Determining the effective dimensionality of the genetic variance-covariance matrix

Hine, E. and Blows, M. W. (2006) Determining the effective dimensionality of the genetic variance-covariance matrix. Genetics, 173 2: 1135-1144. doi:10.1534/genetics.105.054627


Author Hine, E.
Blows, M. W.
Title Determining the effective dimensionality of the genetic variance-covariance matrix
Journal name Genetics   Check publisher's open access policy
ISSN 0016-6731
Publication date 2006-01-01
Sub-type Article (original research)
DOI 10.1534/genetics.105.054627
Volume 173
Issue 2
Start page 1135
End page 1144
Total pages 10
Place of publication Baltimore
Publisher Genetics
Language eng
Subject C1
270207 Quantitative Genetics
780105 Biological sciences
Abstract Determining the dimensionality of G provides an important perspective on the genetic basis of a multivariate suite of traits. Since the introduction of Fisher's geometric model, the number of genetically independent traits underlying a set of functionally related phenotypic traits has been recognized as an important factor influencing the response to selection. Here, we show how the effective dimensionality of G can be established, using a method for the determination of the dimensionality of the effect space from a multivariate general linear model introduced by AMEMIYA (1985). We compare this approach with two other available methods, factor-analytic modeling and bootstrapping, using a half-sib experiment that estimated G for eight cuticular hydrocarbons of Drosophila serrata. In our example, eight pheromone traits were shown to be adequately represented by only two underlying genetic dimensions by Amemiya's approach and factor-analytic modeling of the covariance structure at the sire level. In, contrast, bootstrapping identified four dimensions with significant genetic variance. A simulation study indicated that while the performance of Amemiya's method was more sensitive to power constraints, it performed as well or better than factor-analytic modeling in correctly identifying the original genetic dimensions at moderate to high levels of heritability. The bootstrap approach consistently overestimated the number of dimensions in all cases and performed less well than Amemiya's method at subspace recovery.
Keyword Genetics & Heredity
Maximum-likelihood-estimation
Sexually Selected Traits
Principal Components
Nonnegative Estimation
Quantitative Genetics
Between-group
Models
Selection
Evolution
Definite
Q-Index Code C1

 
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Created: Wed, 15 Aug 2007, 18:36:48 EST