By the same authors

From the same journal

Type I error rates for testing genetic drift with phenotypic covariance matrices: A simulation study

Research output: Contribution to journalArticle



Publication details

JournalEvolution: international journal of organic evolution
DatePublished - 1 Jan 2013
Issue number1
Number of pages11
Pages (from-to)185-195
Original languageEnglish


Studies of evolutionary divergence using quantitative genetic methods are centered on the additive genetic variance-covariance matrix (G) of correlated traits. However, estimating G properly requires large samples and complicated experimental designs. Multivariate tests for neutral evolution commonly replace average G by the pooled phenotypic within-group variance-covariance matrix (W) for evolutionary inferences, but this approach has been criticized due to the lack of exact proportionality between genetic and phenotypic matrices. In this study, we examined the consequence, in terms of type I error rates, of replacing average G by W in a test of neutral evolution that measures the regression slope between among-population variances and within-population eigenvalues (the Ackermann and Cheverud [AC] test) using a simulation approach to generate random observations under genetic drift. Our results indicate that the type I error rates for the genetic drift test are acceptable when using W instead of average G when the matrix correlation between the ancestral G and P is higher than 0.6, the average character heritability is above 0.7, and the matrices share principal components. For less-similar G and P matrices, the type I error rates would still be acceptable if the ratio between the number of generations since divergence and the effective population size (t/N) is smaller than 0.01 (large populations that diverged recently). When G is not known in real data, a simulation approach to estimate expected slopes for the AC test under genetic drift is discussed.

Bibliographical note

© 2012 The Author(s). Evolution© 2012 The Society for the Study of Evolution.

Discover related content

Find related publications, people, projects, datasets and more using interactive charts.

View graph of relations