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

• 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
• ISSN: 0003-0147; 1537-5323
• DOI: 10.1086/681083

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.