Please use this identifier to cite or link to this item: 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)orcid 
Publication Date: 2015
Open Access: Yes
DOI: 10.1111/jeb.12674Open Access Link
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
1420-9101
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
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Appears in Collections:Animal Genetics and Breeding Unit (AGBU)
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