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At least 19 recordsLinked to original sources

PubMed2026

Exploring the shared genetic architecture of sarcopenia using genomic structural equation modeling.

Sarcopenia is a common age-associated condition characterized by the progressive loss of skeletal muscle mass, strength, and physical functionality. While large-scale genome-wide association studies (GWAS) have previously addressed isolated traits of sarcopenia, the multifactorial genetic architecture underlying this condition remains largely undefined. To characterize the common genetic basis of sarcopenia-related traits, genomic structural equation modeling (Genomic-SEM) was implemented. Multiple post-GWAS analytic approaches were integrated to pinpoint susceptibility loci. These analyses encompassed identifying enriched genetic pathways and relevant genomic elements, as well as cell-type-specific enrichment in skeletal muscle satellite stem cells, mesenchymal stem cells, and skeletal muscle satellite cells in limb muscle. Furthermore, based on the integrated GWAS data of sarcopenia-related traits, polygenic risk score (PRS) analysis was conducted to evaluate risk associations at the chromosomal level. A well-fitted Genomic-SEM successfully integrated the GWAS data, revealing the shared genetic architecture of sarcopenia-related traits. We identified 110 single nucleotide polymorphisms (SNPs) reaching genome-wide significance (p < 5 × 10-8), of which 9 represent novel discoveries. Subsequent fine-mapping procedures and gene-set analyses identified 15 causal variants alongside 77 candidate susceptibility genes. This study provides a comprehensive genetic characterization of sarcopenia via Genomic-SEM, offering new insights into the etiological pathways underlying sarcopenia.

Sarcopenia↗

PubMed2025

Genomic structural equation modeling elucidates the shared genetic architecture of allergic disorders.

BACKGROUND: The intricate shared genetic architecture underlying allergic disorders-including allergic asthma, atopic dermatitis, contact dermatitis, allergic rhinitis, allergic conjunctivitis, allergic urticaria, anaphylaxis, and eosinophilic esophagitis-remains incompletely characterized. METHODS: Our study employed genomic structural equation modeling (Genomic SEM) to define the common factor representing the shared genetic architecture of allergic disorders. Coupled with diverse post-GWAS analytical methods, we aimed to discover susceptible loci and investigate genetic associations with external traits. Furthermore, we explored enriched genetic pathways, cellular layers, and genomic elements, and investigated putative plasma protein biomarkers. Polygenic risk score (PRS) analyses, leveraging our integrated GWAS data, were conducted to assess chromosomal-level risk associations for allergic disorders. RESULTS: A well-fitted genomic SEM integrated GWAS data, revealing the shared genetic architecture of allergic disorders. We identified a total of 2038 genome-wide significant SNP loci (p < 5e-8), including 31 previously unreported loci. Fine-mapping of variants and gene sets pinpointed 2 causal variants and 31 candidate susceptible genes. Genetic correlation analyses further illuminated the shared genetic architecture underlying multiple traits, notably psychiatric disorders. Preliminary findings identified four putative causal plasma protein biomarkers. CONCLUSION: Notably, this study presents the first comprehensive genetic characterization of allergic disorders through a GWAS analysis of an unmeasured composite phenotype, providing novel insights into shared etiological pathways across these conditions.

Humans↗

PubMed2025

Distinguishing specific from broad genetic associations between external correlates and common factors.

MOTIVATION: Within the genomic structural equation modelling (genomic SEM) framework, common factors are often used to index shared genetic etiology across constellations of genome-wide associations studies (GWASs) phenotypes. A standard common pathway model, in which a genetic association is estimated between an external GWAS phenotype and a common factor, assumes that all genetic associations between the external GWAS phenotype and the individual indicator phenotypes are mediated through the factor. This assumption can be tested using the QTrait statistic, which compares the common pathway model to an independent pathways model that allows for direct genetic associations between the external GWAS phenotype and the individual indicators of the factor. However, QTrait is not designed to identify either the magnitude or the source of this heterogeneity. RESULTS: We expand upon the QTrait approach by describing an effect size index that quantifies the degree to which the common pathways model is violated, and we provide a systematic approach for empirically identifying specific direct pathways between an external trait and indicator traits. Our method comprises a series of omnibus tests and outlying indicator detection algorithms indexing the heterogeneity of associations between the genetic component of external traits and the individual indicators of common factors. We provide a set of automated functions which we apply to investigate the patterns of genetic associations across a set of external correlates with respect to indicators of general cognitive ability and case-control and proxy GWAS indices of Alzheimer's disease. AVAILABILITY AND IMPLEMENTATION: The Genomic SEM R package and the QTrait function is available at https://github.com/GenomicSEM/GenomicSEM. The QTrait function tutorial is available at https://github.com/GenomicSEM/GenomicSEM/wiki/8.-Tutorials. To ensure reproducibility of the analyses presented in this manuscript, the exact version of the QTrait function used, along with input data and scripts, has been archived on Zenodo (DOI: https://doi.org/10.5281/zenodo.17186083).

Genome-Wide Association Study↗

PubMed2026

Unraveling causal links between chronic rhinosinusitis and peripheral artery diseases: insights from genetic correlations through genome-wide association studies.

OBJECTIVES: Chronic Rhinosinusitis (CRS) shares epidemiological links with Cardiovascular Diseases (CVDs), however, their shared genetic basis remains unclear. We hypothesized that pleiotropic genetic variants underlie CRS-CVDs links via distinct biological pathways. METHODS: Using large-scale GWAS data from European-ancestry individuals, we assessed global and local genetic correlations. We applied Genomic Structural Equation Modeling (Genomic SEM) to dissect shared genetic architecture, performed bidirectional Mendelian Randomization (MR) to infer causality, and conducted cis-eQTL colocalization to identify shared genetic signals. Finally, in vitro endothelial models (HUVECs) validated the functional dynamics of candidate genes under CRS-mimicking inflammatory stress. RESULTS: CRS showed significant genetic correlations with multiple CVDs. Genomic SEM revealed a latent factor structuring shared genetic risk through three pathways: artery diseases, myocardial diseases, and heart failure. Local genetic correlations identified significant local genetic correlations specifically between CRS and Peripheral Atherosclerosis (PAS)/Peripheral Artery Disease (PAD) specifically within the chr6: 31.57‒33.24 Mb locus. MR demonstrated causal effects of CRS on PAD (OR = 1.23, p = 0.022) and PAS (OR = 1.21, p = 0.011), but not vice versa. Genetically predicted HLA-DRB1, APOM, and COL11A2 expression conferred protection, while HLA-DQA2 increased risk. Crucially, in vitro validation corroborated these pathogenic trajectories, inflammatory stress significantly downregulated the protective APOM and upregulated the risk-associated HLA-DQA2 alongside pro-atherogenic VCAM-1, while HLA-DRB1 exhibited a compensatory upregulation (p < 0.05). CONCLUSION: CRS shares global genetic liability with CVDs, structured through three primary etiological pathways. Causal effects of CRS on peripheral artery diseases are mediated by immune and lipid-related genes within the chr6 locus, revealing divergent pleiotropic mechanisms. Our integrated genetic and in vitro evidence provides a mechanistic framework wherein chronic mucosal inflammation contributes to systemic endothelial vulnerability, thereby highlighting candidate targets for mechanism-directed therapy.

Humans↗

PubMed2026

Unraveling 'F' factor: towards a genetic-clinical framework for the musculoskeletal-heart crosstalk in metabolic aging.

BACKGROUND: The rising co-occurrence of cardiometabolic diseases and musculoskeletal degeneration poses a critical challenge to healthy aging, yet the shared biological mechanisms underlying this multimorbidity remain poorly defined. This study aimed to establish an integrative clinical-genetic framework to elucidate the common frailty factor, the 'F' factor, that captures the systemic vulnerability linking cardiometabolic multimorbidity (CMM) and musculoskeletal aging. METHODS: Utilizing the prospective China Health and Retirement Longitudinal Study (CHARLS) cohort, we developed and validated novel Frailty-Integrated Indices for CMM risk prediction, evaluated with machine learning models interpreted via SHapley Additive exPlanations (SHAP). Independently, we applied genomic structural equation modeling (Genomic-SEM) to integrate genome-wide association data from six traits-coronary artery disease, type 2 diabetes, hypertension, bone mineral density, frailty, and telomere length-to model a shared latent genetic factor ('F' factor). This was followed by multivariate GWAS, fine-mapping, transcriptome-wide association study (TWAS), gene-based analysis, and functional annotation to prioritize causal genes, pathways, and cell types. RESULTS: Clinically, several Frailty-Integrated Indices significantly improved CMM risk prediction, with the optimal model achieving an AUC of 0.727. Genetically, we modeled a significant shared latent genetic factor ('F' factor), pinpointing novel risk loci and implicating key genes such as APOE and SLC22A3. These genes were enriched in pathways including cellular senescence and cholesterol metabolism and showed specific expression patterns in developmental brain stages and across multi-organ endothelial cells. CONCLUSION: Our findings provide converging evidence for Musculoskeletal‑Heart crosstalk of metabolic aging and inferred the 'F' factor as a genetic correlate of a transdiagnostic state, which links genetic predisposition to metabolic dysregulation, and systemic functional decline. This work provides a multi-level biological characterization of multimorbidity liability, informing early-risk detection and preventive strategies for complex aging-related comorbidities.

Humans↗

PubMed2026

Genomic Structural Equation Modeling Identifies a Shared Inflammatory Genetic Dimension Across Inflammatory Arthritis Phenotypes and Biomarkers.

BACKGROUND: Inflammatory arthritis (IA), including rheumatoid arthritis (RA), psoriatic arthritis (PsA) and gout, shares systemic inflammatory features indexed by C-reactive protein (CRP) and interleukin-6 (IL-6), yet the extent of their common genetic basis remains unclear. AIMS: We aimed to delineate the shared genetic architecture across IA phenotypes and inflammatory biomarkers. MATERIALS AND METHODS: We applied genomic structural equation modelling (Genomic SEM) to GWAS summary statistics for RA, PsA, gout, CRP and IL-6, fitted a single common factor, and performed multivariate GWAS followed by fine-mapping, transcriptome-wide association, gene-based analysis, pathway enrichment, and cell-type and spatial mapping. RESULTS: A single common factor was fitted (CFI = 0.990, SRMR = 0.045). The multivariate GWAS identified 56 genome-wide significant SNPs across 10 independent lead loci, including one novel signal. Fine-mapping prioritized high-confidence variants near PTPN22, the CRP gene cluster and a urate-associated locus. Gene-level analyses converged on DCLRE1B, PTPN22, IL6R, NLRP3 and HNF1A, with pathway enrichment implicating inflammasome assembly and metabolic-inflammatory overlap. Cell-type enrichment highlighted myeloid populations, and spatial mapping localized signals to lung, kidney, mucosal epithelium and gastrointestinal tissues. DISCUSSION: These results delineate a shared inflammatory genetic dimension across IA phenotypes and biomarkers, anchored in immune, inflammasome, cytokine-receptor and metabolic pathways. CONCLUSION: Together, these findings provide a valuable framework for prioritizing candidate genes and cellular contexts for future investigation.

TWAS↗

PubMed2026

Integrated bioinformatics and SEM analysis reveal GPAM as a key mediator of fibrosis in NAFLD with metabolic dysfunction.

Nonalcoholic fatty liver disease (NAFLD) is a complex condition influenced by metabolic and genetic factors, yet the shared genetic architecture underlying its progression remains poorly understood. The aim of this study was to employ genomic structural equation modeling (GSEM) to elucidate the genetic architecture linking NAFLD with key metabolic traits-including insulin resistance, body mass index (BMI), hemoglobin A1c (HbA1c), and liver fibrosis using summary statistics from large-scale genome-wide association studies. By harmonizing 2.18 million variants across five genome-wide association studies (GWAS) datasets, we identified 134 genome-wide significant loci that mapped to 24 genes. GSEM revealed a latent genetic structure composed of two distinct dimensions: a metabolic regulation factor primarily driven by insulin resistance, BMI, and HbA1c; and a structural pathology factor specifically associated with liver fibrosis. These factors explained 65.5% and 78.1% of the genetic variance in BMI and fibrosis, respectively, with minimal correlation (rg = 0:07), indicating their genetic distinctness. Additionally, integrating Mendelian randomization with liver transcriptome profiling, we characterized how the 24 genes contribute to disease and identified mitochondrial glycerol-3-phosphate acyltransferase (GPAM) as the key gene that causally links lipid metabolism to fibrogenesis. In conclusion, we present the first genetically grounded mechanism for the progression of NAFLD to fibrosis. This mechanism encompasssses genetic variants, dysregulated gene expression, metabolic disturbances, and the processes involved in fibrotic remodeling. This research establishes a genetic framework for understanding the pathogenesis of NAFLD and highlights novel therapeutic targets for intervention.

Non-alcoholic Fatty Liver Disease↗

PubMed2026

Complete mitochondrial genomes and phylogenetic analysis of three species of Indo-Pacific freshwater gobies, Stiphodon (Gobiiformes,Oxudercidae).

Stiphodon is a genus of gobioid fishes found primarily in freshwater streams on islands throughout the Indo-Pacific region. It is the most speciose genus of Sicydiinae, with more than three dozen named species. Complete mitochondrial genome (mtDNA) sequences were determined for three species, namely Stapledon atropurpureus, Stapledon elegans, and Stapledon semoni. Maximum likelihood and Bayes inference inferences from 13 protein-coding genes (11,433 bp) or the COXI gene (1,551 bp) alone were made on these three together with four mitogenomes previously available. S. percnopterygionus and S. tuivi formed an outgroup to the other five Stiphodon species, meanwhile Stapledon atropurpureus and S. semoni were recovered as sister species. The ratios of the non-synonymous to the synonymous ubstitution rates for all PCGs were in the range 0\ (Ka/Ks) \1, which suggests they have been subject to purifying selection.

Animals↗

PubMed2026

SEMPLR: an R package for transcription factor binding prediction.

SUMMARY: SEMPLR is an R package that predicts transcription factor binding and variant effects using SNP Effect Matrices (SEMs), providing efficient, genome-wide scoring, enrichment testing, and visualization tools for comprehensive analysis of regulatory sequences. AVAILABILITY: Available on GitHub at https://github.com/grkenney/SEMPLR and on Bioconductor at https://bioconductor.org/packages/release/bioc/html/SEMPLR.html.

Transcription Factors↗

PubMed2003

Structural equation model-based genome scan for the metabolic syndrome.

BACKGROUND: The metabolic syndrome is characterized by the clustering of several traits, including obesity, hypertension, decreased levels of HDL cholesterol, and increased levels of glucose and triglycerides. Because these traits cluster, there are likely common genetic factors involved. RESULTS: We used a multivariate structural equation model (SEM) approach to scan the genome for loci involved in the metabolic syndrome. We found moderate evidence for linkage on chromosomes 2, 3, 11, 13, and 15, and these loci appear to have different relative effects on the component traits of the metabolic syndrome. CONCLUSION: Our results suggest that the metabolic syndrome components, diabetes, obesity, and hypertension, are under the pleiotropic control of several loci.

Chromosomes, Human, Pair 11↗

PubMed2006

Gene Express Inc.

Gene Express, Inc. is a technology-licensing company and provider of Standardized Reverse Transcription Polymerase Chain Reaction (StaRT-PCR) services. Designed by and for clinical researchers involved in pharmaceutical, biomarker and molecular diagnostic product development, StaRT-PCR is a unique quantitative and standardized multigene expression measurement platform. StaRT-PCR meets all of the performance characteristics defined by the US FDA as required to support regulatory submissions [101,102] , and by the Clinical Laboratory Improvement Act of 1988 (CLIA) as necessary to support diagnostic testing [1] . A standardized mixture of internal standards (SMIS), manufactured in bulk, provides integrated quality control wherein each native template target gene is measured relative to a competitive template internal standard. Bulk production enables the compilation of a comprehensive standardized database from across multiple experiments, across collaborating laboratories and across the entire clinical development lifecycle of a given compound or diagnostic product. For the first time, all these data are able to be directly compared. Access to such a database can dramatically shorten the time from investigational new drug (IND) to new drug application (NDA), or save time and money by hastening a substantiated 'no-go' decision. High-throughput StaRT-PCR is conducted at the company's automated Standardized Expression Measurement (SEM) Center. Currently optimized for detection on a microcapillary electrophoretic platform, StaRT-PCR products also may be analyzed on microarray, high-performance liquid chromatography (HPLC), or matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) platforms. SEM Center services deliver standardized genomic data--data that will accelerate the application of pharmacogenomic technology to new drug and diagnostic test development and facilitate personalized medicine.

Gene Expression Profiling↗

PubMed2005

Genome-wide gene expression profiling of testicular carcinoma in situ progression into overt tumours.

The carcinoma in situ (CIS) cell is the common precursor of nearly all testicular germ cell tumours (TGCT). In a previous study, we examined the gene expression profile of CIS cells and found many features common to embryonic stem cells indicating that initiation of neoplastic transformation into CIS occurs early during foetal life. Progression into an overt tumour, however, typically first happens after puberty, where CIS cells transform into either a seminoma (SEM) or a nonseminoma (N-SEM). Here, we have compared the genome-wide gene expression of CIS cells to that of testicular SEM and a sample containing a mixture of N-SEM components, and analyse the data together with the previously published data on CIS. Genes showing expression in the SEM or N-SEM were selected, in order to identify gene expression markers associated with the progression of CIS cells. The identified markers were verified by reverse transcriptase-polymerase chain reaction and in situ hybridisation in a range of different TGCT samples. Verification showed some interpatient variation, but combined analysis of a range of the identified markers may discriminate TGCT samples as SEMs or N-SEMs. Of particular interest, we found that both DNMT3B (DNA (cytosine-5-)-methyltransferase 3 beta) and DNMT3L (DNA (cytosine-5-)-methyltransferase 3 like) were overexpressed in the N-SEMs, indicating the epigenetic differences between N-SEMs and classical SEM.

Carcinoma in Situ↗

PubMed2004

The modular approach to ligand discovery.

Identifying specific protein-ligand interactions is a long-standing problem in drug discovery and chemical biology, which is only exacerbated by the abundance of uncharacterized proteins revealed by genomics. Last month in Chemistry Biology, Sem et al. described a powerful technique for rapidly screening protein families for ligands.

Drug Evaluation, Preclinical↗

PubMed2006

Ultrastructural and genetic diversity studies of two Sclerocollum (Acanthocephala) species infecting Siganid and Lutianid fishes from the Red Sea, Egypt.

Little is known about the distribution of Acanthocephala in local waters. A survey was carried out on the commercially important herbivorous Siganid fish, Siganus rivulatus and S. luridus, as well as Lutianid fish Centropristisfilamentosus inhabiting the Red Sea to determine the prevalence of Acanthocephala parasites. One hundred and thirteen fish were examined. The infection rates of S. rivulatus and S. luridus with Sclerocollum rubrimaris Schmidt and Paperna 1978 (Rhadinorhynchidae: Gorgorhynchinae) were 59 % & 33%, respectively. Meanwhile, 59% of C. filamentosus were found infected by Sclerocollum sp. The abundance, host-parasite relationships and microhabitat of S. rubrimaris were investigated and discussed. SEM was the employed to investigate the differences between the two species of Sclerocollum. For accurate estimation of genetic diversity of these species, randomly amplified polymorphic DNA (RAPD) genomic fingerprinting was proposed, using four different random primers. SEM studies showed that the two examined species differ in the length of proboscis hooks, the number of longitudinal rows of hooks on proboscis, distance between the bases of hooks and in egg size. The trunk surface of Sclerocollum sp. had minute, scale-like spines that were arranged in oblique lines whereas the trunk surface of S. rubrimaris had small pores and sclerotised plates on its anterior portion. RAPD primers revealed 52 amplification products and species-specific markers were identified. The deduced phenogram comprised two main clusters each includes one of the examined Sclerocollum species. Results indicated that RAPD markers are useful for the assessment of genetic diversity between the investigated Sclerocollum species which concur with SEM outcome.

Acanthocephala↗

PubMed2026

Elevated water levels drive greenhouse gas mitigation in the riparian zone profile.

Wetlands are critical for climate regulation, with their hyporheic zone serving as sensitive interfaces for groundwater-soil-atmosphere exchange. These zones are active hotspots for carbon-nitrogen cycling and greenhouse gas (GHG) emissions (CO2, CH4, N2O), yet the impact of water level fluctuations on these emissions and their microbial drivers in freshwater wetlands remains poorly understood. This study investigated the spatiotemporal dynamics of GHG emissions and carbon-nitrogen coupling processes along riparian soil profiles of Baiyangdian Lake during water level fluctuations. Employing static chamber measurements, microcosms, quantitative PCR, Metagenome-Assembled genome (MAG) analyses, and Structural Equation Modeling (SEM), we observed that GHG emissions were significantly affected by water level fluctuations. Specifically, CO2 and N2O fluxes, as well as CO2 production potential were significantly lower at high-water-level conditions. Water level also emerged as a key driver of microbial community structure, with Methylococcaceae and Methanosarcinaceae as key regulators of CH4 emission, and Anaeromyxobacteraceae as central to N2O dynamics. A high-quality Methylomirabilales-like MAG, possessing the complete pathway for coupled nitrate reduction and methane oxidation, was identified. Its abundance negatively correlated with water level, suggesting that these C-N coupling bacteria contribute to reducing GHG emissions. This study provides crucial theoretical insights and identifies microbial targets for mitigating wetland GHG emission through hydrological management.

Greenhouse Gases↗

PubMed2026

The trypanosome flagellum as model for parasitology, cell biology and ciliopathies.

Cilia and flagella exhibit widely conserved structures and functions across species. In humans, defects in these organelles are responsible for diseases called ciliopathies and many model organisms are used to study them. In this review, we will discuss one of them, the parasite Trypanosoma brucei, which is particularly well-suited to investigate general aspects of cilia and flagella, such as construction or protein localisation. Its flagellum remains present throughout the cell cycle, offering the opportunity to monitor flagellum maintenance and assembly within the same cell. This model organism is very convenient for flagellum live imaging as well as expansion microscopy and ultrastructural studies, including focused ion beam - scanning electron microscopy (FIB-SEM). Efficient tools exist to manipulate the genome, including endogenous tagging, inducible expression system, RNA interference and CRISPR-Cas9 approaches. Here, we review original contributions from studies in trypanosome to our understanding of flagellum construction and intraflagellar transport, as well as the impact of gene mutations in some ciliopathies.

Flagella↗

PubMed2001

Atomic Force Microscopy and Scanning Electron Microscopy Reveal Genome-dependent Ultrastructure of Seed Surface.

Both scanning electron microscopy (SEM) and contact mode imaging via atomic force microscopy (AFM) have been utilized to elucidate the ultrastructure of mung bean seed surfaces. The results indicate: 1) that AFM is useful in the examination of seed surface ultrastructure ex-vaccuo without the need for additional complex preparative procedures; and 2) that both the cotyledon and seed coat of different strains of mung beans bear specific ultrastructural details unique to each strain. To our knowledge, these are the first AFM images of seed surfaces.

Journal Article↗

PubMed2025

Hybridization as driving force for cryptic species diversity in the Caribbean coral genus Madracis.

Species boundaries in scleractinian corals remain highly elusive due to conflicting patterns between morphological and molecular phylogenies, often caused by morphological plasticity, occurrence of cryptic species, incomplete lineage sorting or introgressive hybridization. Here, we use an integrated systematics approach, which combines reduced representation genome sequencing (nextRAD), micro-morphometric characterization, SEM analyses and compilation of life history traits, to infer phylogenetic relationships among closely related species in the Caribbean coral genus Madracis. In total, we analyzed 235 Madracis specimens from Curaçao and Bermuda collected from 10-90 m depth. Sequence- and SNP-based analyses for 115 samples generated unprecedented species resolution in Madracis, greatly supporting the morphology-based taxonomy of the current, accepted Caribbean species M. senaria, M. decactis, M. formosa, M. carmabi and M. mirabilis (M. auretenra). The exception was M. pharensis, in which we found evidence for three separate lineages, and for which we found signatures of admixture and introgression. These three M. pharensis lineages showed distinct depth distributions (thus classified as shallow, deep and very deep) and were partially distinguishable on the basis of fine microstructural elements of the collumella, septa and coenosteum. Further taxonomic comparisons are needed to formalize these putative cryptic species. Overall, our integrated systematics approach further resolves species relationships in the Caribbean genus Madracis, supports the morphological descriptions for most of the recognized species, but also reveals the existence of cryptic diversity in groups marked by high admixture, thus suggesting hybridization as a driving force in coral species diversity.

Animals↗