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PubMed2026

Genetic dissection of cardiac iron regulation using transcriptome network analysis and systems genetics in BXD mice.

Cardiac iron homeostasis is essential for myocardial energy metabolism and contractile function, yet the genetic and molecular mechanisms governing iron levels within the heart remain poorly understood. We used a systems genetics approach to dissect the transcriptional regulation of cardiac iron homeostasis. Myocardial iron level varies substantially across BXD strains (40-112 μg/g) and is under heritable genetic control (H2 = 0.38). Elevated cardiac iron is associated with reduced ventricular mass, increased ventricular ectopy, and prolonged atrioventricular conduction in the BXD population. Weighted gene co-expression network analysis of the BXD heart transcriptome identified a co-expression module that was significantly and negatively correlated with cardiac iron levels in both young and old BXD mice and enriched for pathways related to metabolic regulation, cyclic AMP (cAMP) signaling, circadian entrainment, and cardiovascular physiology. The module showed substantial overlap with a curated cardiac iron gene set, and cross-species enrichment analysis confirmed its conservation in human cardiomyopathy differentially expressed genes (enrichment ratio = 1.49; false discovery rate [FDR] = 0.0342). Quantitative trait locus (QTL) mapping of the first principal component of the overlapping module iron genes (n = 38), corroborated by individual gene mapping, identified trans-eQTL hotspots on multiple chromosomes, implicating Fcho2, Gcc2, and Rmdn1 as candidate upstream regulators operating through sequential steps of intracellular iron trafficking. Together, these findings establish a systems-level map of cardiac iron gene regulation, identify candidate genetic regulators, and provide a molecular framework linking disruption of iron-related transcriptional networks to structural and electrical cardiac dysfunction with implications for iron-related heart diseases.

BXD mouse population↗

PubMed2026

Duplication-based genetic dissection of the Down syndrome critical region reveals its complex functional organization.

Down syndrome (DS), associated with trisomy 21, is the most common genetic cause of developmental delay and intellectual disability, yet the specific dosage-sensitive genes and the associated genetic mechanisms underlying these phenotypes remain incompletely defined. Here, we applied an additive genetic strategy to dissect the Down syndrome critical region (DSCR) by generating 2 complementary mouse models using Cre/loxP-mediated chromosome engineering that together span the entire DSCR on mouse chromosome 16: Dp(16)5Yey, duplicating the Setd4-Kcnj6 interval, and Dp(16)6Yey, duplicating the Kcnj15-Mx2 interval. In addition, we engineered a third duplication model, Dp(16)7Yey, carrying a selective duplication of the Dyrk1a-Kcnj6 interval containing only these 2 genes. Building upon our previously reported results, cognitive behavioral analyses of these 3 models reveal a complex functional genetic architecture of the DSCR, including dosage-sensitive genetic elements, interactions among these elements, and their contributions to DS-associated cognitive deficits. Together, these findings highlight the complexity of dosage-dependent genetic interactions, which provide important insights into DSCR functional organization and have major implications for the development of effective therapeutic strategies for DS-associated cognitive deficits. In addition, these duplication mouse models represent valuable resources for further genetic dissection of DS phenotypes beyond cognition.

Animals↗

PubMed2026

Dissecting genetic architecture and improving machine learning‑based genomic prediction of flowering time in Osmanthus fragrans by integrating structural variants.

Sweet osmanthus (Osmanthus fragrans), a traditional ornamental plant in China, exhibits substantial variation in autumn flowering time, which significantly affects landscape application and cultivation efficiency. Here, we performed a genome-wide association study on 127 resequenced accessions classified into early, intermediate, and late flowering types, using a set of 2,325,410 single-nucleotide polymorphisms (SNPs) and 246,824 structural variants (SVs). By integrating SNP/insertion and deletion (Indel) and SV data with weighted gene co-expression network analysis, machine learning, and genomic prediction, we dissected the genetic architecture of flowering time. We identified 24 associated SNP/Indels and six SVs, mapping to 30 candidate genes, including known flowering regulators FLK, LOS1, Y14, MIF2, and GID1B. These genes showed tissue-specific expression, with some responding to low temperature. The two hub genes, GUX1 and LYG027904, were located within modules of the co-expression network associated with low-temperature treatment. Haplotype analysis revealed a specific three-SNP haplotype associated with late flowering and linked to LOS1, and epistatic interactions among combined genotypes contributed to phenotypic variation. Notably, integrating SVs with SNP/Indels improved genomic prediction accuracy; the gradient boosting decision tree model outperformed other machine learning algorithms, achieving a mean accuracy of 0.859 and an AUC > 0.8 (where AUC is area under receiver operating characteristic curve) for all flowering types. These findings provide insights into the genetic mechanisms underlying flowering time variation in O. fragrans, offer candidate genes and haplotypes for molecular breeding, and highlight the value of integrating SVs with machine learning for genomic prediction in woody ornamentals.

Machine Learning↗

PubMed2026

Advancing precision tacrolimus therapy: a systems genetics dissection in BXD platform.

BACKGROUND: Tacrolimus is a core immunosuppressant in organ transplantation, but its narrow therapeutic window and significant pharmacokinetic variability hinder precision dosing. Although CYP3A5-guided strategies have established clinical relevance for tacrolimus initial dose adjustment, they do not fully account for the marked interindividual variability in tacrolimus exposure, highlighting the need for complementary models to decode more complex genetic regulation. This study aimed to identify candidate genetic modulators of tacrolimus metabolism and develop an integrated predictive framework for individualized therapy. METHODS: Using 46 BXD recombinant inbred mouse strains, we characterized transcriptomics and machine learning, and validated key genes. We then constructed a clinical model using data from 168 renal transplant recipients. RESULTS: We identified 19 genomic loci associated with tacrolimus pharmacokinetic traits and supported DBP/CYP2A6 as candidate modulators associated with tacrolimus disposition. The clinical prediction model, incorporating these genes and clinical variables, achieved robust AUROC. CONCLUSIONS: These findings support a polygenic contribution to tacrolimus metabolism and provide an experimental and computational framework for identifying candidate modulators relevant to individualized dosing. The BXD mouse platform offers a systems-genetics approach for mechanistic discovery that may inform future translational studies on tacrolimus precision dosing.

Animals↗

PubMed2026

Genomic and genetic dissection underlying seedling drought resilience in oats.

Drought threatens global crop yields, and common oat, a vital nutritional source for food and feed, is particularly constrained in the semi‑arid regions where it is widely cultivated. Here, we report two high-quality genome assemblies for drought-resilient (Borris37) and drought-sensitive (XymC06) oat accessions with distinct seedling survival rates and genome sizes of 10.92 Gb and 10.96 Gb, and construct comprehensive landscapes of insertion‑deletions (InDels) and structural variants (SVs). Integrating population-level genomic, transcriptomic and phenotypic (seedling survival rate), we demonstrate that InDels and SVs underpin divergent drought resilience and identify 52 candidate genes associated with drought resistance whose expression is significantly modulated by these variants. Borris37 accumulates 36 favorable alleles of these genes. An InDel in the AsNF-YB3 promoter enhances binding to AsARF1, upregulating AsNF‑YB3 under drought, and overexpression of AsNF‑YB3 reduces ROS accumulation. Our findings provide resources and targets for drought‑resistance breeding in oat, thereby supporting global food security.

Drought Resistance↗

PubMed2026

Dissecting genetic architecture of growth and yield traits in horsegram using GWAS.

Horsegram (Macrotyloma uniflorum), a member of the Fabaceae family, is a nutritious and low-cost legume used for both grain and fodder. This study employed a genome-wide association approach to identify loci linked to key agronomic traits in horsegram. Plant height, seed size, and shoot fresh weight were evaluated in a panel of 96 diverse genotypes. GBS was performed using the Illumina HiSeq platform, yielding 20,241 high-quality SNPs after filtering at a 5% minor allele frequency. Population structure analysis classified genotypes into three admixed subgroups. Phenotyping was conducted over three consecutive years at two locations in Himachal Pradesh (Palampur and Bajaura) using a randomized block design with two replications. GWAS analyses using GLM, MLM, FarmCPU, and BLINK models identified eight markers for plant height, three for seed size, and five for shoot fresh weight across different chromosomes. These markers provide valuable tools for accelerating trait improvement in future horsegram breeding programs.

Genome-Wide Association Study↗

PubMed2026

Dissecting genetic variance structure and evaluating genomic prediction models for single-cross hybrids derived from Stiff Stalk and Non-Stiff Stalk maize heterotic groups.

The early 20th-century discovery of heterosis and the establishment of heterotic groups transformed maize (Zea mays L.) into a keystone of global agriculture. However, maize breeding faces two significant challenges: the gradual decline of general combining ability (GCA) variance within heterotic groups and the impracticality of testing all possible single crosses in the early stages of a breeding program. Here, we developed genomic best linear unbiased prediction (GBLUP)-based multikernel models, using additive and two alternative nonadditive genomic relationship matrices, to estimate the variance components associated with the general combining ability of Stiff Stalk (SS) and Non-Stiff Stalk (NSS) heterotic groups and the specific combining ability arising from their crosses. We further applied these models to predict the performance of untested single-cross combinations under varying levels of parental information. We showed that the SS and NSS groups retained significant GCA variance across traits in both early- and late-maturity groups. The SS group, in contrast, exhibited no detectable GCA variance in grain yield for the intermediate-flowering subset of hybrids, highlighting a limitation for future genetic improvement. Furthermore, our results showed that GBLUP-based multikernel models effectively identified superior hybrids when parental information was available. In the absence of this information, however, these models underperformed compared to covariance-based approaches. Both nonadditive matrices yielded similar results, indicating that they capture comparable genetic relationship patterns despite their distinct formulations. Overall, this study sheds light on the future use of US maize commercial germplasm and demonstrates how GBLUP-based multikernel models can improve the efficiency of hybrid breeding programs.

Zea mays↗

PubMed2025

Post-genome-wide association study dissects genetic vulnerability and risk gene expression of Sjögren's disease for cardiovascular disease.

OBJECTIVES: This study aims to clarify the genetic associations between Sjögren's Disease (SD) and cardiovascular disease (CVD) outcomes, and to conduct an in-depth exploration of specific pleiotropic susceptibility genes. METHODS: We performed two-sample and multivariable Mendelian randomization (MR) analysis to investigate the association between SD and the risk of ischemic heart disease (IHD) and stroke. Linkage disequilibrium score regression (LDSC) and Bayesian co-localization analyses were employed to assess the genetic associations between traits. Cross-phenotype analyses were employed to identify shared variants and genes, followed by a Transcriptome-Wide Association Study (TWAS) and Multi-marker Analysis of Genomic Annotation (MAGMA) based on Multi-Trait Analysis of GWAS (MTAG) results. To validate the pleiotropic genes, we further analyzed tissue-specific differentially expressed genes (DEGs) related to SD using RNA sequencing data. RESULTS: The two-sample and multivariable MR analyses revealed that SD confers a genetic vulnerability to IHD and stroke. LDSC and co-localization analyses indicated a strong genetic linkage between SD and CVDs. Cross-phenotype analyses identified 38 and 37 pleiotropic single nucleotide polymorphisms (SNPs) for SD-Stroke and SD-IHD, respectively, primarily located within the MHC class region on 6p21.32:33 loci. Additionally, TWAS and MAGMA analyses identified pleiotropic genes located outside the MHC regions-seven associated with stroke (UHRF1BP1, SNRPC, BLK, FAM167A, ARHGAP27, C8orf12, and PLEKHM1) and two associated with IHD (UHRF1BP1 and SNRPC). Proxy variants within these genes in SD suggested an increased causal risk for stroke or IHD. Co-localization analysis further reinforced that SD and stroke share significant SNPs within the loci of FAM167A, BLK, C8orf12, SNRPC, and UHRF1BP1. DEG analysis revealed a significant up-regulation of the identified genes in SD-specific tissues. CONCLUSIONS: SD appears genetically predisposed to an increased risk of CVDs. Moreover, this research not only identified pleiotropic genes shared between SD and CVDs, but also, for the first time, detected key gene expressions that elevate CVD risk in SD patients-findings that may offer promising therapeutic targets for patient management.

Humans↗

PubMed2026

Dissecting the genetics of forage quality traits in soft red winter wheat in the U.S. southeast region.

Winter wheat plays a viable role in agriculture, not only as a primary grain crop but also as a valuable forage source that bridges fall-spring forage gaps in many regions, including the southeastern (SE) U.S. Despite its nutritive potential, genetic basis of forage-quality traits remains insufficiently understood, limiting breeding efforts for dual-purpose cultivars. This study aimed to dissect the genetic architecture of forage quality in 182 soft red winter wheat (SRWW) genotypes adapted to the SE U.S. using genome-wide association study (GWAS). Field experiments were carried out in randomized complete block design across three Georgia locations over two growing seasons (2023-2025), with forage sampled at the end of tillering and evaluated using near-infrared reflectance spectroscopy. Significant phenotypic variation was observed for dry matter (DM), crude protein (CP), acid and neutral detergent fiber (ADF, NDF), acid detergent lignin (ADL), total digestible nutrients (TDN), sugars (SUG), and relative forage quality (RFQ). Heritability estimates ranged from low-to-moderate in combined environments and from low-to-high within individual locations. Correlation analysis revealed strong positive associations among fiber-related traits and negative associations with TDN, RFQ, and SUG, while CP declined with increasing fiber. Genome-wide association analysis identified 282 significant marker-trait associations (P < 1×10-4) across 19 chromosomes, which were consolidated into 121 QTLs, including 27 major-effect QTLs. Three QTLs QRfq.uga-3B.1, QRfq.uga-3B.2 (RFQ) and QDm/Sug.uga-7A (DM, SUG) were stable across locations while QAdf/Adl.uga-2A (ADF, ADL) and QDm/Sug.uga-7A (DM, SUG) indicated multi-trait control. Notably, 25 of the 27 major QTLs were putatively novel, highlighting substantial untapped allelic diversity for forage-quality improvement in SE SRWW. Favorable allele accumulation resulted in an overall improvement in forage quality, increasing desirable nutritive traits (DM, RFQ, SUG, CP) while reducing undesirable traits (ADF, ADL). Candidate gene analysis linked six major QTLs with genes implicated in abiotic stress response, plant development, and metabolic regulation, supporting their functional relevance in forage-quality determination. Incorporating these loci into breeding programs provides a robust genetic framework for marker-assisted selection, enabling the development of dual-purpose wheat cultivars with enhanced forage quality, thereby strengthening wheat's utility as a reliable forage resource during periods of seasonal feed scarcity in SE production systems.

GWAS↗

PubMed2026

Dissecting the genetic basis underlying drought tolerance at different development stages in soybean.

INTRODUCTION: Soybean is an indispensable crop supplying protein and oil for humans and animals, and playing an essential role in global food security. Drought represses soybean seed germination, reducing biomass accumulation and even inhibiting yield. METHODS: In order to dissect the genetic components underlying soybean drought tolerance during different development stage, a natural population containing 140 accessions was employed to evaluate seven drought tolerance-related traits under water-welled and drought stress conditions. Subsequently, genome-wide association study (GWAS) was conducted based on 150K single nucleotide polymorphism (SNP) markers of "Zhongdouxin-1". And the drought tolerance coefficient of seven different traits were analyzed with seven GWAS models. RESULTS: A total of 1807 significant SNPs were detected across 20 chromosome, including 569 SNPs for germination stage, and 1242 SNPs for seedling stage. Of 569 SNPs identified in germination stage, 354 SNPs on chromosomes 2, 7, 13, 14, and 17 accounting for 62.21%. Among 1242 SNPs found in seedling stage, 869 SNPs on chromosomes 11, 14, 15, 17 and 18 accounting for 69.97%. Moreover, among 1807 significant SNPs, 163 SNPs exhibited pleiotropic effects, of which 23 were located in exon, 21 in intron, 12 in 5'UTR or 3'UTR and 11 in upstream or downstream. Furthermore, 249 stable SNPs were detected by more than four GWAS models. According to these stable SNPs, RNA expression levels and gene annotations, four causal genes (Glyma.02G080200, Glyma.11G056200, Glyma.12G188900, and Glyma.18G110200) conferring soybean drought tolerance were detected, which participated in ethylene stimulus response, water deprivation response, and proteolysis. DISCUSSION: Collectively, 249 stable SNPs, 163 pleiotropic SNPs and four candidate genes identified in present study provided promising molecular resources and reliable foundation for drought resistance improvement and marker-assisted selective breeding in soybean.

GWAS↗

PubMed2025

Genome-wide association study to dissect the genetic architecture of bolting-related traits in carrot (Daucus carota).

As a crucial root vegetable, the phenomenon of bolting and flowering presents a significant challenge to the commercial value of carrot. However, the genetic mechanism of carrot bolting remains to be fully elucidated. In this study, we conducted a two-year cultivation experiment with a population of 240 carrots to examine traits associated with bolting. We conducted whole-genome sequencing on these carrots and subsequently performed population structure analysis, as well as genome-wide association studies (GWAS). A total of nine single nucleotide polymorphism (SNP) loci were identified across diverse environmental conditions that exhibited significant associations with bolting speed and other traits. Furthermore, within 93 candidate genes identified, the RING-domain zinc-finger protein LOC108205243 was determined to play a significant role in the regulation of bolting traits. The SNPs and candidate genes identified in this research may serve as molecular markers for bolting traits, thereby offering essential resources for genetic engineering breeding efforts aimed at managing bolting in carrots.

Daucus carota↗

PubMed2026

CYClones: a highly powered, fully genotyped, eight-parent yeast mapping population.

The budding yeast Saccharomyces cerevisiae is a remarkably adaptable organism that thrives in diverse environments. Global sequencing of natural isolates has revealed extensive genetic diversity within the species. Here, we describe the construction and characterization of CYClones (Collaborative Yeast Cross clones), a library of 11,392 segregants generated from a multiparent funnel cross of eight genetically diverse parental strains. To enable the genetic dissection of complex traits, we imputed whole-genome sequences for all segregants and show that CYClones captures a substantial fraction of the global genetic diversity of S. cerevisiae. Haplotype representation is well maintained, with each parental haplotype present at >5% frequency across >95% of the genome. Simulations demonstrate that CYClones has ≥95% power to detect variants with heritability as low as 0.36%, with mapping resolution often finer than the length of a single gene. In summary, CYClones is a powerful community resource for dissecting the genetic architecture of complex and quantitative traits, uncovering context-dependent mutational effects, and identifying causal variants underlying phenotypic diversity.

Saccharomyces cerevisiae↗

PubMed2004

Use of a dense single nucleotide polymorphism map for in silico mapping in the mouse.

Rapid expansion of available data, both phenotypic and genotypic, for multiple strains of mice has enabled the development of new methods to interrogate the mouse genome for functional genetic perturbations. In silico mapping provides an expedient way to associate the natural diversity of phenotypic traits with ancestrally inherited polymorphisms for the purpose of dissecting genetic traits. In mouse, the current single nucleotide polymorphism (SNP) data have lacked the density across the genome and coverage of enough strains to properly achieve this goal. To remedy this, 470,407 allele calls were produced for 10,990 evenly spaced SNP loci across 48 inbred mouse strains. Use of the SNP set with statistical models that considered unique patterns within blocks of three SNPs as an inferred haplotype could successfully map known single gene traits and a cloned quantitative trait gene. Application of this method to high-density lipoprotein and gallstone phenotypes reproduced previously characterized quantitative trait loci (QTL). The inferred haplotype data also facilitates the refinement of QTL regions such that candidate genes can be more easily identified and characterized as shown for adenylate cyclase 7.

Adenylyl Cyclases↗

PubMed2025

Genetic evidence for repurposing GLP-1 receptor agonists in chronic kidney disease and IgA nephropathy: Metabolic and anti-inflammatory pathways beyond glycaemic control.

AIMS: Despite observational links between glucagon-like peptide-1 receptor agonists (GLP-1RAs) and kidney benefits, causal mechanisms remain unclear. This study aims to dissect genetic causality and mediation pathways underlying the effects of GLP-1RAs on chronic kidney disease (CKD) and related renal outcomes. MATERIALS AND METHODS: Using large-scale Genome - Wide Association Study (GWAS) data, we applied two-sample Mendelian randomisation (MR) to estimate the causal effects of GLP-1RAs on CKD, estimated glomerular filtration rate (eGFR) and subtypes (IgA nephropathy, membranous nephropathy, nephrotic syndrome and chronic glomerulonephritis), with sensitivity analyses. The glycaemic markers (glycated haemoglobin [HbA1c] and blood glucose), type 2 diabetes mellitus (T2DM) and diabetic nephropathy (DN) served as positive controls. Mediation MR assessed body mass index (BMI), lipids, glycaemic markers and inflammatory proteins. Data were sourced from MRC Integrative Epidemiology Unit Open Genome - Wide Association Studies OpenGWAS, FinnGen, GWAS Catalogue and cohort-specific studies. RESULTS: Positive control analyses revealed that genetically predicted GLP-1R activation was associated with reduced levels of HbA1c (p = 4.93E-15) and blood glucose (p = 9.73E-5), as well as a decreased risk of T2DM (p = 2.45E-4) and DN (p = 6.35E-4), fully validating the reliability of the genetic instruments. Genetic proxies for GLP-1R activation lowered risks of CKD (odds ratio [OR] = 0.83, p = 9.22E-9), immunoglobulin A nephropathy (IgAN) (OR = 0.70, p = 2.11E-3) and kidney function preservation (β = 0.01, p = 9.11E-3), but showed null effects on other CKD subtypes. Mediation analyses indicated that fibroblast growth factor 23 (FGF23) suppression mediated 26.57% of the effect on eGFR and 13.50% of CKD protection, whereas metabolic traits (BMI: 2.08% for CKD, 5.51% for eGFR; high-density lipoprotein: 0.79% for CKD, 2.34% for eGFR; HbA1c: 8.25% for eGFR) partially explained the benefits on CKD and eGFR. Only BMI exhibited a mediation effect on IgAN. Sensitivity analyses confirmed minimal pleiotropy. CONCLUSIONS: This study provides robust genetic evidence for repurposing GLP-1RAs in CKD and IgAN through anti-inflammatory (FGF23) and metabolic pathways, extending their utility beyond glucose control. While European ancestry data limit generalisability, our framework prioritises FGF23 and metabolic modulation as key targets for clinical trials in renal protection.

Humans↗

PubMed2026

Pervasive context-dependent effects in the genetic architecture of complex and quantitative traits revealed by a powerful multiparent mapping population in yeast.

The genetic dissection of complex traits remains a major challenge in basic and biomedical research, but is essential for understanding the molecular pathways that shape phenotypic variation and for developing predictive models of trait and disease susceptibility. Here, we leverage a novel multiparent mapping population of budding yeast, CYClones, comprising 9,344 haploid strains derived from eight genetically diverse founders (~270,000 SNVs, ~ 1 per 44 bp, capturing 56% of common variants and 32% of all variants with a minor allele frequency greater than 0.005 in the global population), to identify quantitative trait loci (QTL) and systematically investigate the genetic architecture of growth rates across ten environmental conditions. In total, we identified 349 QTL (ranging from 18 to 49 QTL per growth condition) that explained between 60% and 100% of narrow sense heritability across traits. The high power and resolution of CYClones revealed that growth traits exhibited distinct, condition-specific genetic architectures with extensive allelic heterogeneity, where a QTL was the result of multiple tightly linked causal variants. We also observed pleiotropy among QTL with complex, trait-dependent allele effects that are also consistent with allelic heterogeneity. Genetic complexity varied widely, with some traits showing nearly Mendelian architectures, while others were highly polygenic. Introgressed loci played a prominent role in the landscape of growth rate QTL, including a QTL localized to a 2.4 kb interval in the PCA1 cadmium transporter that explains 72% of variation in cadmium resistance and is largely driven by an introgression, and a non-additive interaction between the GAL3 regulator and introgressed GAL1/7/10 alleles, extending a previously described three-locus GAL-pathway incompatibility to a four-locus interaction. In both cadmium and galactose conditions, we show that allelic variation at a small number of loci stratifies the population into regulatory or physiological subgroups, each with distinct genetic architectures, a specific manifestation of epistasis we term allele-dependent stratification. Collectively, our results provide novel insights into the genetics of growth rates in budding yeast, the architectural features of genetic complexity, and demonstrate that CYClones is a powerful platform for revealing the molecular basis of complex trait variation.

Quantitative Trait Loci↗

PubMed2025

Mechanisms of Globin Gene Regulation in Mammals.

Studies of globin gene clusters have established many paradigms of gene regulation. This review focuses on the α- and β-globin gene clusters of humans and mice, summarizing important insights from high-throughput biochemical assays and directed genetic dissections and emphasizing similarities across the types of gene clusters and between species. The overall arrangements and architectures are similar, with each gene cluster being localized within a topologically constrained unit of chromatin containing a multicomponent enhancer (i.e., a locus control region) and other regulatory elements bound by a similar set of transcription factors and coactivators. Differential expression of the globin genes within each cluster during ontogeny is associated with changes in contacts with the locus control region and involves the action of gene-specific repressors. Detailed study of the fetal β-like HBG1 and HBG2 globin genes has revealed a remarkable diversity of regulatory pathways that provide candidates for therapeutic approaches to reactivate these genes for β-hemoglobinopathies.

Animals↗

PubMed2025

Dissecting the shared genetic architecture of schizophrenia with ventricular subregion volumes.

Schizophrenia is characterized by cerebral ventricular enlargement as an early and consistent structural anomaly. While genetic factors significantly influence both schizophrenia and cerebral ventricular enlargement, the shared genetic etiology between them requires further investigation. Using summary statistics from recent large genome-wide association studies on schizophrenia and 9 ventricular subregion volumes phenotypes. Gaussian causal mixture modeling was applied to characterize the genetic architecture and overlap between schizophrenia and ventricular subregion volumes phenotypes. Local genetic correlation was investigated with Local Analysis of Variant Association. The conjunctional false discovery rate framework was used to identify the specific shared genetic loci, annotated with FUMA. Gaussian causal mixture modeling estimated schizophrenia to be more polygenic more polygenic (9574 trait-influencing variants) than ventricular subregion volumes phenotypes (157-1267 trait-influencing variants). Conjunctional false discovery rate analysis identified 42 shared genetic loci, 17 loci were identified as novel for both schizophrenia and the ventricular subregion volumes phenotypes. Local Analysis of Variant Association revealed that 11 distinct loci demonstrated significant differences, among which 4 loci were situated in the Major Histocompatibility Complex region. Annotated genes in shared loci were enriched in molecular signaling pathways involved in inflammation and the brain structure. The shared loci between them were annotated and enriched in Major Histocompatibility Complex and inflammation-related pathways, highlighting new opportunities for future investigation.

Schizophrenia↗

PubMed2026

Dissecting the shared genetic architecture between migraine subtypes and cardiovascular diseases: a multi-layered genomic analysis.

BACKGROUND: Epidemiological studies have linked migraine to an increased risk of cardiovascular disease (CVD); however, the shared genetic basis and putative causal relationships between migraine subtypes and cardiovascular traits remain poorly understood. METHODS: Leveraging large-scale GWAS summary statistics for migraine phenotypes (overall migraine, migraine with aura [MA], and migraine without aura [MO]) from FinnGen R12, along with seven cardiovascular diseases from publicly available consortia, we conducted a multi-layered genetic analysis. This integrative framework encompassed genetic correlation [linkage disequilibrium score regression (LDSC) and high-definition likelihood (HDL)], cross-trait meta-analysis (CPASSOC and PLACO), Bayesian colocalization, summary-data-based Mendelian randomization (SMR) using GTEx v8 eQTL data, and bidirectional two-sample Mendelian randomization (MR). RESULTS: Significant genetic correlations were identified between migraine and multiple cardiovascular traits, with hypertension and coronary artery disease (CAD) showing the most robust associations. MA exhibited broader genetic overlap with cardiovascular diseases than MO, including a notably stronger correlation with ischemic stroke, whereas MO demonstrated a stronger correlation with hypertension. Cross-trait meta-analysis identified 160 pleiotropic loci across 17 of 21 trait pairs. Colocalization analysis confirmed 32 loci harboring shared causal variants, mapped to 13 candidate genes, of which 7 (PHACTR1, LRP1, SOX7, ABO, FHOD3, MEI1, XKR6) were further validated by SMR as exhibiting tissue-specific regulatory effects. Among these, PHACTR1 displayed the broadest pleiotropic profile across migraine phenotypes and vascular diseases. After MR-PRESSO outlier removal, bidirectional MR identified 10 MR-supported associations, two of which (genetic liability to hypertension on overall migraine, and CAD on MA) survived Bonferroni correction, all free of detectable horizontal pleiotropy. Genetic liability to hypertension was associated with increased migraine risk (OR = 1.90, 95% CI 1.25-2.90, P = 2.64 × 10⁻³), atherosclerotic diseases showed subtype-specific effects (inverse for MO, positive for MA), and, in the reverse direction, migraine was associated with increased ischemic stroke risk. CONCLUSIONS: This study provides a comprehensive and systematic characterization of the shared genetic architecture between migraine subtypes and cardiovascular diseases. By identifying pleiotropic genes and bidirectional putative causal relationships with subtype-specific patterns, our findings carry implications for the development of targeted therapeutics and subtype-specific cardiovascular risk stratification.

Humans↗