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Approximate Bayesian computation for modular inference problems with many parameters: the example of migration rates.
MedLine Citation:
PMID:  23301635     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
We propose a two-step procedure for estimating multiple migration rates in an approximate Bayesian computation (ABC) framework, accounting for global nuisance parameters. The approach is not limited to migration, but generally of interest for inference problems with multiple parameters and a modular structure (e.g. independent sets of demes or loci). We condition on a known, but complex demographic model of a spatially subdivided population, motivated by the reintroduction of Alpine ibex (Capra ibex) into Switzerland. In the first step, the global parameters ancestral mutation rate and male mating skew have been estimated for the whole population in Aeschbacher et al. (Genetics 2012; 192: 1027). In the second step, we estimate in this study the migration rates independently for clusters of demes putatively connected by migration. For large clusters (many migration rates), ABC faces the problem of too many summary statistics. We therefore assess by simulation if estimation per pair of demes is a valid alternative. We find that the trade-off between reduced dimensionality for the pairwise estimation on the one hand and lower accuracy due to the assumption of pairwise independence on the other depends on the number of migration rates to be inferred: the accuracy of the pairwise approach increases with the number of parameters, relative to the joint estimation approach. To distinguish between low and zero migration, we perform ABC-type model comparison between a model with migration and one without. Applying the approach to microsatellite data from Alpine ibex, we find no evidence for substantial gene flow via migration, except for one pair of demes in one direction.
Authors:
S Aeschbacher; A Futschik; M A Beaumont
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2013-1-10
Journal Detail:
Title:  Molecular ecology     Volume:  -     ISSN:  1365-294X     ISO Abbreviation:  Mol. Ecol.     Publication Date:  2013 Jan 
Date Detail:
Created Date:  2013-1-10     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9214478     Medline TA:  Mol Ecol     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Copyright Information:
© 2013 Blackwell Publishing Ltd.
Affiliation:
Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, EH9 3JT, UK; Institute of Science and Technology Austria (IST Austria), 3400, Klosterneuburg, Austria.
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