Comparing G: multivariate analysis of genetic variation in multiple populations

Aguirre, J. D., Hine, E., McGuigan, K. and Blows, M. W. (2014) Comparing G: multivariate analysis of genetic variation in multiple populations. Heredity, 112 1: 21-29. doi:10.1038/hdy.2013.12


Author Aguirre, J. D.
Hine, E.
McGuigan, K.
Blows, M. W.
Title Comparing G: multivariate analysis of genetic variation in multiple populations
Journal name Heredity   Check publisher's open access policy
ISSN 0018-067X
1365-2540
Publication date 2014-01
Year available 2013
Sub-type Article (original research)
DOI 10.1038/hdy.2013.12
Open Access Status Not Open Access
Volume 112
Issue 1
Start page 21
End page 29
Total pages 9
Place of publication London, United Kingdom
Publisher Nature Publishing
Collection year 2014
Language eng
Abstract The additive genetic variance-covariance matrix (G) summarizes the multivariate genetic relationships among a set of traits. The geometry of G describes the distribution of multivariate genetic variance, and generates genetic constraints that bias the direction of evolution. Determining if and how the multivariate genetic variance evolves has been limited by a number of analytical challenges in comparing G-matrices. Current methods for the comparison of G typically share several drawbacks: metrics that lack a direct relationship to evolutionary theory, the inability to be applied in conjunction with complex experimental designs, difficulties with determining statistical confidence in inferred differences and an inherently pair-wise focus. Here, we present a cohesive and general analytical framework for the comparative analysis of G that addresses these issues, and that incorporates and extends current methods with a strong geometrical basis. We describe the application of random skewers, common subspace analysis, the 4th-order genetic covariance tensor and the decomposition of the multivariate breeders equation, all within a Bayesian framework. We illustrate these methods using data from an artificial selection experiment on eight traits in Drosophila serrata, where a multi-generational pedigree was available to estimate G in each of six populations. One method, the tensor, elegantly captures all of the variation in genetic variance among populations, and allows the identification of the trait combinations that differ most in genetic variance. The tensor approach is likely to be the most generally applicable method to the comparison of G-matrices from any sampling or experimental design.
Keyword Genetic variance
G-matrix
Tensor
Bayesian
MCMC
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Published online: 13 March 2013.

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2014 Collection
School of Biological Sciences Publications
 
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Created: Sun, 14 Apr 2013, 23:26:03 EST by Gail Walter on behalf of School of Biological Sciences