):S706-S711].
  
  
  METHODS
  We consider 20 simulations of linkage data obtained under two different generating models. The SSVS is applied to a multiple regression extension [Genet Epidemiol 2001;21(suppl 1): S706-S711] of the Haseman-Elston [Behav Genet 1972;2:3-19; Genet Epidemiol 2000;19:1-17] methods. Four prior distributions are considered. We apply the IBF criterion to those samples where different prior distributions result in different top models.
  
  
  RESULTS
  In those samples where three different models were obtained using the four priors, application of the IBFs eliminated one of the two wrong models in 4 out of 5 situations. Further elimination using the IBF criterion for situations with two different subsets did not serve as well.
  
  
  CONCLUSIONS
  When different priors result in three or more different subsets of markers, one can use the IBF to get this number down to two for consideration. When two subsets result we recommend that both be considered.
authors.affiliation: Department of Statistics, Seoul National University, Seoul, Korea.
authors.forename: Young Ju
authors.initials: YJ
authors.lastname: Suh
authors.forename: Kenny Q
authors.initials: KQ
authors.lastname: Ye
authors.forename: Nancy R
authors.initials: NR
authors.lastname: Mendell
mesh_terms: D001499:Bayes Theorem; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D008040:Genetic Linkage; D005819:Genetic Markers; D006801:Humans
chemical_list: D005819:Genetic Markers