Evaluating HWE and Association in Genome Wide Association Studies: A Unified Procedure

2026-06-29

AI Summary

Traditional genome-wide association studies (GWAS) typically perform separate single-nucleotide polymorphism (SNP) association tests and Hardy–Weinberg equilibrium (HWE) tests, relying on arbitrary thresholds to filter loci—a practice that can introduce false positives or discard valuable information. This work proposes a unified conditional testing framework that integrates HWE information directly into the association analysis by conditioning the Pearson χ² statistic from the 3×2 case–control contingency table on the HWE χ² statistic computed from controls alone. By leveraging asymptotic distribution theory, this approach eliminates the need for a standalone HWE filtering step and yields more accurate p-values. Both simulations and real-data analyses on alopecia demonstrate that the method substantially improves statistical power and SNP ranking accuracy compared to existing retrospective approaches, thereby reducing replication costs and enhancing fine-mapping resolution.

Abstract

In genome wide association studies (GWASs) based on a case-control design, single nucleotide polymorphisms (SNPs) are typically evaluated for an association test and a Hardy-Weinberg equilibrium (HWE) goodness-of-fit test. SNPs are then excluded from analysis based on a HWE cutoff to avoid false positives. In order to avoid cutoffs based on arbitrary threshold values, we propose a conditional genotype--based test that conditions the Pearson $χ^2$-statistic in the 3x2 contingency table on the $χ^2$-statistic for HWE in the control group, and develop the relevant asymptotic distribution theory. We show by simulations that our test in most scenarios is more powerful than two competing retrospective procedures. Another important advantage of the proposed method is a better ranking of SNPs in GWASs as HWE is accounted for in computing p-values of SNP association. We demonstrate this effect on a data set in an alopecia study. In conclusion, our test makes separate HWE testing superfluous by providing a unified framework and strictly improves on the standard procedure in terms of power and interpretability, thereby making replication more cost effective and improving subsequent fine mapping.\par

Problem

• GWAS
• Hardy-Weinberg equilibrium
• SNP association
• case-control study
• false positives

Innovation

• conditional genotype test
• Hardy-Weinberg equilibrium
• GWAS
• unified statistical framework
• association testing