Interactions of genes with alcohol consumption affect insulin sensitivity and beta cell function.

AIMS/HYPOTHESIS: Alcohol consumption has complex effects on diabetes and metabolic disease, but there is widespread heterogeneity within populations and the specific reasons are unclear. Genetic factors may play a role and warrant exploration. The aim of this study was to elucidate genetic variants modulating the impact of alcohol consumption on insulin sensitivity and pancreatic beta cell function within populations presenting normal glucose tolerance (NGT). METHODS: We recruited 4194 volunteers in Nanjing, 854 in Jurong and an additional 5833 in Nanjing for Discovery cohorts 1 and 2 and a Validation cohort, respectively. We performed an OGTT on all participants, establishing a stringent NGT group, and then assessed insulin sensitivity and beta cell function. Alcohol consumption was categorised as abstinent, light-to-moderate (<210 g per week) or heavy (≥210 g per week). After excluding ineligible individuals, an exploratory genome-wide association study identified potential variants interacting with alcohol consumption in 1862 NGT individuals. These findings were validated in an additional cohort of 2169 NGT individuals. Cox proportional hazard regression was further employed to evaluate the effect of the interaction between the potential variants and alcohol consumption on the risk of type 2 diabetes within the UK Biobank cohort. RESULTS: A significant correlation was observed between drinking levels and insulin sensitivity, accompanied by a consequent inverse relationship with insulin resistance and beta cell insulin secretion after adjusting for confounding factors in NGT individuals. However, no significant associations were noted in the disposition indexes. The interaction of variant rs56221195 with alcohol intake exhibited a pronounced effect on the liver insulin resistance index (LIRI) in the discovery set, corroborated in the validation set (combined p=1.32 × 10-11). Alcohol consumption did not significantly affect LIRI in rs56221195 wild-type (TT) carriers, but a strong negative association emerged in heterozygous (TA) and homozygous (AA) individuals. The rs56221195 variant also significantly interacts with alcohol consumption, influencing the total insulin secretion index INSR120 (the ratio of the AUC of insulin to glucose from 0 to 120 min) (p=2.06 × 10-9) but not disposition index. In the UK Biobank, we found a significant interaction between rs56221195 and alcohol consumption, which was linked to the risk of type 2 diabetes (HR 0.897, p=0.008). CONCLUSIONS/INTERPRETATION: Our findings reveal the effects of the interaction of alcohol and rs56221195 on hepatic insulin sensitivity in NGT individuals. It is imperative to weigh potential benefits and detriments thoughtfully when considering alcohol consumption across diverse genetic backgrounds.

A Large-Scale Exome-Wide Association Study Identifies Novel Germline Mutations in Lung Cancer.

Rationale: Genome-wide association studies have identified common variants of lung cancer. However, the contribution of rare exome-wide variants, especially protein-coding variants, to cancers remains largely unexplored. Objectives: To evaluate the role of human exomes in genetic predisposition to lung cancer. Methods: We performed exome-wide association studies to detect the association of exomes with lung cancer in 30,312 patients and 652,902 control subjects. A scalable and accurate implementation of a generalized mixed model was used to detect the association signals for loss-of-function, missense, and synonymous variants and gene-level sets. Furthermore, we performed association and Bayesian colocalization analyses to evaluate their relationships with intermediate exposures. Measurements and Main Results: We systematically analyzed 216,739 single-nucleotide variants in the human exome. The loss-of-function variants exhibited the most notable effects on lung cancer risk. We identified four novel variants, including two missense variants (rs202197044TET3 [Pmeta (P values of meta-analysis) = 3.60 × 10-8] and rs202187871POT1 [Pmeta = 2.21 × 10-8]) and two synonymous variants (rs7447927TMEM173 [Pmeta = 1.32 × 10-9] and rs140624366ATRN [Pmeta = 2.97 × 10-9]). rs202197044TET3 was significantly associated with emphysema (odds ratio, 3.55; Pfdr = 0.015), whereas rs7447927POT1 was strongly associated with telomere length (ß = 1.08; Pfdr (FDR corrected P value) = 3.76 × 10-53). Functional evidence of expression of quantitative trait loci, splicing quantitative trait loci, and isoform expression was found for the four novel genes. Gene-level association tests identified several novel genes, including POT1 (protection of telomeres 1), RTEL1, BSG, and ZNF232. Conclusions: Our findings provide insights into the genetic architecture of human exomes and their role in lung cancer predisposition.

Risk-stratified Approach for Never- and Ever-Smokers in Lung Cancer Screening: A Prospective Cohort Study in China.

Rationale: Over 40% of lung cancer cases occurred in never-smokers in China. However, high-risk never-smokers were precluded from benefiting from lung cancer screening as most screening guidelines did not consider them. Objectives: We sought to develop and validate prediction models for 3-year lung cancer risks for never- and ever-smokers, named the China National Cancer Center Lung Cancer models (China NCC-LCm2021 models). Methods: 425,626 never-smokers and 128,952 ever-smokers from the National Lung Cancer Screening program were used as the training cohort and analyzed using multivariable Cox models. Models were validated in two independent prospective cohorts: one included 369,650 never-smokers and 107,678 ever-smokers (841 and 421 lung cancers), and the other included 286,327 never-smokers and 78,469 ever-smokers (503 and 127 lung cancers). Measurements and Main Results: The areas under the receiver operating characteristic curves in the two validation cohorts were 0.698 and 0.673 for never-smokers and 0.728 and 0.752 for ever-smokers. Our models had higher areas under the receiver operating characteristic curves than other existing models and were well calibrated in the validation cohort. The China NCC-LCm2021 ⩾0.47% threshold was suggested for never-smokers and ⩾0.51% for ever-smokers. Moreover, we provided a range of threshold options with corresponding expected screening outcomes, screening targets, and screening efficiency. Conclusion: The construction of the China NCC-LCm2021 models can accurately reflect individual risk of lung cancer, regardless of smoking status. Our models can significantly increase the feasibility of conducting centralized lung cancer screening programs because we provide justified thresholds to define the high-risk population of lung cancer and threshold options to adapt different configurations of medical resources.

Novel trace elements detected in multiple stages of pregnancy impact neonatal birth weight by affecting gestational age.

Prenatal exposure to elements may be associated with birth weight via shortening of gestation. This study aimed to determine if prenatal exposure is associated with birth weight, and to explore the potential mediating role of gestational age in the association. Within an established Bangladesh prospective birth cohort (2008-2011), we analyzed the concentrations of 15 elements in maternal serum samples collected during the first (n = 780) and second (n = 610) trimesters using inductively coupled plasma mass spectrometry. Mediation analyses explored the relationships between these elements, gestational age, and birth weight. Serum concentrations of cobalt (Co) (first trimester: b = 56.5; 95% confidence interval [CI]: 13.5-99.0; false discovery rate [FDR]-q = 0.035; second trimester: b = 73.3; 95% CI: 20.4-130.2; FDR-q = 0.037) and antimony (Sb) in both trimesters (first trimester:b = 92.1; 95% CI: 66.0-118.9; FDR-q < 0.001; second trimester: b = 93.3; 95% CI: 67.3-118.4; FDR-q < 0.001), and strontium (Sr) in the first trimester (b = 142.4; 95% CI: 41.6-247.9; FDR-q = 0.035) were positively associated with birth weight, while negative associations were observed for barium (Ba) (first trimester: b = -154.8; 95% CI: -217.9 to 91.8; FDR-q <0.001; second trimester: b = -26.7; 95% CI: -44.9 to 10.2; FDR-q < 0.001). These elements act partially by affecting gestation age and appear to have heightened impact among smaller infants. Further research is needed to determine the biological underpinnings of these effects, which may inform strategies to avert low birth weight.

Comprehensive estimation for the length and dispersion of COVID-19 incubation period: a systematic review and meta-analysis.

PURPOSE: To estimate the central tendency and dispersion for incubation period of COVID-19 and, in turn, assess the effect of a certain length of quarantine for close contacts in active monitoring. METHODS: Literature related to SARS-CoV-2 and COVID-19 was searched through April 26, 2020. Quality was assessed according to Agency for Healthcare Research and Quality guidelines. Log-normal distribution for the incubation period was assumed to estimate the parameters for each study. Incubation period median and dispersion were estimated, and distribution was simulated. RESULTS: Fifty-six studies encompassing 4095 cases were included in this meta-analysis. The estimated median incubation period for general transmissions was 5.8 days [95% confidence interval (95% CI): 5.3, 6.2]. Incubation period was significantly longer for asymptomatic transmissions (median: 7.7 days; 95% CI 6.3, 9.4) than for general transmissions (P = 0.0408). Median and dispersion were higher for SARS-CoV-2 incubation compared to other viral respiratory infections. Furthermore, about 12 in 10,000 contacts in active monitoring would develop symptoms after 14 days, or below 1 in 10,000 for asymptomatic transmissions. Meta-regression suggested that each 10-year increase in age resulted in an average 16% increment in length of median incubation (incubation period ratio, 1.16, 95% CI 1.01, 1.32; P = 0.0250). CONCLUSION: This study estimated the median and dispersion of the SARS-CoV-2 incubation period more precisely. A 14-day quarantine period is sufficient to trace and identify symptomatic infections.

Comprehensive analyses of m6A regulators and interactive coding and non-coding RNAs across 32 cancer types.

N6-Methyladenosine (m6A) is an RNA modification that interacts with numerous coding and non-coding RNAs and plays important roles in the development of cancers. Nonetheless, the clinical impacts of m6A interactive genes on these cancers largely remain unclear since most studies focus only on a single cancer type. We comprehensively evaluated m6A modification patterns, including 23 m6A regulators and 83 interactive coding and non-coding RNAs among 9,804 pan-cancer samples. We used clustering analysis to identify m6A subtypes and constructed the m6A signature based on an unsupervised approach. We used the signatures to identify potential m6A modification targets across the genome. The prognostic value of one target was further validated in 3,444 samples from six external datasets. We developed three distinct m6A modification subtypes with different tumor microenvironment cell infiltration degrees: immunological, intermediate, and tumor proliferative. They were significantly associated with overall survival in 24 of 27 cancer types. Our constructed individual-level m6A signature was associated with survival, tumor mutation burden, and classical pathways. With the signature, we identified 114 novel genes as potential m6A targets. The gene shared most commonly between cancer types, BCL9L, is an oncogene and interacts with m6A patterns in the Wnt signaling pathway. In conclusion, m6A regulators and their interactive genes impact the outcome of various cancers. Evaluating the m6A subtype and the signature of individual tumors may inform the design of adjuvant treatments.

Potentially functional variants of ERAP1, PSMF1 and NCF2 in the MHC-I-related pathway predict non-small cell lung cancer survival.

BACKGROUND: Cellular immunity against tumor cells is highly dependent on antigen presentation by major histocompatibility complex class I (MHC-I) molecules. However, few published studies have investigated associations between functional variants of MHC-I-related genes and clinical outcomes of lung cancer patients. METHODS: We performed a two-phase Cox proportional hazards regression analysis by using two previously published genome-wide association studies to evaluate associations between genetic variants in the MHC-I-related gene set and the survival of non-small cell lung cancer (NSCLC) patients, followed by expression quantitative trait loci analysis. RESULTS: Of the 7811 single-nucleotide polymorphisms (SNPs) in 89 genes of 1185 NSCLC patients in the discovery dataset of the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial, 24 SNPs remained statistically significant after validation in additional 984 NSCLC patients from the Harvard Lung Cancer Susceptibility Study. In a multivariate stepwise Cox model, three independent functional SNPs (ERAP1 rs469783 T > C, PSMF1 rs13040574 C > A and NCF2 rs36071574 G > A) remained significant with an adjusted hazards ratio (HR) of 0.83 [95% confidence interval (CI) = 0.77-0.89, P = 8.0 × 10-7], 0.86 (0.80-0.93, P = 9.4 × 10-5) and 1.31 (1.11-1.54, P = 0.001) for overall survival (OS), respectively. Further combined genotypes revealed a poor survival in a dose-response manner in association with the number of unfavorable genotypes (Ptrend < 0.0001 and 0.0002 for OS and disease-specific survival, respectively). Also, ERAP1 rs469783C and PSMF1 rs13040574A alleles were associated with higher mRNA expression levels of their genes. CONCLUSION: These potentially functional SNPs of the MHC-I-related genes may be biomarkers for NSCLC survival, possibly through modulating the expression of corresponding genes.

Novel genetic variants in KIF16B and NEDD4L in the endosome-related genes are associated with nonsmall cell lung cancer survival.

The endosome is a membrane-bound organ inside most eukaryotic cells, playing an important role in adaptive immunity by delivering endocytosed antigens to both MHC class I and II pathways. Here, by analyzing genotyping data from two published genome-wide association studies (GWASs), we evaluated associations between genetic variants in the endosome-related gene-set and survival of patients with nonsmall cell lung cancer (NSCLC). The discovery included 44,112 (3,478 genotyped and 40,634 imputed) single-nucleotide polymorphisms (SNPs) in 220 genes in a singlelocus analysis for their associations with survival of 1,185 NSCLC patients from the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial. After validation of the 821 survival-associated significant SNPs in additional 984 NSCLC patients from the Harvard Lung Cancer Susceptibility Study, 14 SNPs remained significant. The final multivariate stepwise Cox proportional hazards regression modeling of the PLCO dataset identified three potentially functional and independent SNPs (i.e., KIF16B rs1555195 C>T, NEDD4L rs11660748 A>G and rs73440898 A>G) with an adjusted hazards ratio (HR) of 0.86 (95% confidence interval [CI] = 0.79-0.94, p = 0.0007), 1.31 (1.16-1.47, p = 6.0 × 10-5 ) and 1.27 (1.12-1.44, p = 0.0001) for overall survival (OS), respectively. Combined analysis of the adverse genotypes of these three SNPs revealed a trend in the genotype-survival association (ptrend < 0.0001 for OS and ptrend < 0.0001 for disease-specific survival). Furthermore, the survival-associated KIF16B rs1555195T allele was significantly associated with decreased mRNA expression levels of KIF16B in both lung tissues and blood cells. Therefore, genetic variants of the endosome-related genes may be biomarker for NSCLC survival, possibly through modulating the expression of corresponding genes.

Potentially functional genetic variants in PLIN2, SULT2A1 and UGT1A9 genes of the ketone pathway and survival of nonsmall cell lung cancer.

The ketone metabolism pathway is a principle procedure in physiological homeostasis and induces cancer cells to switch between glycolysis and oxidative phosphorylation for energy production. We conducted a two-phase analysis for associations between genetic variants in the ketone metabolism pathway genes and survival of nonsmall cell lung cancer (NSCLC) by analyzing genotyping data from two published genome-wide association studies (GWASs). In the discovery, we used a genotyping dataset from the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial in the multivariable Cox proportional hazards regression analysis. We used Bayesian false discovery probability (≤0.80) for multiple testing correction to evaluate associations between 25,819 (2,176 genotyped and 23,643 imputed) single-nucleotide polymorphisms (SNPs) in 162 genes and survival of 1,185 NSCLC patients. Subsequently, we validated the identified significant SNPs with an additional 984 NSCLC patients from the Harvard Lung Cancer Susceptibility GWAS study. Finally, we found that three independent and potentially functional SNPs in three different genes (i.e., PLIN2 rs7867814 G>A, SULT2A1 rs2547235 C>T and UGT1A9 rs2011404 C>T) were independently associated with risk of death from NSCLC, with a combined hazards ratio of 1.22 [95% confidence interval = 1.09-1.36 and p = 0.0003], 0.82 (0.74-0.91 and p = 0.0002) and 1.21 (1.10-1.33 and p = 0.0001), respectively. Additional expression quantitative trait loci analysis found that the survival-associated PLIN2 rs7867814 GA + AA genotypes, but not the genotypes of other two SNPs, were significantly associated with increased mRNA expression levels (p = 0.005). These results indicated that PLIN2 variants may be potential predictors of NSCLC survival through regulating the PLIN2 expression.

Novel genetic variants in HDAC2 and PPARGC1A of the CREB-binding protein pathway predict survival of non-small-cell lung cancer.

The CREB-binding protein (CBP) pathway plays an important role in transcription and activity of acetyltransferase that acetylates lysine residues of histones and nonhistone proteins. In the present study, we hypothesized that genetic variants in the CBP pathway genes played a role in survival of non-small-cell lung cancer (NSCLC). We tested this hypothesis using the genotyping data from the genome-wide association study (GWAS) dataset from the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial. In the single-locus analysis, we evaluated associations between 13 176 (1107 genotyped and 12 069 imputed) single-nucleotide polymorphisms (SNPs) in 72 genes and survival of 1185 patients with NSCLC. The identified 106 significant SNPs in the discovery were further validated in additional genotyping data from another GWAS dataset of 984 patients with NSCLC in the Harvard Lung Cancer Susceptibility Study. The combined results of two datasets showed that two independent, potentially functional SNPs (i.e., HDAC2 rs13213007G>A and PPARGC1A rs60571065T>A) were significantly associated with NSCLC overall survival, with a combined hazards ratio (HR) of 1.26 (95% confidence interval (CI), 1.09-1.45; P = .002) and 1.23 (1.04-1.47; P = .017), respectively. Furthermore, we performed an expression quantitative trait loci analysis and found that the survival-associated HDAC2 rs13213007A allele (GA+AA), but not PPARGC1A rs60571065A allele (TA+AA), was significantly associated with increased messenger RNA expression levels of HDAC2 in 373 lymphoblastoid cell lines. These results indicate that the HDAC2 rs13213007A allele is a potential predictor of NSCLC survival, likely by altering the HDAC2 expression.

Genetic variants in RUNX3, AMD1 and MSRA in the methionine metabolic pathway and survival in nonsmall cell lung cancer patients.

Abnormal methionine dependence in cancer cells has led to methionine restriction as a potential therapeutic strategy. We hypothesized that genetic variants involved in methionine-metabolic genes are associated with survival in nonsmall cell lung cancer (NSCLC) patients. Therefore, we investigated associations of 16,378 common single-nucleotide polymorphisms (SNPs) in 97 methionine-metabolic pathway genes with overall survival (OS) in NSCLC patients using genotyping data from two published genome-wide association study (GWAS) datasets. In the single-locus analysis, 1,005 SNPs were significantly associated with NSCLC OS (p < 0.05 and false-positive report probability < 0.2) in the discovery dataset. Three SNPs (RUNX3 rs7553295 G > T, AMD1 rs1279590 G > A and MSRA rs73534533 C > A) were replicated in the validation dataset, and their meta-analysis showed an adjusted hazards ratio [HR] of 0.82 [95% confidence interval (CI) =0.75-0.89] and pmeta = 2.86 × 10-6 , 0.81 (0.73-0.91) and pmeta = 4.63 × 10-4 , and 0.77 (0.68-0.89) and pmeta = 2.07 × 10-4 , respectively). A genetic score of protective genotypes of these three SNPs revealed an increased OS in a dose-response manner (ptrend < 0.0001). Further expression quantitative trait loci (eQTL) analysis showed significant associations between these genotypes and mRNA expression levels. Moreover, differential expression analysis further supported a tumor-suppressive effect of MSRA, with lower mRNA levels in both lung squamous carcinoma and adenocarcinoma (p < 0.0001 and < 0.0001, respectively) than in adjacent normal tissues. Additionally, low mutation rates of these three genes indicated the critical roles of these functional SNPs in cancer progression. Taken together, these genetic variants of methionine-metabolic pathway genes may be promising predictors of survival in NSCLC patients.

Potentially functional genetic variants in the complement-related immunity gene-set are associated with non-small cell lung cancer survival.

The complement system plays an important role in the innate and adaptive immunity, complement components mediate tumor cytolysis of antibody-based immunotherapy, and complement activation in the tumor microenvironment may promote tumor progression or inhibition, depending on the mechanism of action. In the present study, we conducted a two-phase analysis of two independently published genome-wide association studies (GWASs) for associations between genetic variants in a complement-related immunity gene-set and overall survival of non-small cell lung cancer (NSCLC). The GWAS dataset from Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial was used as the discovery, and multivariate Cox proportional hazards regression with false-positive report probability for multiple test corrections were performed to evaluate associations between 14,699 single-nucleotide polymorphisms (SNPs) in 111 genes and survival of 1,185 NSCLC patients. The identified significant SNPs in a single-locus analysis were further validated with 984 NSCLC patients in the GWAS dataset from the Harvard Lung Cancer Susceptibility (HLCS) Study. The results showed that two independent, potentially functional SNPs in two genes (VWF rs73049469 and ITGB2 rs3788142) were significantly associated with NSCLC survival, with a combined hazards ratio (HR) of 1.22 [95% confidence interval (CI) = 1.07-1.40, P = 0.002] and 1.16 (1.07-1.27, 6.45 × 10-4 ), respectively. Finally, we performed expression quantitative trait loci (eQTL) analysis and found that survival-associated genotypes of VWF rs73049469 were also significantly associated with mRNA expression levels of the gene. These results indicated that genetic variants of the complement-related immunity genes might be predictors of NSCLC survival, particularly for the short-term survival, possibly by modulating the expression of genes involved in the host immunity.

Shared genetics of asthma and mental health disorders: a large-scale genome-wide cross-trait analysis.

Epidemiological studies demonstrate an association between asthma and mental health disorders, although little is known about the shared genetics and causality of this association. Thus, we aimed to investigate shared genetics and the causal link between asthma and mental health disorders.We conducted a large-scale genome-wide cross-trait association study to investigate genetic overlap between asthma from the UK Biobank and eight mental health disorders from the Psychiatric Genomics Consortium: attention deficit hyperactivity disorder (ADHD), anxiety disorder (ANX), autism spectrum disorder, bipolar disorder, eating disorder, major depressive disorder (MDD), post-traumatic stress disorder and schizophrenia (sample size 9537-394 283).In the single-trait genome-wide association analysis, we replicated 130 previously reported loci and discovered 31 novel independent loci that are associated with asthma. We identified that ADHD, ANX and MDD have a strong genetic correlation with asthma at the genome-wide level. Cross-trait meta-analysis identified seven loci jointly associated with asthma and ADHD, one locus with asthma and ANX, and 10 loci with asthma and MDD. Functional analysis revealed that the identified variants regulated gene expression in major tissues belonging to the exocrine/endocrine, digestive, respiratory and haemic/immune systems. Mendelian randomisation analyses suggested that ADHD and MDD (including 6.7% sample overlap with asthma) might increase the risk of asthma.This large-scale genome-wide cross-trait analysis identified shared genetics and potential causal links between asthma and three mental health disorders (ADHD, ANX and MDD). Such shared genetics implicate potential new biological functions that are in common among them.

Potentially functional genetic variants in the TNF/TNFR signaling pathway genes predict survival of patients with non-small cell lung cancer in the PLCO cancer screening trial.

The tumor necrosis factor (TNF)/TNF receptor (TNFR) pathway is known to influence survival of patients with cancer. We hypothesize that single nucleotide polymorphisms (SNPs) in the TNF/TNFR pathway genes related to apoptosis are associated with survival of patients with non-small cell lung cancer (NSCLC). We used 1185 patients with NSCLC in the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial and 984 patients with NSCLC in the Harvard Lung Cancer Susceptibility Study as the discovery and validation datasets, respectively. We selected 6788 SNPs in 71 genes in the TNF/TNFR signaling pathway and extracted their genotyping data from the PLCO genowide-association study (GWAS) dataset. We performed Cox proportional hazards regression analysis to evaluate associations between the identified SNPs and survival and validated the significant SNPs, which were further analyzed for their functional relevance. We found that genotypes of two validated SNPs, IKBKAP rs4978754 CT + TT and TNFRSF1B rs677844 TC + CC, as well as their combined genotypes predicted a better overall survival (P = 0.004, 0.002 and <0.001, respectively). These two validated SNPs were predicted by the RegulomeDB score to be potentially functional. In addition, IKBKAP mRNA expression levels were significantly higher, while TNFRSF1B mRNA expression levels were significantly lower in lung cancer tissues than in adjacent normal tissues (P < 0.001). The Cancer Genome Atlas (TCGA)-based expression quantitative trait loci analysis showed that IKBKAP rs4978754 and TNFRSF1B rs677844 genotypes were significantly associated with their corresponding mRNA expression levels in lung cancer tissues in a recessive model (P = 0.035 and 0.045, respectively). Therefore, we identified two potentially functional SNPs (IKBKAP rs4978754 C > T and TNFRSF1B rs677844 T > C) to be associated with survival of patients with NSCLC.

Genetic overlap of chronic obstructive pulmonary disease and cardiovascular disease-related traits: a large-scale genome-wide cross-trait analysis.

BACKGROUND: A growing number of studies clearly demonstrate a substantial association between chronic obstructive pulmonary disease (COPD) and cardiovascular diseases (CVD), although little is known about the shared genetics that contribute to this association. METHODS: We conducted a large-scale cross-trait genome-wide association study to investigate genetic overlap between COPD (Ncase = 12,550, Ncontrol = 46,368) from the International COPD Genetics Consortium and four primary cardiac traits: resting heart rate (RHR) (N = 458,969), high blood pressure (HBP) (Ncase = 144,793, Ncontrol = 313,761), coronary artery disease (CAD)(Ncase = 60,801, Ncontrol = 123,504), and stroke (Ncase = 40,585, Ncontrol = 406,111) from UK Biobank, CARDIoGRAMplusC4D Consortium, and International Stroke Genetics Consortium data. RESULTS: RHR and HBP had modest genetic correlation, and CAD had borderline evidence with COPD at a genome-wide level. We found evidence of local genetic correlation with particular regions of the genome. Cross-trait meta-analysis of COPD identified 21 loci jointly associated with RHR, 22 loci with HBP, and 3 loci with CAD. Functional analysis revealed that shared genes were enriched in smoking-related pathways and in cardiovascular, nervous, and immune system tissues. An examination of smoking-related genetic variants identified SNPs located in 15q25.1 region associated with cigarettes per day, with effects on RHR and CAD. A Mendelian randomization analysis showed a significant positive causal effect of COPD on RHR (causal estimate = 0.1374, P = 0.008). CONCLUSION: In a set of large-scale GWAS, we identify evidence of shared genetics between COPD and cardiac traits.

Bayesian variable selection for parametric survival model with applications to cancer omics data.

BACKGROUND: Modeling thousands of markers simultaneously has been of great interest in testing association between genetic biomarkers and disease or disease-related quantitative traits. Recently, an expectation-maximization (EM) approach to Bayesian variable selection (EMVS) facilitating the Bayesian computation was developed for continuous or binary outcome using a fast EM algorithm. However, it is not suitable to the analyses of time-to-event outcome in many public databases such as The Cancer Genome Atlas (TCGA). RESULTS: We extended the EMVS to high-dimensional parametric survival regression framework (SurvEMVS). A variant of cyclic coordinate descent (CCD) algorithm was used for efficient iteration in M-step, and the extended Bayesian information criteria (EBIC) was employed to make choice on hyperparameter tuning. We evaluated the performance of SurvEMVS using numeric simulations and illustrated the effectiveness on two real datasets. The results of numerical simulations and two real data analyses show the well performance of SurvEMVS in aspects of accuracy and computation. Some potential markers associated with survival of lung or stomach cancer were identified. CONCLUSIONS: These results suggest that our model is effective and can cope with high-dimensional omics data.

Integrated Transcriptomics, Metabolomics, and Lipidomics Profiling in Rat Lung, Blood, and Serum for Assessment of Laser Printer-Emitted Nanoparticle Inhalation Exposure-Induced Disease Risks.

Laser printer-emitted nanoparticles (PEPs) generated from toners during printing represent one of the most common types of life cycle released particulate matter from nano-enabled products. Toxicological assessment of PEPs is therefore important for occupational and consumer health protection. Our group recently reported exposure to PEPs induces adverse cardiovascular responses including hypertension and arrythmia via monitoring left ventricular pressure and electrocardiogram in rats. This study employed genome-wide mRNA and miRNA profiling in rat lung and blood integrated with metabolomics and lipidomics profiling in rat serum to identify biomarkers for assessing PEPs-induced disease risks. Whole-body inhalation of PEPs perturbed transcriptional activities associated with cardiovascular dysfunction, metabolic syndrome, and neural disorders at every observed time point in both rat lung and blood during the 21 days of exposure. Furthermore, the systematic analysis revealed PEPs-induced transcriptomic changes linking to other disease risks in rats, including diabetes, congenital defects, auto-recessive disorders, physical deformation, and carcinogenesis. The results were also confirmed with global metabolomics profiling in rat serum. Among the validated metabolites and lipids, linoleic acid, arachidonic acid, docosahexanoic acid, and histidine showed significant variation in PEPs-exposed rat serum. Overall, the identified PEPs-induced dysregulated genes, molecular pathways and functions, and miRNA-mediated transcriptional activities provide important insights into the disease mechanisms. The discovered important mRNAs, miRNAs, lipids and metabolites may serve as candidate biomarkers for future occupational and medical surveillance studies. To the best of our knowledge, this is the first study systematically integrating in vivo, transcriptomics, metabolomics, and lipidomics to assess PEPs inhalation exposure-induced disease risks using a rat model.

Welding fume exposure is associated with inflammation: a global metabolomics profiling study.

BACKGROUND: Increasing evidence suggests that welding fume exposure is associated with systemic inflammation. Although celluar metabolites may be associated with inflammation, there is limited information on metabolomic changes during welding fume exposure. Such changes may play an important role in the occurrence, development, and prevention of metal-associated diseases. We aim to investigate human metabolomics changes pre- and post-welding fume exposure. METHODS: This study included 52 boilermakers totally. We collected plasma samples pre- and post-shift welding fume exposure and prepared samples using the automated MicroLab STAR® system. Metabolite concentrations were measured using ultra performance liquid chromatography - tandem mass spectrometer (UPLC-MS/MS) methods. Two-way analysis of variance was used to test the significance of metabolite changes with false discovery rate correction. RESULTS: Analysis detected several metabolic changes after welding fume exposure, mainly involved in the lipid pathway [glucocorticoid class (cortisol, corticosterone, and cortisone), acylcarnitine class, and DiHOME species (9,10-DiHOME and 12,13-DiHOME)], amino acid utilization (isoleucine, proline and phenylalanine), and S-(3-hydroxypropyl) mercapturic acid (3-HPMA). These compounds are all associated with inflammation according to previous studies. Further, additive interaction effects linked smoking and 3-HPMA levels. In the metabolite set enrichment analysis for diseases, the top two disease-associated metabolite pathways were systemic inflammation-related diseases including rheumatoid arthritis and systemic lupus erythematosus. CONCLUSIONS: This global metabolomics study shows evidence that metabolite changes during welding fume exposure are closely associated with systemic inflammation. The altered metabolites detected may be potential health monitoring biomarkers for boilermakers, especially for inflammation-related disease prevention.

A fast algorithm for Bayesian multi-locus model in genome-wide association studies.

Genome-wide association studies (GWAS) have identified a large amount of single-nucleotide polymorphisms (SNPs) associated with complex traits. A recently developed linear mixed model for estimating heritability by simultaneously fitting all SNPs suggests that common variants can explain a substantial fraction of heritability, which hints at the low power of single variant analysis typically used in GWAS. Consequently, many multi-locus shrinkage models have been proposed under a Bayesian framework. However, most use Markov Chain Monte Carlo (MCMC) algorithm, which are time-consuming and challenging to apply to GWAS data. Here, we propose a fast algorithm of Bayesian adaptive lasso using variational inference (BAL-VI). Extensive simulations and real data analysis indicate that our model outperforms the well-known Bayesian lasso and Bayesian adaptive lasso models in accuracy and speed. BAL-VI can complete a simultaneous analysis of a lung cancer GWAS data with ~3400 subjects and ~570,000 SNPs in about half a day.

Systematic characterization of m6A proteomics across 12 cancer types: a multi-omics integration study.

The modification patterns of N6-methyladenosine (m6A) regulators and interacting genes are deeply involved in tumors. However, the effect of m6A modification patterns on human proteomics remains largely unknown. We evaluated the molecular characteristics and clinical relevance of m6A modification proteomics patterns among 1013 pan-cancer samples from the Clinical Proteomic Tumor Analysis Consortium (CPTAC). More than half of the m6A proteins were expressed at higher levels in tumor tissues and presented oncogenic characteristics. Furthermore, we performed multi-omics analyses integrating with transcriptomics data of m6A regulators and interactive coding and non-coding RNAs and developed a m6A multi-omics signature to identify potential m6A modification target proteins across global proteomics. It was significantly associated with overall survival in nine cancer types, tumor mutation burden (P = 0.01), and immune checkpoints including PD-L1 (P = 4.9 × 10-8) and PD-1 (P < 0.01). We identified 51 novel proteins associated with the multi-omics signature (PFDR < 0.05). These proteins were functional through pathway enrichment analyses. The protein with the highest hit frequency was CHORDC1, which was significantly up-regulated in tumor tissues in nine cancer types. Its higher abundance was significantly associated with a poorer prognosis in seven cancer types. The identified m6A target proteins might provide infomation for the study of molecular mechanism of cancer.