Estimating Population Parameters using the Structured Serial Coalescent with Bayesian MCMC Inference when some Demes are Hidden
Using the structured serial coalescent with Bayesian MCMC and serial samples, we estimate population size when some demes are not sampled or are hidden, ie ghost demes. It is found that even with the presence of a ghost deme, accurate inference was possible if the parameters are estimated with the t...
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Series: | Evolutionary Bioinformatics |
Online Access: | https://doi.org/10.1177/117693430600200026 |
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doaj-fe09845120e640af997113fca68e75742020-11-25T02:23:02ZengSAGE PublishingEvolutionary Bioinformatics1176-93432006-01-01210.1177/117693430600200026Estimating Population Parameters using the Structured Serial Coalescent with Bayesian MCMC Inference when some Demes are HiddenGreg Ewing0Allen Rodrigo1Bioinformatics Institute, University of Auckland, Private Bag 92019, Auckland, New Zealand.Bioinformatics Institute, University of Auckland, Private Bag 92019, Auckland, New Zealand.Using the structured serial coalescent with Bayesian MCMC and serial samples, we estimate population size when some demes are not sampled or are hidden, ie ghost demes. It is found that even with the presence of a ghost deme, accurate inference was possible if the parameters are estimated with the true model. However with an incorrect model, estimates were biased and can be positively misleading. We extend these results to the case where there are sequences from the ghost at the last time sample. This case can arise in HIV patients, when some tissue samples and viral sequences only become available after death. When some sequences from the ghost deme are available at the last sampling time, estimation bias is reduced and accurate estimation of parameters associated with the ghost deme is possible despite sampling bias. Migration rates for this case are also shown to be good estimates when migration values are low.https://doi.org/10.1177/117693430600200026 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Greg Ewing Allen Rodrigo |
spellingShingle |
Greg Ewing Allen Rodrigo Estimating Population Parameters using the Structured Serial Coalescent with Bayesian MCMC Inference when some Demes are Hidden Evolutionary Bioinformatics |
author_facet |
Greg Ewing Allen Rodrigo |
author_sort |
Greg Ewing |
title |
Estimating Population Parameters using the Structured Serial Coalescent with Bayesian MCMC Inference when some Demes are Hidden |
title_short |
Estimating Population Parameters using the Structured Serial Coalescent with Bayesian MCMC Inference when some Demes are Hidden |
title_full |
Estimating Population Parameters using the Structured Serial Coalescent with Bayesian MCMC Inference when some Demes are Hidden |
title_fullStr |
Estimating Population Parameters using the Structured Serial Coalescent with Bayesian MCMC Inference when some Demes are Hidden |
title_full_unstemmed |
Estimating Population Parameters using the Structured Serial Coalescent with Bayesian MCMC Inference when some Demes are Hidden |
title_sort |
estimating population parameters using the structured serial coalescent with bayesian mcmc inference when some demes are hidden |
publisher |
SAGE Publishing |
series |
Evolutionary Bioinformatics |
issn |
1176-9343 |
publishDate |
2006-01-01 |
description |
Using the structured serial coalescent with Bayesian MCMC and serial samples, we estimate population size when some demes are not sampled or are hidden, ie ghost demes. It is found that even with the presence of a ghost deme, accurate inference was possible if the parameters are estimated with the true model. However with an incorrect model, estimates were biased and can be positively misleading. We extend these results to the case where there are sequences from the ghost at the last time sample. This case can arise in HIV patients, when some tissue samples and viral sequences only become available after death. When some sequences from the ghost deme are available at the last sampling time, estimation bias is reduced and accurate estimation of parameters associated with the ghost deme is possible despite sampling bias. Migration rates for this case are also shown to be good estimates when migration values are low. |
url |
https://doi.org/10.1177/117693430600200026 |
work_keys_str_mv |
AT gregewing estimatingpopulationparametersusingthestructuredserialcoalescentwithbayesianmcmcinferencewhensomedemesarehidden AT allenrodrigo estimatingpopulationparametersusingthestructuredserialcoalescentwithbayesianmcmcinferencewhensomedemesarehidden |
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