2016

  • de Villemereuil, P., Gaggiotti, O. E., Mouterde, M., & Till-Bottraud, I.. (2016). Common garden experiments in the genomic era: new perspectives and opportunities. Heredity, 116(3), 249-254. doi:10.1038/hdy.2015.93
    [BibTeX] [Abstract] [Download PDF]

    The study of local adaptation is rendered difficult by many evolutionary confounding phenomena (for example, genetic drift and demographic history). When complex traits are involved in local adaptation, phenomena such as phenotypic plasticity further hamper evolutionary biologists to study the complex relationships between phenotype, genotype and environment. In this perspective paper, we suggest that the common garden experiment, specifically designed to deal with phenotypic plasticity, has a clear role to play in the study of local adaptation, even (if not specifically) in the genomic era. After a quick review of some high-throughput genotyping protocols relevant in the context of a common garden, we explore how to improve common garden analyses with dense marker panel data and recent statistical methods. We then show how combining approaches from population genomics and genome-wide association studies with the settings of a common garden can yield to a very efficient, thorough and integrative study of local adaptation. Especially, evidence from genomic (for example, genome scan) and phenotypic origins constitute independent insights into the possibility of local adaptation scenarios, and genome-wide association studies in the context of a common garden experiment allow to decipher the genetic bases of adaptive traits.

    @article{de_villemereuil_common_2016,
    title = {Common garden experiments in the genomic era: new perspectives and opportunities},
    volume = {116},
    copyright = {© 2015 Nature Publishing Group},
    issn = {0018-067X},
    shorttitle = {Common garden experiments in the genomic era},
    url = {http://devillemereuil.legtux.org/publis/de Villemereuil et al. - 2015 - Common garden experiments in the genomic era new.pdf},
    doi = {10.1038/hdy.2015.93},
    abstract = {The study of local adaptation is rendered difficult by many evolutionary confounding phenomena (for example, genetic drift and demographic history). When complex traits are involved in local adaptation, phenomena such as phenotypic plasticity further hamper evolutionary biologists to study the complex relationships between phenotype, genotype and environment. In this perspective paper, we suggest that the common garden experiment, specifically designed to deal with phenotypic plasticity, has a clear role to play in the study of local adaptation, even (if not specifically) in the genomic era. After a quick review of some high-throughput genotyping protocols relevant in the context of a common garden, we explore how to improve common garden analyses with dense marker panel data and recent statistical methods. We then show how combining approaches from population genomics and genome-wide association studies with the settings of a common garden can yield to a very efficient, thorough and integrative study of local adaptation. Especially, evidence from genomic (for example, genome scan) and phenotypic origins constitute independent insights into the possibility of local adaptation scenarios, and genome-wide association studies in the context of a common garden experiment allow to decipher the genetic bases of adaptive traits.},
    language = {en},
    number = {3},
    urldate = {2016-04-08},
    journal = {Heredity},
    author = {de Villemereuil, Pierre and Gaggiotti, Oscar E. and Mouterde, Médéric and Till-Bottraud, Irène},
    month = mar,
    year = {2016},
    pages = {249--254},
    file = {de Villemereuil et al. - 2015 - Common garden experiments in the genomic era new .pdf:/home/flyos/.mozilla/firefox/e3zxcz18.default/zotero/storage/5FDSMEFH/de Villemereuil et al. - 2015 - Common garden experiments in the genomic era new .pdf:application/pdf;de Villemereuil et al. - 2015 - Common garden experiments in the genomic era new .pdf:/home/flyos/.mozilla/firefox/e3zxcz18.default/zotero/storage/HKQMUCWI/de Villemereuil et al. - 2015 - Common garden experiments in the genomic era new .pdf:application/pdf;Full Text PDF:/home/flyos/.mozilla/firefox/e3zxcz18.default/zotero/storage/BSIR9RA7/de Villemereuil et al. - 2016 - Common garden experiments in the genomic era new .pdf:application/pdf;Full Text PDF:/home/flyos/.mozilla/firefox/e3zxcz18.default/zotero/storage/SD4JWBDP/de Villemereuil et al. - 2016 - Common garden experiments in the genomic era new .pdf:application/pdf}
    }

  • Till-Bottraud, I., & de Villemereuil, P.. (2016). Kin recognition or phenotype matching?. New Phytologist, 209(1), 13-14. doi:10.1111/nph.13554
    [BibTeX] [Download PDF]
    @article{till-bottraud_kin_2016,
    title = {Kin recognition or phenotype matching?},
    volume = {209},
    issn = {1469-8137},
    url = {http://devillemereuil.legtux.org/publis/Till-Bottraud et de Villemereuil - 2016 - Kin recognition or phenotype matching.pdf},
    doi = {10.1111/nph.13554},
    language = {en},
    number = {1},
    urldate = {2016-01-23},
    journal = {New Phytologist},
    author = {Till-Bottraud, Irène and de Villemereuil, Pierre},
    month = jan,
    year = {2016},
    keywords = {Arabidopsis thaliana, Arabidopsis thaliana, kin selection, kin selection, light signal, light signal, neighbor recognition, neighbor recognition, phenotype matching, phenotype matching, shade avoidance, shade avoidance},
    pages = {13--14},
    file = {Till-Bottraud et de Villemereuil - 2016 - Kin recognition or phenotype matching.pdf:/home/flyos/.mozilla/firefox/e3zxcz18.default/zotero/storage/565D8PPM/Till-Bottraud et de Villemereuil - 2016 - Kin recognition or phenotype matching.pdf:application/pdf;Till-Bottraud et de Villemereuil - 2016 - Kin recognition or phenotype matching.pdf:/home/flyos/.mozilla/firefox/e3zxcz18.default/zotero/storage/QVMF9EJS/Till-Bottraud et de Villemereuil - 2016 - Kin recognition or phenotype matching.pdf:application/pdf}
    }

  • de Villemereuil, P., Schielzeth, H., Nakagawa, S., & Morrissey, M. B.. (2016). General methods for evolutionary quantitative genetic inference from generalised mixed models. Genetics, 204(3), 1281-1294. doi:10.1534/genetics.115.186536
    [BibTeX] [Abstract] [Download PDF]

    Methods for inference and interpretation of evolutionary quantitative genetic parameters, and for prediction of the response to selection, are best developed for traits with normal distributions. Many traits of evolutionary interest, including many life history and behavioral traits, have inherently nonnormal distributions. The generalized linear mixed model (GLMM) framework has become a widely used tool for estimating quantitative genetic parameters for nonnormal traits. However, whereas GLMMs provide inference on a statistically convenient latent scale, it is often desirable to express quantitative genetic parameters on the scale upon which traits are measured. The parameters of fitted GLMMs, despite being on a latent scale, fully determine all quantities of potential interest on the scale on which traits are expressed. We provide expressions for deriving each of such quantities, including population means, phenotypic (co)variances, variance components including additive genetic (co)variances, and parameters such as heritability. We demonstrate that fixed effects have a strong impact on those parameters and show how to deal with this by averaging or integrating over fixed effects. The expressions require integration of quantities determined by the link function, over distributions of latent values. In general cases, the required integrals must be solved numerically, but efficient methods are available and we provide an implementation in an R package, QGglmm. We show that known formulas for quantities such as heritability of traits with binomial and Poisson distributions are special cases of our expressions. Additionally, we show how fitted GLMM can be incorporated into existing methods for predicting evolutionary trajectories. We demonstrate the accuracy of the resulting method for evolutionary prediction by simulation and apply our approach to data from a wild pedigreed vertebrate population.

    @article{de_villemereuil_general_2016,
    title = {General methods for evolutionary quantitative genetic inference from generalised mixed models},
    volume = {204},
    copyright = {Copyright © 2016 de Villemereuil et al.. Available freely online through the author-supported open access option.This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.},
    issn = {0016-6731, 1943-2631},
    url = {http://devillemereuil.legtux.org/publis/Villemereuil et al. - 2016 - General methods for evolutionary quantitative gene.pdf},
    doi = {10.1534/genetics.115.186536},
    abstract = {Methods for inference and interpretation of evolutionary quantitative genetic parameters, and for prediction of the response to selection, are best developed for traits with normal distributions. Many traits of evolutionary interest, including many life history and behavioral traits, have inherently nonnormal distributions. The generalized linear mixed model (GLMM) framework has become a widely used tool for estimating quantitative genetic parameters for nonnormal traits. However, whereas GLMMs provide inference on a statistically convenient latent scale, it is often desirable to express quantitative genetic parameters on the scale upon which traits are measured. The parameters of fitted GLMMs, despite being on a latent scale, fully determine all quantities of potential interest on the scale on which traits are expressed. We provide expressions for deriving each of such quantities, including population means, phenotypic (co)variances, variance components including additive genetic (co)variances, and parameters such as heritability. We demonstrate that fixed effects have a strong impact on those parameters and show how to deal with this by averaging or integrating over fixed effects. The expressions require integration of quantities determined by the link function, over distributions of latent values. In general cases, the required integrals must be solved numerically, but efficient methods are available and we provide an implementation in an R package, QGglmm. We show that known formulas for quantities such as heritability of traits with binomial and Poisson distributions are special cases of our expressions. Additionally, we show how fitted GLMM can be incorporated into existing methods for predicting evolutionary trajectories. We demonstrate the accuracy of the resulting method for evolutionary prediction by simulation and apply our approach to data from a wild pedigreed vertebrate population.},
    language = {en},
    number = {3},
    urldate = {2016-12-27},
    journal = {Genetics},
    author = {de Villemereuil, Pierre and Schielzeth, Holger and Nakagawa, Shinichi and Morrissey, Michael B.},
    month = nov,
    year = {2016},
    keywords = {additive genetic variance, Evolution, generalised linear mixed model, Generalized linear model, G matrix, Quantitative Genetics, Statistics, theory},
    pages = {1281--1294},
    file = {Villemereuil et al. - 2016 - General methods for evolutionary quantitative gene.pdf:/home/flyos/.mozilla/firefox/e3zxcz18.default/zotero/storage/VT9IFDR5/Villemereuil et al. - 2016 - General methods for evolutionary quantitative gene.pdf:application/pdf}
    }

2015

  • Aguilée, R., de Villemereuil, P., & Guillon, J.. (2015). Dispersal evolution and resource matching in a spatially and temporally variable environment. Journal of Theoretical Biology, 370, 184-196. doi:10.1016/j.jtbi.2015.01.018
    [BibTeX] [Abstract] [Download PDF]

    Metapopulations may consist of patches of different quality, and are often disturbed by extrinsic processes causing variation of patch quality. The persistence of such metapopulations then depends on the species׳ dispersal strategy. In a temporally constant environment, the evolution of dispersal rates follows the resource matching rule, i.e. at the evolutionarily stable dispersal strategy the number of competitors in each patch matches the resource availability in each patch. Here, we investigate how the distribution of individuals resulting from convergence stable dispersal strategies would match the distribution of resources in an environment which is temporally variable due to extrinsic disturbance. We develop an analytically tractable asexual model with two qualities of patches. We show that convergence stable dispersal rates are such that resource matching is predicted in expectation before habitat quality variation, and that the distribution of individuals undermatches resources after habitat quality variation. The overall flow of individuals between patches matches the overall flow of resources between patches resulting from environmental variation. We show that these conclusions can be generalized to organisms with sexual reproduction, and to a metapopulation with three qualities of patches when there is no mutational correlation between dispersal rates.

    @article{aguilee_dispersal_2015,
    title = {Dispersal evolution and resource matching in a spatially and temporally variable environment},
    volume = {370},
    issn = {0022-5193},
    url = {http://devillemereuil.legtux.org/publis/Aguil%C3%A9e%20et%20al.%20-%202015%20-%20Dispersal%20evolution%20and%20resource%20matching%20in%20a%20spa.pdf},
    doi = {10.1016/j.jtbi.2015.01.018},
    abstract = {Metapopulations may consist of patches of different quality, and are often disturbed by extrinsic processes causing variation of patch quality. The persistence of such metapopulations then depends on the species׳ dispersal strategy. In a temporally constant environment, the evolution of dispersal rates follows the resource matching rule, i.e. at the evolutionarily stable dispersal strategy the number of competitors in each patch matches the resource availability in each patch. Here, we investigate how the distribution of individuals resulting from convergence stable dispersal strategies would match the distribution of resources in an environment which is temporally variable due to extrinsic disturbance. We develop an analytically tractable asexual model with two qualities of patches. We show that convergence stable dispersal rates are such that resource matching is predicted in expectation before habitat quality variation, and that the distribution of individuals undermatches resources after habitat quality variation. The overall flow of individuals between patches matches the overall flow of resources between patches resulting from environmental variation. We show that these conclusions can be generalized to organisms with sexual reproduction, and to a metapopulation with three qualities of patches when there is no mutational correlation between dispersal rates.},
    urldate = {2015-02-25},
    journal = {Journal of Theoretical Biology},
    author = {Aguilée, Robin and de Villemereuil, Pierre and Guillon, Jean-Michel},
    year = {2015},
    pages = {184--196},
    file = {Aguilée et al. - 2015 - Dispersal evolution and resource matching in a spa.pdf:/home/flyos/.mozilla/firefox/e3zxcz18.default/zotero/storage/FDGSMM6P/Aguilée et al. - 2015 - Dispersal evolution and resource matching in a spa.pdf:application/pdf;Aguilée et al. - 2015 - Dispersal evolution and resource matching in a spa.pdf:/home/flyos/.mozilla/firefox/e3zxcz18.default/zotero/storage/VFHP5MXU/Aguilée et al. - 2015 - Dispersal evolution and resource matching in a spa.pdf:application/pdf}
    }

  • Frichot, É., Schoville, S. D., de Villemereuil, P., Gaggiotti, O. E., & François, O.. (2015). Detecting adaptive evolution based on association with ecological gradients: Orientation matters!. Heredity, 115(1), 22-28. doi:10.1038/hdy.2015.7
    [BibTeX] [Abstract]

    Population genetic signatures of local adaptation are frequently investigated by identifying loci with allele frequencies that exhibit high correlation with ecological variables. One difficulty with this approach is that ecological associations might be confounded by geographic variation at selectively neutral loci. Here, we consider populations that underwent spatial expansion from their original range, and for which geographical variation of adaptive allele frequency coincides with habitat gradients. Using range expansion simulations, we asked whether our ability to detect genomic regions involved in adaptation could be impacted by the orientation of the ecological gradients. For three ecological association methods tested, we found, counter-intuitively, fewer false-positive associations when ecological gradients aligned along the main axis of expansion than when they aligned along any other direction. This result has important consequences for the analysis of genomic data under non-equilibrium population genetic models. Alignment of gradients with expansion axes is likely to be common in scenarios in which expanding species track their ecological niche during climate change while adapting to changing environments at their rear edge.

    @article{frichot_detecting_2015,
    title = {Detecting adaptive evolution based on association with ecological gradients: {Orientation} matters!},
    volume = {115},
    copyright = {© 2015 Nature Publishing Group},
    issn = {0018-067X},
    shorttitle = {Detecting adaptive evolution based on association with ecological gradients},
    doi = {10.1038/hdy.2015.7},
    abstract = {Population genetic signatures of local adaptation are frequently investigated by identifying loci with allele frequencies that exhibit high correlation with ecological variables. One difficulty with this approach is that ecological associations might be confounded by geographic variation at selectively neutral loci. Here, we consider populations that underwent spatial expansion from their original range, and for which geographical variation of adaptive allele frequency coincides with habitat gradients. Using range expansion simulations, we asked whether our ability to detect genomic regions involved in adaptation could be impacted by the orientation of the ecological gradients. For three ecological association methods tested, we found, counter-intuitively, fewer false-positive associations when ecological gradients aligned along the main axis of expansion than when they aligned along any other direction. This result has important consequences for the analysis of genomic data under non-equilibrium population genetic models. Alignment of gradients with expansion axes is likely to be common in scenarios in which expanding species track their ecological niche during climate change while adapting to changing environments at their rear edge.},
    language = {en},
    number = {1},
    urldate = {2015-06-15},
    journal = {Heredity},
    author = {Frichot, Éric and Schoville, Sean D. and de Villemereuil, Pierre and Gaggiotti, Oscar E. and François, Olivoer},
    month = jul,
    year = {2015},
    pages = {22--28},
    file = {Frichot et al. - 2015 - Detecting adaptive evolution based on association .pdf:/home/flyos/.mozilla/firefox/e3zxcz18.default/zotero/storage/4V7NW6HU/Frichot et al. - 2015 - Detecting adaptive evolution based on association .pdf:application/pdf;Frichot et al. - 2015 - Detecting adaptive evolution based on association .pdf:/home/flyos/.mozilla/firefox/e3zxcz18.default/zotero/storage/HZKXA6HA/Frichot et al. - 2015 - Detecting adaptive evolution based on association .pdf:application/pdf}
    }

  • de Villemereuil, P., & Gaggiotti, O. E.. (2015). A new FST-based method to uncover local adaptation using environmental variables. Methods in Ecology and Evolution, 6(11), 1248-1258. doi:10.1111/2041-210X.12418
    [BibTeX] [Abstract] [Download PDF]

    Genome-scan methods are used for screening genome-wide patterns of DNA polymorphism to detect signatures of positive selection. There are two main types of methods: (i) “outlier” detection methods based on FST that detect loci with high differentiation compared to the rest of the genome, and (ii) environmental association methods that test the association between allele frequencies and environmental variables. We present a new FST-based genome-scan method, BayeScEnv, which incorporates environmental information in the form of “environmental differentiation”. It is based on the F model, but, as opposed to existing approaches, it considers two locus-specific effects; one due to divergent selection, and another due to various other processes different from local adaptation (e.g. range expansions, differences in mutation rates across loci or background selection). The method was developped in C++ and is avaible at http://github.com/devillemereuil/bayescenv. A simulation study shows that our method has a much lower false positive rate than an existing FST-based method, BayeScan, under a wide range of demographic scenarios. Although it has lower power, it leads to a better compromise between power and false positive rate. We apply our method to a human dataset and show that it can be used successfully to study local adaptation. We discuss its scope and compare it to other existing methods. This article is protected by copyright. All rights reserved.

    @article{de_villemereuil_new_2015,
    title = {A new {FST}-based method to uncover local adaptation using environmental variables},
    volume = {6},
    issn = {2041210X},
    url = {http://devillemereuil.legtux.org/publis/de Villemereuil et Gaggiotti - 2015 - A new FST-based method to uncover local adaptation.pdf},
    doi = {10.1111/2041-210X.12418},
    abstract = {Genome-scan methods are used for screening genome-wide patterns of DNA polymorphism to detect signatures of positive selection. There are two main types of methods: (i) “outlier” detection methods based on FST that detect loci with high differentiation compared to the rest of the genome, and (ii) environmental association methods that test the association between allele frequencies and environmental variables. We present a new FST-based genome-scan method, BayeScEnv, which incorporates environmental information in the form of “environmental differentiation”. It is based on the F model, but, as opposed to existing approaches, it considers two locus-specific effects; one due to divergent selection, and another due to various other processes different from local adaptation (e.g. range expansions, differences in mutation rates across loci or background selection). The method was developped in C++ and is avaible at http://github.com/devillemereuil/bayescenv. A simulation study shows that our method has a much lower false positive rate than an existing FST-based method, BayeScan, under a wide range of demographic scenarios. Although it has lower power, it leads to a better compromise between power and false positive rate. We apply our method to a human dataset and show that it can be used successfully to study local adaptation. We discuss its scope and compare it to other existing methods. This article is protected by copyright. All rights reserved.},
    language = {en},
    number = {11},
    urldate = {2015-07-13},
    journal = {Methods in Ecology and Evolution},
    author = {de Villemereuil, Pierre and Gaggiotti, Oscar E.},
    month = nov,
    year = {2015},
    keywords = {Bayesian methods, Bayesian methods, environment, environment, false discovery rate, false discovery rate, F model, F model, genome-scan, genome-scan, local adaptation, local adaptation},
    pages = {1248--1258},
    file = {de Villemereuil et Gaggiotti - 2015 - A new FST-based method to uncover local adaptation.pdf:/home/flyos/.mozilla/firefox/e3zxcz18.default/zotero/storage/9PIG5ZAQ/de Villemereuil et Gaggiotti - 2015 - A new FST-based method to uncover local adaptation.pdf:application/pdf;de Villemereuil et Gaggiotti - 2015 - A new FST-based method to uncover local adaptation.pdf:/home/flyos/.mozilla/firefox/e3zxcz18.default/zotero/storage/KQFNXIXM/de Villemereuil et Gaggiotti - 2015 - A new FST-based method to uncover local adaptation.pdf:application/pdf}
    }

2014

  • de Villemereuil, P., Frichot, É., Bazin, É., François, O., & Gaggiotti, O. E.. (2014). Genome scan methods against more complex models: when and how much should we trust them?. Molecular Ecology, 23(8), 2006-2019. doi:10.1111/mec.12705
    [BibTeX] [Abstract] [Download PDF]

    The recent availability of next-generation sequencing (NGS) has made possible the use of dense genetic markers to identify regions of the genome that may be under the influence of selection. Several statistical methods have been developed recently for this purpose. Here, we present the results of an individual-based simulation study investigating the power and error rate of popular or recent genome scan methods: linear regression, Bayescan, BayEnv and LFMM. Contrary to previous studies, we focus on complex, hierarchical population structure and on polygenic selection. Additionally, we use a false discovery rate (FDR)-based framework, which provides an unified testing framework across frequentist and Bayesian methods. Finally, we investigate the influence of population allele frequencies versus individual genotype data specification for LFMM and the linear regression. The relative ranking between the methods is impacted by the consideration of polygenic selection, compared to a monogenic scenario. For strongly hierarchical scenarios with confounding effects between demography and environmental variables, the power of the methods can be very low. Except for one scenario, Bayescan exhibited moderate power and error rate. BayEnv performance was good under nonhierarchical scenarios, while LFMM provided the best compromise between power and error rate across scenarios. We found that it is possible to greatly reduce error rates by considering the results of all three methods when identifying outlier loci.

    @article{de_villemereuil_genome_2014,
    title = {Genome scan methods against more complex models: when and how much should we trust them?},
    volume = {23},
    copyright = {© 2014 John Wiley \& Sons Ltd},
    issn = {1365-294X},
    shorttitle = {Genome scan methods against more complex models},
    url = {http://devillemereuil.legtux.org/publis/de Villemereuil et al. - 2014 - Genome scan methods against more complex models w.pdf},
    doi = {10.1111/mec.12705},
    abstract = {The recent availability of next-generation sequencing (NGS) has made possible the use of dense genetic markers to identify regions of the genome that may be under the influence of selection. Several statistical methods have been developed recently for this purpose. Here, we present the results of an individual-based simulation study investigating the power and error rate of popular or recent genome scan methods: linear regression, Bayescan, BayEnv and LFMM. Contrary to previous studies, we focus on complex, hierarchical population structure and on polygenic selection. Additionally, we use a false discovery rate (FDR)-based framework, which provides an unified testing framework across frequentist and Bayesian methods. Finally, we investigate the influence of population allele frequencies versus individual genotype data specification for LFMM and the linear regression. The relative ranking between the methods is impacted by the consideration of polygenic selection, compared to a monogenic scenario. For strongly hierarchical scenarios with confounding effects between demography and environmental variables, the power of the methods can be very low. Except for one scenario, Bayescan exhibited moderate power and error rate. BayEnv performance was good under nonhierarchical scenarios, while LFMM provided the best compromise between power and error rate across scenarios. We found that it is possible to greatly reduce error rates by considering the results of all three methods when identifying outlier loci.},
    language = {en},
    number = {8},
    urldate = {2014-04-09},
    journal = {Molecular Ecology},
    author = {de Villemereuil, Pierre and Frichot, Éric and Bazin, Éric and François, Olivier and Gaggiotti, Oscar E.},
    year = {2014},
    keywords = {Adaptation, Adaptation, Bayesian methods, Bayesian methods, false discovery rate, false discovery rate, Genome scan, genome scan, power simulation study, power simulation study},
    pages = {2006--2019},
    file = {de Villemereuil et al. - 2014 - Genome scan methods against more complex models w.pdf:/home/flyos/.mozilla/firefox/e3zxcz18.default/zotero/storage/W2BC75WC/de Villemereuil et al. - 2014 - Genome scan methods against more complex models w.pdf:application/pdf;de Villemereuil et al. - 2014 - Genome scan methods against more complex models w.pdf:/home/flyos/.mozilla/firefox/e3zxcz18.default/zotero/storage/X4E5F8MS/de Villemereuil et al. - 2014 - Genome scan methods against more complex models w.pdf:application/pdf}
    }

  • Morrissey, M. B., de Villemereuil, P., Doligez, B., & Gimenez, O.. (2014). Bayesian approaches to the quantitative genetic analysis of natural populations. In Charmantier, A., Garant, D., & Kruuk, L. E. B. (Eds.), In Quantitative Genetics in the Wild (, pp. 228-253). Oxford (UK): Oxford University Press.
    [BibTeX]
    @incollection{morrissey_bayesian_2014,
    address = {Oxford (UK)},
    title = {Bayesian approaches to the quantitative genetic analysis of natural populations},
    isbn = {978-0-19-165595-1},
    language = {en},
    booktitle = {Quantitative {Genetics} in the {Wild}},
    publisher = {Oxford University Press},
    author = {Morrissey, Michael B. and de Villemereuil, Pierre and Doligez, Blandine and Gimenez, Olivier},
    editor = {Charmantier, Anne and Garant, Dany and Kruuk, Loeske E.B.},
    month = apr,
    year = {2014},
    pages = {228--253},
    file = {Morrissey et al. - 2014 - Bayesian approaches to the quantitative genetic an.pdf:/home/flyos/.mozilla/firefox/e3zxcz18.default/zotero/storage/8DKX8JG9/Morrissey et al. - 2014 - Bayesian approaches to the quantitative genetic an.pdf:application/pdf}
    }

  • de Villemereuil, P., & Nakagawa, S.. (2014). General Quantitative Genetic Methods for Comparative Biology. In Garamszegi, L. Z. (Ed.), In Modern Phylogenetic Comparative Methods and Their Application in Evolutionary Biology (, pp. 287-303). Berlin, Heidelberg: Springer Berlin Heidelberg.
    [BibTeX] [Download PDF]
    @incollection{garamszegi_general_2014,
    address = {Berlin, Heidelberg},
    title = {General {Quantitative} {Genetic} {Methods} for {Comparative} {Biology}},
    isbn = {978-3-662-43550-2},
    url = {http://link.springer.com/10.1007/978-3-662-43550-2},
    language = {en},
    urldate = {2014-08-14},
    booktitle = {Modern {Phylogenetic} {Comparative} {Methods} and {Their} {Application} in {Evolutionary} {Biology}},
    publisher = {Springer Berlin Heidelberg},
    author = {de Villemereuil, Pierre and Nakagawa, Shinichi},
    editor = {Garamszegi, László Zsolt},
    year = {2014},
    pages = {287--303},
    file = {de Villemereuil et Nakagawa - 2014 - General Quantitative Genetic Methods for Comparati.pdf:/home/flyos/.mozilla/firefox/e3zxcz18.default/zotero/storage/UIU9TSZK/de Villemereuil et Nakagawa - 2014 - General Quantitative Genetic Methods for Comparati.pdf:application/pdf;de Villemereuil et Nakagawa - 2014 - General Quantitative Genetic Methods for Comparati.pdf:/home/flyos/.mozilla/firefox/e3zxcz18.default/zotero/storage/WNWT969J/de Villemereuil et Nakagawa - 2014 - General Quantitative Genetic Methods for Comparati.pdf:application/pdf}
    }

2013

  • de Villemereuil, P., Gimenez, O., & Doligez, B.. (2013). Comparing parent–offspring regression with frequentist and Bayesian animal models to estimate heritability in wild populations: a simulation study for Gaussian and binary traits. Methods in Ecology and Evolution, 4(3), 260-275. doi:10.1111/2041-210X.12011
    [BibTeX] [Abstract] [Download PDF]

    * Estimating heritability of traits in wild populations is a major prerequisite to understand their evolution. Until recently, most heritability estimates had been obtained using parent-offspring regressions. However, the popularity of animal models, that is, (generalized) linear mixed models assessing the genetic variance component based on population pedigree information, has markedly increased in the past few years. Animal models are claimed to perform better than parent–offspring regressions mainly because they use full between-individual relatedness information and they allow explicit modelling of the environmental effects shared by individuals. However, the differences between heritability estimates obtained using both approaches are not straight forward, and the factors influencing these differences remain unclear. * We performed a simulation study to evaluate and compare the accuracy and precision of estimates obtained from parent–offspring regressions and animal models using both Frequentist (REML, PQL) and Bayesian (MCMC) estimation methods. We explored the influence of (i) the presence and type of shared environmental effects (non-transgenerational or transgenerational), (ii) the distribution of the phenotypic trait considered (Gaussian or binary trait) and (iii) data quantity and quality (sample size, pedigree connectivity) on heritability estimates obtained from the two approaches for different levels of true heritability. * In the absence of shared environmental effects, the animal model using the REML method performed best for a Gaussian trait, while the animal model using MCMC was more appropriate for a binary trait. For low quantity and quality data, and a binary trait, the parent–offspring regression yielded very imprecise estimates. * Estimates from the parent–offspring regression were not influenced by a non-transgenerational shared environmental effect, whereas estimates from animal models in which environmental effects are ignored were affected by both non-transgenerational and transgenerational effects. * We discuss the relevance of each approach and estimation method for estimating heritability in wild populations. Importantly, because most effects fitted in animal models are, in fact, non-transgenerational (including environmental maternal effects), we advocate a systematic comparison between parent–offspring regression and animal model estimates to detect potentially missing non-transgenerational environmental effects.

    @article{de_villemereuil_comparing_2013,
    title = {Comparing parent–offspring regression with frequentist and {Bayesian} animal models to estimate heritability in wild populations: a simulation study for {Gaussian} and binary traits},
    volume = {4},
    issn = {2041-210X},
    shorttitle = {Comparing parent–offspring regression with frequentist and {Bayesian} animal models to estimate heritability in wild populations},
    url = {http://devillemereuil.legtux.org/publis/de Villemereuil et al. - 2013 - Comparing parent–offspring regression with frequen.pdf},
    doi = {10.1111/2041-210X.12011},
    abstract = {* Estimating heritability of traits in wild populations is a major prerequisite to understand their evolution. Until recently, most heritability estimates had been obtained using parent-offspring regressions. However, the popularity of animal models, that is, (generalized) linear mixed models assessing the genetic variance component based on population pedigree information, has markedly increased in the past few years. Animal models are claimed to perform better than parent–offspring regressions mainly because they use full between-individual relatedness information and they allow explicit modelling of the environmental effects shared by individuals. However, the differences between heritability estimates obtained using both approaches are not straight forward, and the factors influencing these differences remain unclear. * We performed a simulation study to evaluate and compare the accuracy and precision of estimates obtained from parent–offspring regressions and animal models using both Frequentist (REML, PQL) and Bayesian (MCMC) estimation methods. We explored the influence of (i) the presence and type of shared environmental effects (non-transgenerational or transgenerational), (ii) the distribution of the phenotypic trait considered (Gaussian or binary trait) and (iii) data quantity and quality (sample size, pedigree connectivity) on heritability estimates obtained from the two approaches for different levels of true heritability. * In the absence of shared environmental effects, the animal model using the REML method performed best for a Gaussian trait, while the animal model using MCMC was more appropriate for a binary trait. For low quantity and quality data, and a binary trait, the parent–offspring regression yielded very imprecise estimates. * Estimates from the parent–offspring regression were not influenced by a non-transgenerational shared environmental effect, whereas estimates from animal models in which environmental effects are ignored were affected by both non-transgenerational and transgenerational effects. * We discuss the relevance of each approach and estimation method for estimating heritability in wild populations. Importantly, because most effects fitted in animal models are, in fact, non-transgenerational (including environmental maternal effects), we advocate a systematic comparison between parent–offspring regression and animal model estimates to detect potentially missing non-transgenerational environmental effects.},
    language = {en},
    number = {3},
    urldate = {2013-03-15},
    journal = {Methods in Ecology and Evolution},
    author = {de Villemereuil, Pierre and Gimenez, Olivier and Doligez, Blandine},
    year = {2013},
    pages = {260--275},
    file = {de Villemereuil et al. - 2013 - Comparing parent–offspring regression with frequen.pdf:/home/flyos/.mozilla/firefox/e3zxcz18.default/zotero/storage/ER73RK3C/de Villemereuil et al. - 2013 - Comparing parent–offspring regression with frequen.pdf:application/pdf;de Villemereuil et al. - 2013 - Comparing parent–offspring regression with frequen.pdf:/home/flyos/.mozilla/firefox/e3zxcz18.default/zotero/storage/T4IFWIFR/de Villemereuil et al. - 2013 - Comparing parent–offspring regression with frequen.pdf:application/pdf}
    }

2012

  • de Villemereuil, P., Wells, J. A., Edwards, R. D., & Blomberg, S. P.. (2012). Bayesian models for comparative analysis integrating phylogenetic uncertainty. BMC Evolutionary Biology, 12(1), 102. doi:10.1186/1471-2148-12-102
    [BibTeX] [Abstract] [Download PDF]

    Uncertainty in comparative analyses can come from at least two sources: a) phylogenetic uncertainty in the tree topology or branch lengths, and b) uncertainty due to intraspecific variation in trait values, either due to measurement error or natural individual variation. Most phylogenetic comparative methods do not account for such uncertainties. Not accounting for these sources of uncertainty leads to false perceptions of precision (confidence intervals will be too narrow) and inflated significance in hypothesis testing (e.g. p-values will be too small). Although there is some application-specific software for fitting Bayesian models accounting for phylogenetic error, more general and flexible software is desirable.

    @article{de_villemereuil_bayesian_2012,
    title = {Bayesian models for comparative analysis integrating phylogenetic uncertainty},
    volume = {12},
    issn = {1471-2148},
    url = {http://devillemereuil.legtux.org/publis/de Villemereuil et al. - 2012 - Bayesian models for comparative analysis integrati.pdf},
    doi = {10.1186/1471-2148-12-102},
    abstract = {Uncertainty in comparative analyses can come from at least two sources: a) phylogenetic uncertainty in the tree topology or branch lengths, and b) uncertainty due to intraspecific variation in trait values, either due to measurement error or natural individual variation. Most phylogenetic comparative methods do not account for such uncertainties. Not accounting for these sources of uncertainty leads to false perceptions of precision (confidence intervals will be too narrow) and inflated significance in hypothesis testing (e.g. p-values will be too small). Although there is some application-specific software for fitting Bayesian models accounting for phylogenetic error, more general and flexible software is desirable.},
    language = {en},
    number = {1},
    urldate = {2013-02-27},
    journal = {BMC Evolutionary Biology},
    author = {de Villemereuil, Pierre and Wells, Jessie A. and Edwards, Robert D. and Blomberg, Simon P.},
    month = jun,
    year = {2012},
    pages = {102},
    file = {de Villemereuil et al. - 2012 - Bayesian models for comparative analysis integrati.pdf:/home/flyos/.mozilla/firefox/e3zxcz18.default/zotero/storage/QUBVS2JU/de Villemereuil et al. - 2012 - Bayesian models for comparative analysis integrati.pdf:application/pdf;de Villemereuil et al. - 2012 - Bayesian models for comparative analysis integrati.pdf:/home/flyos/.mozilla/firefox/e3zxcz18.default/zotero/storage/UNN27BAP/de Villemereuil et al. - 2012 - Bayesian models for comparative analysis integrati.pdf:application/pdf}
    }

2011

  • de Villemereuil, P., & López-Sepulcre, A.. (2011). Consumer functional responses under intra- and inter-specific interference competition. Ecological Modelling, 222(3), 419-426. doi:10.1016/j.ecolmodel.2010.10.011
    [BibTeX] [Download PDF]
    @article{de_villemereuil_consumer_2011,
    title = {Consumer functional responses under intra- and inter-specific interference competition},
    volume = {222},
    issn = {03043800},
    url = {http://devillemereuil.legtux.org/publis/de Villemereuil et López-Sepulcre - 2011 - Consumer functional responses under intra- and int.pdf},
    doi = {10.1016/j.ecolmodel.2010.10.011},
    number = {3},
    urldate = {2011-02-23},
    journal = {Ecological Modelling},
    author = {de Villemereuil, Pierre and López-Sepulcre, Andrés},
    month = feb,
    year = {2011},
    pages = {419--426},
    file = {de Villemereuil et López-Sepulcre - 2011 - Consumer functional responses under intra- and int.pdf:/home/flyos/.mozilla/firefox/e3zxcz18.default/zotero/storage/T4GGW89J/de Villemereuil et López-Sepulcre - 2011 - Consumer functional responses under intra- and int.pdf:application/pdf;de Villemereuil et López-Sepulcre - 2011 - Consumer functional responses under intra- and int.pdf:/home/flyos/.mozilla/firefox/e3zxcz18.default/zotero/storage/UG6ACIID/de Villemereuil et López-Sepulcre - 2011 - Consumer functional responses under intra- and int.pdf:application/pdf}
    }