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Journal Article

Genetic Variation, Simplicity, and Evolutionary Constraints for Function-Valued Traits

Joel G. Kingsolver, Nancy Heckman, Jonathan Zhang, Patrick A. Carter, Jennifer L. Knies, John R. Stinchcombe and Karin Meyer
The American Naturalist
Vol. 185, No. 6 (June 2015), pp. E166-E181
DOI: 10.1086/681083
Stable URL: http://www.jstor.org/stable/10.1086/681083
Page Count: 16
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Abstract

AbstractUnderstanding the patterns of genetic variation and constraint for continuous reaction norms, growth trajectories, and other function-valued traits is challenging. We describe and illustrate a recent analytical method, simple basis analysis (SBA), that uses the genetic variance-covariance (G) matrix to identify “simple” directions of genetic variation and genetic constraints that have straightforward biological interpretations. We discuss the parallels between the eigenvectors (principal components) identified by principal components analysis (PCA) and the simple basis (SB) vectors identified by SBA. We apply these methods to estimated G matrices obtained from 10 studies of thermal performance curves and growth curves. Our results suggest that variation in overall size across all ages represented most of the genetic variance in growth curves. In contrast, variation in overall performance across all temperatures represented less than one-third of the genetic variance in thermal performance curves in all cases, and genetic trade-offs between performance at higher versus lower temperatures were often important. The analyses also identify potential genetic constraints on patterns of early and later growth in growth curves. We suggest that SBA can be a useful complement or alternative to PCA for identifying biologically interpretable directions of genetic variation and constraint in function-valued traits.

Notes and References

This item contains 44 references.

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