期刊
JOURNAL OF EVOLUTIONARY BIOLOGY
卷 28, 期 8, 页码 1542-1549出版社
WILEY
DOI: 10.1111/jeb.12674
关键词
evolution; evolvability; G matrix; quantitative genetics; restricted maximum likelihood; sampling error
资金
- U.S. National Science Foundation [DEB-0950002]
- Meat and Livestock Australia [B.BFG.0050]
- working group 'Darwinian Morphometrics: Cross-topology registration of shape' at the National Institute for Mathematical and Biological Synthesis
- National Science Foundation
- US Department of Homeland Security
- US Department of Agriculture through NSF [EF-083285, DBI-1300426]
- University of Tennessee, Knoxville
- Direct For Biological Sciences
- Div Of Biological Infrastructure [1300426] Funding Source: National Science Foundation
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.
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