Testing Hardy-Weinberg equilibrium with a simple root-mean-square statistic

Abstract

We provide evidence that a root-mean-square test of goodness-of-fit can be significantly more powerful than state-of-the-art exact tests in detecting deviations from Hardy-Weinberg equilibrium. Unlike Pearson's chi-square test, the log--likelihood-ratio test, and Fisher's exact test, which are sensitive to relative discrepancies between genotypic frequencies, the root-mean-square test is sensitive to absolute discrepancies. This can increase statistical power, as we demonstrate using benchmark datasets and through asymptotic analysis. With the aid of computers, exact P-values for the root-mean-square statistic can be calculated eeffortlessly, and can be easily implemented using the author's freely available code.

29 pages, 6 figures

References in corpus (3)

• Computing the confidence levels for a root-mean-square test of goodness-of-fit, II
• Testing the significance of assuming homogeneity in contingency-tables/cross-tabulations
• An introduction to how chi-square and classical exact tests often wildly misreport significance and how the remedy lies in computers