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. 2009 Jul;32(3):619-25.
doi: 10.1590/S1415-47572009000300028. Epub 2009 Sep 1.

A Straightforward Multiallelic Significance Test for the Hardy-Weinberg Equilibrium Law

Free PMC article

A Straightforward Multiallelic Significance Test for the Hardy-Weinberg Equilibrium Law

Marcelo S Lauretto et al. Genet Mol Biol. .
Free PMC article


Much forensic inference based upon DNA evidence is made assuming Hardy-Weinberg Equilibrium (HWE) for the genetic loci being used. Several statistical tests to detect and measure deviation from HWE have been devised, and their limitations become more obvious when testing for deviation within multiallelic DNA loci. The most popular methods-Chi-square and Likelihood-ratio tests-are based on asymptotic results and cannot guarantee a good performance in the presence of low frequency genotypes. Since the parameter space dimension increases at a quadratic rate on the number of alleles, some authors suggest applying sequential methods, where the multiallelic case is reformulated as a sequence of "biallelic" tests. However, in this approach it is not obvious how to assess the general evidence of the original hypothesis; nor is it clear how to establish the significance level for its acceptance/rejection. In this work, we introduce a straightforward method for the multiallelic HWE test, which overcomes the aforementioned issues of sequential methods. The core theory for the proposed method is given by the Full Bayesian Significance Test (FBST), an intuitive Bayesian approach which does not assign positive probabilities to zero measure sets when testing sharp hypotheses. We compare FBST performance to Chi-square, Likelihood-ratio and Markov chain tests, in three numerical experiments. The results suggest that FBST is a robust and high performance method for the HWE test, even in the presence of several alleles and small sample sizes.

Keywords: FBST; Hardy-Weinberg equilibrium; significance tests.


Figure 1
Figure 1
Parameter space representation for two biallelic examples, x = (5,5,10) and x = (3,7,10), and the respective evidence against the HWE hypothesis.
Figure 2
Figure 2
Datasets for numerical experiments, given by Louis and Dempster (1987) (a), Guo and Thompson (1992) (b) and Wordsworth et al. (1992) (c).
Figure 3
Figure 3
Average error rates for FBST, MCMC, Chi-square and Likelihood-ratio for simulated data based on examples from Louis and Dempster (1987) (a), Guo and Thompson (1992) (b) and Wordsworth et al. (1992) (c).

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