Human Genetics最新文献

Cardiac desmosomes are specialized cell junctions responsible for cardiomyocytes mechanical coupling. Mutation in desmosomal genes cause autosomal dominant and recessive familial arrhythmogenic cardiomyopathy. Motivated by evidence that Mendelian diseases share genetic architecture with common complex traits, we assessed whether common variants in any desmosomal gene were associated with cardiac conduction traits in the general population. We analysed data of N = 4342 Cooperative Health Research in South Tyrol (CHRIS) study participants. We tested associations between genotype imputed variants covering the five desmosomal genes Desmoplakin (DSP), junction plakoglobin (JUP), plakophilin 2 (PKP2), desmoglein 2 (DSG2), and desmocollin 2 (DSC2), and P-wave, PR, QRS, and QT electrocardiographic intervals, using linear mixed models. Functional annotation and interrogation of publicly available genome-wide association study resources implicated potential connection with antisense long non-coding RNAs (lncRNAs), DNA methylation sites, and complex traits. Causality was tested via two-sample Mendelian randomization (MR) analysis and validated with functional in vitro follow-up in human induced pluripotent stem cell derived cardiomyocytes (hiPSC-CMs). DSP variant rs2744389 was associated with QRS (P = 3.5 × 10^-6), with replication in the Microisolates in South Tyrol (MICROS) study (n = 636; P = 0.010). Observing that rs2744389 was associated with DSP-AS1 antisense lncRNA but not with DSP expression in multiple Genotype-Tissue Expression (GTEx) v8 tissues, we conducted two-sample Mendelian randomization analyses that identified causal effects of DSP-AS1 on DSP expression (P = 6.33 × 10^-5; colocalization posterior probability = 0.91) and QRS (P = 0.015). In hiPSC-CMs, DSP-AS1 expression downregulation through a specific GapmerR matching sequence led to significant DSP upregulation at both mRNA and protein levels. The evidence that DSP-AS1 has a regulatory role on DSP opens the venue for further investigations on DSP-AS1's therapeutic potential for conditions caused by reduced desmoplakin production.

The human leukocyte antigen (HLA) region is a critical genetic locus associated with diverse complex traits, yet its intricate genetic architecture poses significant challenges to elucidation. Leveraging recent advances in regional heritability estimation and extensive datasets from the Million Veteran Program (MVP), we conducted a comprehensive investigation of the HLA region's genetic architecture. This involved heritability estimation and genetic correlation analyses within the HLA region across European Americans (EAs) and African Americans (AAs). Our analyses demonstrated that in EAs, the HLA region exhibited significantly greater local heritability than other genomic regions of comparable length for lipid metabolic traits (triglycerides [TG], total cholesterol [TC], high-density lipoprotein [HDL], low-density lipoprotein [LDL]), anthropometric measures (body mass index [BMI]), and suicide-related traits (suicidal ideation without suicide attempts [IDE] and suicidal thoughts and behaviors [SITB]) (false discovery rate [FDR]-adjusted empirical p-values < 0.05). Notably, this enrichment was not observed in AAs. Genetic correlation analyses revealed disparities between local HLA and genome-wide findings. EAs exhibited 16 significant local HLA correlations and 32 genome-wide correlations. Conversely, AAs displayed more significant local genetic correlations within the HLA region (14 pairs) than genome-wide (3 pairs), with two pairs (IDE-SITB, LDL-TC) concordantly significant. These findings underscore the HLA region's substantial contribution to the variance of these lipid metabolic traits, BMI, and suicide-related traits. Further investigation into the genetic mechanisms by which HLA-mediated pathways influence these phenotypes is crucial for elucidating the complex role of this region, particularly concerning lipid metabolism and suicidal behaviors.

Genome-wide genetic correlation studies have demonstrated widespread shared genetic architecture between complex traits, yet the impact of vertical pleiotropy on these genetic correlation estimates remains unclear. To address this, we propose the Horizontal and Vertical Pleiotropy (HVP) model, designed to disentangle horizontal from vertical pleiotropy effects. This approach provides unbiased genetic correlation estimates specifically attributed to horizontal pleiotropy. Through simulations, we verify that the HVP model corrects biases introduced by vertical pleiotropy-particularly the causal influence of exposure on outcomes-across various scenarios, improving the accuracy of heritability and genetic correlation estimates. Vertical pleiotropy biases genetic variances and covariances, influencing essential estimates such as SNP-based heritability and genetic correlation in traditional methods. By addressing these biases, the HVP model enhances accuracy in parameter estimation. Real data analysis shows that horizontal pleiotropy significantly contributes to genetic correlations between metabolic syndrome (MetS) and traits such as type 2 diabetes, C-reactive protein (CRP), sleep apnoea, and cholelithiasis, whereas vertical pleiotropy is more relevant between body mass index (BMI) and MetS, and MetS and cardiovascular diseases. These findings suggest that action on modifiable factors like lowering BMI may effectively reduce MetS risk, while CRP-though not causative-serves as a useful marker in risk prediction through horizontal pleiotropic genes. These results confirm the HVP model's relevance and utility in revealing the complex genetic architecture underlying traits such as metabolic syndrome, highlighting its potential to inform precision healthcare.

Rare variants in prokineticin 2 pathway genes (PROK2; PROKR2), cause isolated hypogonadotropic hypogonadism (IHH) in humans, leading to pubertal failure and infertility. In addition to reproduction, this pathway is also implicated in cardiovascular, metabolic, and inflammatory regulation. The role of naturally occurring PROK2/R2 variants in the general population remains unknown. Thus, we aimed to investigate the role of PROK2/R2 variants in the overall human health. We performed a recall-by-genotype study in rare PROK2/R2 variant carriers and non-carrier controls from a large hospital dataset [Massachusetts General Brigham Biobank (MGBB)]. All recalled participants underwent medical history, physical exam, completed detailed questionnaires and laboratory evaluation including a frequently sampled intravenous glucose tolerance test. Continuous and categorical variables were analyzed with a t-test/non-parametric Wilcoxon rank sum test and a Fisher's exact test, respectively. Twenty-five rare PROKR2 variant carriers (11 males and 14 females, mean age 45.6 years ± SD 11.7) and 24 non-carrier controls (16 males and 8 females, mean age 44.8 years ± SD 10) were recruited. Male variant carriers were more likely to seek fertility evaluation compared to non-carrier controls (p = 0.03) and carriers of the founder PROKR2 (p.L173R) variant (44% of the cohort) in both sexes were more likely to be diagnosed with lower gastrointestinal phenotypes compared to controls (p = 0.02). This novel clinical association is in line with the reported role of prokineticin 2 in intestinal smooth muscle function in preclinical models. Rare heterozygous PROK2/R2 variants contribute to known reproductive and novel gastrointestinal phenotypes within a hospital-based population cohort.

Genomics is revolutionizing medical science, offering transformative potential for the future of medicine. Advances in whole-genome sequencing have deepened our understanding of genome structure and function, paving the way for genomic medicine. The Human Genome Project has been instrumental in identifying genetic variations linked to increased disease risks, such as cancer, enabling genome-based diagnostics and personalized therapeutic strategies. Human genomics research focuses on developing precise therapies to enhance public health and address rare genetic disorders, including Spinal muscular atrophy, Duchenne muscular dystrophy, Parkinson's disease, and Huntington's disease. Cutting-edge gene-editing tools like CRISPR allow precise and targeted modifications with minimal side effects, improving treatment efficacy. By examining the interplay of genetic factors in health and disease, genomics lays the foundation for personalized medicine. This review highlights the impact of genomics on public health and its potential to reshape healthcare through innovative treatment strategies.

Hepatocellular carcinoma (HCC) is a primary liver malignancy with a dismal prognosis. This study established and validated a prognostic model based on antigen-processing and presenting machinery (APM)-related genes through Mendelian randomization and publicly available datasets. Systematic analysis revealed CXCL5, SGPP2, and GLP1R as critical prognostic biomarkers, which were subsequently integrated into a risk model. The model demonstrated significant associations with pathways linked to bile acid, fatty acid, and amino acid metabolism, alongside variations in immune cell infiltration and genomic mutations, including TTN, TP53, and MUC16. Patient stratification into high- and low-risk groups indicated that low-risk individuals exhibited reduced immune infiltration, potentially correlating with enhanced immunotherapy sensitivity. These findings offer a robust gene signature for HCC prognosis and a framework for evaluating responses to immunotherapy.

Cross-border transfer of human genetic data is a crucial prerequisite for sharing such data globally. However, given the unique nature of human genetic data, this aspiration may not be easily realized. China, a country rich in biological resources, possesses a vast wealth of human genetic data. However, due to China's domestic laws and policies, human genetic data is considered both personal information, subject to regulations like the Personal Information Protection Law, and human genetic resources, governed by the Regulations on Management of Human Genetic Resources. This dual nature necessitates a double security review of human genetic data for cross-border transfer, rendering cross-border transfer of China's human genetic data a seemingly impossible mission. However, in recent years, China has refined its security review rules for cross-border data transfer to increase transparency and, in practice, has eased review criteria, issuing numerous administrative licenses. Such a shift of stance is not fortuitous but rather a planned and purposeful reflection of China's top-level design to promote self-reliance and self-improvement in science and technology, as well as its strategic goal of joining international digital economy organizations.

Genetic causes of steroid-resistant-nephrotic-syndrome (SRNS) represent a rapidly growing number of monogenic diseases. The reported diagnostic yield of various studies applying genetic panels and exome-sequencing to diagnose SRNS is usually < 30%. We performed genome-sequencing in a cohort of Egyptian SRNS patients. We recruited 47 SRNS patients belonging to 41 unrelated families [28 males/19 females; median (range): 6 (0.5-22 years)]. We established a pipeline for genome sequencing, bioinformatics analysis, variant curation and protein modeling at the Egypt Center for Research and Regenerative Medicine (ECRRM). Disease-causing variants were detected in 27/47 patients (57.4%) belonging to 23/41 families (56.1%), including nine novel variants in NPHS1, NPHS2, COL4A3, MYO1E, NUP93, PLCE1, PODXL, SMARCAL1 and WT1. Novel variants were confirmed by Sanger sequencing and were segregated in families of affected patients. NPHS2 was the most common causative gene in 8/23 (34.8%) of confirmed families, followed by NPHS1, WT1, and SMARCAL1 in 2/23 families (8.7%) each. All detected missense variants were evaluated through protein modeling and were predicted deleterious. Our study expanded the spectrum of SRNS disease-causing variants and revealed a monogenic cause in 56.1% of investigated families. In our cohort, no deep intronic or regulatory variants were detected by genome-sequencing. Pursuing genetic diagnosis in SRNS patients is crucial to inform clinical decision making, genetic counseling, transplantation strategy and prenatal diagnosis thus improving clinical outcome of affected patients.

Discovering cancer driver genes is critical for improving survival rates. Current methods often overlook the varying functional impacts of mutations. It is necessary to develop a method integrating mutation pathogenicity and gene expression data, enhancing the identification of novel cancer drivers. To predict cancer drivers, we have developed a framework (DGAT-cancer) that integrates the pathogenicity of somatic mutation in tumors and germline variants in the healthy population, with topological networks of gene expression in tumors, and the gene expressions in tumor and paracancerous tissues. This integration overcomes the limitations of current methods that assume a uniform impact of all mutations by leveraging a comprehensive view of mutation function within its biological context. These features were filtered by an unsupervised approach, Laplacian selection, and combined by Hotelling and Box-Cox transformations to score genes. By using gene scores as weights, Gibbs sampling was performed to identify cancer drivers. DGAT-cancer was applied to seven types of cancer cohorts, and achieved the best area under the precision-recall curve (AUPRC ranging from 0.646 to 0.862) compared to five commonly used methods (AUPRC ranging from 0.357 to 0.629). DGAT-cancer has identified 505 cancer drivers. Knockdown of the top ranked gene, EEF1A1 indicated a ~ 41-50% decrease in glioma size and improved the temozolomide sensitivity of glioma cells. By combining heterogeneous genomics and transcriptomics data, DGAT-cancer has significantly improved our ability to detect novel cancer drivers, and is an innovative approach revealing cancer therapeutic targets, thereby advancing the development of more precise and effective cancer treatments.