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At least 19 recordsLinked to original sources

Osteoarthritis Year in Review 2026: Genetics, genomics and epigenetics.

OBJECTIVE: The purpose of this narrative review is to highlight advances made over the past 12 months in the field of osteoarthritis (OA) genetics, genomics and epigenomics, with a particular focus on the interpretation of OA risk loci through functional genomic and regulatory approaches. DESIGN: PubMed and Europe PMC were searched to identify studies relevant to OA genetics, genomics and epigenomics published between 1st March 2025 and 30th April 2026. Searches used combinations of terms relating to genetics, genomics, epigenomics, functional genomics, molecular quantitative trait loci, chromatin accessibility and enhancer biology. Studies were limited to human subjects and English-language publications, with additional articles identified through citation screening and expert knowledge of the field. RESULTS: Over the past year, the field has continued to transition from large-scale locus discovery towards biological interpretation of OA genetic risk. Major advances included the largest OA genome-wide association study to date, further development of polygenic risk score approaches, and increasing integration of molecular quantitative trait loci, chromatin accessibility, and enhancer biology datasets to prioritise effector genes and elucidate regulatory mechanisms. Several studies highlighted the highly context-dependent nature of OA genetic risk mechanisms, demonstrating that distinct tissues, cell types, and regulatory layers can identify different candidate effector genes at the same locus. Additional developments included increasing application of singlecell and multi-omic technologies to study OA-relevant tissues. CONCLUSION: Recent advances in OA genetics have shifted the field from locus discovery towards mechanistic interpretation. Emerging evidence demonstrates that the biological consequences of genetic variation are highly dependent upon tissue, cell state and disease context, with different functional genomic approaches often prioritising distinct candidate genes and regulatory mechanisms at the same susceptibility locus. Together, these findings suggest that OA risk loci should increasingly be viewed as dynamic regulatory systems rather than simple variant-to-gene relationships, providing a framework for future studies aimed at resolving causal mechanisms, defining disease endotypes, and identifying therapeutic targets.

Genetics↗

Quantitative trait loci and molecular markers associated with wheat allelopathy.

Wheat ( Triticum aestivum L.) has been examined for allelopathic potential against annual ryegrass ( Lolium rigidum). The bioassay technique, 'equal-compartment-agar-method', was employed to evaluate seedling allelopathy in a doubled-haploid (DH) population derived from cv Sunco (weakly allelopathic) and cv Tasman (strongly allelopathic). A significant difference in allelopathic activity was found among the DH lines, which inhibited the root length of ryegrass across a range from 23.7 to 88.3%. The phenotypic data showed that wheat allelopathic activity was distributed normally within this DH population and a substantial transgressive segregation for seedling allelopathic activity was also found. Analysis of restriction fragment length polymorphism (RFLP), amplified fragment length polymorphism (AFLP) and microsatellite (SSRs) markers identified two major QTLs on chromosome 2B associated with wheat allelopathy. The linkage analysis of genetic markers and the QTLs may improve genetic gains for the allelopathic activity through marker-assisted selection in wheat breeding. The development of wheat allelopathic cultivars could reduce the over-reliance of weed control on synthetic herbicides.

Genetic Markers↗

Decoding Primary Open-Angle Glaucoma: A Multi-Omics Approach to Identify Druggable Effector Genes.

PURPOSE: Genomewide association studies (GWAS) have identified numerous primary open angle glaucoma (POAG) risk loci, yet most reside in non-coding regions with unclear function. Mapping these loci to effector genes can elucidate disease mechanisms, identify functionally conserved variants, improve cross-ancestry risk prediction by reducing population-specific noise, and uncover shared therapeutic targets. METHODS: Here, we integrate European POAG GWAS with six types of multi-omics molecular Quantitative Trait Locis (xQTLs) using multi-trait colocalization to identify candidate effector variants and evaluate their cross-population relevance using genetic risk score (GRS) analysis, and their therapeutic potential through drug target prioritization. RESULTS: We identified 25 POAG effector variants colocalized with at least one xQTLs. In non-European populations, effector variants showed stronger effect size correlations with Europeans than non-colocalized variants (Pearson r2 = African 0.85 vs. 0.71; East Asian 0.81 vs. 0.69; and Latin American 0.91 vs. 0.75). Effector variants also had smaller allele frequency variations across populations (average interquartile range [IQR] = 0.15 vs. 0.20). The genetic risk score based on effector variants performed comparably to the genome-wide significant single-nucleotide polymorphism (SNP)-based GRS in non-European populations. Drug prioritization identified zinc, copper, sunitinib, probucol, and astemizole as potential common therapeutic agents for POAG and its subtypes. CONCLUSIONS: Our findings offer deeper insight into the molecular mechanisms underlying glaucoma and effector variants for developing more robust GRS models and broadly effective therapeutic strategies for POAG.

Humans↗

Linkage analysis of molecular markers and quantitative trait loci in populations of inbred backcross lines of Brassica napus L.

Backcross populations are often used to study quantitative trait loci (QTL) after they are initially discovered in balanced populations, such as F(2), BC(1), or recombinant inbreds. While the latter are more powerful for mapping marker loci, the former have the reduced background genetic variation necessary for more precise estimation of QTL effects. Many populations of inbred backcross lines (IBLs) have been developed in plant and animal systems to permit simultaneous study and dissection of quantitative genetic variation introgressed from one source to another. Such populations have a genetic structure that can be used for linkage estimation and discovery of QTL. In this study, four populations of IBLs of oilseed Brassica napus were developed and analyzed to map genomic regions from the donor parent (a winter-type cultivar) that affect agronomic traits in spring-type inbreds and hybrids. Restriction fragment length polymorphisms (RFLPs) identified among the IBLs were used to calculate two-point recombination fractions and LOD scores through grid searches. This information allowed the enrichment of a composite genetic map of B. napus with 72 new RFLP loci. The selfed and hybrid progenies of the IBLs were evaluated during two growing seasons for several agronomic traits. Both pedigree structure and map information were incorporated into the QTL analysis by using a regression approach. The number of QTL detected for each trait and the number of effective factors calculated by using biometrical methods were of similar magnitude. Populations of IBLs were shown to be valuable for both marker mapping and QTL analysis.

Brassica↗

[Molecular mapping of quantitative trait loci (QTL) for characters of vascular bundles in peduncle related to indica-japonica differentiation in rice (Oryza sativa L.)].

A doubled-haploid population, consisting of 81 DH lines derived from the F1 hybrid of a cross between an indica cultivar and a japonica cultivar, was used to map quantitative trait loci (QTL) controlling numbers of vascular bundles in peduncle, primary rachis branches and the ratio of vascular bundles to primary rachis branches (V/R ratio). For vascular bundles, three QTL were detected. Among them, the QTL qVB-8 with the largest effect individually accounted for 31.1% of the total variation. Two QTL controlling primary rachis branches were identified and they were co-located with 2 of the 3 QTL for vascular bundles respectively. Three QTL for the V/R ratio were mapped on chromosome 1, 2 and 8, respectively. Of the three QTL, the QTL qV/R-1 with the largest additive effect, explained 25.3% of the total variation, was located on chromosome 1 and found to be closely linked to the gene sh-2, a major gene underlying grain-shattering ability. In addition, four and two pairs of significant epistatic QTL were detected for vascular bundles and the V/R ratio, respectively, but none for rachis branches. Our results suggested that the numbers of vascular bundles and primary rachis branches were independently controlled by different polygenic systems, but the two polygenic systems shared a fraction of quantitative trait loci. It was also shown that the chromosome region carrying the QTL qV/R-1 on chromosome 1 might play an important role in the processes of indica-japonica differentiation in rice.

Chromosome Mapping↗

Quantitative trait loci in Drosophila.

Phenotypic variation for quantitative traits results from the simultaneous segregation of alleles at multiple quantitative trait loci. Understanding the genetic architecture of quantitative traits begins with mapping quantitative trait loci to broad genomic regions and ends with the molecular definition of quantitative trait loci alleles. This has been accomplished for some quantitative trait loci in Drosophila. Drosophila quantitative trait loci have sex-, environment- and genotype-specific effects, and are often associated with molecular polymorphisms in non-coding regions of candidate genes. These observations offer valuable lessons to those seeking to understand quantitative traits in other organisms, including humans.

Animals↗

Quantitative trait loci mapped to single-nucleotide resolution in yeast.

Identifying the genetic variation underlying quantitative trait loci remains problematic. Consequently, our molecular understanding of genetically complex, quantitative traits is limited. To address this issue directly, we mapped three quantitative trait loci that control yeast sporulation efficiency to single-nucleotide resolution in a noncoding regulatory region (RME1) and to two missense mutations (TAO3 and MKT1). For each quantitative trait locus, the responsible polymorphism is rare among a diverse set of 13 yeast strains, suggestive of genetic heterogeneity in the control of yeast sporulation. Additionally, under optimal conditions, we reconstituted approximately 92% of the sporulation efficiency difference between the two genetically distinct parents by engineering three nucleotide changes in the appropriate yeast genome. Our results provide the highest resolution to date of the molecular basis of a quantitative trait, showing that the interaction of a few genetic variants can have a profound phenotypic effect.

Adaptor Proteins, Signal Transducing↗

Quantitative trait loci in Two Soybean Recombinant Inbred Line Populations Segregating for Yield and Disease Resistance.

Molecular makers linked to quantitative trait loci (QTL) can assist soybean [Glycine max (L.) Merr.] breeders to combine traits of low heritability, such as yield, with disease resistance. The objective of this study was to identify markers linked to yield QTL in two recombinant inbred line (RIL) populations ['Essex' x 'Forrest' (ExF; n = 100) and 'Flyer' x 'Hartwig' (FxH; n = 94)] that also segregate for soybean cyst nematode (SCN) resistance genes (rhg1 and Rhg4). Each population was yield tested in four environments between 1996 and 1999. The resistant parents produced lower yields. Heritability of yield across four environments was 47% for ExF and 57% for FxH. Yield was normally distributed in both populations. High yielding, SCN resistant transgressive segregants were not observed. In the ExF RIL population, 134 microsatellite markers were compared against yield by ANOVA and MAPMAKER QTL. Regions associated with yield were identified by SATT294 on linkage group (LG.) C1 (P = 0.006, R(2) = 10%), SATT440 on LG. I (P = 0.007, R(2) = 10%), and SATT337 on LG. K (P = 0.004, R(2) = 10%). Essex provided the beneficial allele at SATT337. Mean yields among FxH RILs were compared against 33 microsatellite markers from LG. K. In addition 136 markers from randomly selected LGs were compared with extreme phenotypes by bulk segregant analysis. Two regions on LG. K (20 cM apart) associated with yield were identified by SATT326 (P = 0.0004, R(2) = 15%) and SATT539 (P = 0.0008, R(2) = 14%). Flyer provided both beneficial alleles. Both populations revealed a yield QTL in the interval (5 cM) between SATT337 and SATT326. These populations may share a common allele for yield in this region, given that about 40% of Flyer genome derived from Essex.

Journal Article↗

A pangenome framework uncovers the role of deletions in repeated evolution of cave-derived traits.

Structural variants (SVs) are increasingly recognized as key contributors to adaptive evolution, yet they remain underexplored compared with single-nucleotide variation. To understand how large-scale genomic changes shape repeated evolution, we leveraged multiple levels of sequence data across the powerful evolutionary model system of the Mexican tetra fish (Astyanax mexicanus). We constructed one of the first pangenome graphs from a naturally evolving vertebrate, enabling comprehensive discovery of SVs among 120 fish from 11 populations. We discover substantial amounts of structural variation and explore the roles of genomic biases and selection in shaping the distribution of these variants. More than 2400 high-confidence cave-specific deletions are enriched in biological pathways involved in vision, metabolism, and behavior and cluster nonrandomly in quantitative trait loci linked to cavefish traits. Additionally, 67 genes harbor unique deletions between independent cavefish lineages. These reused genes show evidence of population-specific selection (99% contain selective sweeps compared with 8%-15% in genes lacking SVs), indicating that deletions likely rose in frequency through repeated positive selection rather than drift. Together, these results reveal that recurrent deletion events have repeatedly contributed to the evolution of cave-adapted phenotypes and highlight deletions as underexplored contributors of adaptive evolution in extreme environments.

Animals↗

From genetic causality to druggable targets: A multiomics framework identifies ZSCAN16 in gout pathogenesis.

ObjectiveGout is a prevalent form of inflammatory arthritis in which many patients respond suboptimally to current therapies. Drug development is hampered by a lack of genetically validated targets, leading to high clinical trial attrition. This study aimed to systematically identify and prioritize novel, druggable targets for gout via a multilayered genetic and functional genomics approach.MethodsWe performed two-sample Mendelian randomization (MR) using cis-expression quantitative trait locus (cis-eQTL) data and dual independent gout genome-wide association study (GWAS) cohorts (openGWAS and FinnGen). The candidate genes were subjected to a rigorous validation pipeline including Bayesian colocalization, phenome-wide association studies (PheWASs) to assess pleiotropy and on-target safety, and single-cell RNA sequencing (scRNA-seq) to delineate the cellular context. Molecular docking was used to evaluate the structural druggability of prioritized targets.ResultsMR analysis revealed 15 genes causally associated with gout. Colocalization analysis (PPH4 > 0.8) prioritized two targets: ZSCAN16 (risk-increasing, OR = 1.04, 95% CI [1.02-1.06]) and TRIM10 (protective, OR = 0.96, 95% CI [0.94-0.98]). Crucially, PheWAS revealed that ZSCAN16 is highly specific to gout, whereas TRIM10 exhibited extensive pleiotropy with hematological and cardiometabolic traits, indicating significant safety risks. Single-cell analysis provided orthogonal validation, demonstrating flare-specific upregulation of ZSCAN16 in cytotoxic T/NK cells. Molecular docking confirmed ZSCAN16 as a structurally druggable target, showing high-affinity binding with known compounds (e.g. digoxin, binding energy = -9.6 kcal/mol).ConclusionsOur study identifies ZSCAN16 as a high-potential, druggable therapeutic target for gout, highlighting its genetic influence on specific immune cell activities during acute flares. Conversely, TRIM10 was deprioritized owing to substantial pleiotropic liabilities and poor chemical tractability. These findings suggest that ZSCAN16 could play a crucial role in the pathogenesis of gout and may provide a valuable lead for future drug discovery efforts.

Humans↗

Multi-omic underpinnings of heterogeneous aging across multiple organ systems.

Aging is the main determinant of chronic diseases and mortality, yet organ-specific aging trajectories vary, and the molecular basis underlying this heterogeneity remains unclear. To elucidate this, we integrated genomic, epigenomic, transcriptomic, proteomic, and metabolomic data, employing post-genome-wide association study methodologies to systematically investigate the molecular mechanisms of nine organ-specific aging clocks and four blood-based epigenetic clocks. We uncovered genetic correlations and specific phenotypic clusters among these aging-related traits, identified prioritized genetic drug targets for heterogeneous aging, and elucidated downstream proteomic and metabolomic effects mediated by heterogeneous aging. We constructed a cross-layer molecular interaction network of heterogeneous aging across multiple organ systems and characterized detectable biomarkers of this heterogeneity. Integrating these findings, we developed an R/Shiny-based framework that provides a comprehensive multi-omic molecular landscape of heterogeneous aging, thereby advancing the understanding of aging heterogeneity and informing precision medicine strategies to delay organ-specific aging and prevent or treat its associated chronic diseases.

Aging↗

Association of genetically proxied cancer-targeted drugs with cardiovascular diseases through Mendelian randomization analysis.

BACKGROUND: Cancer-targeted therapies are progressively pivotal in oncological care. Observational studies underscore the emergence of cancer therapy-related cardiovascular toxicity (CTR-CVT), impacting patient outcomes. We aimed to investigate the causal relationship between different types of cancer-targeted therapies and cardiovascular disease (CVD) outcomes through a two-sample Mendelian randomization (MR) study. METHODS: This genome-wide association study was conducted using a two-sample Mendelian randomization framework. Genetic instruments for drug target gene expression were extracted from the eQTLGen consortium (31684 individuals, 37 cohorts). Genome-wide association study (GWAS) summary statistics for 19 cardiovascular diseases were derived from the FinnGen database. Primary analysis was carried out using the summary-data-based MR (SMR) method, with sensitivity analysis for validation. Colocalization analysis identifies shared causal variants between exposure eQTLs and CVD-associated single-nucleotide polymorphisms (SNPs). RESULTS: Among the 39 drug target genes, 8 were identified with detectable cis-eQTLs and were subsequently validated through positive control analysis for further investigation. In the SMR and sensitivity analyses, genetically proxied VEGFA inhibition showed significantly strong association with stroke (odds ratio [OR] = 1.17, 95% confidence interval [CI] = 1.09-1.26, p = 1.33 × 10- 5). Additionally, the inhibition of FGFR1, FLT1, and MAP2K2 exhibited suggestive association with corresponding cardiovascular disease outcomes. Nevertheless, only VEGFA expression and stroke shared a causal variant (93.6%), whereas FGFR1, MAP2K2, and FLT1 did not share causal variants with corresponding cardiovascular diseases in the colocalization analysis. CONCLUSIONS: This genetic association study revealed evidence supporting the genetic association between the use of VEGFA inhibitors and increased stroke risk, highlighting the need for enhanced pharmacovigilance. These findings underscore the delicate balance between cardiovascular toxicity risk and the benefits of cancer-targeted therapy.

Humans↗

Marker-assisted introgression of quantitative trait loci.

The use of molecular markers for the introgression of one or several superior QTL alleles into a recipient line is investigated using analytic and simulation results. The positions of the markers devoted to the control of the genotype at the QTLs in a "foreground selection" step are optimized given the confidence interval of the QTL position. Results demonstrate that using at least three markers per QTL allows a good control over several generations. Population sizes that should be recommended for various numbers of QTLs are calculated and are used to determine the limit in the number of QTLs that can be monitored simultaneously. If "background selection" devoted to accelerate the return to the recipient parent genotype outside the QTL regions is applied, the positions of the markers devoted to the control of the QTLs have to be reconsidered. When several QTLs are monitored simultaneously, background selection among the limited number of individuals resulting from the foreground selection step accelerates the increase in genomic similarity with the recipient parent, with only limited costs. Background selection is even more efficient in a pyramidal backcross program where QTLs are first monitored one by one.

Alleles↗

[A linkage analysis of quantitative trait loci for familial schizophrenia on chromosome 1].

OBJECTIVE: To explore the molecular genetic relationship between chromosome 1 and quantitative trait loci for familial schizophrenia. METHODS: A series of assessment scales included positive and negative syndrome scale (PANSS), global assessment of functional scale (GAFS), premorbid schizoid and schizotypal traits scale (PSST), premorbid social adjustment scale (PSA) were applied to quantify the phenotypes of schizophrenia. Non-parametric linkage analysis of quantitative traits was conducted in 32 multiplex pedigrees with schizophrenia by using 29 microsatellite makers on chromosome 1. RESULTS: Haseman-Elston quantitative trait analysis detected a maximum Traditional H-E Lods of 1.73 and a maximum EH H-E Lods of 1.65 of negative symptoms (PANSS-N ) at 147.64 cM, which was overlapped to the positive region of 1q21-23 in qualitative linkage analysis. CONCLUSION: The results suggest there might be an independent quantitative trait locus of negative symptoms on 1q21-23 for familial schizophrenia.

Chromosomes, Human, Pair 1↗

Molecular-marker-facilitated investigations of quantitative-trait loci in maize. I. Numbers, genomic distribution and types of gene action.

Individual genetic factors which underlie variation in quantitative traits of maize were investigated in each of two F2 populations by examining the mean trait expressions of genotypic classes at each of 17-20 segregating marker loci. It was demonstrated that the trait expression of marker locus classes could be interpreted in terms of genetic behavior at linked quantitative trait loci (QTLs). For each of 82 traits evaluated, QTLs were detected and located to genomic sites. The numbers of detected factors varied according to trait, with the average trait significantly influenced by almost two-thirds of the marked genomic sites. Most of the detected associations between marker loci and quantitative traits were highly significant, and could have been detected with fewer than the 1800-1900 plants evaluated in each population. The cumulative, simple effects of marker-linked regions of the genome explained between 8 and 40% of the phenotypic variation for a subset of 25 traits evaluated. Single marker loci accounted for between 0.3% and 16% of the phenotypic variation of traits. Individual plant heterozygosity, as measured by marker loci, was significantly associated with variation in many traits. The apparent types of gene action at the QTLs varied both among traits and between loci for given traits, although overdominance appeared frequently, especially for yield-related traits. The prevalence of apparent overdominance may reflect the effects of multiple QTLs within individual marker-linked regions, a situation which would tend to result in overestimation of dominance. Digenic epistasis did not appear to be important in determining the expression of the quantitative traits evaluated. Examination of the effects of marked regions on the expression of pairs of traits suggests that genomic regions vary in the direction and magnitudes of their effects on trait correlations, perhaps providing a means of selecting to dissociate some correlated traits. Marker-facilitated investigations appear to provide a powerful means of examining aspects of the genetic control of quantitative traits. Modifications of the methods employed herein will allow examination of the stability of individual gene effects in varying genetic backgrounds and environments.

Chromosome Mapping↗

Efficient algorithms for quantitative trait loci mapping problems.

Rapid advances in molecular genetics push the need for efficient data analysis. Advanced algorithms are necessary for extracting all possible information from large experimental data sets. We present a general linear algebra framework for quantitative trait loci (QTL) mapping, using both linear regression and maximum likelihood estimation. The formulation simplifies future comparisons between and theoretical analyses of the methods. We show how the common structure of QTL analysis models can be used to improve the kernel algorithms, drastically reducing the computational effort while retaining the original analysis results. We have evaluated our new algorithms on data sets originating from two large F(2) populations of domestic animals. Using an updating approach, we show that 1-3 orders of magnitude reduction in computational demand can be achieved for matrix factorizations. For interval-mapping/composite-interval-mapping settings using a maximum likelihood model, we also show how to use the original EM algorithm instead of the ECM approximation, significantly improving the convergence and further reducing the computational time. The algorithmic improvements makes it feasible to perform analyses which have previously been deemed impractical or even impossible. For example, using the new algorithms, it is reasonable to perform permutation testing using exhaustive search on populations of 200 individuals using an epistatic two-QTL model.

Algorithms↗

Seven suggestive quantitative trait loci influence hygienic behavior of honey bees.

In 1964, Walter Rothenbuhler proposed a two-gene model to explain phenotypic variance in the remarkable behavior in which honey bee workers remove dead brood from their colonies. Rothenbuhler's model proposed that one locus controls the uncapping of brood cells containing dead pupae, while a second controls the removal of the cell contents. We show here, through molecular techniques and quantitative trait loci (QTL) linkage mapping, that the genetic basis of hygienic behavior is more complex, and that many genes are likely to contribute to the behavior. In our cross, we detected seven suggestive QTLs associated with hygienic behavior. Each detected QTL controlled only 9-15% of the observed phenotypic variance in the character.

Animals↗