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Using Correlation Proximity Graphs to Study Phenotypic Integration

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Abstract

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.

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  1. Department of Biology, Duke University, 90338, Durham, NC, 27708, USA

    Paul M. Magwene

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  1. Paul M. Magwene
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Correspondence to Paul M. Magwene.

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Magwene, P.M. Using Correlation Proximity Graphs to Study Phenotypic Integration. Evol Biol 35, 191–198 (2008). https://doi.org/10.1007/s11692-008-9030-y

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  • DOI: https://doi.org/10.1007/s11692-008-9030-y

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