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Power for detecting genetic divergence: differences between statistical methods and marker loci

Journal article
Authors N. Ryman
S. Palm
Carl André
Gary R. Carvalho
Thomas G. Dahlgren
P. E. Jorde
L. Laikre
L. C. Larsson
A. Palme
D. E. Ruzzante
Published in Molecular Ecology
Volume 15
Issue 8
Pages 2031-2045
ISSN 0962-1083
Publication year 2006
Published at Department of Marine Ecology, Tjärnö Marine Biological Laboratory
Department of Zoology
Department of Marine Ecology
Pages 2031-2045
Language en
Keywords chi-square, fisher's exact test, fisher's method, genetic differentiation, g-test, statistical power, differentiation, conservation, populations, distances, evolution, patterns, program, tests
Subject categories Marine ecology


Information on statistical power is critical when planning investigations and evaluating empirical data, but actual power estimates are rarely presented in population genetic studies. We used computer simulations to assess and evaluate power when testing for genetic differentiation at multiple loci through combining test statistics or P values obtained by four different statistical approaches, viz. Pearson's chi-square, the log-likelihood ratio G-test, Fisher's exact test, and an F-ST-based permutation test. Factors considered in the comparisons include the number of samples, their size, and the number and type of genetic marker loci. It is shown that power for detecting divergence may be substantial for frequently used sample sizes and sets of markers, also at quite low levels of differentiation. The choice of statistical method may be critical, though. For multi-allelic loci such as microsatellites, combining exact P values using Fisher's method is robust and generally provides a high resolving power. In contrast, for few-allele loci (e.g. allozymes and single nucleotide polymorphisms) and when making pairwise sample comparisons, this approach may yield a remarkably low power. In such situations chi-square typically represents a better alternative. The G-test without Williams's correction frequently tends to provide an unduly high proportion of false significances, and results from this test should be interpreted with great care. Our results are not confined to population genetic analyses but applicable to contingency testing in general.

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