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Application of a Bayesian Method for Optimal Subset Regression to Linkage Analysis of Q1 and Q2
Author(s) -
Ju Suh Young,
Finch Stephen J.,
Mendell Nancy R.
Publication year - 2001
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.2001.21.s1.s706
Subject(s) - linkage (software) , bayesian probability , regression analysis , regression , statistics , mathematics , econometrics , biology , genetics , gene
We explore an approach that allows us to consider a trait for which we wish to determine the optimal subset of markers out of a set of p ≥ 3 candidate markers being considered in a linkage analysis. The most effective analysis would find the model that only includes the q markers closest to the q major genes which determine the trait. Finding this optimal model using classical “frequentist” multiple regression techniques would require consideration of all 2 p possible subsets. We apply the work of George and McCulloch [J Am Stat Assoc 88:881–9, 1993], who have developed a Bayesian approach to optimal subset selection regression, to a modification of the Baseman ‐ Elston linkage statistic [Elston et al., Genet Epidemiol 19:1–17, 2000] in the analysis of the two quantitative traits simulated in Problem 2. The results obtained using this Bayesian method are compared to those obtained using (1) multiple regression and (2) the modified Haseman‐Elston method (single variable regression analysis). We note upon doing this that for both Q1 and Q2, (1) we have extremely low power with all methods using the samples as given and have to resort to combining several simulated samples in order to have power of 50%, (2) the multivariate analysis does not have greater power than the univariate analysis for these traits, and (3) the Bayesian approach identifies the correct model more frequently than the frequentist approaches but shows no clear advantage over the multivariate approach. © 2001 Wiley‐Liss, Inc.