Document Detail

A Bayesian method for the joint estimation of outcrossing rate and inbreeding depression.
MedLine Citation:
PMID:  22990309     Owner:  NLM     Status:  MEDLINE    
The population outcrossing rate (t) and adult inbreeding coefficient (F) are key parameters in mating system evolution. The magnitude of inbreeding depression as expressed in the field can be estimated given t and F via the method of Ritland (1990). For a given total sample size, the optimal design for the joint estimation of t and F requires sampling large numbers of families (100-400) with fewer offspring (1-4) per family. Unfortunately, the standard inference procedure (MLTR) yields significantly biased estimates for t and F when family sizes are small and maternal genotypes are unknown (a common occurrence when sampling natural populations). Here, we present a Bayesian method implemented in the program BORICE (Bayesian Outcrossing Rate and Inbreeding Coefficient Estimation) that effectively estimates t and F when family sizes are small and maternal genotype information is lacking. BORICE should enable wider use of the Ritland approach for field-based estimates of inbreeding depression. As proof of concept, we estimate t and F in a natural population of Mimulus guttatus. In addition, we describe how individual maternal inbreeding histories inferred by BORICE may prove useful in studies of inbreeding and its consequences.
V A Koelling; P J Monnahan; J K Kelly
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't     Date:  2012-09-19
Journal Detail:
Title:  Heredity     Volume:  109     ISSN:  1365-2540     ISO Abbreviation:  Heredity (Edinb)     Publication Date:  2012 Dec 
Date Detail:
Created Date:  2012-11-15     Completed Date:  2013-05-06     Revised Date:  2013-12-04    
Medline Journal Info:
Nlm Unique ID:  0373007     Medline TA:  Heredity (Edinb)     Country:  England    
Other Details:
Languages:  eng     Pagination:  393-400     Citation Subset:  IM    
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MeSH Terms
Bayes Theorem*
Biological Evolution*
Models, Theoretical*
Population / genetics
Reproduction / genetics
Grant Support

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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