TYPE I ERROR RATES FOR TESTING GENETIC DRIFT WITH PHENOTYPIC COVARIANCE MATRICES: A SIMULATION STUDY. (22nd August 2012)

• Record Type: Journal Article
• Authors: Prôa, Miguel; O'Higgins, Paul; Monteiro, Leandro R.
• Is Part Of: Evolution. Volume 67:Number 1(2013:Jan.)
• Page Start: 185
• Page End: 195
• Publication Date: 2012-08-22
• Subjects: Cheverud's conjecture -- divergence rates -- G‐matrix -- genetic drift test -- multivariate evolution; Evolution -- Periodicals; Heredity -- Periodicals; Évolution (Biologie) -- Périodiques; Hérédité -- Périodiques; 338.47004094
• DOI: 10.1111/j.1558-5646.2012.01746.x
• Languages: English
• ISSNs: 0014-3820

Abstract

Abstract : Studies of evolutionary divergence using quantitative genetic methods are centered on the additive genetic variance–covariance matrix (G ) of correlated traits. However, estimatingG properly requires large samples and complicated experimental designs. Multivariate tests for neutral evolution commonly replace averageG 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 averageG byW 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 usingW instead of averageG when the matrix correlation between the ancestralG andP is higher than 0.6, the average character heritability is above 0.7, and the matrices share principal components. For less‐similarG andP 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 / Ne ) is smaller than 0.01 (large populations that diverged recently). WhenG is not known in real data, a simulation approach to estimate expected slopes for the AC test under genetic drift is discussed.