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Titolo: Application of a Bayesian method for optimal subset regression to linkage analysis of Q1 and Q2
Autore: Suh, YJ; Finch, SJ; Mendell, NR;
 Indirizzi:
 SUNY Stony Brook, Dept Appl Math & Stat, Stony Brook, NY 11794 USA SUNY Stony Brook Stony Brook NY USA 11794 Stat, Stony Brook, NY 11794 USA
 Titolo Testata:
 GENETIC EPIDEMIOLOGY
,
volume: 21,
anno: 2001,
supplemento:, 1
pagine: S706  S711
 SICI:
 07410395(2001)21:<S706:AOABMF>2.0.ZU;2W
 Fonte:
 ISI
 Lingua:
 ENG
 Soggetto:
 QUANTITATIVE TRAIT;
 Keywords:
 Bayesian optimal subset regression; identical by descent; linkage analysis;
 Tipo documento:
 Article
 Natura:
 Periodico
 Settore Disciplinare:
 Life Sciences
 Citazioni:
 6
 Recensione:
 Indirizzi per estratti:
 Indirizzo: Finch, SJ SUNY Stony Brook, Dept Appl Math & Stat, Stony Brook, NY 11794 USA SUNY Stony Brook Stony Brook NY USA 11794 y Brook, NY 11794 USA



 Citazione:
 Y.J. Suh et al., "Application of a Bayesian method for optimal subset regression to linkage analysis of Q1 and Q2", GENET EPID, 21, 2001, pp. S706S711
Abstract
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 greater thanor equal to 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 88:8819, 1993], who have developed a Bayesian approach to optimal subset selection regression, to a modification of the Haseman  Elston linkage statistic [Elston et al., Genet Epidemiol 19:117, 2000] in the analysis of the two quantitative traits simulatedin Problem 2. The results obtained using this Bayesian method are comparedto those obtained using (1) multiple regression and (2) the modified HasemanElston 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. (C) 2001 WileyLiss, Inc.
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Documento generato il 06/04/20 alle ore 08:34:01