A General Statistical Framework for Hardy-Weinberg Equilibrium Inference on the X Chromosome

TL;DR

This paper introduces a comprehensive statistical framework for Hardy-Weinberg equilibrium testing on the X chromosome, addressing complexities like sex-specific genotypes and minor allele frequency differences.

Contribution

It unifies existing HWE tests under a regression-based model, clarifies their assumptions, and enhances robustness and interpretability for X-chromosomal data.

Findings

Existing tests can have inflated error with sdMAF

The framework unifies and clarifies HWE testing assumptions

Simulation and real data validate improved robustness

Abstract

Testing for Hardy-Weinberg equilibrium (HWE) is a fundamental component of genetic data analysis, widely used for quality control and model validation. Although HWE testing is well established for autosomal loci, inference on the X chromosome is more complex due to sex-specific genotype structures and potential sex differences in minor allele frequency (sdMAF). Existing tests differ in their assumptions about sdMAF and male sample inclusion, often leading to distinct but poorly characterized null hypotheses. We develop a general statistical framework for HWE inference using the robust allele-based regression model. By formulating HWE testing as an assessment of allele-level dependence, the framework directly parameterizes Hardy-Weinberg disequilibrium, unifies existing Pearson chi-square-based tests under explicit modeling assumptions, and clarifies their null hypotheses, degrees of freedom, and sensitivity to sdMAF. The framework also accommodates covariate and population-structure adjustment within a unified regression-based formulation. The proposed framework provides robust, interpretable, and flexible inference, establishing a unified statistical foundation for HWE testing across autosomal and X-chromosomal regions. Simulation studies and analysis of high-coverage 1000 Genomes Project data demonstrate that commonly used X-chromosome tests can exhibit inflated type I error or misleading inference when sdMAF is present.