Bayesian Computation and Model Selection Without Likelihoods
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TLDR
This work proposes a reformulation of the regression adjustment of population subdivision among western chimpanzees in terms of a general linear model (GLM), which allows the integration into the sound theoretical framework of Bayesian statistics and the use of its methods, including model selection via Bayes factors.Abstract:
Until recently, the use of Bayesian inference was limited to a few cases because for many realistic probability models the likelihood function cannot be calculated analytically. The situation changed with the advent of likelihood-free inference algorithms, often subsumed under the term approximate Bayesian computation (ABC). A key innovation was the use of a postsampling regression adjustment, allowing larger tolerance values and as such shifting computation time to realistic orders of magnitude. Here we propose a reformulation of the regression adjustment in terms of a general linear model (GLM). This allows the integration into the sound theoretical framework of Bayesian statistics and the use of its methods, including model selection via Bayes factors. We then apply the proposed methodology to the question of population subdivision among western chimpanzees, Pan troglodytes verus.read more
Citations
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Robust demographic inference from genomic and SNP data.
Laurent Excoffier,Isabelle Dupanloup,Emilia Huerta-Sanchez,Vitor C. Sousa,Matthieu Foll,Matthieu Foll +5 more
TL;DR: A flexible and robust simulation-based framework to infer demographic parameters from the site frequency spectrum (SFS) computed on large genomic datasets and shows that it allows one to study evolutionary models of arbitrary complexity, which cannot be tackled by other current likelihood-based methods.
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Approximate Bayesian Computation (ABC) in practice
TL;DR: It is argued that the use of ABC should incorporate all aspects of Bayesian data analysis: formulation, fitting, and improvement of a model if these principles are carefully applied.
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Approximate Bayesian Computation in Evolution and Ecology
TL;DR: Although the method arose in population genetics, ABC is increasingly used in other fields, including epidemiology, systems biology, ecology, and agent-based modeling, and many of these applications are briefly described.
Journal ArticleDOI
Great ape genetic diversity and population history
Javier Prado-Martinez,Peter H. Sudmant,Jeffrey M. Kidd,Jeffrey M. Kidd,Heng Li,Joanna L. Kelley,Belen Lorente-Galdos,Krishna R. Veeramah,August E. Woerner,Timothy D. O’Connor,Gabriel Santpere,Alex Cagan,Christoph Theunert,Ferran Casals,Hafid Laayouni,Kasper Munch,Asger Hobolth,Anders E. Halager,Maika Malig,Jessica Hernandez-Rodriguez,Irene Hernando-Herraez,Kay Prüfer,Marc Pybus,Laurel Johnstone,Michael Lachmann,Can Alkan,Dorina Twigg,Natalia Petit,Carl Baker,Fereydoun Hormozdiari,Marcos Fernandez-Callejo,Marc Dabad,Michael L. Wilson,Laurie S. Stevison,Cristina Camprubí,Tiago Carvalho,Aurora Ruiz-Herrera,Laura Vives,Marta Melé,Teresa Abello,Ivanela Kondova,Ronald E. Bontrop,Anne E. Pusey,Felix Lankester,John Kiyang,Richard A. Bergl,Elizabeth V. Lonsdorf,Simon Myers,Mario Ventura,Pascal Gagneux,David Comas,Hans R. Siegismund,Julie Blanc,Lidia Agueda-Calpena,Marta Gut,Lucinda Fulton,Sarah A. Tishkoff,James C. Mullikin,Richard K. Wilson,Ivo Gut,Mary Katherine Gonder,Oliver A. Ryder,Beatrice H. Hahn,Arcadi Navarro,Arcadi Navarro,Joshua M. Akey,Jaume Bertranpetit,David Reich,Thomas Mailund,Mikkel H. Schierup,Christina Hvilsom,Christina Hvilsom,Aida M. Andrés,Jeffrey D. Wall,Carlos Bustamante,Michael F. Hammer,Evan E. Eichler,Evan E. Eichler,Tomas Marques-Bonet,Tomas Marques-Bonet +79 more
TL;DR: This comprehensive catalogue of great ape genome diversity provides a framework for understanding evolution and a resource for more effective management of wild and captive great ape populations.
References
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Journal ArticleDOI
Arlequin (version 3.0): An integrated software package for population genetics data analysis
TL;DR: Arlequin ver 3.0 as discussed by the authors is a software package integrating several basic and advanced methods for population genetics data analysis, like the computation of standard genetic diversity indices, the estimation of allele and haplotype frequencies, tests of departure from linkage equilibrium, departure from selective neutrality and demographic equilibrium, estimation or parameters from past population expansions, and thorough analyses of population subdivision under the AMOVA framework.
Journal ArticleDOI
On the number of segregating sites in genetical models without recombination.
TL;DR: The distribution is obtained for the number of segregating sites observed in a sample from a population which is subject to recurring, new, mutations but not subject to recombination, and applies approximately to three population models.
Journal ArticleDOI
Approximate Bayesian computation in population genetics.
TL;DR: A key advantage of the method is that the nuisance parameters are automatically integrated out in the simulation step, so that the large numbers of nuisance parameters that arise in population genetics problems can be handled without difficulty.
Journal Article
Approximate Bayesian computation in population genetics.
TL;DR: In this paper, the authors proposed a new method for approximate Bayesian statistical inference on the basis of summary statistics, which is suited to complex problems that arise in population genetics, extending ideas developed in this setting by earlier authors.