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Abstract Background Structural variation (SV) detection methods using third-generation sequencing data are widely employed, yet accurately detecting SVs remains challenging. Different methods often yield inconsistent results for certain SV types, complicating tool selection and revealing biases in detection. Results This study comprehensively evaluates 53 SV detection pipelines using simulated and real data from PacBio (CLR: Continuous Long Read, CCS: Circular Consensus Sequencing) and Nanopore (ONT) platforms. We assess their performance in detecting various sizes and types of SVs, breakpoint biases, and genotyping accuracy with various sequencing depths. Notably, pipelines such as Minimap2-cuteSV2, NGMLR-SVIM, PBMM2-pbsv, Winnowmap-Sniffles2, and Winnowmap-SVision exhibit comparatively higher recall and precision. Our findings also show that combining multiple pipelines with the same aligner, like pbmm2 or winnowmap, can significantly enhance performance. The individual pipelines’ detailed ranking and performance metrics can be viewed in a dynamic table: http://pmglab.top/SVPipelinesRanking . Conclusions This study comprehensively characterizes the strengths and weaknesses of numerous pipelines, providing valuable insights that can improve SV detection in third-generation sequencing data and inform SV annotation and function prediction.

Abstract Background The high mutation rate throughout the entire melanoma genome presents a major challenge in stratifying true driver events from the background mutations. Numerous recurrent non-coding alterations, such as those in enhancers, can shape tumor evolution, thereby emphasizing the importance in systematically deciphering enhancer disruptions in melanoma. Results Here, we leveraged 297 melanoma whole-genome sequencing samples to prioritize highly recurrent regions. By performing a genome-scale CRISPR interference (CRISPRi) screen on highly recurrent region-associated enhancers in melanoma cells, we identified 66 significant hits which could have tumor-suppressive roles. These functional enhancers show unique mutational patterns independent of classical significantly mutated genes in melanoma. Target gene analysis for the essential enhancers reveal many known and hidden mechanisms underlying melanoma growth. Utilizing extensive functional validation experiments, we demonstrate that a super enhancer element could modulate melanoma cell proliferation by targeting MEF2A, and another distal enhancer is able to sustain PTEN tumor-suppressive potential via long-range interactions. Conclusions Our study establishes a catalogue of crucial enhancers and their target genes in melanoma growth and progression, and illuminates the identification of novel mechanisms of dysregulation for melanoma driver genes and new therapeutic targeting strategies.

Abstract Background Oxidative stress (OS) is a key pathophysiological mechanism in Crohn’s disease (CD). OS-related genes can be affected by environmental factors, intestinal inflammation, gut microbiota, and epigenetic changes. However, the role of OS as a potential CD etiological factor or triggering factor is unknown, as differentially expressed OS genes in CD can be either a cause or a subsequent change of intestinal inflammation. Herein, we used a multi-omics summary data-based Mendelian randomization (SMR) approach to identify putative causal effects and underlying mechanisms of OS genes in CD. Methods OS-related genes were extracted from the GeneCards database. Intestinal transcriptome datasets were collected from the Gene Expression Omnibus (GEO) database and meta-analyzed to identify differentially expressed genes (DEGs) related to OS in CD. Integration analyses of the largest CD genome-wide association study (GWAS) summaries with expression quantitative trait loci (eQTLs) and DNA methylation QTLs (mQTLs) from the blood were performed using SMR methods to prioritize putative blood OS genes and their regulatory elements associated with CD risk. Up-to-date intestinal eQTLs and fecal microbial QTLs (mbQTLs) were integrated to uncover potential interactions between host OS gene expression and gut microbiota through SMR and colocalization analysis. Two additional Mendelian randomization (MR) methods were used as sensitivity analyses. Putative results were validated in an independent multi-omics cohort from the First Affiliated Hospital of Sun Yat-sen University (FAH-SYS). Results A meta-analysis from six datasets identified 438 OS-related DEGs enriched in intestinal enterocytes in CD from 817 OS-related genes. Five genes from blood tissue were prioritized as candidate CD-causal genes using three-step SMR methods: BAD, SHC1, STAT3, MUC1, and GPX3. Furthermore, SMR analysis also identified five putative intestinal genes, three of which were involved in gene–microbiota interactions through colocalization analysis: MUC1, CD40, and PRKAB1. Validation results showed that 88.79% of DEGs were replicated in the FAH-SYS cohort. Associations between pairs of MUC1–Bacillus aciditolerans and PRKAB1–Escherichia coli in the FAH-SYS cohort were consistent with eQTL–mbQTL colocalization. Conclusions This multi-omics integration study highlighted that OS genes causal to CD are regulated by DNA methylation and host-microbiota interactions. This provides evidence for future targeted functional research aimed at developing suitable therapeutic interventions and disease prevention.

Abstract Background The variation in the rate at which humans age may be rooted in early events acting through the genomic regions that are influenced by such events and subsequently are related to health phenotypes in later life. The parent-of-origin-effect (POE)-regulated methylome includes regions enriched for genetically controlled imprinting effects (the typical type of POE) and regions influenced by environmental effects associated with parents (the atypical POE). This part of the methylome is heavily influenced by early events, making it a potential route connecting early exposures, the epigenome, and aging. We aim to test the association of POE-CpGs with early and later exposures and subsequently with health-related phenotypes and adult aging. Results We perform a phenome-wide association analysis for the POE-influenced methylome using GS:SFHS (N discovery = 5087, N replication = 4450). We identify and replicate 92 POE-CpG-phenotype associations. Most of the associations are contributed by the POE-CpGs belonging to the atypical class where the most strongly enriched associations are with aging (DNAmTL acceleration), intelligence, and parental (maternal) smoking exposure phenotypes. A proportion of the atypical POE-CpGs form co-methylation networks (modules) which are associated with these phenotypes, with one of the aging-associated modules displaying increased within-module methylation connectivity with age. The atypical POE-CpGs also display high levels of methylation heterogeneity, fast information loss with age, and a strong correlation with CpGs contained within epigenetic clocks. Conclusions These results identify the association between the atypical POE-influenced methylome and aging and provide new evidence for the “early development of origin” hypothesis for aging in humans.

Abstract Whole -genome sequencing projects of millions of subjects contain enormous genotypes, entailing a huge memory burden and time for computation. Here, we present GBC, a toolkit for rapidly compressing large-scale genotypes into highly addressable byte-encoding blocks under an optimized parallel framework. We demonstrate that GBC is up to 1000 times faster than state-of-the-art methods to access and manage compressed large-scale genotypes while maintaining a competitive compression ratio. We also showed that conventional analysis would be substantially sped up if built on GBC to access genotypes of a large population. GBC’s data structure and algorithms are valuable for accelerating large-scale genomic research.

Abstract Background Due to large genetic and phenotypic heterogeneity, the conventional workup for Charcot-Marie-Tooth (CMT) diagnosis is often underpowered, leading to diagnostic delay or even lack of diagnosis. In the present study, we explored how bioinformatics analysis on whole-exome sequencing (WES) data can be used to diagnose patients with CMT disease efficiently. Case presentation The proband is a 29-year-old female presented with a severe amyotrophy and distal skeletal deformity that plagued her family for over 20 years since she was 5-year-old. No other aberrant symptoms were detected in her speaking, hearing, vision, and intelligence. Similar symptoms manifested in her younger brother, while her parents and her older brother showed normal. To uncover the genetic causes of this disease, we performed exome sequencing for the proband and her parents. Subsequent bioinformatics analysis on the KGGSeq platform and further Sanger sequencing identified a novel homozygous GDAP1 nonsense mutation (c.218C > G, p.Ser73*) that responsible for the family. This genetic finding then led to a quick diagnosis of CMT type 4A (CMT4A), confirmed by nerve conduction velocity and electromyography examination of the patients. Conclusions The patients with severe muscle atrophy and distal skeletal deformity were caused by a novel homozygous nonsense mutation in GDAP1 (c.218C > G, p.Ser73*), and were diagnosed as CMT4A finally. This study expanded the mutation spectrum of CMT disease and demonstrated how affordable WES could be effectively employed for the clinical diagnosis of unexplained phenotypes.

Abstract Background Recent genome-wide association studies (GWASs) have suggested several susceptibility loci of hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) by statistical analysis at individual single-nucleotide polymorphisms (SNPs). However, these loci only explain a small fraction of HBV-related HCC heritability. In the present study, we aimed to identify additional susceptibility loci of HBV-related HCC using advanced knowledge-based analysis. Methods We performed knowledge-based analysis (including gene- and gene-set-based association tests) on variant-level association p-values from two existing GWASs of HBV-related HCC. Five different types of gene-sets were collected for the association analysis. A number of SNPs within the gene prioritized by the knowledge-based association tests were selected to replicate genetic associations in an independent sample of 965 cases and 923 controls. Results The gene-based association analysis detected four genes significantly or suggestively associated with HBV-related HCC risk: SLC39A8, GOLGA8M, SMIM31, and WHAMMP2. The gene-set-based association analysis prioritized two promising gene sets for HCC, cell cycle G1/S transition and NOTCH1 intracellular domain regulates transcription. Within the gene sets, three promising candidate genes (CDC45, NCOR1 and KAT2A) were further prioritized for HCC. Among genes of liver-specific expression, multiple genes previously implicated in HCC were also highlighted. However, probably due to small sample size, none of the genes prioritized by the knowledge-based association analyses were successfully replicated by variant-level association test in the independent sample. Conclusions This comprehensive knowledge-based association mining study suggested several promising genes and gene-sets associated with HBV-related HCC risks, which would facilitate follow-up functional studies on the pathogenic mechanism of HCC.

Abstract The driver tissues or cell types in which susceptibility genes initiate diseases remain elusive. We develop a unified framework to detect the causal tissues of complex diseases or traits according to selective expression of disease-associated genes in genome-wide association studies (GWASs). This framework consists of three components which run iteratively to produce a converged prioritization list of driver tissues. Additionally, this framework also outputs a list of prioritized genes as a byproduct. We apply the framework to six representative complex diseases or traits with GWAS summary statistics, which leads to the estimation of the lung as an associated tissue of rheumatoid arthritis.

Abstract The clinical diagnosis of neurodegenerative disorders based on phenotype is difficult in heterogeneous conditions with overlapping symptoms. It does not take into account the disease etiology or the highly variable clinical course even amongst patients diagnosed with the same disorder. The advent of next generation sequencing (NGS) has allowed for a system-wide, unbiased approach to identify all gene variants in the genome simultaneously. With the plethora of new genes being identified, genetic rather than phenotype-based classification of Mendelian diseases such as spinocerebellar ataxia (SCA), hereditary spastic paraplegia (HSP) and Charcot-Marie-Tooth disease (CMT) has become widely accepted. It has also become clear that gene variants play a role in common and predominantly sporadic neurodegenerative diseases such as Parkinson’s disease (PD) and amyotrophic lateral sclerosis (ALS). The observation of pleiotropy has emerged, with mutations in the same gene giving rise to diverse phenotypes, which further increases the complexity of phenotype-genotype correlation. Possible mechanisms of pleiotropy include different downstream effects of different mutations in the same gene, presence of modifier genes, and oligogenic inheritance. Future directions include development of bioinformatics tools and establishment of more extensive public genotype/phenotype databases to better distinguish deleterious gene variants from benign polymorphisms, translation of genetic findings into pathogenic mechanisms through in-vitro and in-vivo studies, and ultimately finding disease-modifying therapies for neurodegenerative disorders.

Abstract Clinical treatment of hepatocellular carcinoma (HCC) with 5-fluorouracil (5-FU), the primary anticancer agent, remains unsatisfactory due to the glutathione (GSH)-associated drug resistance and immunosuppressive microenvironment of HCC. To develop a facile yet robust strategy to overcome 5-FU resistance for enhanced immunotherapy treatment of HCC via all dimensional GSH exhaustion, we report in this study construction of a minimalist prodrug consisting of 5-FU linked to an indoleamine-(2,3)-dioxygenase (IDO) inhibitor (IND) via a disulfide bridge, FU-SS-IND that can further self-assemble into stabilized nanoparticles, FU-SS-IND NPs. Specifically, besides the disulfide linker-induced GSH exhaustion, IND inhibits GSH biosynthesis and enhances the effector function of T cells for turning a “cold” tumor to a “hot” one, which synergistically achieving a tumor inhibition rate (TIR) of 92.5% in a 5-FU resistant mice model. Most importantly, FU-SS-IND NPs could upregulate programmed death ligand 1 (PD-L1) expression on the surface of tumor cells, which enables facile combination with immune checkpoint blockade (ICB) for a ultimate prolonged survival lifetime of 5-FU-resistant tumors-bearing mice. Overall, the minimalist bioreducible nano-prodrug developed herein demonstrates great translatable potential for efficiently reversing drug resistance and enhancing immunotherapy of HCC.

Abstract Background Air pollution is a major environmental risk factor. Hypertension is one of the most important modifiable risk factors for cardiovascular diseases. However, few studies have evaluated the impact of exposure to ambient air pollution on hypertension hospitalizations. Objective This study aims to explore the correlation between exposure to air pollution and hospital admissions for hypertension, to evaluate the short-term effects of air pollution on hypertension hospitalizations and its lag effects. Methods We collected air pollution concentration and hypertension hospitalization data from 2013 to 2020. Distributed lag non-linear models were employed to assess the impact of air pollution on hypertension hospitalizations in Lanzhou City. We also performed subgroup analyses and sensitivity analyses. Results A total of 47,884 cases of hypertension hospitalizations were included. Short-term exposure to NO2 and CO increased the risk of hypertension hospitalization. For each 10 µg/m3 increase in NO2 and each 1 mg/m3 increase in CO, the relative risk (RR) for hypertension hospitalization were highest at lag0-3 (RR: 1.0427; 95% CI: 1.0196, 1.0663) and lag3 (RR: 1.0986; 95% CI: 1.0350, 1.1661), respectively. SO2 at different lag structures showed opposite effects on hypertension hospitalization. O3 was significantly negatively associated with hypertension hospitalization. No correlation was observed between PM2.5 and PM10 and hypertension hospitalization. Females and individuals aged ≥ 65 were more susceptible to air pollution. The harmful effects of air pollution are greater in the cold season. Conclusion Short-term exposure to ambient air pollution increases risk of hypertension hospitalization.

Abstract Background With the backdrop of global climate change, the impact of climate change on respiratory diseases like asthma is receiving increasing attention. However, the effects of temperature and diurnal temperature range (DTR) on asthma are complex, and understanding these effects across different seasons, age groups, and sex is of utmost importance. Methods This study utilized asthma hospitalization data from Lanzhou, China, and implemented a distributed lag nonlinear model (DLNM) to investigate the relationship between temperature and DTR and asthma hospitalizations. It considered differences in the effects across various seasons and population subgroups. Results The study revealed that low temperatures immediately increase the risk of asthma hospitalization (RR = 1.2010, 95% CI: 1.1464, 1.2580), and this risk persists for a period of time. Meanwhile, both high and low DTR were associated with an increased risk of asthma hospitalization. Lower temperatures (RR = 2.9798, 95% CI: 1.1154, 7.9606) were associated with higher asthma risk in the warm season, while in the cold season, the risk significantly rose for the general population (RR = 3.6867, 95% CI: 1.7494, 7.7696), females (RR = 7.2417, 95% CI: 2.7171, 19.3003), and older individuals (RR = 18.5425, 95% CI: 5.1436, 66.8458). In the warm season, low DTR conditions exhibited a significant association with asthma hospitalization risk in males (RR = 7.2547, 95% CI: 1.2612, 41.7295) and adults aged 15–64 (RR = 9.9494, 95% CI: 2.2723, 43.5643). Children also exhibited noticeable risk within specific DTR ranges. In the cold season, lower DTR increases the risk of asthma hospitalization for the general population (RR = 3.1257, 95% CI: 1.4004, 6.9767). High DTR significantly increases the risk of asthma hospitalization in adults (RR = 5.2563, 95% CI: 2.4131, 11.4498). Conclusion This study provides crucial insights into the complex relationship between temperature, DTR, and asthma hospitalization, highlighting the variations in asthma risk across different seasons and population subgroups.

Abstract Background Angina is a crucial risk signal for cardiovascular disease. However, few studies have evaluated the effects of ambient air pollution exposure on angina. Objective We aimed to explore the short-term effects of air pollution on hospitalization for angina and its lag effects. Methods We collected data on air pollutant concentrations and angina hospitalizations from 2013 to 2020. Distributed lag nonlinear model (DLNM) was used to evaluate the short-term effects of air pollutants on angina hospitalization under different lag structures. Stratified analysis by sex, age and season was obtained. Results A total of 39,110 cases of angina hospitalization were included in the study. The results showed a significant positive correlation between PM2.5, SO2, NO2, and CO and angina hospitalization. Their maximum harmful effects were observed at lag0-7 (RR = 1.042; 95% CI: 1.017, 1.068), lag0-3 (RR = 1.067; 95% CI: 1.005, 1.133), lag0-6 (RR = 1.078; 95% CI: 1.041, 1.117), and lag0-6 (RR = 1.244; 95% CI: 1.109, 1.397), respectively. PM10 did not have an overall risk effect on angina hospitalization, but it did have a risk effect on women and the elderly. O3 was significantly negatively correlated with angina hospitalization, with the most pronounced effect observed at lag0-6 (RR = 0.960; 95% CI: 0.940, 0.982). Stratified analysis results showed that women and the elderly were more susceptible to pollutants, and the adverse effects of pollutants were stronger in the cold season. Conclusion Short-term exposure to PM2.5, SO2, NO2, and CO increases the risk of hospitalization for angina.