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

The significance of the work lies in the gap it addresses. The roots of P. praeruptorum are rich in coumarins and flavonoids, two classes of plant secondary metabolites with well-documented pharmacological activity, yet the genetic basis underlying the biosynthesis of these compounds has remained insufficiently characterized. For medicinal plant researchers, that is a familiar frustration: the chemistry is known, the clinical tradition is deep, but the genes and regulatory networks controlling production of the active ingredients stay hidden. PeuDB is designed to change that by giving scientists the tools to move from a compound of interest to candidate genes, and from candidate genes to testable hypotheses about biosynthetic pathways.

Technically, the platform is built on a dual-assembly foundation that reflects the current reality of plant genomics. It incorporates the PP_2024v2 telomere-to-telomere assembly, representing the most complete end-to-end reconstruction of the plant’s chromosomes, alongside the PP_2024v1 chromosome-level assembly generated independently. Maintaining both assemblies side by side is a deliberate choice rather than redundancy. Different research groups have already deposited data mapped against different references, and by supporting both, the database allows users to work within whichever coordinate system their data or collaborators use. To make this practical, PeuDB links 24,846 accepted cross-assembly gene pairs, enabling identifier conversion so that a gene discovered in one assembly can be traced to its counterpart in the other.

Functional annotation of predicted genes was carried out against a battery of established reference databases, including NR, Swiss-Prot, TAIR10, TrEMBL, GO, KEGG, and Pfam. This layered annotation strategy means users can query a gene and retrieve not only its likely protein family but also its predicted molecular function, biological process, cellular component, and the metabolic pathways it may participate in. The scale of the classification effort is substantial: the team catalogued six major gene-family categories, comprising 5,981 transcription factors and transcriptional regulators, 2,916 protein kinases, 1,940 carbohydrate-active enzymes, 1,879 transporters, 3,112 ubiquitin-related proteins, and 594 cytochrome P450 genes. That last number is particularly noteworthy for natural-products research, since cytochrome P450 enzymes are the workhorses of plant secondary metabolism and frequently catalyze the tailoring steps that give medicinal compounds their final, bioactive structures.

The transcriptomic component of the database required careful statistical treatment before it could support reliable conclusions. Expression data from nine BioProjects inevitably carry batch effects, systematic technical differences between sequencing runs, laboratories, and library preparations that can masquerade as biology. The researchers applied tissue-stratified batch correction using mean-only parametric ComBat, applied separately within root, leaf, and stem tissues, with BioProject as the batch variable and estimable within-project biological-condition contrasts built into the model. Tissues that appeared in only a single BioProject, namely callus and flower, were passed through unchanged. Principal component analysis diagnostics before and after correction showed a substantial reduction, though not complete elimination, of measurable project-associated variation, a level of transparency that the authors document in their supplementary tables rather than glossing over.

With corrected expression profiles in hand, the team constructed co-expression networks using two complementary filtering approaches: direct Pearson-correlation and mutual-rank filtering. The baseline thresholds, a Pearson correlation coefficient above 0.70 combined with a mutual rank below 30, identified 427,999 positive co-expression relationships, of which 255,633 belong to the PP_2024v2 assembly and 172,366 to PP_2024v1. Importantly, the researchers did not treat these thresholds as sacred. A sensitivity analysis evaluated all nine combinations of strict correlation cutoffs from 0.65 to 0.75 and mutual-rank cutoffs from 20 to 40, quantifying how network size and overlap respond to parameter choices. This kind of threshold-sensitivity reporting is increasingly seen as a hallmark of rigorous network biology, since it tells downstream users exactly how much confidence to place in any individual predicted relationship.

Beyond co-expression, PeuDB extends into protein-interaction prediction through a strategy borrowed from comparative genomics. The team projected evidence-filtered Arabidopsis interaction data onto P. praeruptorum using strict reciprocal-best-hit analysis, yielding 5,864 confidence-filtered interolog predictions, split as 3,027 for the T2T assembly and 2,837 for the chromosome-level assembly. The logic is that if two proteins are known to interact in the well-studied reference plant Arabidopsis thaliana, and their best-matching counterparts in P. praeruptorum are each other’s best matches in turn, the interaction is likely conserved. It is an inference, not a measurement, and the database is explicit about that distinction, presenting these predictions as computational hypotheses awaiting experimental validation.

To demonstrate the platform in action, the authors built two reproducible case-study workflows, one centered on the F6’H enzyme family and another on WRKY transcription factors. Both are directly relevant to the plant’s medicinal value: feruloyl 6-hydroxylase enzymes participate in coumarin biosynthesis, while WRKY transcription factors are candidate regulators of secondary-metabolite pathways. The case studies walk users through candidate prioritization while carefully distinguishing what the computational evidence supports from what still requires laboratory confirmation. Notably, the WRKY analysis showed that biosynthesis of unsaturated fatty acids remained significantly enriched across all nine threshold combinations tested, while other baseline-significant pathways and individual network edges proved threshold-dependent, a finding the authors report honestly rather than burying.

The platform itself runs on a LAMP architecture, the long-established combination of Linux, Apache, MySQL, and PHP, and offers a user-friendly web interface with an unusually complete analytical toolkit. Users can perform BLAST searches, browse genomes with JBrowse2, generate expression heatmaps, explore co-expression and predicted protein-interaction networks, run enrichment and differential-expression analyses, design primers, convert identifiers between assemblies, and conduct multiple-sequence alignments. For programmatic users, PeuDB provides a versioned, read-only REST API with OpenAPI/Swagger documentation, enabling automated retrieval of assembly information, gene annotations and sequences, raw TPM expression profiles, cross-assembly mappings, co-expression relationships, and predicted protein interactions. That API layer matters for reproducibility, allowing pipelines to query the database directly rather than relying on manual downloads that quickly go stale.

PeuDB is freely accessible at https://www.gzybioinformatics.cn/PeuDB, and its arrival reflects a broader shift in how medicinal plant science is done. The era in which a single reference genome and a handful of transcriptomes could anchor a research program is giving way to integrated platforms that manage multiple assemblies, large sample collections, and the statistical corrections needed to make heterogeneous public data usable. For P. praeruptorum, the practical payoff could be faster identification of the genes governing coumarin and flavonoid production, with implications for breeding, cultivation, and potentially metabolic engineering of a herb whose therapeutic reputation spans centuries. For the wider community of researchers working on under-characterized medicinal species, the database offers a template: assemble thoroughly, annotate broadly, correct for batch effects rigorously, report threshold sensitivity honestly, and always keep the line between computational prediction and experimental proof clearly visible.

Subject of Research: A genomic and transcriptomic database for gene-function and coumarin biosynthesis analysis in the medicinal plant Peucedanum praeruptorum

Article Title: PeuDB: a comprehensive genomic and transcriptomic platform for gene function analysis in Peucedanum praeruptorum

Article References: Yang, J., Yang, H., Shu, G., Xiao, Q., & Yang, J. (2026). PeuDB: a comprehensive genomic and transcriptomic platform for gene function analysis in Peucedanum praeruptorum. BMC Genomics. https://doi.org/10.1186/s12864-026-13302-9

Image Credits: AI Generated

DOI: 10.1186/s12864-026-13302-9

Keywords: Peucedanum praeruptorum, PeuDB, genomics, transcriptomics, co-expression network, coumarin biosynthesis, cytochrome P450, telomere-to-telomere assembly, gene annotation, protein interaction prediction, medicinal plants, biological database

The challenge that the team set out to overcome was technical rather than conceptual. For decades, scientists have known that G4-forming sequences exist throughout the genome, but the most repetitive parts of human DNA, such as centromeres and the long arrays of ribosomal DNA, resisted assembly with standard short-read sequencing technologies. These regions are composed of long, near-identical repeats that short reads cannot be uniquely placed, leaving gaps in even the most celebrated reference assemblies. The recent arrival of long-read sequencing and the resulting gap-less, telomere-to-telomere human genome assemblies changed the picture entirely. For the first time, researchers could scan the entirety of a complete human genome, from end to end, for sequences with the potential to form G-quadruplexes, and then compare those findings across many individuals.

In the new study, the researchers systematically identified potential G4-forming sequences in the gap-less reference human genome assembly and extended their analysis to 88 haplotypes, or individual genome copies, drawn from people of diverse ancestries. This breadth mattered. By looking across many genomes rather than a single reference, the team could ask not only where G4s sit in the genome but also how variable they are between people, a direct window into their evolutionary and mutational behavior. The scale of the analysis allowed them to move beyond anecdotal observations and build a population-level picture of G4 landscape and instability.

One of the most striking findings concerns where G4s concentrate. The team reports that G4-forming sequences are highly enriched in specific repetitive regions of the genome, including certain centromeric and pericentromeric repeat types, the specialized DNA that surrounds and structures the centromere, the point where chromosomes attach to the segregation machinery during cell division. They also found marked enrichment in ribosomal DNA arrays, the tandem repeats that encode the RNA components of ribosomes, the molecular machines that build proteins in every cell. These are precisely the regions that had been invisible to earlier G4 surveys, which means that a substantial fraction of the human G4 landscape had effectively been missing from the scientific record until now.

Importantly, the researchers did not rely on sequence patterns alone. Computational predictions of G4 formation are useful, but they can be misleading, because not every guanine-rich motif actually folds into a quadruplex under physiological conditions. To address this, the team experimentally validated the most prevalent G4 structures they detected, confirming that the sequences identified in these repetitive regions can indeed form the characteristic four-stranded architecture. This biophysical validation anchors the computational survey in laboratory reality and strengthens the case that the newly mapped G4s are genuine structural features of human DNA rather than artifacts of pattern matching.

The study also examined the chemical modification landscape around G4s, with intriguing results. DNA methylation, the addition of a methyl group to cytosine bases, is a key epigenetic mark that influences whether genes are active or silent. The researchers found that G4-forming regions tend to show lower methylation than would be expected by chance. This observation fits with a growing body of evidence that alternative DNA structures can influence the epigenetic state of the sequences that carry them, potentially affecting how nearby genes are regulated. While the study establishes the correlation across the complete genome, it also raises new questions about cause and effect: whether G4 formation actively discourages methylation, or whether unmethylated regions are simply more permissive to quadruplex folding, remains an open avenue for future work.

Perhaps the most consequential finding relates to genome stability. By comparing G4 loci across the 88 haplotypes, the researchers could measure how often these sequences change from one generation to the next. The answer was unambiguous: G4-forming regions are genomically unstable, showing an excess of insertions and deletions relative to the surrounding genome. This makes mechanistic sense. A four-stranded structure folded out of one DNA strand creates an awkward substrate for the enzymes that copy and repair DNA, and stalled replication forks at stable secondary structures are a classic source of small insertions and deletions. The new data suggest that this mutagenic tendency is a genome-wide property of G4s, measurable at the population scale, and that these structures act as ongoing engines of genetic variation in repetitive DNA.

The team then connected G4s to one of the most dramatic events in the life of a genome: meiosis, the specialized cell division that produces sperm and eggs. During meiosis, programmed double-strand breaks are deliberately introduced at thousands of hotspots throughout the genome to initiate recombination, the shuffling of genetic material between chromosomes. Which sites are cut is largely determined by PRDM9, a protein whose DNA-binding domain varies between individuals and whose genotype therefore reshapes the recombination landscape from person to person. Remarkably, the researchers found that G4-forming motifs are consistently enriched at meiotic double-strand break hotspots across individuals with different PRDM9 genotypes. This consistency suggests that the physical propensity of DNA to fold into quadruplexes may contribute to the targeting or processing of meiotic breaks in a way that transcends the specific PRDM9 variant an individual carries, hinting at a structural layer of regulation overlaid on the well-known genetic one.

Taken together, the findings reframe G-quadruplexes not as curiosities of guanine-rich sequence but as dynamic, widespread, and functionally meaningful elements of the human genome. Their enrichment in centromeric and ribosomal DNA places them at the heart of chromosome architecture and protein synthesis; their reduced methylation ties them to epigenetic regulation; their excess of insertions and deletions marks them as drivers of mutation; and their presence at recombination hotspots links them to the machinery of heredity itself. Each of these threads also points toward disease. Genome instability at repetitive regions is a recurring theme in cancer and in a range of neurological and developmental disorders, and a complete map of where G4s form, and where they mutate, gives researchers a new tool for interpreting disease-associated variation in parts of the genome that were previously unreadable.

The study also illustrates how technological progress reshapes entire fields. Just as the telomere-to-telomere assemblies revealed previously hidden genes and structural variants, they have now revealed a hidden structural genome, one written not in the order of bases alone but in the capacity of those bases to fold. With 88 diverse haplotypes already surveyed, the stage is set for broader population studies that could trace how G4 landscapes differ across ancestries, how they evolve under selection, and how their instability contributes to both human evolution and human disease. For a structure first noticed as an oddity of guanine chemistry, the G-quadruplex has come a long way, and the complete human genome, it turns out, is full of it.

Subject of Research: Genomic landscape and mutational dynamics of G-quadruplex DNA structures in the complete human genome

Article Title: Landscape and mutational dynamics of G-quadruplexes across the complete human genome

Article References: Landscape and mutational dynamics of G-quadruplexes across the complete human genome. (n.d.). https://doi.org/10.1186/s13059-026-04255-z

Image Credits: AI Generated

DOI: 10.1186/s13059-026-04255-z

Keywords: G-quadruplexes, human genome, telomere-to-telomere assembly, centromeres, ribosomal DNA, genomic instability, DNA methylation, meiotic recombination, PRDM9, long-read sequencing, insertions and deletions, genome biology