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A comparative method for both discrete and continuous characters using the threshold model.
MedLine Citation:
PMID:  22218305     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
Abstract The threshold model developed by Sewall Wright in 1934 can be used to model the evolution of two-state discrete characters along a phylogeny. The model assumes that there is a quantitative character, called liability, that is unobserved and that determines the discrete character according to whether the liability exceeds a threshold value. A Markov chain Monte Carlo algorithm is used to infer the evolutionary covariances of the liabilities for discrete characters, sampling liability values consistent with the phylogeny and with the observed data. The same approach can also be used for continuous characters by assuming that the tip species have values that have been observed. In this way, one can make a comparative-methods analysis that combines both discrete and continuous characters. Simulations are presented showing that the covariances of the liabilities are successfully estimated, although precision can be achieved only by using a large number of species, and we must always worry whether the covariances and the model apply throughout the group. An advantage of the threshold model is that the model can be straightforwardly extended to accommodate within-species phenotypic variation and allows an interface with quantitative-genetics models.
Authors:
Joseph Felsenstein
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Publication Detail:
Type:  Journal Article     Date:  2011-12-15
Journal Detail:
Title:  The American naturalist     Volume:  179     ISSN:  1537-5323     ISO Abbreviation:  Am. Nat.     Publication Date:  2012 Feb 
Date Detail:
Created Date:  2012-01-05     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  2984688R     Medline TA:  Am Nat     Country:  United States    
Other Details:
Languages:  eng     Pagination:  145-56     Citation Subset:  IM    
Affiliation:
Department of Genome Sciences and Department of Biology, University of Washington, Seattle, Washington 98195.
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