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Open Access Research article

A comparison of tests for Hardy-Weinberg Equilibrium in national genetic household surveys

Yan Li

Author affiliations

Joint Program in Survey Methodology, University of Maryland at College Park, 7965 Baltimore Ave, College Park, MD, 20742, USA

Department of Mathematics, University of Texas at Arlington, 701 S Nedderman Dr, Arlington, TX, 76019, USA

Citation and License

BMC Genetics 2013, 14:14  doi:10.1186/1471-2156-14-14

Published: 1 March 2013

Abstract

Background

This study is motivated by National Household Surveys that collect genetic data, in which complex samples (e.g. stratified multistage cluster sample), partially from the same family, are selected. In addition to the differential selection probabilities of selecting households and persons within the sampled households, there are two levels of correlations of the collected genetic data in National Genetic Household Surveys (NGHS). The first level of correlation is induced by the hierarchical geographic clustered sampling of households and the second level of correlation is induced by biological inheritances from individuals sampled in the same household.

Results

To test for Hardy-Weinberg Equilibrium (HWE) in NGHS, two test statistics, the CCS method [1] and the QS method [2], appear to be the only existing methods that take account of both correlations. In this paper, I evaluate both methods in terms of the test size and power under a variety of complex designs with different weighting schemes and varying magnitudes of the two correlation effects. Both methods are applied to a real data example from the Hispanic Health and Nutrition Examination Survey with simulated genotype data.

Conclusions

The QS method maintains the nominal size well and consistently achieves higher power than the CCS method in testing HWE under a variety of sample designs, and therefore is recommended for testing HWE of genetic survey data with complex designs.

Keywords:
Complex sampling; Condensed coefficients of identity; Quasi-score test; Taylor linearization