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Genetics, Vol. 168, 2295-2306, December 2004, Copyright © 2004
doi:10.1534/genetics.104.029181
Direct Estimation of Genetic Principal Components
Simplified Analysis of Complex Phenotypes
Mark Kirkpatrick*,1 and
Karin Meyer
* Section of Integrative Biology, University of Texas, Austin, Texas 78712
Animal Genetics and Breeding Unit, University of New England, Armidale NSW 2351, Australia
1 Corresponding author: Section of Integrative Biology, 1 University Station C-0930, University of Texas, Austin TX 78712.
E-mail: kirkp{at}mail.utexas.edu
Estimating the genetic and environmental variances for multivariate and function-valued phenotypes poses problems for estimation and interpretation. Even when the phenotype of interest has a large number of dimensions, most variation is typically associated with a small number of principal components (eigen-vectors or eigenfunctions). We propose an approach that directly estimates these leading principal components; these then give estimates for the covariance matrices (or functions). Direct estimation of the principal components reduces the number of parameters to be estimated, uses the data efficiently, and provides the basis for new estimation algorithms. We develop these concepts for both multivariate and function-valued phenotypes and illustrate their application in the restricted maximum-likelihood framework.
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