Performing the Exact Test of Hardy-Weinberg Proportion for Multiple Alleles

Department of Statistics, University of Washington Seattle, Seattle, Washington, United States
Biometrics (Impact Factor: 1.57). 07/1992; 48(2):361-72. DOI: 10.2307/2532296
Source: PubMed


The Hardy-Weinberg law plays an important role in the field of population genetics and often serves as a basis for genetic inference. Because of its importance, much attention has been devoted to tests of Hardy-Weinberg proportions (HWP) over the decades. It has long been recognized that large-sample goodness-of-fit tests can sometimes lead to spurious results when the sample size and/or some genotypic frequencies are small. Although a complete enumeration algorithm for the exact test has been proposed, it is not of practical use for loci with more than a few alleles due to the amount of computation required. We propose two algorithms to estimate the significance level for a test of HWP. The algorithms are easily applicable to loci with multiple alleles. Both are remarkably simple and computationally fast. Relative efficiency and merits of the two algorithms are compared. Guidelines regarding their usage are given. Numerical examples are given to illustrate the practicality of the algorithms.

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    • "The presence of null alleles was evaluated using the software MICRO-CHECKER version 2.2.0 (Van Oosterhout et al. 2004). The probability of significant deviation from Hardy–Weinberg equilibrium (HWE) was assessed using the Markov chain-based method (Guo & Thompson 1992) implemented in GENEPOP version 3.4 (Raymond & Rousset 1995). Significance criteria were adjusted for the number of simultaneous tests using the standard Bonferroni correction. "
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    • "pairwise linkage disequilibria for each pair of loci in each population and deviation from Hardy- Weinberg equilibrium (HWE) were tested using the exact test implemented in GenePop version 4.0 (Rousset 2008). Statistical significance was evaluated by running a Markov Chain Monte Carlo (MCMC) consisting of 10,000 batches of 10,000 iterations each, with the first 10,000 iterations discarded before sampling (Guo & Thompson 1992). "
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    • ".edu/*miller/tfpga.htm), permutations were performed using at least 1000 randomisations. Evidence of linkage disequilibrium was assessed using Markov chain approximations (Guo and Thompson 1992). Similarly, deviations from Hardy–Weinberg equilibrium were tested for each locus and population separately with sequential Bonferroni correction Table 2 Details of the sampled Moor frog populations (N = sample size, H "

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