Sex-Aware Quality Check Revisits Hardy-Weinberg Equilibrium in Telomere-to-Telomere 1000 Genomes Data

One of the oldest rules in population genetics is quietly doing some of the heaviest lifting in modern genomics. The Hardy-Weinberg equilibrium, a simple mathematical relationship described more than a century ago, predicts how often different versions of a gene should appear in a population that is not being shaped by evolutionary forces. When the observed genetic makeup of a group of people departs from that prediction, researchers often take it as a warning sign that something has gone wrong, either in the biology being studied or, more commonly, in the measurement itself. A new study published in PLOS Genetics has now subjected this venerable quality-control tool to a rigorous re-examination, using the most complete human genome assembly ever produced and a framework that explicitly accounts for a factor most analyses have long ignored: sex.

The research, led by Elika Garg, Jaffa Romain, Lei Sun and Andrew D. Paterson, turned to the 1000 Genomes Project, one of the most widely used reference resources in human genetics. Rather than relying on the standard reference genome that has underpinned most sequencing studies for two decades, the team realigned high-coverage whole genome sequencing data from 2,490 individuals to the telomere-to-telomere T2T-v2 assembly. This assembly, completed by the Telomere-to-Telomere Consortium, closes gaps that plagued earlier versions of the human reference sequence, particularly in repetitive and structurally complex regions where short-read sequencing has historically struggled. The choice matters because misalignment in these difficult regions can produce spurious genotype calls, and spurious genotypes can masquerade as violations of Hardy-Weinberg expectations.

The motivation for the study came from two converging lines of evidence. First, previous work had suggested that sex-based selection operates at numerous autosomal loci in cohorts with active recruitment, meaning that the genetic composition of men and women in a study sample may differ in ways that standard analyses never detect. Second, sequences derived from the sex chromosomes can interfere with the mapping of reads to autosomes, contaminating genotype calls in ways that differ between males and females. Because Hardy-Weinberg testing is usually implemented on a homogeneous population without stratifying by sex, both of these effects could distort the results of a routine quality-control step without anyone noticing. The researchers set out to determine how much of the apparent disequilibrium in a large, diverse dataset could be explained once sex and population structure were properly modelled.

To do this, the team imposed strict filters before any testing began. Only bi-allelic single nucleotide polymorphisms, or SNPs, with non-missing genotypes and a minor allele frequency of at least 5 percent in both sexes of each of the five super-populations represented in the 1000 Genomes Project were retained. This ensured that the statistical tests had adequate power in every stratum and that rare, poorly characterised variants would not drive the conclusions. The five super-populations, spanning African, European, East Asian, South Asian and American ancestry groups, allowed the researchers to ask whether any deviation from equilibrium was a universal feature of a genomic region or something specific to a particular ancestral background.

Methodologically, the study departed from the conventional approach of simply rejecting or accepting equilibrium at each SNP. Instead, the researchers applied an allele-based framework that quantifies both the magnitude and the direction of Hardy-Weinberg disequilibrium, distinguishing an excess of heterozygotes from a deficit. This distinction is informative: an excess of heterozygotes often points to technical artefacts such as collapsed duplications or mapping errors, whereas a deficit can suggest inbreeding, copy-number variation or genuine selection. On top of the per-SNP estimates, the team performed a second-order omnibus meta-analysis that combined disequilibrium results across populations and across sexes, allowing them to separate signals that were consistent everywhere from those that varied by ancestry or by sex.

At a genome-wide significance threshold of p less than 5 times 10 to the minus 8, the same stringent standard used in genome-wide association studies, only 0.9 percent of autosomal SNPs showed significant deviations from Hardy-Weinberg equilibrium. That figure might sound reassuringly small, but with millions of SNPs tested it still represents thousands of individual markers. The crucial finding was that the majority of these deviations were associated with genomic features indicative of poor sequence quality, such as segmental duplications and other regions where read mapping is unreliable. In other words, most of the apparent departures from equilibrium were not biological surprises but artefacts of the measurement process, exactly the kind of noise that quality control is designed to catch.

When the analysis was restricted to reliable genomic regions, the number of signals dropped dramatically, leaving 255 autosomal SNPs and a single non-pseudoautosomal chromosome X SNP that remained significantly out of equilibrium. This refinement is one of the study’s most practical contributions: it demonstrates that aligning sequencing reads to the gapless T2T assembly, combined with careful masking of problematic regions, can strip away a large fraction of false positives that would otherwise clutter downstream association analyses. Among the surviving signals, 140 autosomal SNPs displayed significant heterogeneity across populations but not across sexes, suggesting that ancestry-specific factors, whether demographic history or local genomic architecture, drive much of the residual disequilibrium rather than any systematic difference between males and females.

One cluster of results stood out. Eight SNPs within a remarkably tight 15-base-pair window on chromosome 14q31.3 showed an excess of heterozygosity in both sexes of the African super-population. The consistency of the signal across sexes within a single ancestry group, and its confinement to such a small genomic interval, makes it a compelling candidate for closer inspection, whether the underlying cause turns out to be a mapping artefact specific to African haplotypes, a structural variant not yet fully resolved even by the T2T assembly, or a genuine biological phenomenon. The finding illustrates the value of the study’s population-stratified design, since an analysis that pooled all super-populations together would likely have diluted or obscured this ancestry-specific pattern.

Beyond cataloguing individual deviations, the researchers distilled their findings into a practical tool: a multivariate predictor of Hardy-Weinberg disequilibrium built from sequence features. By learning which characteristics of a genomic locus, such as its repetitiveness, GC content and mapping properties, are associated with spurious disequilibrium, the predictor can flag problematic SNPs before they contaminate an analysis. Because it is multivariate, it integrates many weak indicators into a single score, offering a more nuanced assessment than the binary pass-or-fail verdict of a traditional equilibrium test. The authors position this predictor as something that can be slotted directly into existing quality-control pipelines for whole genome sequencing studies, which is where its impact is likely to be felt first.

The broader lesson of the study is that even the most routine statistical checks deserve periodic re-examination as the underlying technology changes. Hardy-Weinberg testing was developed for an era of gene frequencies in defined populations, yet it now serves as a first line of defence against sequencing artefacts in datasets of thousands of genomes. The transition to telomere-to-telomere reference assemblies, the growing recognition that sex must be modelled explicitly, and the increasing diversity of cohorts all change what the test is telling us. By quantifying how much apparent disequilibrium dissolves once these factors are handled properly, and by providing a predictor that separates technical noise from genuine signal, the work offers the genomics community a clearer view of where its measurements are trustworthy and where they still need scrutiny, ensuring that one of genetics’ oldest equations remains a sharp instrument in the telomere-to-telomere era.

Subject of Research: Sex-aware assessment of Hardy-Weinberg equilibrium deviations in telomere-to-telomere-aligned 1000 Genomes Project whole genome sequencing data

Article Title: Assessing Hardy-Weinberg equilibrium in T2T-aligned 1000 genomes project

Article References: Garg, E., Romain, J., Sun, L., & Paterson, A. D. (2026). Assessing Hardy-Weinberg equilibrium in T2T-aligned 1000 genomes project. PLOS Genetics, 22(9), e1012280. https://doi.org/10.1371/journal.pgen.1012280

Image Credits: AI Generated

DOI: 10.1371/journal.pgen.1012280

Keywords: Hardy-Weinberg equilibrium, 1000 Genomes Project, telomere-to-telomere assembly, T2T-v2, whole genome sequencing, quality control, Hardy-Weinberg disequilibrium, single nucleotide polymorphisms, population stratification, sex-specific analysis, minor allele frequency, meta-analysis