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EMPIRICAL COMPARISON OF G MATRIX TEST STATISTICS: FINDING BIOLOGICALLY RELEVANT CHANGE
Author(s) -
Calsbeek Brittny,
Goodnight Charles J.
Publication year - 2009
Publication title -
evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.84
H-Index - 199
eISSN - 1558-5646
pISSN - 0014-3820
DOI - 10.1111/j.1558-5646.2009.00735.x
Subject(s) - selection (genetic algorithm) , statistics , test statistic , covariance matrix , biology , matrix (chemical analysis) , statistic , mathematics , statistical hypothesis testing , computer science , artificial intelligence , materials science , composite material
A central assumption of quantitative genetic theory is that the breeder's equation ( R = GP −1 S ) accurately predicts the evolutionary response to selection. Recent studies highlight the fact that the additive genetic variance–covariance matrix ( G ) may change over time, rendering the breeder's equation incapable of predicting evolutionary change over more than a few generations. Although some consensus on whether G changes over time has been reached, multiple, often‐incompatible methods for comparing G matrices are currently used. A major challenge of G matrix comparison is determining the biological relevance of observed change. Here, we develop a “selection skewers” G matrix comparison statistic that uses the breeder's equation to compare the response to selection given two G matrices while holding selection intensity constant. We present a bootstrap algorithm that determines the significance of G matrix differences using the selection skewers method, random skewers, Mantel's and Bartlett's tests, and eigenanalysis. We then compare these methods by applying the bootstrap to a dataset of laboratory populations of Tribolium castaneum . We find that the results of matrix comparison statistics are inconsistent based on differing a priori goals of each test, and that the selection skewers method is useful for identifying biologically relevant G matrix differences.