HGG Advances最新文献

Despite considerable advances in identifying risk factors for obesity, gaps remain in our understanding about its etiology. Genetic variants explain only a small portion of variation in obesity-related traits such as body mass index (BMI). Epigenetic regulation, which controls gene expression and is influenced by environmental and genetic factors, may account for additional variability in BMI. Epigenetic studies of BMI have largely been conducted in European ancestry populations, despite the disproportionate burden of obesity in African Americans (AAs). We conducted a sex-stratified BMI epigenome-wide association study meta-analysis in AA participants from the Jackson Heart Study (n = 1,604) and the Multi-Ethnic Study of Atherosclerosis (n = 179) with Illumina EPIC (850,000) array data. Linear regression models with methylation as the outcome and continuous BMI as the predictor were stratified by study and sex and meta-analyzed. We identified 208 methylation sites (CpGs, p < 8.72 × 10^-8) significantly associated with BMI; 151 had not been previously reported in the literature. Replication was performed in a separate sample of AA participants with 450,000 array data, which lacks many CpGs present in the 850,000 array. Replication testing was possible for only 29 of the 151 CpGs; 19 were statistically significant (p < 1.72 × 10^-3). Sex-specific results showed 4 female-only and 3 male-only BMI-CpGs not identified in the sex-combined results. Differentially methylated region (DMR) analysis resulted in 66 DMRs, including several regions near genes previously implicated for obesity (e.g., SOCS3, TGFB1). Further analyses showed enrichment of genes and traits related to the immune system and inflammation-related pathways (e.g., the IL-6/JAK/STAT pathway).

Asymptomatic individuals with pathogenic variants in OTC, the gene encoding ornithine transcarbamylase are increasingly being identified through cascade testing, carrier screening, or as secondary findings from genome-wide sequencing tests. However, guidance for counseling and management of such individuals is currently lacking. We selected two common OTC variants for phenotypic and functional characterization: NM_000531.6:c.118C>T p.(Arg40Cys) and NM_000531.6:c.1061T>G p.(Phe354Cys). The former is the most frequently reported pathogenic/likely pathogenic missense variant present in gnomAD, and the latter has been frequently encountered in our clinical practice. We performed a retrospective chart review at our center, queried the database of the Urea Cycle Disorders Consortium, and performed a literature review to create cohorts of individuals with these variants. Functional studies were pursued using a validated yeast-based assay. We identified 14 individuals (6 females, 8 males) with the p.(Arg40Cys) variant and 14 individuals (5 females, 9 males) with the p.(Phe354Cys) variant. There were no reported episodes of neonatal hyperammonemia in males and no hyperammonemic events reported in females with either variant. In our functional assay, both variants reduced yeast growth to the hypomorphic range. Our findings support the classification of both p.(Arg40Cys) and p.(Phe354Cys) variants in OTC as hypomorphic variants that are typically associated with late-onset OTCD in males.

Heart failure (HF) is a common cardiovascular syndrome that poses significant morbidity and mortality risks. While genome-wide association studies reporting on HF abound, its genetic etiology remains poorly elucidated, primarily due to its inherent polygenic nature. Furthermore, these genetic insights have not been fully leveraged to develop effective primary treatment strategies for HF. In this study, we conducted a large-scale integrated multi-trait analysis using European ancestry genome-wide association study summary statistics of coronary artery disease and HF, involving nearly 2 million samples to identify risk loci associated with HF. Seventy-two loci were identified for HF using MTAG, of which 58 were supported in the replication phase. Transcriptome association analysis revealed 215 HF risk genes, including EDNRA and FURIN. Pathway enrichment analysis of risk genes revealed their enrichment in pathways closely related to HF, such as response to endogenous stimulus (adjusted p = 8.83 × 10^-3), phosphate-containing compound metabolic process (adjusted p = 1.91 × 10^-2), myofibroblast differentiation (adjusted p = 4.26 × 10^-2), and regulation of muscle adaptation (adjusted p = 4.96 × 10^-2). Single-cell analysis indicated significant enrichments of these genes in smooth muscle cells, fibroblasts of cardiac tissue, and cardiac endothelial cells. Additionally, our analysis of HF risk genes identified 81 potential drugs for further pharmacological evaluation. These findings provide insights into the genetic determinants of HF, highlighting MTAG-identified genetic loci as potential interventional targets for HF treatment, with significant implications for public health and clinical practice.

Oxidative stress has been implicated in Parkinson disease (PD). Genes involved in PD, such as PRKN, PINK1, and PARK7, contribute to oxidative stress in dopaminergic neurons. The X-linked G6PD gene encodes glucose 6-phosphate dehydrogenase, an important regulator of oxidative stress. Recent studies suggested that alpha-synuclein aggregates may impair G6PD activity and contribute to dopaminergic neuron loss, and that G6PD mutations may independently increase the risk of PD. In this study, we aimed to examine the role of common and rare G6PD variants in PD across 6 cohorts, including 8,905 PD cases, 16,770 proxy cases, and 394,098 controls. These cohorts were analyzed after stratification by sex and then combined to account for the G6PD X-linked location. Using logistic regression, we did not identify significant associations for common variants in any of the cohorts. The optimized sequence Kernel association (SKAT-O) test was performed to assess the effect of rare variants (minor allele frequency <0.01) across six cohorts, followed by a meta-analysis using metaSKAT, also demonstrating lack of association. In conclusion, we did not find evidence for a role for G6PD in PD.

Large language models (LLMs) perform well on general medical benchmarks, but their ability to reason about rare diseases (RDs) remains unclear. Rather than challenge LLMs to diagnose a limited number of cases that are unlikely to represent all RDs or RD-associated genes, we instead sought to comprehensively probe LLM understanding of RD-associated genes and phenotypes. We systematically evaluated six leading general-domain LLMs (GPT-4, Claude 3.7, Llama-3.3 70B, Gemma-2 27B, Llama-3.2, and Phi-4) for their ability to generate core phenotypic features and causal genes required to support reasoning for 10,892 Orphanet diseases. Outputs were mapped to Human Phenotype Ontology (HPO) terms and HGNC gene symbols and compared with curated references using set overlap, semantic similarity, and disease ranking via the likelihood ratio interpretation of clinical abnormality (LIRICAL) framework applied to 8,000 patient Phenopackets. LLM recall of curated RD knowledge was generally low, with gene associations retrieved more accurately than phenotypes. Commercial models, particularly GPT-4 and Claude, achieved over 60% recall for gene associations but struggled with precise phenotype recovery. Despite low exact overlaps, moderate semantic similarity scores indicated partial alignment with curated data. When used in LIRICAL, LLM-derived phenotypic profiles yielded ranking performance close to that of gold standard profiles, although direct diagnostic accuracy remained limited. Interestingly, convergent non-curated terms across models suggest potential for hypothesis generation. Current generalist LLMs lack the precision to replace curated RD knowledge bases but offer complementary, semantically relevant information. Our results support hybrid approaches that combine expert curation with selectively integrated LLM outputs to enhance and scale ontology-driven RD diagnostics.

Accurate inference of genetic ancestry is a fundamental step in population genetics, disease association studies, and understanding human history. However, most existing tools, whether model-based or model-free, are limited by dataset-specific characteristics, which restrict reproducibility and hinder cross-study comparisons. Additionally, these tools often struggle to resolve fine-scale population structure, requiring multiple processing steps, such as sample subsetting and repeated program execution. These practices introduce bias and reduce replicability, particularly in evolutionary and migration studies. We present GrafAnc, a robust tool for inferring ancestry at both continental and subcontinental levels without requiring dataset partitioning, iterative processing, or manual sample curation. Building upon and extending GRAF-pop, GrafAnc infers an individual's ancestry background by comparing genotypes with allele frequencies from 26 reference populations compiled from publicly available databases. The current version of GrafAnc generates 18 ancestry scores per individual and classifies individuals into 8 continental and 38 subcontinental ancestry groups, including Middle East and North Africa. These scores are invariant to the specific composition of the study dataset and can be used directly as continuous covariates or for ancestry group assignments. GrafAnc enables seamless integration of population structure across studies and datasets, facilitating consistent interpretation in large-scale genomics. We benchmark GrafAnc using the 1000 Genomes Project, UK Biobank, and Human Genome Diversity Project datasets, demonstrating its accuracy and robustness across diverse ancestries and genotyping platforms. GrafAnc is implemented in C++ with multithreading support and is freely available.

5-Methylcytosine (5mC) is the most common DNA modification in the human genome. Bisulfite conversion combined with short-read sequencing captures this modification at single-nucleotide resolution but introduces PCR duplication bias and limits co-methylation analysis between distant cytosines. To resolve these limitations, we used nanopore long-read sequencing to profile human methylation and performed long-range co-methylation analysis with native DNA modification information. We analyzed the nanopore demo data in the adaptive sampling sequencing targeting the CpG islands and applied the linkage disequilibrium (LD) R² to identified methylation haplotype blocks (MHBs). We found that the cancer genome exhibited significantly smaller MHBs, higher CpG density, and a lower methylation LD R² value compared to normal cells. Additionally, we demonstrated the superiority of long-read sequencing in capturing large MHBs compared with short-read sequencing. By profiling the methylation changes near the JASPAR motif and actual chromatin immunoprecipitation sequencing (ChIP-seq) peaks, we also studied the epigenetic changes related to protein binding. Based on adaptive sampling technology, we conducted nanopore sequencing targeting regions with methylation quantitative trait loci (mQTLs) and genome-wide association study (GWAS) risk variants in the 22Rv1 cell line. After analyses, we inspected the closest haplotype-specific methylated region near the variant and identified allele-specific methylated regions with allele-specific accessibility signals in the ATAC-seq data. This study demonstrates the feasibility of nanopore sequencing for methylome profiling while preserving haplotype information, offering an innovative approach to elucidate the epigenetic changes driven by noncoding variants in the human genome.

Rare diseases (RDs) collectively affect >400 million people worldwide, but fragmented infrastructure and biospecimen scarcity impede progress. China's heterogeneous healthcare landscape magnifies these challenges. Since 2016, Peking Union Medical College Hospital (PUMCH) RD Biobank has pioneered a scalable model integrating 446 institutions via national networks, and setup Quality Management System since 2019 with the ISO 20387:2018, that received accreditation in 2022. Our secure digital platform standardizes biospecimen protocols (acquisition/processing/storage) and enables ethical data/specimen sharing through auditable Material Transfer Agreements/Data Use Agreements. Among 49,759 enrolled patients, 73.13% were diagnosed, while 26.87% were undiagnosed, with pediatric cases (50.75%) and males (52.39%) predominating. Phenotypic analysis showed 78.51% single-system versus 21.20% multisystem involvement. Top diagnoses included congenital scoliosis and progressive muscular dystrophy. Specimen diversity revealed system-specific patterns: musculoskeletal/nervous/sensory systems linked to multiple specimen types; immune/genitourinary to fluids; cardiovascular/neoplastic to derivatives; endocrine uniquely to tissues. Nucleic acids (93.4%) and blood specimens (21.4%) formed core resources, while induced pluripotent stem cells/organoids prioritized for cardiovascular/neoplastic RDs enable functional validation. This framework transcends biospecimen fragmentation by uniting clinical, molecular, and institutional dimensions. It demonstrates how centralized governance and interoperable systems can accelerate RD research globally. By transforming isolated data into collaborative discovery engines, we provide a blueprint for converting RD challenges into precision diagnostics and therapies, which are urgently needed for the millions of individuals worldwide who remain undiagnosed.

Venous thromboembolism (VTE) is a major cause of mortality, influenced by genetic and environmental factors. von Willebrand factor (VWF) mediates hemostasis by promoting platelet adhesion, and its plasma levels are associated with thrombotic risk. Although many non-coding variants in ABO are associated with VWF levels, VTE risk, and COVID-19 severity, the mechanisms underlying these associations remain unclear. In this study, we identified the ABO locus as the genomic region with the highest concentration of variants associated with VWF levels. Chromatin conformation analyses in endothelial cells revealed non-coding ABO variants (rs657152, rs9411377, rs660340, and rs505922) associated with VWF levels, VTE risk, and COVID-19 severity, located in spatial proximity to ADAMTS13. ADAMTS13 is a key regulator of VWF activity, and both ADAMTS13 and VWF play crucial roles in coagulation and thrombosis. Chromatin activation (CRISPRa) of the region near the non-coding ABO variant rs657152 increased ADAMTS13 transcription in endothelial cells, suggesting that this variant resides in a regulatory region with the potential to modulate long-range transcriptional control of ADAMTS13. Luciferase assay revealed reduced transcriptional activity driven by the rs505922-C allele in endothelial cells. These findings provide insights into the spatial organization of the ABO locus and its potential role in ADAMTS13 regulation.

Cystic kidney disease and related ciliopathies are caused by pathogenic variants in genes that commonly result in ciliary dysfunction. For a substantial number of individuals affected by those cilia-related diseases, the causative gene remains unknown. Using massively parallel sequencing, we here identified a pathogenic bi-allelic variant in the gene encoding PALS1-associated tight junction protein ([PATJ] also known as inactivation-no-afterpotential D-like, INADL) in an individual with ciliopathy. The affected fetus carried the homozygous truncating PATJ nonsense variant c.830delC (p.Pro277fsX), and presented with a syndromic phenotype mainly characterized by polycystic kidney disease and hydrocephalus. Using zebrafish (Danio rerio) as a vertebrate in vivo model organism, we could validate our patient findings and demonstrated a ciliopathy phenotype. In addition, we were able to address a hitherto not described role of Patj for cilia formation and function. Taken together, with the Crumbs cell polarity complex member PATJ, we add a new member to the large family of ciliopathy-related human disease proteins that is different from the classical ciliopathy protein classes, and may offer new perspectives for drug development.