The importance of haplotype structure is difficult to overstate in modern crop genetics. When a genome-wide association study flags a region linked to flowering time or disease resistance, the next question is almost always which accessions carry which allele combinations, whether those combinations translate into changes in the encoded protein, and which parental lines should be crossed to combine useful allelic diversity. A third question follows quickly: where should a diagnostic marker be placed so that breeders can track the desired version of the gene in a breeding population without sequencing every seedling. Each of these questions has traditionally demanded its own software. Alignment browsers display read-level evidence at a locus, variant databases catalog allele frequencies across a panel, and marker design tools work in isolation from both. The read counts supporting a variant call and the panel-wide pattern of alleles sit in separate views, and neither view leads naturally to haplotype classification or to primer design.

Hap-Browser consolidates these steps by reading precomputed pileups, the compressed records of sequencing reads aligned to a reference genome, and rendering an entire panel of accessions as a sample-by-position matrix. In practical terms, a researcher opens a gene of interest and immediately sees, for every accession in the panel, which nucleotide is present at every variant position within that gene. The application then groups accessions by haplotype over any set of positions the user selects, so that varieties sharing the same combination of alleles cluster together. Crucially, the tool also reports the amino-acid changes that follow from the nucleotide differences, translating synonymous changes, missense mutations, and premature stop codons into a functional interpretation of each haplotype class. This translation step is what turns a table of single-nucleotide polymorphisms into a hypothesis about which haplotype is likely to alter protein function and therefore which haplotype is worth pursuing in a breeding program.

The marker design capability is where the application departs most sharply from existing tools. From the same view that displays the haplotype matrix, Hap-Browser designs allele-specific KASP primers, the competitive PCR chemistry widely used in breeding laboratories to distinguish two alleles at a single position, as well as primers for insertion-deletion polymorphisms. The design step is variant-aware in a way that matters enormously for panel-scale breeding: the software checks every variant within the primer-binding regions across the entire panel, so that a candidate primer set that would fail, or produce misleading calls, on some accessions is flagged before the primers are ever ordered from a synthesis company. In large diversity panels, hidden polymorphisms under primer-binding sites are a notorious source of genotyping failure, and discovering the problem after ordering primers wastes both time and money. By surfacing those conflicts at design time, the tool shifts quality control to the earliest possible point in the marker development pipeline.

The application also handles the reverse problem, assigning an unknown sample to a known haplotype class. Query sequences can be matched against the cataloged haplotypes using BLAST, the Basic Local Alignment Search Tool, allowing a breeder who has sequenced a new variety to determine immediately which haplotype group it belongs to and what that membership implies for the trait under study. This closes the loop between discovery and application: the same interface that reveals haplotype structure in the reference panel can classify new material against that structure, which is precisely the operation a breeding program performs when it screens germplasm for a version of a gene worth introgressing.

Performance considerations shaped the architecture in ways that will be visible to users. Because the pileups are precomputed rather than generated on the fly, a 200-accession panel is fetched and cached once when a gene is first opened. After that initial load, reclassifying haplotypes over a new set of selected positions and recomputing the display are effectively instantaneous. That responsiveness matters for exploratory analysis, where a researcher may want to try several different position sets within a gene, comparing how the haplotype groupings shift depending on whether the analysis is restricted to coding variants, expanded to promoter regions, or focused on a candidate causal polymorphism identified from a GWAS peak. A tool that forces a wait on every reclassification discourages exactly the kind of iterative reasoning that produces biological insight.

The demonstration dataset accompanying the paper covers 23 rice heading-date genes plus Sub1A, the well-known submergence-tolerance gene, evaluated across 200 accessions from the International Rice Research Institute panel. Heading-date genes are a natural showcase: flowering time in rice is controlled by a network of genes whose allelic variation underpins adaptation to different latitudes and growing seasons, making haplotype-level inspection genuinely informative for breeders. Sub1A, meanwhile, is one of the most successfully deployed genes in modern rice breeding, having been introgressed into numerous varieties to confer tolerance of complete submergence. Extending the demonstration set, the authors note, requires only editing a single tab-separated line and rerunning a Snakemake pipeline, the workflow management system that automates the precomputation steps. Nothing in the design of the software ties it to rice in its inputs, although the demonstration presented in the paper is confined to a single species.

That extensibility point deserves emphasis because it positions Hap-Browser as infrastructure rather than a one-off resource. Any crop species with a reference genome, a resequenced diversity panel, and standard annotation files in General Feature Format can be loaded by preparing the pileups through the pipeline and registering the gene of interest. The same logic applies to non-crop organisms: any research community maintaining a panel of resequenced individuals, whether studying natural variation in a model organism or cataloging variation in a livestock breed, faces the same gap between raw variant data and haplotype-level interpretation. The authors make the source code, the pipeline, and the demonstration data openly available, inviting other groups to adapt the system to their own organisms and to extend its functionality.

The broader context is the steady industrialization of genomics-assisted breeding. Marker-assisted selection, in which DNA markers stand in for laborious phenotyping, depends on a supply of reliable, well-validated diagnostic markers, and the rate at which such markers can be developed has been limited less by sequencing capacity than by the fragmented software chain connecting variant discovery to primer design. Tools that remove manual hand-offs from that chain compress the timeline from a GWAS hit to a working KASP assay from weeks to, potentially, an afternoon. The Jeonbuk National University team, whose work was supported by the Rural Development Administration of the Republic of Korea and the Jeonbuk RISE program, has effectively asked what a breeder actually needs to see at a candidate gene, and built the interface around that answer: the full panel of alleles, the haplotype groupings, the protein consequences, and the marker design, all in one session.

Whether Hap-Browser becomes a standard fixture in breeding laboratories will depend on adoption, but the problem it addresses is real and widely felt. As diversity panels grow from hundreds toward thousands of accessions, and as gene-level interpretation becomes the routine first step after any association study, the value of an interface that keeps panel-wide haplotype inspection and variant-aware marker design within a single view will only increase. For a discipline whose progress has often been gated by the friction between tools, the consolidation itself is the innovation.

Subject of Research: A web application for gene-level haplotype visualization and marker design in crop genomics

Article Title: Hap-Browser: a web application for gene-level haplotype visualization and marker design

Article References: Lee, H.-O., Kim, M., Jun, Y., Yang, C.-Y., Kwak, S.-H., & Mo, Y. (2026). Hap-Browser: a web application for gene-level haplotype visualization and marker design. BMC Bioinformatics. https://doi.org/10.1186/s12859-026-06651-5

Image Credits: AI Generated

DOI: 10.1186/s12859-026-06651-5

Keywords: haplotype analysis, crop breeding, KASP markers, rice genomics, genome browser, marker-assisted selection, variant visualization, GWAS, bioinformatics, resequencing, InDel markers, BLAST

The challenge for geneticists has always been that Verticillium wilt resistance is a quantitative trait. Rather than being governed by a single gene, it emerges from the combined effects of multiple quantitative trait loci, or QTL, scattered across the genome. Detecting these loci reliably, let alone pinpointing the causal genes within them, demands dense genetic maps, replicated disease evaluations across environments, and systematic functional validation. Earlier mapping efforts in cotton had identified resistance regions on chromosomes D7 and D9, and genome-wide association studies added further candidate intervals, but the leap from a broad QTL to a specific gene has remained difficult, particularly in the large and complex allotetraploid cotton genome.

To narrow that gap, the team, led by Yunlei Zhao, Baimei Cheng and Jinfa Zhang with corresponding authors Jianhua Lu and Hongmei Wang of the Institute of Cotton Research at the Chinese Academy of Agricultural Sciences, constructed high-density genetic maps of the regions harboring major resistance QTL. The key tool was a genotyping technology called Kompetitive Allele-Specific PCR, or KASP, which allows thousands of individual plants to be scored rapidly and cheaply at specific single-nucleotide polymorphisms. By saturating the target QTL intervals with KASP markers, the researchers were able to fine-map the loci, shrinking previously vague genomic neighborhoods down to precise physical intervals on the reference genome.

The mapping effort identified six QTL for Verticillium wilt resistance, five of which qualified as major QTL because they were detected in at least two independent environments, a critical test of whether a locus genuinely contributes to resistance rather than reflecting environmental noise. Four of these loci, named qVW-D05-1, qVW-D05-2, qVW-D05-3 and qVW-A01-1, were fine mapped to remarkably compact physical regions of just 15.2 to 241.3 kilobases on chromosomes D05 and A01. In a genome spanning roughly 2.5 billion base pairs, intervals of that size contain only a handful of genes, transforming an intractable search into a manageable candidate list.

With the intervals defined, the researchers deployed a multi-layered strategy to find the genes that matter. They combined gene annotation of the reference genome with whole-genome resequencing of resistant and susceptible lines, transcriptome sequencing to capture differences in gene activity after pathogen challenge, and quantitative reverse-transcription PCR to confirm expression patterns independently. A gene only earned candidate status if it showed both DNA sequence variation between resistant and susceptible germplasm and a measurable difference in expression. This double filter proved powerful: fifteen genes emerged that carried both genetic variants and differential expression, making them the strongest current leads for the molecular basis of wilt resistance in upland cotton.

The functional work went further. Four candidate genes associated with the qVW-D05-1 locus were cloned and subjected to functional analysis using virus-induced gene silencing, a technique that temporarily suppresses a target gene’s activity in the plant, together with overexpression studies that boost it. These complementary approaches allowed the team to observe what happens to disease resistance when each gene’s function is dialled down or up. The results indicated that the genes associated with the major QTL do contribute to resistance, strengthening the case that the fine-mapped intervals harbor genuine resistance determinants rather than statistical artifacts of mapping.

Perhaps the most mechanistically revealing finding concerns the nature of the sequence variation itself. The researchers found that variation in functional gene sequences, especially variations that change amino acids within protein domains, appears to be the underlying molecular mechanism distinguishing resistant from susceptible cotton lines. In other words, resistance is not simply a matter of genes being switched on or off; the resistant alleles encode subtly altered proteins whose functional domains differ in ways that presumably improve recognition of the pathogen or the execution of defense responses. This kind of allelic diversity is exactly what marker-assisted breeding can exploit, because DNA markers linked to the favorable variants can be tracked through generations without waiting years for field evaluations.

The study also delivered a result with immediate practical implications for breeding programs: the power of pyramiding. When the team assembled lines that carried different combinations of the major resistance QTL, a clear dosage effect appeared. The more resistant QTL a line accumulated, the stronger and more durable its Verticillium wilt resistance became. Lines that polymerized all four major resistance loci showed markedly higher and more stable resistance across every environment tested. Because quantitative resistance built from multiple loci is generally harder for pathogens to overcome than single-gene resistance, these findings suggest that stacking the favorable alleles from qVW-D05-1, qVW-D05-2, qVW-D05-3 and qVW-A01-1 could produce cotton cultivars whose resistance endures in the field.

The candidate genes themselves connect to well-established pillars of plant immunity. Prior research in cotton has shown that lignin deposition in vascular tissue, driven by phenylpropanoid metabolism, physically reinforces the xylem vessels the fungus colonizes, while hormones such as jasmonic acid and brassinosteroids orchestrate defense signaling. Receptor-like kinases, including the wall-associated kinase GhWAK7A, have been shown to sense fungal chitin and trigger immune responses, and transcription factors of the MYB and bHLH families regulate defense gene networks. The newly identified candidates, evaluated against this background, fit into pathways that plausibly shape how cotton detects Verticillium dahliae and walls it off before the fungus can spread through the vascular stream.

For a crop grown on tens of millions of hectares and facing a pathogen that chemical fungicides struggle to reach, the study offers a concrete toolkit. The fine-mapped intervals and the KASP markers developed for them give breeders the ability to select resistant plants at the seedling stage, accelerating the introgression of resistance alleles into elite cultivars. The fifteen candidate genes and four functionally validated qVW-D05-1 genes provide starting points for deeper mechanistic study, potentially including genome editing of favorable alleles directly into susceptible varieties. As Verticillium wilt continues to constrain cotton production across major growing regions, the convergence of fine mapping, multi-omics screening and functional validation demonstrated here offers a template for dissecting other quantitative disease-resistance traits, moving cotton breeding decisively closer to durable, genetically anchored resistance.

Subject of Research: Identification of candidate genes within major quantitative trait loci controlling Verticillium wilt resistance in upland cotton (Gossypium hirsutum L.).

Article Title: Identification of candidate genes associated with major QTL controlling Vertcillium wilt resistance in cotton (Gossypium hirsutum L.)

Article References: Identification of candidate genes associated with major QTL controlling Vertcillium wilt resistance in cotton (Gossypium hirsutum L.). (n.d.). https://doi.org/10.1007/s00122-026-05355-x

Image Credits: AI Generated

DOI: 10.1007/s00122-026-05355-x

Keywords: cotton, Verticillium wilt, Gossypium hirsutum, QTL mapping, candidate genes, KASP markers, fine mapping, virus-induced gene silencing, plant disease resistance, Theoretical and Applied Genetics, marker-assisted selection, plant immunity

Septoria nodorum blotch has grown into an economically significant problem over recent decades across the eastern United States, northern Europe, Australia, and parts of North Asia. The fungus attacks both leaves, producing leaf blotch, and spikes, causing glume blotch, and can slash yields by as much as 50 percent on susceptible cultivars. In the Great Plains, where hard winter wheat dominates production, the disease has become increasingly prevalent in recent years. Farming practices such as minimum or no-till agriculture, which leave more infected crop residue on the soil surface, may have contributed to the surge. Compounding the problem, isolates of the fungus collected from hard winter wheat in Oklahoma show unusually high genetic and virulence diversity, suggesting local pathogen populations have adapted to the region’s cultivars.

The central puzzle of the wheat-P. nodorum system lies in its inverted genetics. Unlike classic disease resistance, where a plant gene recognizes a pathogen molecule and triggers defense, susceptibility here follows an inverse gene-for-gene model. The fungus produces proteinaceous molecules called necrotrophic effectors, and when a corresponding sensitivity gene in the wheat host recognizes one of these effectors, it triggers programmed cell death. That cell death, paradoxically, feeds the necrotrophic fungus, which thrives on dead tissue. To date, researchers have cloned seven wheat sensitivity genes, including Tsn1-B1, Snn1-B1, Snn2, Snn3-B1, Snn3-B2, Snn3-D1, and Snn5-B1, along with five fungal effector genes named SnToxA, SnTox1, SnTox3, SnTox5, and SnTox267. Thirteen distinct sensitivity gene-effector interactions have been documented in this pathosystem.

To determine how widespread these susceptibility genes are in contemporary hard winter wheat, the research team, led by scientists at Oklahoma State University in collaboration with the USDA Agricultural Research Service and Kansas State University, assembled a panel of 619 genotypes. The panel included 532 doubled-haploid breeding lines derived from 14 bi-parental crosses, 38 elite breeding lines from the Oklahoma State University program, and 49 cultivars from the Oklahoma State and Kansas State breeding programs. Each genotype was tested at the seedling stage against five P. nodorum isolates originally collected from the winter wheat cultivar Gallagher in Canadian County, Oklahoma in 2016, and against purified preparations of the five known necrotrophic effectors.

The results revealed a sobering picture of genetic vulnerability. When inoculated with the fungal isolates, between 40 and 67 percent of the genotypes proved susceptible, depending on the isolate, while resistant genotypes ranged from only 5 to 21 percent. Effector infiltration assays, in which purified effector proteins were injected directly into leaves using a needleless syringe, showed that 54 percent of genotypes were sensitive to SnToxA, 37 percent to SnTox3, 15 percent to SnTox5, 13 percent to SnTox267, and just 2 percent to SnTox1. These frequencies indicate that Tsn1-B1 and Snn3-B1/B2 are the most common sensitivity genes in this germplasm, while functional Snn1-B1 is rare. Notably, 75 genotypes showed resistant to intermediate reactions against all five isolates, including the Oklahoma cultivars Uncharted, Bentley, Big Country, and OK Corral.

The team genotyped the entire panel using genotyping-by-sequencing, which yielded 34,357 high-quality single nucleotide polymorphism markers after filtering, along with diagnostic kompetitive allele-specific PCR, or KASP, markers for the sensitivity genes Tsn1-B1, Snn1-B1, and Snn3-B1/B2. The diagnostic markers performed remarkably well for some genes: the Tsn1-B1 markers achieved 98 percent prediction accuracy against SnToxA sensitivity, and the Snn3-B1/B2 markers reached 92 percent accuracy against SnTox3. The Snn1-B1 marker, by contrast, showed only 75 percent accuracy, with a 25 percent false positive rate, suggesting that structural variations such as mutations, deletions, or inversions may have disabled the gene in many lines without being detectable by the current marker.

Genome-wide association studies using three statistical models, the mixed linear model, FarmCPU, and BLINK, confirmed the presence of the known sensitivity genes Tsn1-B1 on chromosome 5BL, Snn1-B1 on 1BS, Snn3-B1/B2 on 5BS, Snn2 on 2DS, and Snn5-B1 on 4BL, each associated with responses to its corresponding effector. But the analysis went further, uncovering numerous novel loci scattered across the wheat genome that had never before been linked to septoria nodorum blotch responses. In total, the BLINK model identified 23 significant loci associated with responses to the fungal isolates and 49 loci associated with effector responses, positioned on chromosome arms including 1AS, 1AL, 1BL, 1DS, 2AL, 2BL, 3AS, 3AL, 3BS, 3BL, 4AL, 4BL, 5AS, 5AL, 6AL, 6BS, 6BL, 7BS, 7BL, and 7DL.

The most striking discovery was a locus on chromosome arm 2AS, designated Qsnb.osu-2AS, which was associated with responses to all five P. nodorum isolates tested. For three of the isolates, the effect of this locus on disease response actually exceeded that of the Tsn1 gene. The significant markers in this region spanned a physical interval of roughly 17.69 megabases containing 268 high-confidence genes, several of which are annotated as involved in disease resistance and defense-related pathways. Encouragingly, the resistant allele of Qsnb.osu-2AS was already present at frequencies of roughly 45 to 60 percent in the germplasm, and the region shows some overlap with a previously reported resistance quantitative trait locus, Qsnb.cur-2AS2, mapped in an earlier study.

Translating these discoveries into practical breeding tools, the researchers developed new KASP markers linked to Snn5-B1, Snn2, and Qsnb.osu-2AS, sensitivity and resistance loci that previously lacked diagnostic markers. The marker KASP_S4B_643615365, targeting the Snn5-B1 region on chromosome 4BL, achieved 89 percent accuracy in predicting SnTox5 sensitivity across the full panel, while KASP_S2D_16184991, linked to Snn2 on chromosome 2DS, reached 80 percent accuracy for SnTox267 sensitivity. Two additional markers, KASP_S2A_9833162 and KASP_S2A_14367498, were validated for selecting the resistant allele at Qsnb.osu-2AS. Together with the existing diagnostic markers for Tsn1-B1 and Snn3-B1/B2, these tools give breeders the ability to deliberately eliminate susceptibility genes and stack resistance alleles through marker-assisted selection.

The study also revealed evidence of fungal weapons not yet characterized. Several cultivars, including Green Hammer, currently the second most grown wheat variety in Oklahoma, were insensitive to all five known effectors and lacked the corresponding sensitivity genes, yet remained susceptible to particular isolates. This pattern strongly suggests that P. nodorum harbors additional, undiscovered effectors and that wheat carries uncharacterized sensitivity genes targeted by them. The researchers also noted intriguing epistatic effects among effectors, consistent with earlier findings that SnTox1 expression can be suppressed in the presence of SnToxA, which may explain why the Snn1 locus showed no association with responses to the whole isolates even though it responds to purified SnTox1. Because Tsn1 also confers susceptibility to tan spot and spot blotch, two other major fungal diseases, the authors argue that eliminating Tsn1-B1 and Snn3-B1/B2, which are present at high frequencies in contemporary hard winter wheat, should be the top priority for breeding programs. The study focused on seedling-stage responses, and the team emphasizes that future work will need to identify loci governing adult plant resistance under field conditions, where genotype-by-environment interactions and quantitatively inherited resistance play a larger role.

Subject of Research: Identification of known and novel septoria nodorum blotch susceptibility and resistance genes in US hard winter wheat using effector assays, diagnostic markers, and genome-wide association studies.

Article Title: Identification of septoria nodorum blotch susceptibility genes in hard winter wheat

Article References: Ara, A. M., Holmes, D. J., Friesen, T. L., Carver, B. F., Bai, G., Amand, P. S., Bernado, A., Sharma, R., & Aoun, M. (2026). Identification of septoria nodorum blotch susceptibility genes in hard winter wheat. Theoretical and Applied Genetics, 139(10), Article 268. https://doi.org/10.1007/s00122-026-05359-7

Image Credits: AI Generated

DOI: 10.1007/s00122-026-05359-7

Keywords: septoria nodorum blotch, hard winter wheat, Parastagonospora nodorum, necrotrophic effectors, Tsn1, Snn3, genome-wide association study, KASP markers, marker-assisted selection, wheat breeding, disease resistance, Great Plains