schliessen

Filtern

 

Bibliotheken

PHRAPL: Phylogeographic Inference Using Approximate Likelihoods

The demographic history of most species is complex, with multiple evolutionary processes combining to shape the observed patterns of genetic diversity. To infer this history, the discipline of phylogeography has (to date) used models that simplify the historical demography of the focal organism, for... Full description

Journal Title: Systematic biology 2017-11-01, Vol.66 (6), p.1045-1053
Main Author: Jackson, Nathon D
Other Authors: Morales, Ariadna E , Carstens, Bryan C , O'Meara, Brian C
Format: Electronic Article Electronic Article
Language: English
Subjects:
Quelle: Alma/SFX Local Collection
Publisher: England: Oxford University Press
ID: ISSN: 1063-5157
Link: https://www.ncbi.nlm.nih.gov/pubmed/28204782
Zum Text:
SendSend as email Add to Book BagAdd to Book Bag
Staff View
recordid: cdi_proquest_miscellaneous_1869069287
title: PHRAPL: Phylogeographic Inference Using Approximate Likelihoods
format: Article
creator:
  • Jackson, Nathon D
  • Morales, Ariadna E
  • Carstens, Bryan C
  • O'Meara, Brian C
subjects:
  • Approximation
  • Biological Evolution
  • Coalescence
  • Computer Simulation
  • Data processing
  • Demography
  • Evolution
  • Gene flow
  • Genetic diversity
  • Genetic Variation
  • Likelihood Functions
  • Models
  • Models, Biological
  • Parameter estimation
  • Phylogeny
  • Phylogeography
  • Phylogeography - methods
  • Population number
  • Probability
  • Software for Systematics and Evolution
ispartof: Systematic biology, 2017-11-01, Vol.66 (6), p.1045-1053
description: The demographic history of most species is complex, with multiple evolutionary processes combining to shape the observed patterns of genetic diversity. To infer this history, the discipline of phylogeography has (to date) used models that simplify the historical demography of the focal organism, for example by assuming or ignoring ongoing gene flow between populations or by requiring a priori specification of divergence history. Since no single model incorporates every possible evolutionary process, researchers rely on intuition to choose the models that they use to analyze their data. Here, we describe an approximate likelihood approach that reduces this reliance on intuition. PHRAPL allows users to calculate the probability of a large number of complex demographic histories given a set of gene trees, enabling them to identify the most likely underlying model and estimate parameters for a given system. Available model parameters include coalescence time among populations or species, gene flow, and population size. We describe the method and test its performance in model selection and parameter estimation using simulated data. We also compare model probabilities estimated using our approximate likelihood method to those obtained using standard analytical likelihood. The method performs well under a wide range of scenarios, although this is sometimes contingent on sampling many loci. In most scenarios, as long as there are enough loci and if divergence among populations is sufficiently deep, PHRAPL can return the true model in nearly all simulated replicates. Parameter estimates from the method are also generally accurate in most cases. PHRAPL is a valuable new method for phylogeographic model selection and will be particularly useful as a tool to more extensively explore demographic model space than is typically done or to estimate parameters for complex models that are not readily implemented using current methods. Estimating relevant parameters using the most appropriate demographic model can help to sharpen our understanding of the evolutionary processes giving rise to phylogeographic patterns.
language: eng
source: Alma/SFX Local Collection
identifier: ISSN: 1063-5157
fulltext: fulltext
issn:
  • 1063-5157
  • 1076-836X
url: Link


@attributes
NO1
SEARCH_ENGINEprimo_central_multiple_fe
SEARCH_ENGINE_TYPEPrimo Central Search Engine
RANK2.653261
LOCALfalse
PrimoNMBib
record
control
sourceidjstor_opena
recordidTN_cdi_proquest_miscellaneous_1869069287
sourceformatXML
sourcesystemPC
jstor_id26581830
sourcerecordid26581830
originalsourceidFETCH-LOGICAL-1458t-7e190ec17ede3ce89fc402f666eae80509b8a7b3209a9701432be9753f4c1b733
addsrcrecordideNp1kEtLxDAQgIP42HX16FEvXrxUZ5I2j6MsPllQRMFbSNspdNlt1qQL-u_NUh8gmMvk8OUj8zF2hHCOYMRF_Ihl69N4B8AtNkZQMtNCvm5v7lJkBRZqxPZjnCcAZYF7bMQ1h1xpPma7j7dPl4-zA7bTuEWkw685YS_XV8_T22z2cHM3vZxlmBe6zxShAapQUU2iIm2aKgfeSCnJkYYCTKmdKgUH44wCzAUvyahCNHmFpRJiwu4Hr19R59pAdhXapQsf1rvW1h31tna9q9qerOEcTa2gIpKg0xEGEUuRzEaVjU6ys0G2Cv5tTbG3yzZWtFi4jvw6WtTSgDRcq4Se_kHnfh26tKpFo7TMDaRWE5YNVBV8jIGan98h2E1tO9S2Q-3En3xZ1-WS6h_6O28CxB9h2sz1re_64NrFv9rj4dU89j78WmWhUQsQn-wik8E
sourcetypeOpen Access Repository
isCDItrue
recordtypearticle
pqid1978649006
display
typearticle
titlePHRAPL: Phylogeographic Inference Using Approximate Likelihoods
sourceAlma/SFX Local Collection
creatorJackson, Nathon D ; Morales, Ariadna E ; Carstens, Bryan C ; O'Meara, Brian C
creatorcontribJackson, Nathon D ; Morales, Ariadna E ; Carstens, Bryan C ; O'Meara, Brian C
descriptionThe demographic history of most species is complex, with multiple evolutionary processes combining to shape the observed patterns of genetic diversity. To infer this history, the discipline of phylogeography has (to date) used models that simplify the historical demography of the focal organism, for example by assuming or ignoring ongoing gene flow between populations or by requiring a priori specification of divergence history. Since no single model incorporates every possible evolutionary process, researchers rely on intuition to choose the models that they use to analyze their data. Here, we describe an approximate likelihood approach that reduces this reliance on intuition. PHRAPL allows users to calculate the probability of a large number of complex demographic histories given a set of gene trees, enabling them to identify the most likely underlying model and estimate parameters for a given system. Available model parameters include coalescence time among populations or species, gene flow, and population size. We describe the method and test its performance in model selection and parameter estimation using simulated data. We also compare model probabilities estimated using our approximate likelihood method to those obtained using standard analytical likelihood. The method performs well under a wide range of scenarios, although this is sometimes contingent on sampling many loci. In most scenarios, as long as there are enough loci and if divergence among populations is sufficiently deep, PHRAPL can return the true model in nearly all simulated replicates. Parameter estimates from the method are also generally accurate in most cases. PHRAPL is a valuable new method for phylogeographic model selection and will be particularly useful as a tool to more extensively explore demographic model space than is typically done or to estimate parameters for complex models that are not readily implemented using current methods. Estimating relevant parameters using the most appropriate demographic model can help to sharpen our understanding of the evolutionary processes giving rise to phylogeographic patterns.
identifier
0ISSN: 1063-5157
1EISSN: 1076-836X
2DOI: 10.1093/sysbio/syx001
3PMID: 28204782
languageeng
publisherEngland: Oxford University Press
subjectApproximation ; Biological Evolution ; Coalescence ; Computer Simulation ; Data processing ; Demography ; Evolution ; Gene flow ; Genetic diversity ; Genetic Variation ; Likelihood Functions ; Models ; Models, Biological ; Parameter estimation ; Phylogeny ; Phylogeography ; Phylogeography - methods ; Population number ; Probability ; Software for Systematics and Evolution
ispartofSystematic biology, 2017-11-01, Vol.66 (6), p.1045-1053
rights
0The Author(s) 2017
1The Author(s) 2017. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
2Copyright Oxford University Press, UK Nov 2017
lds50peer_reviewed
oafree_for_read
citedbyFETCH-LOGICAL-1458t-7e190ec17ede3ce89fc402f666eae80509b8a7b3209a9701432be9753f4c1b733
citesFETCH-LOGICAL-1458t-7e190ec17ede3ce89fc402f666eae80509b8a7b3209a9701432be9753f4c1b733
links
openurl$$Topenurl_article
openurlfulltext$$Topenurlfull_article
thumbnail$$Usyndetics_thumb_exl
backlink$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28204782$$D View this record in MEDLINE/PubMed
search
creatorcontrib
0Jackson, Nathon D
1Morales, Ariadna E
2Carstens, Bryan C
3O'Meara, Brian C
title
0PHRAPL: Phylogeographic Inference Using Approximate Likelihoods
1Systematic biology
addtitleSyst Biol
descriptionThe demographic history of most species is complex, with multiple evolutionary processes combining to shape the observed patterns of genetic diversity. To infer this history, the discipline of phylogeography has (to date) used models that simplify the historical demography of the focal organism, for example by assuming or ignoring ongoing gene flow between populations or by requiring a priori specification of divergence history. Since no single model incorporates every possible evolutionary process, researchers rely on intuition to choose the models that they use to analyze their data. Here, we describe an approximate likelihood approach that reduces this reliance on intuition. PHRAPL allows users to calculate the probability of a large number of complex demographic histories given a set of gene trees, enabling them to identify the most likely underlying model and estimate parameters for a given system. Available model parameters include coalescence time among populations or species, gene flow, and population size. We describe the method and test its performance in model selection and parameter estimation using simulated data. We also compare model probabilities estimated using our approximate likelihood method to those obtained using standard analytical likelihood. The method performs well under a wide range of scenarios, although this is sometimes contingent on sampling many loci. In most scenarios, as long as there are enough loci and if divergence among populations is sufficiently deep, PHRAPL can return the true model in nearly all simulated replicates. Parameter estimates from the method are also generally accurate in most cases. PHRAPL is a valuable new method for phylogeographic model selection and will be particularly useful as a tool to more extensively explore demographic model space than is typically done or to estimate parameters for complex models that are not readily implemented using current methods. Estimating relevant parameters using the most appropriate demographic model can help to sharpen our understanding of the evolutionary processes giving rise to phylogeographic patterns.
subject
0Approximation
1Biological Evolution
2Coalescence
3Computer Simulation
4Data processing
5Demography
6Evolution
7Gene flow
8Genetic diversity
9Genetic Variation
10Likelihood Functions
11Models
12Models, Biological
13Parameter estimation
14Phylogeny
15Phylogeography
16Phylogeography - methods
17Population number
18Probability
19Software for Systematics and Evolution
issn
01063-5157
11076-836X
fulltexttrue
rsrctypearticle
creationdate2017
recordtypearticle
recordideNp1kEtLxDAQgIP42HX16FEvXrxUZ5I2j6MsPllQRMFbSNspdNlt1qQL-u_NUh8gmMvk8OUj8zF2hHCOYMRF_Ihl69N4B8AtNkZQMtNCvm5v7lJkBRZqxPZjnCcAZYF7bMQ1h1xpPma7j7dPl4-zA7bTuEWkw685YS_XV8_T22z2cHM3vZxlmBe6zxShAapQUU2iIm2aKgfeSCnJkYYCTKmdKgUH44wCzAUvyahCNHmFpRJiwu4Hr19R59pAdhXapQsf1rvW1h31tna9q9qerOEcTa2gIpKg0xEGEUuRzEaVjU6ys0G2Cv5tTbG3yzZWtFi4jvw6WtTSgDRcq4Se_kHnfh26tKpFo7TMDaRWE5YNVBV8jIGan98h2E1tO9S2Q-3En3xZ1-WS6h_6O28CxB9h2sz1re_64NrFv9rj4dU89j78WmWhUQsQn-wik8E
startdate20171101
enddate20171101
creator
0Jackson, Nathon D
1Morales, Ariadna E
2Carstens, Bryan C
3O'Meara, Brian C
general
0Oxford University Press
1Oxford University Press (OUP)
scope
0CGR
1CUY
2CVF
3ECM
4EIF
5NPM
6AAYXX
7CITATION
8K9.
97X8
10CLFQK
sort
creationdate20171101
titlePHRAPL
authorJackson, Nathon D ; Morales, Ariadna E ; Carstens, Bryan C ; O'Meara, Brian C
facets
frbrtype5
frbrgroupidcdi_FETCH-LOGICAL-1458t-7e190ec17ede3ce89fc402f666eae80509b8a7b3209a9701432be9753f4c1b733
rsrctypearticles
prefilterarticles
languageeng
creationdate2017
topic
0Approximation
1Biological Evolution
2Coalescence
3Computer Simulation
4Data processing
5Demography
6Evolution
7Gene flow
8Genetic diversity
9Genetic Variation
10Likelihood Functions
11Models
12Models, Biological
13Parameter estimation
14Phylogeny
15Phylogeography
16Phylogeography - methods
17Population number
18Probability
19Software for Systematics and Evolution
toplevel
0peer_reviewed
1online_resources
creatorcontrib
0Jackson, Nathon D
1Morales, Ariadna E
2Carstens, Bryan C
3O'Meara, Brian C
collection
0Medline
1MEDLINE
2MEDLINE (Ovid)
3MEDLINE
4MEDLINE
5PubMed
6CrossRef
7ProQuest Health & Medical Complete (Alumni)
8MEDLINE - Academic
9OpenAIRE
jtitleSystematic biology
delivery
delcategoryRemote Search Resource
fulltextfulltext
addata
au
0Jackson, Nathon D
1Morales, Ariadna E
2Carstens, Bryan C
3O'Meara, Brian C
formatjournal
genrearticle
ristypeJOUR
atitlePHRAPL
jtitleSystematic biology
addtitleSyst Biol
date2017-11-01
risdate2017
volume66
issue6
spage1045
epage1053
pages1045-1053
issn1063-5157
eissn1076-836X
abstractThe demographic history of most species is complex, with multiple evolutionary processes combining to shape the observed patterns of genetic diversity. To infer this history, the discipline of phylogeography has (to date) used models that simplify the historical demography of the focal organism, for example by assuming or ignoring ongoing gene flow between populations or by requiring a priori specification of divergence history. Since no single model incorporates every possible evolutionary process, researchers rely on intuition to choose the models that they use to analyze their data. Here, we describe an approximate likelihood approach that reduces this reliance on intuition. PHRAPL allows users to calculate the probability of a large number of complex demographic histories given a set of gene trees, enabling them to identify the most likely underlying model and estimate parameters for a given system. Available model parameters include coalescence time among populations or species, gene flow, and population size. We describe the method and test its performance in model selection and parameter estimation using simulated data. We also compare model probabilities estimated using our approximate likelihood method to those obtained using standard analytical likelihood. The method performs well under a wide range of scenarios, although this is sometimes contingent on sampling many loci. In most scenarios, as long as there are enough loci and if divergence among populations is sufficiently deep, PHRAPL can return the true model in nearly all simulated replicates. Parameter estimates from the method are also generally accurate in most cases. PHRAPL is a valuable new method for phylogeographic model selection and will be particularly useful as a tool to more extensively explore demographic model space than is typically done or to estimate parameters for complex models that are not readily implemented using current methods. Estimating relevant parameters using the most appropriate demographic model can help to sharpen our understanding of the evolutionary processes giving rise to phylogeographic patterns.
copEngland
pubOxford University Press
pmid28204782
doi10.1093/sysbio/syx001
oafree_for_read