Using correlation proximity graphs to study phenotypic integration

Published

Journal Article

Characterizing and comparing the covariance or correlation structure of phenotypic traits lies at the heart of studies concerned with multivariate evolution. I describe an approach that represents the geometric structure of a correlation matrix as a type of proximity graph called a Correlation Proximity graph. Correlation Proximity graphs provide a compact representation of the geometric relationships inherent in correlation matrices, and these graphs have simple and intuitive properties. I demonstrate how this framework can be used to study patterns of phenotypic integration by employing this approach to compare phenotypic and additive genetic correlation matrices within and between species. I also outline a graph-based method for testing whether an inferred correlation proximity graph is one of a number of possible models that are consistent with a "soft" biological hypothesis. © Springer Science+Business Media, LLC 2008.

Full Text

Duke Authors

Cited Authors

  • Magwene, PM

Published Date

  • September 1, 2008

Published In

Volume / Issue

  • 35 / 3

Start / End Page

  • 191 - 198

International Standard Serial Number (ISSN)

  • 0071-3260

Digital Object Identifier (DOI)

  • 10.1007/s11692-008-9030-y

Citation Source

  • Scopus