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A similar conclusion was reached in a California-wide study of valley oak genetic adaptation to rapid climate change, where 90-m climate data provided excellent correlations with the geographic patterns of multivariate genetic variation associated with climatic conditions (Sork et al. 2010).
The small responses of ornamental traits to selection on male attractiveness suggest that we may have selected in a direction that contains little multivariate genetic variation.
The structure of multivariate genetic variation is often analyzed in terms of the eigenvectors of the G-matrix (as in a principal component analysis).
Tradeoffs and sexual antagonism between fitness components, and the possibility that little multivariate genetic variation in suites of ornaments is in the direction of sexual selection both raise the possibility of a new multivariate form of the lek paradox.
It may also be due to the fact that male attractiveness is influenced by a suite of male traits, and that there is little multivariate genetic variation in the direction of selection despite the presence of additive genetic variation in each one of the male traits.
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That greater multivariate additive genetic variation can occur with epistasis is in contrast to previous theory that indicated univariate additive genetic variation is decreased with epistasis under stabilizing selection conditions.
Our results indicate that under stabilizing selection conditions a population with a mixed additive and epistatic genetic architecture can have greater multivariate additive genetic variation and evolvability than a population with a purely additive genetic architecture.
Two recent studies have documented traits that fail to respond to selection as predicted by the univariate breeders' equation [ 31, 32], and suggested that multivariate understanding of genetic variation and the operation of selection is necessary.
Information theoretic extensions of the KLD allow measurement of complex multivariate dependencies among genetic variations and environmental factors without complex modeling and could enable powerful and intuitive methodology for GGI and GEI analyses to be developed [ 14, 15].
The proportion of genetic variance explained was determined for each candidate gene individually and for the combination of transcripts in the final multivariate genetic model which described the strain variation in alcohol consumption.
It remains to be established whether there remains significant additive genetic variation along the major axes of multivariate selection.
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multivariate genetic variance
multivariate genetic profile
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multivariate genetic algorithm
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multivariate genetic model
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multivariate genetic interaction
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