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https://hdl.handle.net/1959.11/18906
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: | 1420-9101 1010-061X |
Fields of Research (FoR) 2008: | 070201 Animal Breeding | Fields of Research (FoR) 2020: | 300305 Animal reproduction and breeding | Socio-Economic Objective (SEO) 2008: | 830399 Livestock Raising not elsewhere classified | Socio-Economic Objective (SEO) 2020: | 100407 Insects | Peer Reviewed: | Yes | HERDC Category Description: | C1 Refereed Article in a Scholarly Journal |
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Appears in Collections: | Animal Genetics and Breeding Unit (AGBU) Journal Article |
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