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BACKGROUND@#A polygenic risk score (PRS) derived from 112 single-nucleotide polymorphisms (SNPs) for gastric cancer has been reported in Chinese populations (PRS-112). However, its performance in other populations is unknown. A functional PRS (fPRS) using functional SNPs (fSNPs) may improve the generalizability of the PRS across populations with distinct ethnicities.@*METHODS@#We performed functional annotations on SNPs in strong linkage disequilibrium (LD) with the 112 previously reported SNPs to identify fSNPs that affect protein-coding or transcriptional regulation. Subsequently, we constructed an fPRS based on the fSNPs by using the LDpred2-infinitesimal model and then analyzed the performance of the PRS-112 and fPRS in the risk prediction of gastric cancer in 457,521 European participants of the UK Biobank cohort. Finally, the performance of the fPRS in combination with lifestyle factors were evaluated in predicting the risk of gastric cancer.@*RESULTS@#During 4,582,045 person-years of follow-up with a total of 623 incident gastric cancer cases, we found no significant association between the PRS-112 and gastric cancer risk in the European population (hazard ratio [HR] = 1.00 [95% confidence interval (CI) 0.93-1.09], P = 0.846). We identified 125 fSNPs, including seven deleterious protein-coding SNPs and 118 regulatory non-coding SNPs, and used them to construct the fPRS-125. Our result showed that the fPRS-125 was significantly associated with gastric cancer risk (HR = 1.11 [95% CI, 1.03-1.20], P = 0.009). Compared to participants with a low fPRS-125 (bottom quintile), those with a high fPRS-125 (top quintile) had a higher risk of incident gastric cancer (HR = 1.43 [95% CI, 1.12-1.84], P = 0.005). Moreover, we observed that participants with both an unfavorable lifestyle and a high genetic risk had the highest risk of incident gastric cancer (HR = 4.99 [95% CI, 1.55-16.10], P = 0.007) compared to those with both a favorable lifestyle and a low genetic risk.@*CONCLUSION@#These results indicate that the fPRS-125 derived from fSNPs may act as an indicator to measure the genetic risk of gastric cancer in the European population.
Subject(s)
Humans , Prospective Studies , Stomach Neoplasms/genetics , Genetic Predisposition to Disease/genetics , Risk Factors , Multifactorial Inheritance/genetics , Polymorphism, Single Nucleotide/genetics , Genome-Wide Association StudyABSTRACT
Although genome-wide association studies have identified more than eighty genetic variants associated with non-small cell lung cancer (NSCLC) risk, biological mechanisms of these variants remain largely unknown. By integrating a large-scale genotype data of 15 581 lung adenocarcinoma (AD) cases, 8350 squamous cell carcinoma (SqCC) cases, and 27 355 controls, as well as multiple transcriptome and epigenomic databases, we conducted histology-specific meta-analyses and functional annotations of both reported and novel susceptibility variants. We identified 3064 credible risk variants for NSCLC, which were overrepresented in enhancer-like and promoter-like histone modification peaks as well as DNase I hypersensitive sites. Transcription factor enrichment analysis revealed that USF1 was AD-specific while CREB1 was SqCC-specific. Functional annotation and gene-based analysis implicated 894 target genes, including 274 specifics for AD and 123 for SqCC, which were overrepresented in somatic driver genes (ER = 1.95, P = 0.005). Pathway enrichment analysis and Gene-Set Enrichment Analysis revealed that AD genes were primarily involved in immune-related pathways, while SqCC genes were homologous recombination deficiency related. Our results illustrate the molecular basis of both well-studied and new susceptibility loci of NSCLC, providing not only novel insights into the genetic heterogeneity between AD and SqCC but also a set of plausible gene targets for post-GWAS functional experiments.
Subject(s)
Humans , Adenocarcinoma of Lung/genetics , Carcinoma, Non-Small-Cell Lung/genetics , Carcinoma, Squamous Cell/genetics , Genetic Heterogeneity , Genetic Predisposition to Disease , Genome-Wide Association Study , Lung Neoplasms/genetics , Polymorphism, Single NucleotideABSTRACT
OBJECTIVE: To explore the influence of work pressure and psychological capital on job burnout of college teachers. METHODS: A total of 287 teachers from 7 universities in Nanjing City were selected as the research subjects using the convenient sampling method. The Maslach Burnout Inventory, Job Stress Scale for University Teachers and Psychological Capital Questionnaire were used to investigate their job burnout, job stress and psychological capital. RESULTS: The total scores of job burnout and job stress were(42.9±12.5) and(48.5±12.4) respectively, and the occurrence of job burnout was 64.1%. The total scores of psychological capital was(106.7±14.7), and the scores of the four dimensions including self-efficacy, hope, resilience and optimism were(27.6±4.6),(26.7±4.8),(27.0±4.2) and(25.4±3.8) respectively. The total score of job stress was positively correlated with the total score of job burnout [correlation coefficient(r)=0.41, P<0.01]. The total score of psychological capital, self-efficacy, hope, resilience and optimism were negatively correlated with the total score of job burnout(r values were-0.42,-0.28,-0.36,-0.36 and-0.42, respectively, P<0.01). The results of multiple linear regression analysis showed that after adjusting the influence of confounding factors and excluding other confounding factors, the higher the job stress, the higher the job burnout level(P<0.01), the higher the psychological capital optimism dimension score, the lower the job burnout level(P<0.01). CONCLUSION: The job stress and psychological capital of college teachers can independently affect their job burnout level, with a dose-effect relationship.
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Objective To describe the genetic structure of populations in different areas of China,and explore the effects of different strategies to control the confounding factors of the genetic structure in cohort studies.Methods By using the genome-wide association study (GWAS) on data of 4 500 samples from 10 areas of the China Kadoorie Biobank (CKB),we performed principal components analysis to extract the fast and second principal components of the samples for the component two-dimensional diagram generation,and then compared them with the source of sample area to analyze the characteristics of genetic structure of the samples from different areas of China.Based on the CKB cohort data,a simulation data set with cluster sample characteristics such as genetic structure differences and extensive kinship was generated;and the effects of different analysis strategies including traditional analysis scheme and mixed linear model on the inflation factor (λ) were evaluated.Results There were significant genetic structure differences in different areas of China.Distribution of the principal components of the population genetic structure was basically consistent with the geographical distribution of the project area.The first principal component corresponds to the latitude of different areas,and the second principal component corresponds to the longitude of different areas.The generated simulation data showed high false positive rate (λ =1.16),even if the principal components of the genetic structure was adjusted or the area specific subgroup analysis was performed,λ could not be effectively controlled (λ ＞ 1.05);while,by using a mixed linear model adjusting for the kinship matrix,λ was effectively controlled regardless of whether the genetic structure principal component was further adjusted (λ =0.99).Conclusions There were large differences in genetic structure among populations in different areas of China.In molecular epidemiology studies,bias caused by population genetic structure needs to be carefully treated.For large cohort data with complex genetic structure and extensive kinship,it is necessary to use a mixed linear model for association analysis.
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Objective To describe the genetic structure of populations in different areas of China,and explore the effects of different strategies to control the confounding factors of the genetic structure in cohort studies.Methods By using the genome-wide association study (GWAS) on data of 4 500 samples from 10 areas of the China Kadoorie Biobank (CKB),we performed principal components analysis to extract the fast and second principal components of the samples for the component two-dimensional diagram generation,and then compared them with the source of sample area to analyze the characteristics of genetic structure of the samples from different areas of China.Based on the CKB cohort data,a simulation data set with cluster sample characteristics such as genetic structure differences and extensive kinship was generated;and the effects of different analysis strategies including traditional analysis scheme and mixed linear model on the inflation factor (λ) were evaluated.Results There were significant genetic structure differences in different areas of China.Distribution of the principal components of the population genetic structure was basically consistent with the geographical distribution of the project area.The first principal component corresponds to the latitude of different areas,and the second principal component corresponds to the longitude of different areas.The generated simulation data showed high false positive rate (λ =1.16),even if the principal components of the genetic structure was adjusted or the area specific subgroup analysis was performed,λ could not be effectively controlled (λ ＞ 1.05);while,by using a mixed linear model adjusting for the kinship matrix,λ was effectively controlled regardless of whether the genetic structure principal component was further adjusted (λ =0.99).Conclusions There were large differences in genetic structure among populations in different areas of China.In molecular epidemiology studies,bias caused by population genetic structure needs to be carefully treated.For large cohort data with complex genetic structure and extensive kinship,it is necessary to use a mixed linear model for association analysis.
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Large-scale cohort study has unique advantages in the field of etiology research for its large sample size a multi-time point data, but it also brings great difficulty in data management and quality control at the same time. Recently, China has initiated a number of large-scale population cohort studies, posing enormous challenges to the management and quality control of related cohort data. This paper summarizes the existing experience and consensus in the field of cohort study in China from the characteristics of the cohort data, aiming at the types and main forms of the four main sources of questionnaire data, clinical diagnosis and treatment data, biological sample detection data and observation outcome data, from the data storage, circulation and transmission work.The contents and methods of queue data management are comprehensively summarized. Corresponding data quality control strategies are advised in the questionnaire evaluation, data logic verification, survey object sampling and multi-database review, etc. The goal of this review is to provide guidance for the management of data and the formulation of quality control strategies in the cohort study in China.
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Objective:To explore the clinical features of recurrence after choledocholithotomy and to analyze the risk factors.Methods:The clinical data of 730 patients with choledocholithiasis who were treated in our hospital from January 2005 to July 2016 were analyzed retrospectively,550 cases who were received choledocholithotomy were defined as laparotomy group,30 cases with laparoscopic common bile duct exploration (LCBDE) were defined as the LCBDE group,and 150 cases with endoscopic sphincterotomy (EST) were defined as EST group.The recurrence rate of the three groups were compared.The patients of three groups were divided into recurrence group (n=227) and non recurrence group (n=503) according to the recurrent situation,then the clinical features and risk factors of recurrent patients were analyzed by univariate and multivariate Logistic regression analysis.Results:The recurrence rate of EST group was 38.67％,which was significantly higher than that of LCBDE group with 26.67％ and the laparotomy group with 29.27％,and there was statistical difference (P＜0.05).The results of univariate analysis showed that there were statistically significant differences in age,history of HBV infection,jaundice,abnormal total bilirubin,peripapillary diverticulum,biliary infection,biliary stricture,papillary stenosis,sphincter of Oddis dysfunction,history of biliary surgery,cholecystectomy,bile duct diameter ≥ 15 mm,bile duct angle ≤120°,operation type,stone quantity ≥ 2 grains,stone diameter ≥ 10 mm,with or without gallstones (P＜0.05).The results of Logistic multivariate regression analysis showed that age,having peripapillary diverticulum,having history of biliary surgery,bile duct diameter ≥ 15 mm,stone quantity ≥ 2grains and EST operation type were the independent risk factors of the recurrence after choledocholithotomy (P＜0.05).Conclusion:There are many risk factors of recurrence after choledocholithotomy,and operation method should be based on the size and the number of the stones,and the constitution of patients.Preventive measures should be strengthened to control the recurrence after choledocholithotomy.
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BACKGROUND AND PURPOSE: Large cohort studies on relationship between family history of stroke (FHS) and stroke risk are lacking in Asians. We aimed to systematically evaluate the association of FHS with stroke risk in a cohort study of 0.5 million Chinese adults. METHODS: Information about FHS was self-reported. The median follow-up time was 7.16 years and the end-point of follow-up was incident stroke, which was entered directly into the China Kadoorie Biobank system. Multivariate analyses were performed with Cox proportional hazards model, and interaction analyses were carried using likelihood-ratio tests. RESULTS: Compared with participants without FHS, the hazard ratio (HR) (95% confidence interval, CI) of stroke for participants with FHS was 1.50 (1.46-1.55). The HRs increased with the number of first degree relatives with stroke (HRs=1.41, 1.98 and 2.47 for 1, 2 and ≥3 relatives, respectively, P(trend) <0.001). The HRs were 1.57 (95% CI: 1.50-1.66) and 1.49 (95% CI: 1.45-1.54) for sibling history and parental history, respectively. Similar associations with offspring stroke risk were observed between paternal history (HR=1.48, 95% CI: 1.43-1.54) and maternal history (HR=1.49, 95% CI: 1.43-1.55). Moreover, significant interactions were detected between FHS and health-risk behaviors (tobacco smoking and alcohol drinking). CONCLUSIONS: FHS is an independent risk factor for stroke in Chinese. The more first degree relatives are affected by stroke, the higher are individuals’ risk of suffering from stroke. The management of the health-risk behaviors for reducing stroke should be highlighted, especially for the individuals with FHS.
Subject(s)
Adult , Humans , Asian People , China , Cohort Studies , Follow-Up Studies , Multivariate Analysis , Parents , Proportional Hazards Models , Risk Factors , Siblings , Smoke , Smoking , StrokeABSTRACT
Genome-wide association studies (GWAS) have identified thousands of genetic loci associated with complex diseases or traits. However, the exact biological functions of these loci are largely unknown. Recent functional annotation indicates that the majority of disease/trait associated loci are concentrated in regulatory DNA of human genome. Expression quantitative trait loci (eQTL) analyses, chromosome conformation capture related methods and genome editing methods (such as CRISPR/Cas9) may facilitate the functional study of these loci. Research on noncoding RNAs and rare variants may improve the functional understanding. These efforts may promise translation of GWAS findings to clinical practices.
Subject(s)
Humans , Genetic Predisposition to Disease , Genome-Wide Association Study , Quantitative Trait LociABSTRACT
<p><b>OBJECTIVE</b>To evaluate the predictive power of risk model by combining traditional epidemiological factors and genetic factors.</p><p><b>METHODS</b>Our previous GWAS data of lung cancer in Chinese were used in training set (Nanjing and Shanghai: 1473 cases vs. 1962 control) and testing set (Beijing and Wuhan: 858 cases vs. 1 115 control). All the single nucleotide polymorphisms (SNPs) associated with lung cancer risk were systematically selected and stepwise logistic regression analysis was used to select independent factors in the training set. The wGRS (weighted genetic score) was further used to calculate genetic risk score. To evaluate the contribution of the genetic factors, 3 risk models were established by using the training set, i.e. smoking model (based on smoking status) , genetic risk model (based on genetic risk score) and combined model (based on smoke and genetic risk score). The predictability of the models were evaluated by the areas under the receiver operating characteristic (ROC) curves, area under curve (AUC), net reclassification improvement (NRI) and integrated discrimination index (IDI). Besides, the results were further verified in the testing set.</p><p><b>RESULTS</b>In the training set, it was found that the AUC of the smoking, genetic risk and combined models were 0.65 (0.63-0.66), 0.60 (0.59-0.62) and 0.69 (0.67-0.71), respectively. Compared with combined model, the predictive power of other two models significantly declined, the difference was statistically significant (P<0.001). Furthermore, compared with the smoking model, the NRI of the combined model increased by 4.57% (2.23%-6.91%) and IDI increased by 3.11% (2.52%-3.69%) in the training set, the difference was statistically significant (P<0.001). Similarly, in the testing set NRI increased by 2.77%, the difference was not statistically significant (P=0.069) , and IDI increased by 3.16%, the difference was statistically significant (P<0.001).</p><p><b>CONCLUSION</b>This study showed that combining 14 genetic variants with traditional epidemiological factors could improve the predictive power of risk model for lung cancer. The model could be used in the screening of high-risk population of lung cancer in Chinese and provide evidence for the early diagnosis and treatment of lung cancer.</p>