A composite likelihood approach in testing for Hardy Weinberg Equilibrium using family-based genetic survey data.

In population-based household surveys, for example, the National Health and Nutrition Examination Survey, households are often sampled by stratified multistage cluster sampling, and multiple individuals related by blood are often sampled within households. Therefore, genetic data collected from these population-based household surveys, called National Genetic Household Surveys, can be correlated because of two levels of correlation. One level of correlation is caused by the multistage geographical cluster sampling and the other is caused by biological inheritance among participants within the same sampled family. In this paper, we develop an efficient Hardy Weinberg Equilibrium (HWE) test utilizing pairwise composite likelihood methods that incorporate the sample weighting effect induced by the differential selection probabilities in complex sample designs, as well as the two-level clustering (correlation) effects described above. Monte Carlo simulation studies show that the proposed HWE test maintains the nominal levels, and is more powerful than existing methods (Li et al. 2011) under various (non)informative sample designs that depend on genotypes (explicitly or implicitly), family relationships or both, especially when within-household sampling depends on the genotypes. The developed tests are further evaluated using simulated genetic data based on the Hispanic Health and Nutrition Survey.