Journal
JOURNAL OF EVOLUTIONARY BIOLOGY
Volume 21, Issue 4, Pages 949-957Publisher
WILEY
DOI: 10.1111/j.1420-9101.2008.01529.x
Keywords
Bayesian analysis; evolution; hierarchical models; quantitative genetics; statistics
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The study of evolutionary quantitative genetics has been advanced by the use of methods developed in animal and plant breeding. These methods have proved to be very useful, but they have some shortcomings when used in the study of wild populations and evolutionary questions. Problems arise from the small size of data sets typical of evolutionary studies, and the additional complexity of the questions asked by evolutionary biologists. Here, we advocate the use of Bayesian methods to overcome these and related problems. Bayesian methods naturally allow errors in parameter estimates to propagate through a model and can also be written as a graphical model, giving them an inherent flexibility. As packages for fitting Bayesian animal models are developed, we expect the application of Bayesian methods to evolutionary quantitative genetics to grow, particularly as genomic information becomes more and more associated with environmental data.
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