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

Understanding 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...

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Bibliographic Details
Published in:The American naturalist 2015-06, Vol.185 (6), p.E166-E181
Main Authors: Kingsolver, Joel G., Heckman, Nancy, Zhang, Jonathan, Carter, Patrick A., Knies, Jennifer L., Stinchcombe, John R., Meyer, Karin
Format: Article
Language:English
Subjects:
Age Factors
Bacteriophages
Biological Evolution
E-Article
Eigenvectors
Evolutionary biology
Evolutionary genetics
Gene-Environment Interaction
Genetic diversity
Genetic variance
Genetic Variation
Genetic vectors
Genotypes
Growth - genetics
Low temperature
Models, Biological
Phenotype
Phenotypic traits
Physical growth
Principal Component Analysis
Principal components analysis
Quantitative Trait, Heritable
Temperature
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Summary:Understanding 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.
ISSN:0003-0147
1537-5323
DOI:10.1086/681083