- THIS ARTICLE
- Full Text
- Full Text (PDF)
-
All Versions of this Article:
genetics.105.054627v1
173/2/1135 most recent - Alert me when this article is cited
- Alert me if a correction is posted
- SERVICES
- Similar articles in this journal
- Similar articles in PubMed
- Alert me to new issues of the journal
- Download to citation manager
- Reprints & Permissions
- CITING ARTICLES
- Citing Articles via HighWire
- Citing Articles via Google Scholar
- GOOGLE SCHOLAR
- Articles by Hine, E.
- Articles by Blows, M. W.
- Search for Related Content
- PUBMED
- PubMed Citation
- Articles by Hine, E.
- Articles by Blows, M. W.
Originally published as Genetics Published Articles Ahead of Print on March 17, 2006.
Genetics, Vol. 173, 1135-1144, June 2006, Copyright © 2006
doi:10.1534/genetics.105.054627
Determining the Effective Dimensionality of the Genetic Variance–Covariance Matrix
Emma Hine1 and Mark W. Blows
School of Integrative Biology, University of Queensland, Brisbane 4072, Australia
1 Corresponding author: Department of Zoology and Entomology, University of Queensland, St. Lucia, Queensland 4072, Australia.
E-mail: ehine{at}zen.uq.edu.au
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.
This article has been cited by other articles:
 |
|
 |
|
  K. Meyer and M. Kirkpatrick Perils of Parsimony: Properties of Reduced-Rank Estimates of Genetic Covariance Matrices Genetics, October 1, 2008; 180(2): 1153 - 1166. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
H. Vehvilainen, A. Kause, C. Quinton, H. Koskinen, and T. Paananen Survival of the Currently Fittest: Genetics of Rainbow Trout Survival Across Time and Space Genetics, September 1, 2008; 180(1): 507 - 516. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. Hunt, M. W. Blows, F. Zajitschek, M. D. Jennions, and R. Brooks Reconciling Strong Stabilizing Selection with the Maintenance of Genetic Variation in a Natural Population of Black Field Crickets (Teleogryllus commodus) Genetics, October 1, 2007; 177(2): 875 - 880. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
C. K. Griswold, B. Logsdon, and R. Gomulkiewicz Neutral Evolution of Multiple Quantitative Characters: A Genealogical Approach Genetics, May 1, 2007; 176(1): 455 - 466. [Abstract] [Full Text] [PDF]
|
|