Kingsolver, Joel G

Heckman, Nancy

Zhang, Jonathan

Carter, Patrick A

Knies, Jennifer L

Stinchcombe, John R

Meyer, Karin

2015

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.

The American Naturalist, 185(6), p. E166-E181

1537-5323

0003-0147

https://hdl.handle.net/1959.11/18905

en

University of Chicago Press

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

Journal Article

Publication