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|Title:||Estimating sampling error of evolutionary statistics based on genetic covariance matrices using maximum likelihood||Contributor(s):||Houle, D (author); Meyer, Karin (author)||Publication Date:||2015||Open Access:||Yes||DOI:||10.1111/jeb.12674||Handle Link:||https://hdl.handle.net/1959.11/18906||Abstract:||We explore the estimation of uncertainty in evolutionary parameters using a recently devised approach for resampling entire additive genetic variance- covariance matrices (G). Large-sample theory shows that maximum-likelihood estimates (including restricted maximum likelihood, REML) asymptotically have a multivariate normal distribution, with covariance matrix derived from the inverse of the information matrix, and mean equal to the estimated G. This suggests that sampling estimates of G from this distribution can be used to assess the variability of estimates of G, and of functions of G. We refer to this as the REML-MVN method. This has been implemented in the mixed-model program WOMBAT. Estimates of sampling variances from REML-MVN were compared to those from the parametric bootstrap and from a Bayesian Markov chain Monte Carlo (MCMC) approach (implemented in the R package MCMCglmm). We apply each approach to evolvability statistics previously estimated for a large, 20-dimensional data set for Drosophila wings. REML-MVN and MCMC sampling variances are close to those estimated with the parametric bootstrap. Both slightly underestimate the error in the best-estimated aspects of the G matrix. REML analysis supports the previous conclusion that the G matrix for this population is full rank. REML-MVN is computationally very efficient, making it an attractive alternative to both data resampling and MCMC approaches to assessing confidence in parameters of evolutionary interest.||Publication Type:||Journal Article||Source of Publication:||Journal of Evolutionary Biology, 28(8), p. 1542-1549||Publisher:||Wiley-Blackwell Publishing Ltd||Place of Publication:||United Kingdom||ISSN:||1010-061X
|Field of Research (FoR) 2008:||070201 Animal Breeding||Field of Research (FoR) 2020:||300305 Animal reproduction and breeding||Socio-Economic Objective (SEO) 2008:||830399 Livestock Raising not elsewhere classified||Peer Reviewed:||Yes||HERDC Category Description:||C1 Refereed Article in a Scholarly Journal||Statistics to Oct 2018:||Visitors: 45|
|Appears in Collections:||Animal Genetics and Breeding Unit (AGBU)|
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