Several authors have concluded that independence testing for HWE and LE do not help validate the product rule (Evett and Buckleton 1996, Buckleton et al. 2001, Forman and Evett 2001). The requirements for Hardy-Weinberg equilibrium (infinite, randomly mating population with no migration or mutation) cannot be met in real human populations (Evett and Buckleton 1996). Thus, obtaining a pvalue of >0.05 and demonstrating that the STR alleles in our population database are not statistically significantly different from HWE does not mean that the samples are in HWE.
In order to use the product rule and multiple genotype frequencies across all tested loci, an assumption of within-locus and between loci independence is made. According to Buckleton et al. (2001), independence testing using Fisher's exact test (Guo and Thomson 1992) does not validate the product rule. Nevertheless, Evett and Buckleton (1996) maintain that the model of perfect independence performs adequately in the context of estimating DNA profile frequencies. However, they argue that classical hypothesis testing has only a small part to play in assessing reliability of a DNA population database. Evett and Buckleton (1996) do not advocate the abandonment of testing for disequi-libria but they do contend that the results of independence testing (e.g., HWE) do not address the questions of practical impact and provide no guidance on how to proceed after testing. Thus, if a pvalue of < 0.05 is observed with a set of alleles measured at a particular STR locus, it does not mean that a laboratory should avoid using this data because it 'failed' a test for Hardy-Weinberg equilibrium (e.g., see Gorman et al. 2004).
Despite their harsh position on the lack of value for testing of independence assumptions, Foreman and Evett (2001) recognize that there is merit in using within-locus significance tests as part of quality control of the data to enable detection of null alleles (see D.N.A. Box 20.1). In short, critics of conventional independence testing question the relevance of HWE and LE evaluation since these tests are not capable of proving the assumption of independence true. However, these tests are useful for quality assurance of databases and can help detect the presence of null alleles.
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