General Quantitative Genetic Methods for Comparative Biology

There is much in common between the aim and tools of the quantitative geneticist and the comparative biologist. One of the most interesting statistical tools of the quantitative genetics (QG) is the mixed model framework, especially the so-called animal model, which can be used for comparative analyses. In this chapter, we describe the phylogenetic generalised linear mixed model (PGLMM), which encompasses phylogenetic (linear) mixed model (PMM). The widely used phylogenetic generalised least square (PGLS) can be seen as a special case of PGLMM. Thus, we demonstrate how PGLMM can be a useful extension of PGLS, hence a useful tool for the comparative biologist. In particular, we show how the PGLMM can tackle issues such as (1) intraspecific variance inference, (2) phylogenetic meta-analysis, (3) non-Gaussian traits analysis, and (4) missing values and data augmentation. Further possible extensions of the PGLMM and applications to phylogenetic comparative (PC) analysis are discussed at the end of the chapter. We provide working examples, using the R package MCMCglmm, in the online practical material (OPM).

[1]  Shinichi Nakagawa,et al.  Strategic female reproductive investment in response to male attractiveness in birds , 2012, Proceedings of the Royal Society B: Biological Sciences.

[2]  I. Cuthill,et al.  Effect size, confidence interval and statistical significance: a practical guide for biologists , 2007, Biological reviews of the Cambridge Philosophical Society.

[3]  J. Hadfield,et al.  General quantitative genetic methods for comparative biology: phylogenies, taxonomies and multi‐trait models for continuous and categorical characters , 2010, Journal of evolutionary biology.

[4]  A. L. Rae,et al.  The analysis of binomial data by a generalized linear mixed model , 1985 .

[5]  Elizabeth E Crone,et al.  Causes and consequences of variation in plant population growth rate: a synthesis of matrix population models in a phylogenetic context. , 2010, Ecology letters.

[6]  Craig K. Enders,et al.  Applied Missing Data Analysis , 2010 .

[7]  T. F. Hansen,et al.  Phylogenies and the Comparative Method: A General Approach to Incorporating Phylogenetic Information into the Analysis of Interspecific Data , 1997, The American Naturalist.

[8]  Eloy Revilla,et al.  Biases in comparative analyses of extinction risk: mind the gap. , 2012, The Journal of animal ecology.

[9]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[10]  Jacek Radwan,et al.  META‐ANALYSIS SUGGESTS CHOOSY FEMALES GET SEXY SONS MORE THAN “GOOD GENES” , 2012, Evolution; international journal of organic evolution.

[11]  László Zsolt Garamszegi,et al.  Nonrandom variation in within-species sample size and missing data in phylogenetic comparative studies. , 2011, Systematic biology.

[12]  E. Dempster,et al.  Heritability of Threshold Characters. , 1950, Genetics.

[13]  T. Garland,et al.  TESTING FOR PHYLOGENETIC SIGNAL IN COMPARATIVE DATA: BEHAVIORAL TRAITS ARE MORE LABILE , 2003, Evolution; international journal of organic evolution.

[14]  Simon P Blomberg,et al.  Extrinsic versus intrinsic factors in the decline and extinction of Australian marsupials , 2003, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[15]  D. Adams,et al.  PHYLOGENETIC META-ANALYSIS , 2008, Evolution; international journal of organic evolution.

[16]  Kerrie Mengersen,et al.  Handbook of Meta-analysis in Ecology and Evolution , 2013 .

[17]  Arthur E. Dunham,et al.  Historical perspectives in ecology and evolutionary biology: the use of phylogenetic comparative analyses , 1993 .

[18]  James A. Davis,et al.  A technique for analyzing the effects of group composition. , 1961 .

[19]  Alejandro Gonzalez-Voyer,et al.  Brains and the city: big-brained passerine birds succeed in urban environments , 2011, Biology Letters.

[20]  R. Fisher XV.—The Correlation between Relatives on the Supposition of Mendelian Inheritance. , 1919, Transactions of the Royal Society of Edinburgh.

[21]  Andrew Thomas,et al.  WinBUGS - A Bayesian modelling framework: Concepts, structure, and extensibility , 2000, Stat. Comput..

[22]  G. Hewitt,et al.  Founder takes all: density-dependent processes structure biodiversity. , 2013, Trends in ecology & evolution.

[23]  Andrew Gelman,et al.  Data Analysis Using Regression and Multilevel/Hierarchical Models , 2006 .

[24]  S. Wright,et al.  An Analysis of Variability in Number of Digits in an Inbred Strain of Guinea Pigs. , 1934, Genetics.

[25]  Shinichi Nakagawa,et al.  Missing inaction: the dangers of ignoring missing data. , 2008, Trends in ecology & evolution.

[26]  A. Ives,et al.  Phylogenetic trait-based analyses of ecological networks. , 2013, Ecology.

[27]  Shinichi Nakagawa,et al.  The influence of male age on within‐pair and extra‐pair paternity in passerines , 2012 .

[28]  Laura Ross,et al.  LARGE POPULATION SIZE PREDICTS THE DISTRIBUTION OF ASEXUALITY IN SCALE INSECTS , 2013, Evolution; international journal of organic evolution.

[29]  James G. Lefevre,et al.  Independent contrasts and PGLS regression estimators are equivalent. , 2012, Systematic biology.

[30]  Thomas F Hansen,et al.  ASSESSING CURRENT ADAPTATION AND PHYLOGENETIC INERTIA AS EXPLANATIONS OF TRAIT EVOLUTION:THE NEED FOR CONTROLLED COMPARISONS , 2005, Evolution; international journal of organic evolution.

[31]  T. Meuwissen,et al.  Computing inbreeding coefficients in large populations , 1992, Genetics Selection Evolution.

[32]  M. Pagel,et al.  Phylogenetic Analysis and Comparative Data: A Test and Review of Evidence , 2002, The American Naturalist.

[33]  M. Lynch,et al.  The Phylogenetic Mixed Model , 2004, The American Naturalist.

[34]  Olivier Gimenez,et al.  Comparing parent–offspring regression with frequentist and Bayesian animal models to estimate heritability in wild populations: a simulation study for Gaussian and binary traits , 2013 .

[35]  J. Felsenstein Comparative Methods with Sampling Error and Within‐Species Variation: Contrasts Revisited and Revised , 2008, The American Naturalist.

[36]  Shinichi Nakagawa,et al.  Meta-analytic insights into evolutionary ecology: an introduction and synthesis , 2012, Evolutionary Ecology.

[37]  Martijn van de Pol,et al.  A simple method for distinguishing within- versus between-subject effects using mixed models , 2009, Animal Behaviour.

[38]  Robin Thompson,et al.  ASREML user guide release 1.0 , 2002 .

[39]  Andy Gardner,et al.  Ecology, Not the Genetics of Sex Determination, Determines Who Helps in Eusocial Populations , 2013, Current Biology.

[40]  S. Chamberlain,et al.  Does phylogeny matter? Assessing the impact of phylogenetic information in ecological meta-analysis. , 2012, Ecology letters.

[41]  Denis Réale,et al.  How do misassigned paternities affect the estimation of heritability in the wild? , 2005, Molecular ecology.

[42]  Jarrod D. Hadfield,et al.  MCMC methods for multi-response generalized linear mixed models , 2010 .

[43]  Joseph Felsenstein,et al.  Controlling for non-independence in comparative analysis of patterns across populations within species , 2011, Philosophical Transactions of the Royal Society B: Biological Sciences.

[44]  J. Felsenstein Phylogenies and the Comparative Method , 1985, The American Naturalist.

[45]  Shinichi Nakagawa,et al.  The costs of parental care: a meta‐analysis of the trade‐off between parental effort and survival in birds , 2012, Journal of evolutionary biology.

[46]  Shinichi Nakagawa,et al.  Methodological issues and advances in biological meta-analysis , 2012, Evolutionary Ecology.

[47]  Bayesian models for comparative analysis integrating phylogenetic uncertainty , 2012, BMC Evolutionary Biology.

[48]  D. Rubin INFERENCE AND MISSING DATA , 1975 .

[49]  Mark Kirkpatrick,et al.  What Animal Breeding Has Taught Us about Evolution , 2010 .

[50]  M. Lajeunesse,et al.  Meta‐Analysis and the Comparative Phylogenetic Method , 2009, The American Naturalist.

[51]  Jacob Cohen Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[52]  M. Lynch METHODS FOR THE ANALYSIS OF COMPARATIVE DATA IN EVOLUTIONARY BIOLOGY , 1991, Evolution; international journal of organic evolution.

[53]  Shinichi Nakagawa,et al.  A Tale of Two Phylogenies: Comparative Analyses of Ecological Interactions , 2013, The American Naturalist.

[54]  Joseph Felsenstein,et al.  Using the quantitative genetic threshold model for inferences between and within species , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[55]  Mollie E. Brooks,et al.  Generalized linear mixed models: a practical guide for ecology and evolution. , 2009, Trends in ecology & evolution.

[56]  C. R. Henderson A simple method for computing the inverse of a numerator relationship matrix used in prediction of breeding values , 1976 .

[57]  Mikko J Sillanpää,et al.  On statistical methods for estimating heritability in wild populations , 2011, Molecular ecology.

[58]  Shinichi Nakagawa,et al.  Nested by design: model fitting and interpretation in a mixed model era , 2013 .

[59]  Stef van Buuren,et al.  Flexible Imputation of Missing Data , 2012 .

[60]  L. Garamszegi,et al.  A meta-analysis of correlated behaviours with implications for behavioural syndromes: mean effect size, publication bias, phylogenetic effects and the role of mediator variables , 2012, Evolutionary Ecology.

[61]  Hanna Kokko,et al.  Meta-analysis and sexual selection: past studies and future possibilities , 2012, Evolutionary Ecology.

[62]  DETECTING CORRELATION BETWEEN CHARACTERS IN A COMPARATIVE ANALYSIS WITH UNCERTAIN PHYLOGENY , 2003, Evolution; international journal of organic evolution.

[63]  N. Breslow,et al.  Approximate inference in generalized linear mixed models , 1993 .

[64]  Stuart A. West,et al.  Promiscuity and the evolutionary transition to complex societies , 2010, Nature.

[65]  Anthony R. Ives,et al.  Generalized linear mixed models for phylogenetic analyses of community structure , 2011 .

[66]  Shinichi Nakagawa,et al.  Repeatability for Gaussian and non‐Gaussian data: a practical guide for biologists , 2010, Biological reviews of the Cambridge Philosophical Society.

[67]  D. Rubin Multiple imputation for nonresponse in surveys , 1989 .

[68]  Jun Zhu,et al.  Statistics for correlated data: phylogenies, space, and time. , 2006, Ecological applications : a publication of the Ecological Society of America.