Exome-wide association study identifies KDELR3 mutations in extreme myopia.

Extreme myopia (EM), defined as a spherical equivalent (SE) ≤ -10.00 diopters (D), is one of the leading causes of sight impairment. Known EM-associated variants only explain limited risk and are inadequate for clinical decision-making. To discover risk genes, we performed a whole-exome sequencing (WES) on 449 EM individuals and 9606 controls. We find a significant excess of rare protein-truncating variants (PTVs) in EM cases, enriched in the retrograde vesicle-mediated transport pathway. Employing single-cell RNA-sequencing (scRNA-seq) and a single-cell polygenic burden score (scPBS), we pinpointed PI16 + /SFRP4+ fibroblasts as the most relevant cell type. We observed that KDELR3 is highly expressed in scleral fibroblast and involved in scleral extracellular matrix (ECM) organization. The zebrafish model revealed that kdelr3 downregulation leads to elongated ocular axial length and increased lens diameter. Together, our study provides insight into the genetics of EM in humans and highlights KDELR3's role in EM pathogenesis.

Protocol for detecting rare and common genetic associations in whole-exome sequencing studies using MAGICpipeline.

Whole-exome sequencing (WES) is a major approach to uncovering gene-disease associations and pinpointing effector genes. Here, we present a protocol for estimating genetic associations of rare and common variants in large-scale case-control WES studies using MAGICpipeline, an open-access analysis pipeline. We describe steps for assessing gene-based rare-variant association analyses by incorporating multiple variant pathogenic annotations and statistical techniques. We then detail procedures for identifying disease-related modules and hub genes using weighted correlation network analysis, a systems biology approach. For complete details on the use and execution of this protocol, please refer to Su et al. (2023).1.

Histone 3 lysine 9 acetylation-specific reprogramming regulates esophageal squamous cell carcinoma progression and metastasis.

Dysregulation of histone acetylation is widely implicated in tumorigenesis, yet its specific roles in the progression and metastasis of esophageal squamous cell carcinoma (ESCC) remain unclear. Here, we profiled the genome-wide landscapes of H3K9ac for paired adjacent normal (Nor), primary ESCC (EC) and metastatic lymph node (LNC) esophageal tissues from three ESCC patients. Compared to H3K27ac, we identified a distinct epigenetic reprogramming specific to H3K9ac in EC and LNC samples relative to Nor samples. This H3K9ac-related reprogramming contributed to the transcriptomic aberration of targeting genes, which were functionally associated with tumorigenesis and metastasis. Notably, genes with gained H3K9ac signals in both primary and metastatic lymph node samples (common-gained gene) were significantly enriched in oncogenes. Single-cell RNA-seq analysis further revealed that the corresponding top 15 common-gained genes preferred to be enriched in mesenchymal cells with high metastatic potential. Additionally, in vitro experiment demonstrated that the removal of H3K9ac from the common-gained gene MSI1 significantly downregulated its transcription, resulting in deficiencies in ESCC cell proliferation and migration. Together, our findings revealed the distinct characteristics of H3K9ac in esophageal squamous cell carcinogenesis and metastasis, and highlighted the potential therapeutic avenue for intervening ESCC through epigenetic modulation via H3K9ac.

Integration of human organoids single-cell transcriptomic profiles and human genetics repurposes critical cell type-specific drug targets for severe COVID-19.

Human organoids recapitulate the cell type diversity and function of their primary organs holding tremendous potentials for basic and translational research. Advances in single-cell RNA sequencing (scRNA-seq) technology and genome-wide association study (GWAS) have accelerated the biological and therapeutic interpretation of trait-relevant cell types or states. Here, we constructed a computational framework to integrate atlas-level organoid scRNA-seq data, GWAS summary statistics, expression quantitative trait loci, and gene-drug interaction data for distinguishing critical cell populations and drug targets relevant to coronavirus disease 2019 (COVID-19) severity. We found that 39 cell types across eight kinds of organoids were significantly associated with COVID-19 outcomes. Notably, subset of lung mesenchymal stem cells increased proximity with fibroblasts predisposed to repair COVID-19-damaged lung tissue. Brain endothelial cell subset exhibited significant associations with severe COVID-19, and this cell subset showed a notable increase in cell-to-cell interactions with other brain cell types, including microglia. We repurposed 33 druggable genes, including IFNAR2, TYK2, and VIPR2, and their interacting drugs for COVID-19 in a cell-type-specific manner. Overall, our results showcase that host genetic determinants have cellular-specific contribution to COVID-19 severity, and identification of cell type-specific drug targets may facilitate to develop effective therapeutics for treating severe COVID-19 and its complications.

DeepGraFT: A novel semantic segmentation auxiliary ROI-based deep learning framework for effective fundus tessellation classification.

Fundus tessellation (FT) is a prevalent clinical feature associated with myopia and has implications in the development of myopic maculopathy, which causes irreversible visual impairment. Accurate classification of FT in color fundus photo can help predict the disease progression and prognosis. However, the lack of precise detection and classification tools has created an unmet medical need, underscoring the importance of exploring the clinical utility of FT. Thus, to address this gap, we introduce an automatic FT grading system (called DeepGraFT) using classification-and-segmentation co-decision models by deep learning. ConvNeXt, utilizing transfer learning from pretrained ImageNet weights, was employed for the classification algorithm, aligning with a region of interest based on the ETDRS grading system to boost performance. A segmentation model was developed to detect FT exits, complementing the classification for improved grading accuracy. The training set of DeepGraFT was from our in-house cohort (MAGIC), and the validation sets consisted of the rest part of in-house cohort and an independent public cohort (UK Biobank). DeepGraFT demonstrated a high performance in the training stage and achieved an impressive accuracy in validation phase (in-house cohort: 86.85 %; public cohort: 81.50 %). Furthermore, our findings demonstrated that DeepGraFT surpasses machine learning-based classification models in FT classification, achieving a 5.57 % increase in accuracy. Ablation analysis revealed that the introduced modules significantly enhanced classification effectiveness and elevated accuracy from 79.85 % to 86.85 %. Further analysis using the results provided by DeepGraFT unveiled a significant negative association between FT and spherical equivalent (SE) in the UK Biobank cohort. In conclusion, DeepGraFT accentuates potential benefits of the deep learning model in automating the grading of FT and allows for potential utility as a clinical-decision support tool for predicting progression of pathological myopia.

Whole-Exome Sequencing Among School-Aged Children With High Myopia.

Importance: High myopia (HM) is one of the leading causes of visual impairment worldwide. Genetic factors are known to play an important role in the development of HM. Objective: To identify risk variants in a large HM cohort and to examine the implications of genetic testing of schoolchildren with HM. Design, Setting, and Participants: This cohort study retrospectively reviewed whole-exome sequencing (WES) results in 6215 schoolchildren with HM who underwent genetic testing between September 2019 and July 2020 in Wenzhou City, China. HM is defined as a spherical equivalent refraction (SER) of -6.00 diopters (D) or less. The study setting was a genetic testing laboratory and a multicenter school census. Data were analyzed from July 2021 to June 2022. Main Outcomes and Measures: The frequency and distribution of positive germline variants, the percentage of individuals with HM in both eyes, and subsequent variant yield for common high myopia (CHM; -8.00 D ≤ SER ≤ -6.00 D), ultra myopia (UM; -10.00 D ≤ SER < -8.00 D), and extreme myopia (EM; SER < -10.00 D). Results: Of the 6215 schoolchildren with HM, 3278 (52.74%) were male. Their mean (SD) age was 14.87 (2.02) years, including 355 students in primary school, 1970 in junior high school, and 3890 in senior high school. The mean (SD) SER was -7.51 (-1.36) D for the right eye and -7.46 (-1.34) D for the left eye. Among schoolchildren with HM, genetic testing yielded 271 potential pathogenic variants in 75 HM candidate genes in 964 diagnoses (15.52%). A total of 36 known variants were found in 490 HM participants (7.88%) and 235 protein-truncating variants (PTVs) in 506 participants (8.14%). Involved variant yield was significantly positively associated with SER (Cochran-Armitage test for trend Z = 2.5492; P = .01), which ranged from 7.66% in the CHM group, 8.70% in the UM group, to 11.90% in the EM group. We also found that primary school students with EM had the highest variant yield of PTVs (8 of 35 students [22.86%]), which was 1.77 and 4.78 times that of the UM and CHM, respectively. Conclusions and Relevance: In this cohort study of WES for HM, several potential pathogenic variants were identified in a substantial number of schoolchildren with HM. The high variation frequency in younger students with EM can provide clues for genetic screening and clinical examinations of HM to promote long-term follow-up assessment.

Determination of genetic correlation between tobacco smoking and coronary artery disease.

Backgrounds: Tobacco smoking is an important risk factor for coronary artery disease (CAD), but the genetic mechanisms linking smoking to CAD remain largely unknown. Methods: We analyzed summary data from the genome-wide association study (GWAS) of the UK Biobank for CAD, plasma lipid concentrations (n = 184,305), and smoking (n = 337,030) using different biostatistical methods, which included LD score regression and Mendelian randomization (MR). Results: We identified SNPs shared by CAD and at least one smoking behavior, the genes where these SNPs are located were found to be significantly enriched in the processes related to lipoprotein metabolic, chylomicron-mediated lipid transport, lipid digestion, mobilization, and transport. The MR analysis revealed a positive correlation between smoking cessation and decreased risk for CAD when smoking cessation was considered as exposure (p = 0.001), and a negative correlation between the increased risk for CAD and smoking cessation when CAD was considered as exposure (p = 2.95E-08). This analysis further indicated that genetic liability for smoking cessation increased the risk of CAD. Conclusion: These findings inform the concomitant conditions of CAD and smoking and support the idea that genetic liabilities for smoking behaviors are strongly associated with the risk of CAD.

Schwann cell-derived extracellular vesicles as a potential therapy for retinal ganglion cell degeneration.

Optic neuropathy is the leading cause of irreversible blindness and is characterized by progressive degeneration of retinal ganglion cells (RGCs). Several studies have demonstrated that transplantation of Schwann cells (SCs) is a promising candidate therapy for optic neuropathy and that intravitreally transplanted cells exert their effect via paracrine actions. Extracellular vesicle (EV)-based therapies are increasingly recognized as a potential strategy for cell replacement therapy. In this study, we aimed to investigate the neuroprotective and regenerative effects of SC-EVs following optic nerve injury. We found that SC-EVs were internalized by RGCs in vitro and in vivo without any transfection reagents. Intriguingly, SC-EVs significantly enhanced the survival and axonal growth of primary RGCs in a coculture system. In a rat optic nerve crush model, SC-EVs mitigated RGC degeneration, prevented RGC loss, and preserved the thickness of the ganglion cell complex, as demonstrated by the statistically significant improvement in RGC counts and thickness measurements. Mechanistically, SC-EVs activated the cAMP-response element binding protein (CREB) signaling pathway and regulated reactive gliosis in ONC rats, which is crucial for RGC protection and axonal regeneration. These findings provide novel insights into the neuroprotective and regenerative properties of SC-EVs, suggesting their potential as a cell-free therapeutic strategy and natural biomaterials for neurodegenerative diseases of the central nervous system.

Liquid biopsies for cancer: From bench to clinic.

Over the past two decades, liquid biopsy has been increasingly used as a supplement, or even, a replacement to the traditional biopsy in clinical oncological practice, due to its noninvasive and early detectable properties. The detections can be based on a variety of features extracted from tumor­derived entities, such as quantitative alterations, genetic changes, and epigenetic aberrations, and so on. So far, the clinical applications of cancer liquid biopsy mainly aimed at two aspects, prediction (early diagnosis, prognosis and recurrent evaluation, therapeutic response monitoring, etc.) and intervention. In spite of the rapid development and great contributions achieved, cancer liquid biopsy is still a field under investigation and deserves more clinical practice. To better open up future work, here we systematically reviewed and compared the latest progress of the most widely recognized circulating components, including circulating tumor cells, cell-free circulating DNA, noncoding RNA, and nucleosomes, from their discovery histories to clinical values. According to the features applied, we particularly divided the contents into two parts, beyond epigenetics and epigenetic-based. The latter was considered as the highlight along with a brief overview of the advances in both experimental and bioinformatic approaches, due to its unique advantages and relatively lack of documentation.

Association of mitochondrial DNA variation with high myopia in a Han Chinese population.

High myopia (HM), which is characterized by oxidative stress, is one of the leading causes of visual impairment and blindness across the world. Family and population genetic studies have uncovered nuclear-genome variants in proteins functioned in the mitochondria. However, whether mitochondrial DNA mutations are involved in HM remains unexplored. Here, we performed the first large-scale whole-mitochondrial genome study in 9613 HM cases and 9606 control subjects of Han Chinese ancestry for identifying HM-associated mitochondrial variants. The single-variant association analysis identified nine novel genetic variants associated with HM reaching the entire mitochondrial wide significance level, including rs370378529 in ND2 with an odds ratio (OR) of 5.25. Interestingly, eight out of nine variants were predominantly located in related sub-haplogroups, i.e. m.5261G > A in B4b1c, m.12280A > G in G2a4, m.7912G > A in D4a3b, m.94G > A in D4e1, m.14857 T > C in D4e3, m.14280A > G in D5a2, m.16272A > G in G2a4, m.8718A > G in M71 and F1a3, indicating that the sub-haplogroup background can increase the susceptible risk for high myopia. The polygenic risk score analysis of the target and validation cohorts indicated a high accuracy for predicting HM with mtDNA variants (AUC = 0.641). Cumulatively, our findings highlight the critical roles of mitochondrial variants in untangling the genetic etiology of HM.

Sequencing of 19,219 exomes identifies a low-frequency variant in FKBP5 promoter predisposing to high myopia in a Han Chinese population.

High myopia (HM) is one of the leading causes of visual impairment and blindness worldwide. Here, we report a whole-exome sequencing (WES) study in 9,613 HM cases and 9,606 controls of Han Chinese ancestry to pinpoint HM-associated risk variants. Single-variant association analysis identified three newly identified -genetic loci associated with HM, including an East Asian ancestry-specific low-frequency variant (rs533280354) in FKBP5. Multi-ancestry meta-analysis with WES data of 2,696 HM cases and 7,186 controls of European ancestry from the UK Biobank discerned a newly identified European ancestry-specific rare variant in FOLH1. Functional experiments revealed a mechanism whereby a single G-to-A transition at rs533280354 disrupted the binding of transcription activator KLF15 to the promoter of FKBP5, resulting in decreased transcription of FKBP5. Furthermore, burden tests showed a significant excess of rare protein-truncating variants among HM cases involved in retinal blood vessel morphogenesis and neurotransmitter transport.

Genome-wide association meta-analysis of 88,250 individuals highlights pleiotropic mechanisms of five ocular diseases in UK Biobank.

BACKGROUND: Ocular diseases may exhibit common clinical symptoms and epidemiological comorbidity. However, the extent of pleiotropic mechanisms across ocular diseases remains unclear. We aim to examine shared genetic etiology in age-related macular degeneration (AMD), diabetic retinopathy (DR), glaucoma, retinal detachment (RD), and myopia. METHODS: We analyzed genome-wide association analyses for the five ocular diseases in 43,877 cases and 44,373 controls of European ancestry from UK Biobank, estimated their genetic relationships (LDSC, GNOVA, and Genomic SEM), and identified pleiotropic loci (ASSET and METASOFT). FINDINGS: The genetic correlation of common SNPs revealed a meaningful genetic structure within these diseases, identifying genetic correlations between AMD, DR, and glaucoma. Cross-trait meta-analysis identified 23 pleiotropic loci associated with at least two ocular diseases and 14 loci unique to individual disorders (non-pleiotropic). We found that the genes associated with these shared genetic loci are involved in neuron differentiation (P = 8.80 × 10-6) and eye development systems (P = 3.86 × 10-5), and single cell RNA sequencing data reveals their heightened gene expression from multipotent progenitors to other differentiated retinal cells during retina developmental process. INTERPRETATION: These results highlighted the potential common genetic architectures among these ocular diseases and can deepen the understanding of the molecular mechanisms underlying the related diseases. FUNDING: The National Natural Science Foundation of China (61871294), Zhejiang Provincial Natural Science Foundation of China (LR19C060001), and the Scientific Research Foundation for Talents of Wenzhou Medical University (QTJ18023).

Integrating single-cell sequencing data with GWAS summary statistics reveals CD16+monocytes and memory CD8+T cells involved in severe COVID-19.

BACKGROUND: Understanding the host genetic architecture and viral immunity contributes to the development of effective vaccines and therapeutics for controlling the COVID-19 pandemic. Alterations of immune responses in peripheral blood mononuclear cells play a crucial role in the detrimental progression of COVID-19. However, the effects of host genetic factors on immune responses for severe COVID-19 remain largely unknown. METHODS: We constructed a computational framework to characterize the host genetics that influence immune cell subpopulations for severe COVID-19 by integrating GWAS summary statistics (N = 969,689 samples) with four independent scRNA-seq datasets containing healthy controls and patients with mild, moderate, and severe symptom (N = 606,534 cells). We collected 10 predefined gene sets including inflammatory and cytokine genes to calculate cell state score for evaluating the immunological features of individual immune cells. RESULTS: We found that 34 risk genes were significantly associated with severe COVID-19, and the number of highly expressed genes increased with the severity of COVID-19. Three cell subtypes that are CD16+monocytes, megakaryocytes, and memory CD8+T cells were significantly enriched by COVID-19-related genetic association signals. Notably, three causal risk genes of CCR1, CXCR6, and ABO were highly expressed in these three cell types, respectively. CCR1+CD16+monocytes and ABO+ megakaryocytes with significantly up-regulated genes, including S100A12, S100A8, S100A9, and IFITM1, confer higher risk to the dysregulated immune response among severe patients. CXCR6+ memory CD8+ T cells exhibit a notable polyfunctionality including elevation of proliferation, migration, and chemotaxis. Moreover, we observed an increase in cell-cell interactions of both CCR1+ CD16+monocytes and CXCR6+ memory CD8+T cells in severe patients compared to normal controls among both PBMCs and lung tissues. The enhanced interactions of CXCR6+ memory CD8+T cells with epithelial cells facilitate the recruitment of this specific population of T cells to airways, promoting CD8+T cell-mediated immunity against COVID-19 infection. CONCLUSIONS: We uncover a major genetics-modulated immunological shift between mild and severe infection, including an elevated expression of genetics-risk genes, increase in inflammatory cytokines, and of functional immune cell subsets aggravating disease severity, which provides novel insights into parsing the host genetic determinants that influence peripheral immune cells in severe COVID-19.

Integrative genomics analysis reveals a 21q22.11 locus contributing risk to COVID-19.

The systematic identification of host genetic risk factors is essential for the understanding and treatment of coronavirus disease 2019 (COVID-19). By performing a meta-analysis of two independent genome-wide association summary datasets (N = 680 128), a novel locus at 21q22.11 was identified to be associated with COVID-19 infection (rs9976829 in IFNAR2-IL10RB, odds ratio = 1.16, 95% confidence interval = 1.09-1.23, P = 2.57 × 10-6). The rs9976829 represents a strong splicing quantitative trait locus for both IFNAR2 and IL10RB genes, especially in lung tissue (P = 1.8 × 10-24). Integrative genomics analysis of combining genome-wide association study with expression quantitative trait locus data showed the expression variations of IFNAR2 and IL10RB have prominent effects on COVID-19 in various types of tissues, especially in lung tissue. The majority of IFNAR2-expressing cells were dendritic cells (40%) and plasmacytoid dendritic cells (38.5%), and IL10RB-expressing cells were mainly nonclassical monocytes (29.6%). IFNAR2 and IL10RB are targeted by several interferons-related drugs. Together, our results uncover 21q22.11 as a novel susceptibility locus for COVID-19, in which individuals with G alleles of rs9976829 have a higher probability of COVID-19 susceptibility than those with non-G alleles.

Integration of summary data from GWAS and eQTL studies identified novel risk genes for coronary artery disease.

ABSTRACT: Several genetic loci have been reported to be significantly associated with coronary artery disease (CAD) by multiple genome-wide association studies (GWAS). Nevertheless, the biological and functional effects of these genetic variants on CAD remain largely equivocal. In the current study, we performed an integrative genomics analysis by integrating large-scale GWAS data (N = 459,534) and 2 independent expression quantitative trait loci (eQTL) datasets (N = 1890) to determine whether CAD-associated risk single nucleotide polymorphisms (SNPs) exert regulatory effects on gene expression. By using Sherlock Bayesian, MAGMA gene-based, multidimensional scaling (MDS), functional enrichment, and in silico permutation analyses for independent technical and biological replications, we highlighted 4 susceptible genes (CHCHD1, TUBG1, LY6G6C, and MRPS17) associated with CAD risk. Based on the protein-protein interaction (PPI) network analysis, these 4 genes were found to interact with each other. We detected a remarkably altered co-expression pattern among these 4 genes between CAD patients and controls. In addition, 3 genes of CHCHD1 (P = .0013), TUBG1 (P = .004), and LY6G6C (P = .038) showed significantly different expressions between CAD patients and controls. Together, we provide evidence to support that these identified genes such as CHCHD1 and TUBG1 are indicative factors of CAD.

Determination of shared genetic etiology and possible causal relations between tobacco smoking and depression.

BACKGROUNDS: Cigarette smoking is strongly associated with major depressive disorder (MDD). However, any genetic etiology of such comorbidity and causal relations is poorly understood, especially at the genome-wide level. METHODS: In the present in silico research, we analyzed summary data from the genome-wide association study of the Psychiatric Genetic Consortium for MDD (n = 191 005) and UK Biobank for smoking (n = 337 030) by using various biostatistical methods including Bayesian colocalization analysis, LD score regression, variant effect size correlation analysis, and Mendelian randomization (MR). RESULTS: By adopting a gene prioritization approach, we identified 43 genes shared by MDD and smoking, which were significantly enriched in membrane potential, gamma-aminobutyric acid receptor activity, and retrograde endocannabinoid signaling pathways, indicating that the comorbid mechanisms are involved in the neurotransmitter system. According to linkage disequilibrium score regression, we found a strong positive correlation between MDD and current smoking (rg = 0.365; p = 7.23 × 10-25) and a negative correlation between MDD and former smoking (rg = -0.298; p = 1.59 × 10-24). MR analysis suggested that genetic liability for depression increased smoking. CONCLUSIONS: These findings inform the concomitant conditions of MDD and smoking and support the use of self-medication with smoking to counteract depression.

Prediction of Smoking Behavior From Single Nucleotide Polymorphisms With Machine Learning Approaches.

Smoking is a complex behavior with a heritability as high as 50%. Given such a large genetic contribution, it provides an opportunity to prevent those individuals who are susceptible to smoking dependence from ever starting to smoke by predicting their inherited predisposition with their genomic profiles. Although previous studies have identified many susceptibility variants for smoking, they have limited power to predict smoking behavior. We applied the support vector machine (SVM) and random forest (RF) methods to build prediction models for smoking behavior. We first used 1,431 smokers and 1,503 non-smokers of African origin for model building with a 10-fold cross-validation and then tested the prediction models on an independent dataset consisting of 213 smokers and 224 non-smokers. The SVM model with 500 top single nucleotide polymorphisms (SNPs) selected using logistic regression (p<0.01) as the feature selection method achieved an area under the curve (AUC) of 0.691, 0.721, and 0.720 for the training, test, and independent test samples, respectively. The RF model with 500 top SNPs selected using logistic regression (p<0.01) achieved AUCs of 0.671, 0.665, and 0.667 for the training, test, and independent test samples, respectively. Finally, we used the combined logistic (p<0.01) and LASSO (λ=10-3) regression to select features and the SVM algorithm for model building. The SVM model with 500 top SNPs achieved AUCs of 0.756, 0.776, and 0.897 for the training, test, and independent test samples, respectively. We conclude that machine learning methods are promising means to build predictive models for smoking.

Genome-wide DNA methylation analysis reveals significant impact of long-term ambient air pollution exposure on biological functions related to mitochondria and immune response.

Exposure to long-term ambient air pollution is believed to have adverse effects on human health. However, the mechanisms underlying these impacts are poorly understood. DNA methylation, a crucial epigenetic modification, is susceptible to environmental factors and likely involved in these processes. We conducted a whole-genome bisulfite sequencing study on 120 participants from a highly polluted region (HPR) and a less polluted region (LPR) in China, where the HPR had much higher concentrations of five air pollutants (PM2.5, PM10, SO2, NO2, and CO) (fold difference 1.6 to 6.6 times; P value 1.80E-07 to 3.19E-23). Genome-wide methylation analysis revealed 371 DMRs in subjects from the two areas and these DMRs were located primarily in gene regulatory elements such as promoters and enhancers. Gene enrichment analysis showed that DMR-related genes were significantly enriched in diseases related to pulmonary disorders and cancers and in biological processes related to mitochondrial assembly and cytokine production. Further, HPR participants showed a higher mtDNA copy number. Of those identified DMRs, 15 were significantly correlated with mtDNA copy number. Finally, cytokine assay indicated that an increased plasma interleukin-5 level was associated with greater air pollution. Taken together, our findings suggest that exposure to long-term ambient air pollution can lead to alterations in DNA methylation whose functions relate to mitochondria and immune responses.

Identification of 34 genes conferring genetic and pharmacological risk for the comorbidity of schizophrenia and smoking behaviors.

The prevalence of smoking is significantly higher in persons with schizophrenia (SCZ) than in the general population. However, the biological mechanisms of the comorbidity of smoking and SCZ are largely unknown. This study aimed to reveal shared biological pathways for the two diseases by analyzing data from two genome-wide association studies with a total sample size of 153,898. With pathway-based analysis, we first discovered 18 significantly enriched pathways shared by SCZ and smoking, which were classified into five groups: postsynaptic density, cadherin binding, dendritic spine, long-term depression, and axon guidance. Then, by using an integrative analysis of genetic, epigenetic, and expression data, we found not only 34 critical genes (e.g., PRKCZ, ARHGEF3, and CDKN1A) but also various risk-associated SNPs in these genes, which convey susceptibility to the comorbidity of the two disorders. Finally, using both in vivo and in vitro data, we demonstrated that the expression profiles of the 34 genes were significantly altered by multiple psychotropic drugs. Together, this multi-omics study not only reveals target genes for new drugs to treat SCZ but also reveals new insights into the shared genetic vulnerabilities of SCZ and smoking behaviors.