Robust Chi-square Test to Distinguish Population Stratification from Other Reasons of Hardy-Weinberg Disequilibrium
Abstract
The Hardy-Weinberg Equilibrium (HWE) test is a standard component of quality control pipelines for genomic studies and databases. However, applying the HWE test to large, multi-ethnic datasets raises substantial challenges: natural departures from HWE due to population stratification and admixture can mask technical errors. We extended the classical chi-square goodness-of-fit test to account for the presence of population stratification. Assuming genotype datasets can be partitioned into relatively homogeneous strata, we changed the null hypothesis of the classical HWE test to assume that all strata are in HWE, and created a test statistic similar to the classical chi-square but applicable to stratified populations. This test statistic, together with a few others, can distinguish HWE deviations caused by population stratification from those resulting from other reasons. We have implemented this method in the software package PopHWE. Test results using simulated data and 1000 Genomes data showed that PopHWE outperforms the classical HWE meta-analysis using Stouffer's method, while being much faster, easier to implement, and more robust to small sample sizes, large numbers of subpopulations, and the presence of ancestry grouping errors. Using PopHWE, HWE deviations caused by population stratification and those caused by genotyping errors can be displayed in different clusters on scatterplots. Furthermore, we demonstrated that the combined z-value of meta-analysis is biased toward excess heterozygosity for genotype datasets with up to moderately large sample sizes, and showed that replacing the combined z-value with its squared value could significantly improve the performance.
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