Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “GWAS”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 289 records · Page 16Linked to original sources
PubMed2026

Genome-wide association studies reveal genetic variants associated with antineoplastic monoterpenoid indole alkaloid accumulation in Catharanthus roseus.

Catharanthus roseus produces pharmacologically important monoterpenoid indole alkaloids (MIAs), yet their natural accumulation is low, limiting therapeutic exploitation. To dissect the genetic basis of natural variation in MIA accumulation, we integrated phenotypic, chemotypic, and genomic analyses of 93 C. roseus accessions sampled from six locations across India, including New Delhi, Lucknow, Jodhpur, Bangalore, and two locations in Gujarat: Navsari and Bardoli. Morphological characterization showed limited differentiation among locations, whereas accessions from Gujarat tended to be taller compared to other locations and more frequently white-flowered. Quantitative HPLC profiling revealed substantial accession- and location-dependent variation in total indole alkaloid levels, with Gujarat accessions showing the highest accumulation, largely driven by vindoline and catharanthine. Genotyping-by-sequencing generated 10,801 high-quality variants comprising 10,087 SNPs and 714 InDels corresponding to an average density of 19.34 variants per Mbp of the genome, revealing three genetic subgroups with overall admixed ancestry and weak geographic stratification. Genome-wide association study (GWAS) using five benchmark models identified 47 variants potentially associated with catharanthine, vindoline, and vinblastine content. These putative candidate loci were located near genes implicated in hormone signaling, mitochondrial function, nitrogen metabolism, and RNA processing, suggesting complex regulatory control of MIA biosynthesis. Notably, two missense variants in a carboxylesterase-like gene were associated with vindoline accumulation, and highly significant intergenic SNP clusters suggested putative regulatory hotspots for vinblastine biosynthesis. These results provide GWAS-based insights into the genetic architecture of MIA metabolism in C. roseus and nominate candidate variants for precision breeding and metabolic engineering to enhance pharmaceutical alkaloid production.

Catharanthus roseus↗
PubMed2021

Genetic Relationship Between Endometriosis and Melanoma.

Epidemiological studies have observed that risk of endometriosis is associated with history of cutaneous melanoma and vice versa. Evidence for shared biological mechanisms between the two traits is limited. The aim of this study was to investigate the genetic correlation and causal relationship between endometriosis and melanoma. Summary statistics from genome-wide association meta-analyses (GWAS) for endometriosis and melanoma were used to estimate the genetic correlation between the traits and Mendelian randomization was used to test for a causal association. When using summary statistics from separate female and male melanoma cohorts we identified a significant positive genetic correlation between melanoma in females and endometriosis (r g = 0.144, se = 0.065, p = 0.025). However, we find no evidence of a correlation between endometriosis and melanoma in males or a combined melanoma dataset. Endometriosis was not genetically correlated with skin color, red hair, childhood sunburn occasions, ease of skin tanning, or nevus count suggesting that the correlation between endometriosis and melanoma in females is unlikely to be influenced by pigmentary traits. Mendelian Randomization analyses also provided evidence for a relationship between the genetic risk of melanoma in females and endometriosis. Colocalization analysis identified 27 genomic loci jointly associated with the two diseases regions that contain different causal variants influencing each trait independently. This study provides evidence of a small genetic correlation and relationship between the genetic risk of melanoma in females and endometriosis. Genetic risk does not equate to disease occurrence and differences in the pathogenesis and age of onset of both diseases means it is unlikely that occurrence of melanoma causes endometriosis. This study instead provides evidence that having an increased genetic risk for melanoma in females is related to increased risk of endometriosis. Larger GWAS studies with increased power will be required to further investigate these associations.

endometriosis↗
PubMed2025

Breast Cancer Risk Stratification in Black Women: Current Status and Potential Solutions to Improve Accuracy.

Breast cancer risk stratification models identify individuals at increased risk, allowing earlier screening than for those at average risk and potentially improving health outcomes. Due to the increasing rates of breast cancer in individuals aged <40 years, especially among Black females, the American College of Radiology now recommends all females initiate breast cancer risk assessment by age 25 years. Several breast cancer risk prediction models are readily available, including the Gail Model, Breast Cancer Surveillance Consortium Risk Calculator, BOADICEA, and Tyrer-Cuzick Model. However, because these models were primarily developed using data from White women of European ancestry, they may underestimate risk in Black women. Indeed, current evidence suggests that these models underpredict breast cancer risk among Black women, particularly those of African ancestry. Although cancer risk prediction models typically incorporate personal characteristics, family history of cancer, and hormonal and lifestyle factors, inherited breast cancer genes can also increase risk for breast cancer. Beyond monogenic inherited breast cancer genes that increase breast cancer risk, emerging data suggest that single nucleotide polymorphisms identified through genome-wide association studies (GWAS) may be used to generate polygenic risk scores, which may further refine breast cancer risk. However, GWAS data are also primarily gathered from European ancestry females, further reducing the ability to accurately stratify breast cancer risk in non-European ancestry populations. Current data highlight the importance of ensuring representation from all populations in developing cancer risk prediction models, conducting genomics research, and designing effective implementation strategies to enhance the use of these models in routine clinical care. Although new analytic methods and models are being developed to improve breast cancer risk stratification across populations, it remains critical to assess the utility and calibration of existing and new models to ensure applicability across non-European ancestry populations.

Humans↗
PubMed2026

Systematic common and rare variant association testing in 392,030 whole genomes in All of Us.

Large-scale genome-wide association studies (GWAS) and rare variant association studies (RVAS) from population biobanks provide valuable resources for gene discovery in complex human traits. We present an analysis of the All of Us Research Program v8 release, which includes whole genome sequencing data and harmonized phenotypic information of 392,030 participants after quality control, enabling a unified investigation of rare and common variants across a spectrum of human traits and diseases. We build an extensive phenome- and genome-wide ("All by All") computational framework to perform GWAS and RVAS on 3,602 phenotypes and identify 49,863 approximately independent, high-quality single-variant and gene-level associations. Meta-analyses of All of Us and UK Biobank, with sample sizes as large as 786,871 participants, further enhance statistical power and find 193 pLoF gene-phenotype associations that are not significant in either cohort alone, including 22 associations not highlighted by previous studies. We also present a public interactive browser that integrates association results for common and rare variants to facilitate interpretation and rapid querying of summary statistics, along with supporting documentation, and a Featured Workspace in the All of Us Researcher Workbench. Our framework will apply to iterative data releases as All of Us grows, empowering researchers worldwide to uncover insights into the functional effects of genetic components on complex traits and diseases.

Journal Article↗
PubMed2026

Meta-analysis of over 8,000 individuals from Hawai'i and Samoa for genetic associations to cardiometabolic phenotypes.

Although genome-wide association studies (GWAS) now routinely reveal genetic associations and biological insights in millions of individuals, underrepresentation of global populations, such as those from Polynesia, continue to persist. These exclusions, often driven by logistical challenges and lack of data, prevent systematic identification of population-enriched associations, such as the association of the missense variant at the CREBRF locus to BMI and type 2 diabetes discovered commonly occurring in Polynesian populations due to its rarity in global populations. Armed with the recently updated TOPMed imputation panel that could benefit studies in diverse populations that previously had poorer imputation performance, we performed the first GWAS of Native Hawaiians and largest to date of Polynesian-ancestry populations (combined N up to 8,461) to identify population-enriched associations for 13 adiposity and cardiometabolic traits available across both cohorts: BMI, fasting glucose, fasting insulin, HDL, height, hip circumference, HOMA-IR, LDL, T2D, total cholesterol, triglycerides, waist circumference, and waist-hip ratio. We found 25 trait-loci associations that met genome-wide significance: 20 previously reported or known associations and 5 associations newly confirmed via meta-analysis. In particular, with improved statistical power, we were able to confirm the suspected association between the missense CREBRF variant with fasting glucose levels. The remaining 4 potentially novel loci-trait associations for BMI, LDL, and waist-hip ratio, however, were not replicated in multi-ethnic datasets from All-of-Us despite having reasonable power to replicate. The lack of Polynesian-enriched findings outside of the CREBRF locus informs the bounds of the effect sizes or frequency of any enriched variants, and suggests that further expansion of cohort sizes from this region of the world and improved imputation references specific to these populations are needed to identify more population-enriched associations.

Journal Article↗
PubMed2026

Calibrated Prediction Intervals for Polygenic Scores: Updated Comparisons, Contextual Calibration, and Data Normalization.

Calibrated prediction intervals for polygenic scores (PGS) are essential for communicating individual-level uncertainty in genomic medicine. We present updated comparisons of two methods for constructing such intervals: CalPred, a parametric approach, and PredInterval, a non-parametric approach. Our results show that both methods can achieve calibrated coverage, although CalPred additionally requires a sufficiently large calibration set. The two methods also exhibit complementary trade-offs with respect to dataset size and risk identification. We further show that contextual calibration, as introduced in Hou et al. and followed in Shi et al., is most naturally achieved through appropriate phenotype normalization and data preprocessing. Apparent miscalibration can arise from inadequate normalization or from providing contextual information to some methods but not others. In UK Biobank, standard GWAS phenotype normalization procedures are sufficient to achieve contextual calibration for traits analyzed. In the extreme simulations of Hou et al. and Shi et al., supplying contextual covariates to PredInterval restores contextual calibration without normalization, and appropriate normalization can achieve contextual calibration without supplying covariates, while also substantially improving upstream tasks including association power and PGS accuracy. Together, these results underscore the central role of phenotype normalization and data preprocessing in GWAS analyses, including reliable uncertainty quantification for PGS.

Journal Article↗
PubMed2026

Multimodal atlas of human atherosclerosis links granular vascular cell states to coronary artery disease risk.

Advances in single-cell and spatial assays have revolutionized the scale and resolution of molecular tissue profiling. Here we present MetaPlaq, a multimodal atlas of human atherosclerotic arterial beds comprising over a million cells across single-cell transcriptomics, epigenomics and high-resolution spatial expression assays. We map granular cell states and disease-relevant transcriptional programs within the native tissue context of coronary arteries. Furthermore, we map cardiovascular GWAS signals to smooth muscle cells (SMCs) and endothelial cells (ECs) and uncover the cis-regulatory architecture governing their phenotypic transitions. Our comprehensive epigenomic reference allowed us to build cell-specific enhancer-gene link maps and multimodal gene regulatory networks (GRNs) underlying disease-relevant states such as osteogenic SMCs and ECs undergoing mesenchymal transition. We also integrate SMC and EC disease-associated gene sets with GRNs to nominate key transcription factors such as PRRX1, BNC2 and ELK3 regulating atherosclerosis-relevant transcriptional programs. Finally, we layer single-cell and spatial modalities to fine-map GWAS variants with improved cell and anatomical context. We highlight candidate cell-specific regulatory mechanisms at less characterized CAD loci, including FGD5 and MCF2L in ECs. Together, this atlas represents an important step towards fully interpreting genetic risk loci and informing new therapeutic strategies for cardiovascular disease.

Journal Article↗
PubMed2026

POISE: Spectral Inference of Parent-of-Origin Effects in Unlabeled Genomic Data.

MOTIVATION: Parent of Origin Effects (POEs), where the effect of an an allele on a phenotype differs based on maternal or paternal inheritance implicated in growth, metabolism, and neurodevelopment. Traditional tests for POEs require family data to determine parental origins of transmitted alleles. Given that such studies are expensive and time consuming compared to genome-wide association studies (GWAS), tests that function absent inheritance information are highly desirable. We develop a method, based on community detection from machine learning, that infers POEs via a spectral decomposition, obtains confidence intervals via a non-parametric bootstrap, and safeguards against confounding by non POE sources of variation. We refer to our method as Parent of Origin Inference via Spectral Estimation (POISE). RESULTS: We demonstrate that POISE is well-calibrated under both Gaussian and heavy-tailed noise in simulation studies, with improved robustness to true POEs compared to existing covariance-based tests. POISE provides per-trait effect estimates with bias-corrected bootstrap confidence intervals and incorporates an information-theoretic minimum detectable effect size that filters unreliable estimates, conferring robustness to covariance-deflating variance QTL. We then apply POISE to GWAS data from the UK Biobank using BMI, LDL cholesterol, and HDL cholesterol. POISE recovers established POE loci and identifies 134 additional variants at genes implicated in lipid metabolism, immune regulation, and growth. AVAILABILITY AND IMPLEMENTATION: The code for this method in Python is available at https://github.com/bystrogenomics/POISE.

Community Detection↗
PubMed2026

Type 2 diabetes genetics in 125,000 admixed adults from Mexico City.

Type 2 diabetes (T2D) is a highly heritable, polygenic disease with over 600 loci identified through genome-wide association studies (GWAS). However, despite possessing unique genetic variation shaped by demographic history and admixture, Latin American populations remain markedly underrepresented in global genomic research. To address this gap, we conducted genome- and exome-wide analyses of 19,431 T2D cases and 105,611 controls from the Mexico City Prospective Study (MCPS). We identified 86 independent GWAS associations, including 21 novel signals, 15 of which replicated in external cohorts. Risk alleles at novel loci were enriched in individuals with Indigenous American ancestry. Exome analyses revealed rare and ultra-rare missense variants with substantial risk effects at HNF1A and GCK, as well as a protein-damaging variant in SLC30A8 that reduced T2D risk by 45% in carriers. Integrative analyses indicate that T2D genetic architecture in Mexico is predominantly driven by common regulatory variation acting in the endocrine pancreas. Polygenic risk scores strongly stratified T2D risk and transferred to Indigenous Mexican populations. These findings demonstrate the power of large-scale genetic discovery in diverse populations to refine disease architecture and identify loci with potential therapeutic relevance.

Journal Article↗
PubMed2026

Retinal Transcriptome-Wide Association Study Identifies Novel Alzheimer's Disease Risk Genes.

INTRODUCTION: Alzheimer's disease (AD) is the leading cause of dementia worldwide. The retina shares molecular pathways with the brain, yet no study has systematically linked retinal gene expression to AD risk. METHODS: We performed transcriptome-wide association studies (TWAS) using two independent retinal eQTL panels (Strunz et al., n = 311; EyeGEx, n = 406) and a large meta-analyzed AD genome-wide association study (GWAS) (Bellenguez et al., 111,326 cases, 677,663 controls). Genes were further validated with GWAS in the independent Alzheimer's Disease Sequencing Project (ADSP) using a matched eQTL-panel strategy. RESULTS: We identified 62 AD-associated genes across the two eQTL panels using Bellenguez et al. as the discovery cohort. Of these, 31 were replicated in the ADSP cohort. The findings highlight shared complement-mediated immune dysregulation (CD55, CD46, TREM2) and provide functional transcriptomic evidence to prioritize novel causal drivers of AD pathogenesis, including STYX and the LRRC37 gene family. DISCUSSION: Retinal data capture core AD genetic architecture and reveal novel risk genes, highlighting the retina as a molecularly informative tissue for dementia research.

Alzheimer&#x2019;s disease↗
PubMed2026

Whole-genome sequencing implicates rare, low-frequency and structural non-coding variation at the SCN5A locus in Brugada syndrome.

Brugada syndrome (BrS) is an inherited cardiac condition characterized by a hallmark ECG pattern and an increased risk of sudden cardiac death. Central to the aetiology of BrS, the SCN5A region harbours both common non-coding risk variants and rare coding variants that are causative in approximately 20% of patients. However, rare non-coding genetic variation in this region remains largely unexplored. Here, we used whole-genome sequencing (WGS) of 752 European-ancestry BrS cases and 1,827 ancestry-matched controls to identify BrS-associated rare non-coding genetic variation at the SCN5A locus. Sliding-window and cis-regulatory element (CRE)-based rare-variant aggregate testing implicated three conserved CREs, including a dense aggregation of case singleton variants within a 178 bp enhancer in intron 17 of SCN5A which replicated in an independent BrS cohort. Prioritised BrS-associated rare and low-frequency non-coding variants within these elements were predicted to alter cardiac transcription factor motifs, and altered CRE activity in hiPSC-CM luciferase assays or were associated with BrS-relevant ECG endophenotypes in the UK Biobank. Single-variant analysis across the region identified a Bonferroni-significant five-fold case-enriched low-frequency variant within a known CRE in intron 1 of SCN5A, which replicated, was associated with slower cardiac conduction in the UK Biobank and accounted for part of the BrS GWAS signal at this locus. Structural variant analyses identified a 10.5 kb deletion upstream of SCN5A in a BrS case that encompassed a cardiac CRE and reduced sodium current density in a hiPSC-CM model, as well as a 6 kb BrS-enriched retrotransposon insertion in SCN5A that appeared to underlie part of the GWAS signal in this region. Together, these findings implicate rare and low-frequency non-coding variation at the SCN5A locus in BrS susceptibility and demonstrate the value of targeted WGS analysis of key disease loci.

Journal Article↗
PubMed2026

Genetic Correlation Between Brain Imaging Phenotypes and Externalizing Behavior: A Large-Scale LDSC Analysis of UK Biobank IDPs.

Externalizing has been associated with differences in brain structure and function; however, it remains unclear whether these associations reflect shared common-variant genetic influences. Cross-trait linkage disequilibrium score regression was used to estimate genome-wide genetic correlations between externalizing genome-wide association study (GWAS) results and 3,935 brain imaging-derived phenotypes from the UK Biobank BIG40 resource. The imaging phenotypes covered structural magnetic resonance imaging (MRI), diffusion MRI, susceptibility-weighted imaging, resting-state functional MRI, and task-based functional MRI. Results were included in the primary analysis when the imaging phenotype had positive single-nucleotide polymorphism (SNP) heritability, a heritability Z statistic of at least 1.96, a mean GWAS chi-square statistic of at least 1.02, at least 200,000 regression SNPs, and a complete LDSC result without a fatal error. Technical imaging quality-control phenotypes were excluded from biological inference. Individual results were corrected using the Benjamini-Hochberg false discovery rate procedure. Aggregated Cauchy association tests (ACATs) were used to evaluate evidence across all imaging phenotypes and within predefined imaging categories. Statistical power, simultaneous confidence bounds, and alternative quality-control definitions were examined in sensitivity analyses. Of the 3,935 imaging phenotypes, 3,716 produced estimable genetic correlations, 2,980 met the primary LDSC quality-control criteria, and 2,967 were classified as biological imaging phenotypes. No individual phenotype survived false discovery rate correction. The smallest unadjusted P value was 0.0005, and the minimum adjusted q value was 0.486. The distribution of genetic correlations was centered near zero, with a median genetic correlation of 0.0014 and a median absolute genetic correlation of 0.0338. ACAT provided no evidence of an aggregate association across all biological imaging phenotypes (P = 0.302), and no predefined imaging category survived multiple-testing correction. The median minimum detectable genetic correlation at 80% power was 0.216. Bonferroni-adjusted simultaneous confidence intervals were fully contained within the interval [-0.30, 0.30] for 80.0% of phenotypes in the primary analysis and 88.0% under the stringent heritability quality-control definition. Broad and stringent sensitivity analyses produced the same overall conclusions. In this study, no statistically robust evidence of genome-wide genetic correlations between externalizing and individual UK Biobank brain imaging phenotypes was found. Nevertheless, small, localized, mixed-direction, or developmentally specific genetic effects remain possible.

Journal Article↗
PubMed2026

PMBB Geno-Pheno Toolkit: A suite of scalable, reproducible pipelines for cross-biobank association analyses.

Electronic health record (EHR)-linked biobanks generate unprecedented genomic and phenotypic datasets, but their scientific utility is constrained by data fragmentation across institutional silos and incompatible computing infrastructures, forcing researchers to rewrite ad-hoc scripts for each new environment. We present the PMBB Geno-Pheno Toolkit, a suite of modular Nextflow pipelines for biobank-scale association analyses. This note focuses on the toolkit's SAIGE family of pipelines - supporting genome-wide (GWAS), exome-wide (ExWAS), and phenome-wide (PheWAS) association testing - together with the companion GWAMA and ExWAS meta-analysis pipelines that enable cross-biobank replication. All components are containerized (Docker/Apptainer) and orchestrated with Nextflow, allowing the same workflows to run unmodified on local HPC clusters, cloud platforms, and the All of Us Research Workbench. Complementary toolkit pipelines for PLINK-based GWAS, polygenic scoring, LD-based clumping, and phenotype harmonization are also available and briefly noted.

Journal Article↗
PubMed2026

Myeloid Dendritic Cell Counts and Coronary Heart Disease: a Bidirectional Mendelian Randomization Study.

BACKGROUND: Coronary heart disease (CHD) remains a leading cause of morbidity and mortality worldwide, with immune and inflammatory mechanisms playing important roles in its pathogenesis. Dendritic cells (DCs) are key regulators of immune responses; however, the relationship between specific DC subsets and CHD risk remains incompletely understood. METHODS: This study conducted a bidirectional two-sample Mendelian randomization (MR) analysis using publicly available genome-wide association study (GWAS) summary statistics to investigate the potential associations between circulating dendritic cell traits and CHD. Genetic instruments for myeloid dendritic cells (Myeloid DCs) and plasmacytoid dendritic cells (Plasmacytoid DCs), including both absolute counts and relative proportions, were obtained from immune cell GWAS datasets. Summary statistics for CHD were derived from a large European-ancestry population. Multiple MR methods were applied, and sensitivity analyses were performed to assess the robustness of the findings and potential pleiotropic effects. RESULTS: Nominal associations between genetically predicted Myeloid DC counts and CHD risk were observed in the MR-Egger and weighted median analyses, whereas the inverse variance weighted analysis demonstrated no significant association. These nominal associations did not remain statistically significant after correction for multiple testing. No significant associations were observed for Plasmacytoid DC counts or for the relative proportions of either DC subset. Reverse MR analyses were inconclusive due to wide confidence intervals, precluding meaningful inference regarding a causal effect of CHD on DC-related traits. Sensitivity analyses revealed no substantial heterogeneity or horizontal pleiotropy. CONCLUSIONS: This bidirectional MR study explored the potential relationships between circulating dendritic cell traits and CHD risk. Although nominal associations involving Myeloid DC counts were observed in secondary MR analyses, no robust evidence supporting an association remained after correction for multiple testing. Further studies using larger datasets and functional approaches are warranted to clarify the role of dendritic cells in CHD.

Humans↗
PubMed2026

TET2 promotes monocyte inflammatory activation in asthma via ALKBH5-m6A regulation and PI3K signaling: evidence from m6A-SNP and single-cell analyses.

Asthma is a complex inflammatory airway disease with strong genetic determinants, yet the functional relevance of most asthma-associated non-coding variants remains unclear. Emerging evidence suggests that N6-methyladenosine (m6A) modification may serve as a critical epitranscriptomic link between genetic variation and immune regulation. In this study, we aimed to systematically identify functionally relevant m6A-regulated genes in asthma by integrating large-scale GWAS data, m6A-SNP annotations, and single-cell transcriptomic analyses, and to investigate their roles in monocyte-driven airway inflammation. We identified TET2 as a key m6A-regulated gene associated with both asthma and lung function, which was selectively upregulated in monocytes during asthma and accompanied by activation of inflammatory and PI3K signaling pathways. Mechanistic experiments further demonstrated that inflammatory stimulation induced ALKBH5 expression, reduced m6A modification of TET2 mRNA, and increased TET2 protein levels, thereby promoting PI3K/AKT signaling and pro-inflammatory cytokine production, whereas inhibition of TET2 or ALKBH5 attenuated these effects. Collectively, these findings demonstrate that ALKBH5-mediated m6A regulation of TET2 enhances PI3K/AKT signaling in monocytes, thereby promoting inflammatory responses in asthma. Our study establishes TET2 as a key m6A-regulated gene linking genetic susceptibility to monocyte-driven inflammation, and highlights the ALKBH5-m6A-TET2 axis as a potential therapeutic target for modulating aberrant immune responses in asthma.

Humans↗
PubMed2025

Construction of a Core Germplasm and Identification of Candidate SNPs Associated with Growth Performance of Epinephelus tukula by Whole-Genome Resequencing.

Epinephelus tukula is an economically important aquaculture animal, and a major parent in grouper crossbreeding. To better preserve and exploit E. tukula germplasm resources, a core collection (containing 34 individuals derived from 10 genetic groups) was first constructed based on phenotypic growth traits and whole-genome resequencing (WGS) data. The phenotypic traits of the individuals within the core collection were not significantly different from those in the original collection, suggesting effective representativeness of the core collection. Additionally, we performed genome-wide association study (GWAS) of E. tukula to identify candidate single nucleotide polymorphisms (SNPs) and genes associated with growth traits, to facilitate the improvements in the growth performance of this species. Twenty-six significant SNPs were identified, scattered among multiple chromosomes. Five SNPs were confirmed to be correlated with growth in another new group of 101 individuals. Based on the annotation results, these five SNPs were located in CCDC102A, NTRK2, CTSL, OTOF, and nestin, and were involved in cell development, differentiation and proliferation, glycolytic metabolism, neurological development, and myoblast differentiation. Our findings not only provide an effective basis for the conservation and utilization of E. tukula germplasm resources, but also promote the development of marker-assisted selection of E. tukula.

Polymorphism, Single Nucleotide↗
PubMed2026

Advancements in the Understanding of the Genetics of Obsessive-Compulsive Disorder (OCD).

PURPOSE OF REVIEW: This review summarizes recent advances in the genetics of Obsessive-Compulsive Disorder (OCD), their contribution to understanding disorder biology, and implication for clinical translation. RECENT FINDINGS: Recent GWAS identified 30 genome-wide significant loci and prioritized 25 putatively causal genes. Rare variant studies implicated specific genes, including CHD8, CELSR3, SLITRK5, and QRICH1. Evidence from common and rare variants support brain- and immune-related pathways. Genetic overlap with obsessive compulsive symptoms and other psychiatric disorders indicate shared underlying biology. Current evidence is largely based on individuals of European ancestry, although global efforts are underway to improve ancestral diversity in OCD genetics. Given the urgent need for improved treatment, genetically informed clinical translation approaches hold promise, including pharmacogenetics and drug repurposing. Recent advances in OCD genetics support a highly polygenic architecture, implicate specific neuro-biological and immune pathways, and provide new opportunities for clinical translation.

Humans↗
PubMed2026

Novel genomic regions associated with adult-plant resistance to multiple fungal pathogens in wheat (Triticum aestivum L.) revealed by DArT marker sequencing.

Wheat is among the top three most important cereal crops globally and serves as a staple food for approximately 40% of the world's population. Fungal leaf diseases such as yellow and leaf rusts (YR, LR), septoria nodorum blotch (SNB), septoria tritici blotch (STB), and powdery mildew (PM) have a major effect on yield loss in wheat, and resistance breeding is so far the most effective strategy to minimize those losses. Adult plant resistance (APR) is a crucial component of durable disease resistance; it reduces the pathogen's infection rate, keeping disease levels below the damage threshold, even in the absence of complete immunity. Therefore, this study aimed to identify sources of resistance in a collection of 411 accessions from diverse global origins. These accessions were phenotyped across 2018-2019. DArTseq technology and Genome-wide association studies (GWAS) analysis were conducted to identify single-nucleotide polymorphisms (SNPs) associated with APR for evaluated pathogens. DArT analysis showed that wheat chromosome 2B contains genomic regions associated with resistance to SNB, and that SNPs on chromosome 3B are associated with resistance to YR. On chromosome 6&#xa0;A, there is a strong potential to explore, as a shared resistance locus for YR and SNB was found. SNPs: 3,937,236, 1,056,817 were consistent in both years, meaning their association with disease resistance is reliable and repeatable. Chromosome 7D is a strong region for SNPs significantly associated with both LR and SNB resistance. While multiple disease resistance genes are present on 7D, the 610&#xa0;Mb LR locus is distinct from known LR, PM, and SNB loci, making it a strong candidate for functional validation. These findings highlight the value of historical resistance sources and uncover novel genomic regions for breeding a broad-spectrum APR-based resistance. Dual-trait loci, especially those effective against both biotrophic and necrotrophic pathogens, represent a promising material for achieving durable resistance in elite wheat cultivars.

Triticum↗