| Model averaging in linkage analysis. | |
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MedLine Citation:
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PMID: 16652369 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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Methods for genetic linkage analysis are traditionally divided into "model-dependent" and "model-independent," but there may be a useful place for an intermediate class, in which a broad range of possible models is considered as a parametric family. It is possible to average over model space with an empirical Bayes prior that weights models according to their goodness of fit to epidemiologic data, such as the frequency of the disease in the population and in first-degree relatives (and correlations with other traits in the pleiotropic case). For averaging over high-dimensional spaces, Markov chain Monte Carlo (MCMC) has great appeal, but it has a near-fatal flaw: it is not possible, in most cases, to provide rigorous sufficient conditions to permit the user safely to conclude that the chain has converged. A way of overcoming the convergence problem, if not of solving it, rests on a simple application of the principle of detailed balance. If the starting point of the chain has the equilibrium distribution, so will every subsequent point. The first point is chosen according to the target distribution by rejection sampling, and subsequent points by an MCMC process that has the target distribution as its equilibrium distribution. Model averaging with an empirical Bayes prior requires rapid estimation of likelihoods at many points in parameter space. Symbolic polynomials are constructed before the random walk over parameter space begins, to make the actual likelihood computations at each step of the random walk very fast. Power analysis in an illustrative case is described. (c) 2006 Wiley-Liss, Inc. |
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Authors:
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Steven Matthysse |
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Publication Detail:
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Type: Comparative Study; Journal Article; Research Support, N.I.H., Extramural |
Journal Detail:
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Title: American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics Volume: 141B ISSN: 1552-4841 ISO Abbreviation: Am. J. Med. Genet. B Neuropsychiatr. Genet. Publication Date: 2006 Jun |
Date Detail:
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Created Date: 2006-05-25 Completed Date: 2006-08-16 Revised Date: 2008-05-21 |
Medline Journal Info:
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Nlm Unique ID: 101235742 Medline TA: Am J Med Genet B Neuropsychiatr Genet Country: United States |
Other Details:
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Languages: eng Pagination: 344-53 Citation Subset: IM |
Affiliation:
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Harvard Medical School, Boston, MA, USA. steven_matthysse@harvard.edu |
Export Citation:
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APA/MLA Format Download EndNote Download BibTex |
| MeSH Terms | |
Descriptor/Qualifier:
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Algorithms Bayes Theorem Computer Simulation Female Gene Frequency Genetic Predisposition to Disease / genetics Humans Linkage (Genetics) / genetics* Linkage Disequilibrium Male Markov Chains Models, Genetic* Monte Carlo Method Pedigree Probability Schizophrenia / genetics |
| Grant Support | |
ID/Acronym/Agency:
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R01-GM57453/GM/NIGMS NIH HHS; R01-MH59513/MH/NIMH NIH HHS |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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