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Jing Dong,
Guangfu Jin,
Chen Wu,
Huan Guo,
Baosen Zhou,
Jiachun Lv,
Daru Lu,
Yongyong Shi,
Yongqian Shu,
Lin Xu, [......],
Ying Yan,
Jibin Liu,
Christopher I Amos, Feng Chen,
Wen Tan,
Li Jin,
Tangchun Wu,
Zhibin Hu,
Dongxin Lin,
Hongbing Shen
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ABSTRACT: Adenocarcinoma (AC) and squamous cell carcinoma (SqCC) are two major histological subtypes of lung cancer. Genome-wide association studies (GWAS) have made considerable advances in the understanding of lung cancer susceptibility. Obvious heterogeneity has been observed between different histological subtypes of lung cancer, but genetic determinants in specific to lung SqCC have not been systematically investigated. Here, we performed the GWAS analysis specifically for lung SqCC in 833 SqCC cases and 3,094 controls followed by a two-stage replication in additional 2,223 lung SqCC cases and 6,409 controls from Chinese populations. We found that rs12296850 in SLC17A8-NR1H4 gene region at12q23.1 was significantly associated with risk of lung SqCC at genome-wide significance level [additive model: odds ratio (OR) = 0.78, 95% confidence interval (CI) = 0.72-0.84, P = 1.19×10(-10)]. Subjects carrying AG or GG genotype had a 26% (OR = 0.74, 95% CI = 0.67-0.81) or 32% (OR = 0.68, 95% CI = 0.56-0.83) decreased risk of lung SqCC, respectively, as compared with AA genotype. However, we did not observe significant association between rs12296850 and risk of lung AC in a total of 4,368 cases with lung AC and 9,486 controls (OR = 0.96, 95% CI = 0.90-1.02, P = 0.173). These results indicate that genetic variations on chromosome 12q23.1 may specifically contribute to lung SqCC susceptibility in Chinese population.
PLoS Genetics 01/2013; 9(1):e1003190. · 8.69 Impact Factor
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Ruyang Zhang,
Yang Zhao,
Minjie Chu,
Chen Wu,
Guangfu Jin,
Juncheng Dai,
Cheng Wang,
Lingmin Hu,
Jianwei Gou,
Chen Qian,
Jianling Bai,
Tangchun Wu,
Zhibin Hu,
Dongxin Lin,
Hongbing Shen, Feng Chen
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ABSTRACT: Genome-wide association studies (GWAS) have identified a number of genetic variants associated with lung cancer risk. However, these loci explain only a small fraction of lung cancer hereditability and other variants with weak effect may be lost in the GWAS approach due to the stringent significance level after multiple comparison correction. In this study, in order to identify important pathways involving the lung carcinogenesis, we performed a two-stage pathway analysis in GWAS of lung cancer in Han Chinese using gene set enrichment analysis (GSEA) method. Predefined pathways by BioCarta and KEGG databases were systematically evaluated on Nanjing study (Discovery stage: 1,473 cases and 1,962 controls) and the suggestive pathways were further to be validated in Beijing study (Replication stage: 858 cases and 1,115 controls). We found that four pathways (achPathway, metPathway, At1rPathway and rac1Pathway) were consistently significant in both studies and the values for combined dataset were 0.012, 0.010, 0.022 and 0.005 respectively. These results were stable after sensitivity analysis based on gene definition and gene overlaps between pathways. These findings may provide new insights into the etiology of lung cancer.
PLoS ONE 01/2013; 8(3):e57763. · 4.09 Impact Factor
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Ruyang Zhang,
Yang Zhao,
Minjie Chu,
Amar Mehta,
Yongyue Wei,
Yao Liu,
Pengcheng Xun,
Jianling Bai,
Hao Yu,
Li Su,
Hongxi Zhang,
Zhibin Hu,
Hongbing Shen, Feng Chen,
David C Christiani
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ABSTRACT: Occupational exposure to endotoxin is associated with decrements in pulmonary function, but how much variation in this association is explained by genetic variants is not well understood.
We aimed to identify single nucleotide polymorphisms (SNPs) that are associated with the rate of forced expiratory volume in one second (FEV1) decline by a large scale genetic association study in newly-hired healthy young female cotton textile workers.
DNA samples were genotyped using the Illumina Human CVD BeadChip. Change rate in FEV1 was modeled as a function of each SNP genotype in linear regression model with covariate adjustment. We controlled the type 1 error in study-wide level by permutation method. The false discovery rate (FDR) and the family-wise error rate (FWER) were set to be 0.10 and 0.15 respectively.
Two SNPs were found to be significant (P<6.29×10(-5)), including rs1910047 (P = 3.07×10(-5), FDR = 0.0778) and rs9469089 (P = 6.19×10(-5), FDR = 0.0967), as well as other eight suggestive (P<5×10(-4)) associated SNPs. Gene-gene and gene-environment interactions were also observed, such as rs1910047 and rs1049970 (P = 0.0418, FDR = 0.0895); rs9469089 and age (P = 0.0161, FDR = 0.0264). Genetic risk score analysis showed that the more risk loci the subjects carried, the larger the rate of FEV1 decline occurred (P trend = 3.01×10(-18)). However, the association was different among age subgroups (P = 7.11×10(-6)) and endotoxin subgroups (P = 1.08×10(-2)). Functional network analysis illustrates potential biological connections of all interacted genes.
Genetic variants together with environmental factors interact to affect the rate of FEV1 decline in cotton textile workers.
PLoS ONE 01/2013; 8(3):e59035. · 4.09 Impact Factor
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ABSTRACT: Genome-wide association study (GWAS) is a promising approach for identifying common genetic variants of the diseases on the basis of millions of single nucleotide polymorphisms (SNPs). In order to avoid low power caused by overmuch correction for multiple comparisons in single locus association study, some methods have been proposed by grouping SNPs together into a SNP set based on genomic features, then testing the joint effect of the SNP set. We compare the performances of principal component analysis (PCA), supervised principal component analysis (SPCA), kernel principal component analysis (KPCA), and sliced inverse regression (SIR). Simulated SNP sets are generated under scenarios of 0, 1 and ≥2 causal SNPs model. Our simulation results show that all of these methods can control the type I error at the nominal significance level. SPCA is always more powerful than the other methods at different settings of linkage disequilibrium structures and minor allele frequency of the simulated datasets. We also apply these four methods to a real GWAS of non-small cell lung cancer (NSCLC) in Han Chinese population.
PLoS ONE 01/2013; 8(5):e62495. · 4.09 Impact Factor
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ABSTRACT: Satellite-based remote sensing provides a unique opportunity to monitor air quality from space at global, continental, national and regional scales. Most current research focused on developing empirical models using ground measurements of the ambient particulate. However, the application of satellite-based exposure assessment in environmental health is still limited, especially for acute effects, because the development of satellite PM(2.5) model depends on the availability of ground measurements. We tested the hypothesis that MODIS AOD (aerosol optical depth) exposure estimates, obtained from NASA satellites, are directly associated with daily health outcomes. Three independent healthcare databases were used: unscheduled outpatient visits, hospital admissions, and mortality collected in Beijing metropolitan area, China during 2006. We use generalized linear models to compare the short-term effects of air pollution assessed by ground monitoring (PM(10)) with adjustment of absolute humidity (AH) and AH-calibrated AOD. Across all databases we found that both AH-calibrated AOD and PM(10) (adjusted by AH) were consistently associated with elevated daily events on the current day and/or lag days for cardiovascular diseases, ischemic heart diseases, and COPD. The relative risks estimated by AH-calibrated AOD and PM(10) (adjusted by AH) were similar. Additionally, compared to ground PM(10), we found that AH-calibrated AOD had narrower confidence intervals for all models and was more robust in estimating the current day and lag day effects. Our preliminary findings suggested that, with proper adjustment of meteorological factors, satellite AOD can be used directly to estimate the acute health impacts of ambient particles without prior calibrating to the sparse ground monitoring networks.
Environment international 12/2012; 51C:150-159. · 4.79 Impact Factor
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ABSTRACT: BACKGROUND: Population structure (PS), including population stratification and admixture, is a significant confounder in genome-wide association studies (GWAS), as it may produce spurious associations. Random forest (RF) has been increasingly applied in GWAS data analysis because of its advantage in analysing high dimensional genetic data. RF creates importance measures for single nucleotide polymorphisms (SNPs), which are helpful for feature selections. However, if PS is not appropriately corrected, RF tends to give high importance to disease-unrelated SNPs with different frequencies of allele or genotype among subpopulations, leading to inaccurate results. METHODS: In this study, the authors propose to correct for the confounding effect of PS by including the information of PS in RF analysis. The correction procedure starts by extracting the information of PS using EIGENSTRAT or multi-dimensional scaling clustering procedure from a large number of structure inference SNPs. Phenotype and genotypes adjusted by the information of PS are then used as the outcome and predictors in RF analysis. RESULTS: Extensive simulations indicate that the importance measure of the causal SNP is increased following the PS correction. By analysing a real dataset, the proposed correction removes the spurious association between the lactase gene and height. CONCLUSION: The authors propose a simple method to correct for PS in RF analysis on GWAS data. Further studies in real GWAS datasets are required to validate the robustness of the proposed approach.
International Journal of Epidemiology 11/2012; · 6.41 Impact Factor
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Yue-Jia Cheng,
Jessie Norris,
Chang-Jun Bao,
Qi Liang,
Jian-Li Hu,
Ying Wu,
Fen-Yang Tang,
Wen-Dong Liu,
Ke-Qin Ding,
Yang Zhao,
Zhi-Hang Peng,
Rong-Bin Yu,
Hua Wang,
Hong-Bing Shen, Feng Chen
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ABSTRACT: Spatial distribution rules and risk factors for syphilis were studied in Jiangsu province, People's Republic of China during 2005 and 2009. Trend surface analysis, spatial autocorrelation analysis and spatio-temporal clustering were applied with the incidence rates of the various counties in the province to determine spatial distribution rules and risk factors. Syphilis was found to be most severe in the southern region of the province where many counties could be shown to be hotspots with positive autocorrelation. Clusters were detected in the south-western region of Jiangsu with the county-level city of Yixing as the centre. Temperature, distance from railways and highways, and the normalised difference vegetation index were determined as supporting variables with regard to the transmission of the disease by both univariate and multivariate spatial correlation analyses. Interventions, including health education and awareness campaigns, should be strengthened throughout the province targeting the south-western areas, especially the clusters and hotspots detected in order to improve the situation.
Geospatial health 11/2012; 7(1):63-72. · 3.00 Impact Factor
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Guangfu Jin,
Hongxia Ma,
Chen Wu,
Juncheng Dai,
Ruyang Zhang,
Yongyong Shi,
Jiachun Lu,
Xiaoping Miao,
Meilin Wang,
Yifeng Zhou, [......],
Wen Tan,
Baosen Zhou,
Daru Lu,
Tangchun Wu,
Zhengdong Zhang, Feng Chen,
Xinru Wang,
Zhibin Hu,
Dongxin Lin,
Hongbing Shen
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ABSTRACT: Cancer susceptibility loci identified in reported genome-wide association studies (GWAS) are often tumor-specific; however, evidence of pleiotropy of some genes/loci has also been observed and biologically plausible. We hypothesized that there are important regions in the genome harboring genetic variants associated with risk of multiple types of cancer. In the current study, we attempted to map genetic variants that have consistent effects on risk of multiple cancers using our existing genome-wide scan data of lung cancer, noncardia gastric cancer, and esophageal squamous-cell carcinoma with overall 5,368 cases and 4,006 controls (GWAS stage), followed by a further evaluation in additional 9,001 cases with one of these cancer types and 11,436 controls (replication stage). Five variants satisfying the criteria of pleiotropy with p values from 1.10 × 10(-8) to 8.96 × 10(-6) for genome-wide scans of three cancer types were further evaluated in the replication stage. We found consistent associations of rs2494938 at 6p21.1 and rs2285947 at 7p15.3 with these three cancers in both GWAS and replication stages. In combined samples of GWAS and replication stages, the minor alleles of rs2494938 and rs2285947 were significantly associated with an increased risk of the cancers (odds ratio [OR] = 1.15, 95% confidence interval [CI], 1.10-1.19 and OR = 1.17, 95% CI, 1.12-1.21), with the p values being 1.20 × 10(-12) and 1.26 × 10(-16), respectively, which are at a genome-wide significance level. Our findings highlight the potential importance of variants at 6p21.1 and 7p15.3 in the susceptibility to multiple cancers.
The American Journal of Human Genetics 10/2012; · 10.60 Impact Factor
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Paula Tejera,
Nuala J Meyer, Feng Chen,
Rui Feng,
Yang Zhao,
D Shane O'Mahony,
Lin Li,
Chau-Chyun Sheu,
Rihong Zhai,
Zhaoxi Wang, [......],
Amy M Ahasic,
Peter F Clardy,
Michelle N Gong,
Angela J Frank,
Paul N Lanken,
B Taylor Thompson,
Jason D Christie,
Mark M Wurfel,
Grant E O'Keefe,
David C Christiani
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ABSTRACT: BACKGROUND: The role of genetics in the development of acute lung injury (ALI)/acute respiratory distress syndrome (ARDS) from direct or indirect lung injury has not been specifically investigated. The aim of this study was to identify genetic variants contributing to ALI/ARDS from pulmonary or extrapulmonary causes. METHODS: We conducted a multistage genetic association study. We first performed a large-scale genotyping (50K ITMAT-Broad_CARe Chip) in 1717 critically ill Caucasian patients with either pulmonary or extrapulmonary injury, to identify single nucleotide polymorphisms (SNPs) associated with the development of ARDS from direct or indirect insults to the lung. Identified SNPs (p≤0.0005) were validated in two separated populations (Stage II), with trauma (Population I; n=765) and pneumonia/pulmonary sepsis (Population II; n=838), as causes for ALI/ARDS. Genetic variants replicating their association with trauma related-ALI in Stage II were validated in a second trauma-associated ALI population (n=224, Stage III). RESULTS: In Stage I, non-overlapping SNPs were significantly associated with ARDS from direct/indirect lung injury, respectively. The association between rs1190286 (POPDC3) and reduced risk of ARDS from pulmonary injury was validated in Stage II (p<0.003). SNP rs324420 (FAAH) was consistently associated with increased risk of ARDS from extrapulmonary causes in two independent ALI-trauma populations (p<0.006, Stage II; p<0.05, Stage III). Meta-analysis confirmed these associations. CONCLUSIONS: Different genetic variants may influence ARDS susceptibility depending on direct versus indirect insults. Functional SNPs in POPDC3 and FAAH genes may be driving the association with direct and indirect ALI/ARDS, respectively.
Journal of Medical Genetics 10/2012; · 6.36 Impact Factor
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ABSTRACT: GWAS has facilitated greatly the discovery of risk SNPs associated with complex diseases. Traditional methods analyze SNP individually and are limited by low power and reproducibility since correction for multiple comparisons is necessary. Several methods have been proposed based on grouping SNPs into SNP sets using biological knowledge and/or genomic features. In this article, we compare the linear kernel machine based test (LKM) and principal components analysis based approach (PCA) using simulated datasets under the scenarios of 0 to 3 causal SNPs, as well as simple and complex linkage disequilibrium (LD) structures of the simulated regions. Our simulation study demonstrates that both LKM and PCA can control the type I error at the significance level of 0.05. If the causal SNP is in strong LD with the genotyped SNPs, both the PCA with a small number of principal components (PCs) and the LKM with kernel of linear or identical-by-state function are valid tests. However, if the LD structure is complex, such as several LD blocks in the SNP set, or when the causal SNP is not in the LD block in which most of the genotyped SNPs reside, more PCs should be included to capture the information of the causal SNP. Simulation studies also demonstrate the ability of LKM and PCA to combine information from multiple causal SNPs and to provide increased power over individual SNP analysis. We also apply LKM and PCA to analyze two SNP sets extracted from an actual GWAS dataset on non-small cell lung cancer.
PLoS ONE 09/2012; · 4.09 Impact Factor
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Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi 09/2012; 33(9):977-82.
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ABSTRACT: To investigate the performance of random forest method as a SNP screening procedure in high dimensional case-control data of lung cancer.
This study included 500 lung cancer patients and 517 controls. A total of 5 ml venous blood sample was collected from each participant. The genotypes were classified by GoldenGate platform, and 399 SNPs were selected. The random forest method was first applied to reduce the dimension, and then the traditional logistic regression method was used to analyze the variables and the genetic susceptibility between lung cancer and multiple SNPs was analyzed by AUC (areas under receiver operation characteristics (ROC) curves).
Fifty important variables, whose average importance scores were highest and whose error rates were lowest, were selected by random forest method. The importance scores of environmental variables (smoking, age and gender) were all listed at top 20, which were respectively 4.05, 3.12 and 1.16. After adjusting 3 environmental variables and false discovery rate (FDR), 6 SNPs were still significantly associated with lung cancer (FDR-P < 0.05). However, if traditional logistic regression analysis were directly applied, no significant SNPs were found. The likelihood testing result of AUC of the 2 ROC (one curve only included environmental variables and the other curve included environmental variables and SNPs) were 0.6491 ± 0.0172 and 0.6811 ± 0.0166 respectively; showed statistical significance of the association between the 6 SNPs and lung cancer (χ(2) = 43.82, P = 3.6×10(-11)).
Random forest analysis could first remove the turbulent SNPs and then make the analysis by logistic regression method. This could improve the testing efficacy, which is significantly better than single logistic regression analysis.
Zhonghua yu fang yi xue za zhi [Chinese journal of preventive medicine] 09/2012; 46(9):845-9.
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Jing Dong,
Zhibin Hu,
Chen Wu,
Huan Guo,
Baosen Zhou,
Jiachun Lv,
Daru Lu,
Kexin Chen,
Yongyong Shi,
Minjie Chu, [......],
Yuxia Zhao,
Haibo Zhang,
Ying Yan,
Christopher I Amos, Feng Chen,
Wen Tan,
Li Jin,
Tangchun Wu,
Dongxin Lin,
Hongbing Shen
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ABSTRACT: To find additional susceptibility loci for lung cancer, we tested promising associations from our previous genome-wide association study (GWAS) of lung cancer in the Chinese population in an extended validation sample size of 7,436 individuals with lung cancer (cases) and 7,483 controls. We found genome-wide significant (P < 5.0 × 10(-8)) evidence for three additional lung cancer susceptibility loci at 10p14 (rs1663689, close to GATA3, P = 2.84 × 10(-10)), 5q32 (rs2895680 in PPP2R2B-STK32A-DPYSL3, P = 6.60 × 10(-9)) and 20q13.2 (rs4809957 in CYP24A1, P = 1.20 × 10(-8)). We also found consistent associations for rs247008 at 5q31.1 (IL3-CSF2-P4HA2, P = 7.68 × 10(-8)) and rs9439519 at 1p36.32 (AJAP1-NPHP4, P = 3.65 × 10(-6)). Four of these loci showed evidence for interactions with smoking dose (P = 1.72 × 10(-10), P = 5.07 × 10(-3), P = 6.77 × 10(-3) and P = 4.49 × 10(-2) for rs2895680, rs4809957, rs247008 and rs9439519, respectively). These results advance our understanding of lung cancer susceptibility and highlight potential pathways that integrate genetic variants and smoking in the development of lung cancer.
Nature Genetics 07/2012; 44(8):895-9. · 35.53 Impact Factor
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ABSTRACT: Acute kidney injury frequently complicates septic shock and independently predicts mortality in this population. Clinical factors alone do not entirely account for differences in risk of acute kidney injury between patients. Genetic variants are likely to explain this differential susceptibility. To identify genetic variants linked to acute kidney injury susceptibility, we conducted a high-density genotyping association study in a large population of patients with septic shock.
Retrospective study.
Tertiary academic medical center.
One thousand two hundred and sixty-four patients with septic shock were analyzed to elucidate clinical risk factors associated with the development of acute kidney injury. Among them, 887 Caucasian patients were randomly split into discovery and validation cohorts and genotyped using the Illumina Human-CVD BeadChip (Illumina, San Diego, CA).
None.
Six hundred and twenty-seven of the 1,264 patients with septic shock and 441 of the 887 patients with genotyping data developed acute kidney injury within the first 72 hrs of intensive care unit admission. Five single nucleotide polymorphisms were associated with acute kidney injury in both the discovery and validation cohorts. Two of these were in the BCL2 gene and both were associated with a decreased risk of acute kidney injury (rs8094315: odds ratio 0.61, p = .0002; rs12457893: odds ratio 0.67, p = .0002, both for combined data). Bcl-2 is involved in the apoptosis pathway, which has previously been implicated in acute kidney injury. Another single nucleotide polymorphism was in the SERPINA4 gene, whose protein product, kallistatin, has been linked to apoptosis in the kidney.
Large-scale genotyping reveals two single nucleotide polymorphisms in the BCL2 gene and a single nucleotide polymorphism in the SERPINA4 gene associated with a decreased risk of developing acute kidney injury, supporting the putative role of apoptosis in the pathogenesis of acute kidney injury.
Critical care medicine 07/2012; 40(7):2116-23. · 6.37 Impact Factor
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ABSTRACT: To explore the gene-based principal component logistic regression model and its application in genome-wide association study. Using the simulated genome-wide single nucleotide polymorphism (SNPs) genotypes data, we proposed a practical statistical analysis strategy-'the principal component logistic regression model', based on the gene levels to assess the association between genetic variations and complex diseases. The simulation results showed that the P value of genes in related diseases was the smallest among the results from all the genes. The results of simulation indicated that not only it could reduce the degree of freedom through hypothesis testing but could also better understand the correlations between SNPs. The gene-based principal component logistic regression model seemed to have certain statistical power for testing the association between genetic genes and diseases in the genome-wide association studies.
Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi 06/2012; 33(6):622-5.
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ABSTRACT: The study was to investigate the incidence of HIV-1 and related factors, as well as predictors associated with retention in a cohort study among men who have sex with men (MSM) in Yangzhou, Jiangsu Province, China. A carefully designed 12-month prospective cohort study was conducted.
A total of 278 sero-negative MSM were recruited and followed up for 12 months starting from May, 2008. Participants were tested for HIV-1 at baseline, 6-month, and 12-month follow-up visits. Questionnaire interviews were conducted to collect information. The retention rate and HIV incidence were analyzed as functions of demographic and behavioral variables. Risk factors were identified by estimating the relative risks (RR) and respective 95% confidence intervals (CI) using a Poisson regression model, univariate and multivariate analyses and risk factors analyses. 71 (25.5%) and 45 (16.2%) of the 278 participants were retained at the 6-month and 12-month follow-up visits respectively. The incidence rates of HIV-1 were 5.65 and 6.67 per 100 person years (PY) respectively. Both having received condoms and having received lubricant were negatively associated with HIV sero-conversion at the 12 months' follow-up. Predictors associated with 12-month retention rate include Yangzhou residency (RR = 0.471, 95%CI: 0.275∼0.807, P = 0.006), having received condoms (RR = 0.065, 95%CI: 0.007∼0.572, P = 0.014), and having received VCTs (RR = 0.093, 95%CI: 0.010∼0.818, P = 0.032).
The incidence of HIV-1 among MSM in Yangzhou is relatively high and effective interventions are needed urgently. More attention should be focused on maintaining a higher retention rate.
PLoS ONE 01/2012; 7(12):e52731. · 4.09 Impact Factor
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ABSTRACT: Family based association study (FBAS) has the advantages of controlling for population stratification and testing for linkage and association simultaneously. We propose a retrospective multilevel model (rMLM) approach to analyze sibship data by using genotypic information as the dependent variable. Simulated data sets were generated using the simulation of linkage and association (SIMLA) program. We compared rMLM to sib transmission/disequilibrium test (S-TDT), sibling disequilibrium test (SDT), conditional logistic regression (CLR) and generalized estimation equations (GEE) on the measures of power, type I error, estimation bias and standard error. The results indicated that rMLM was a valid test of association in the presence of linkage using sibship data. The advantages of rMLM became more evident when the data contained concordant sibships. Compared to GEE, rMLM had less underestimated odds ratio (OR). Our results support the application of rMLM to detect gene-disease associations using sibship data. However, the risk of increasing type I error rate should be cautioned when there is association without linkage between the disease locus and the genotyped marker.
PLoS ONE 01/2012; 7(2):e31134. · 4.09 Impact Factor
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ABSTRACT: This research aimed to explore the application of ARIMA model of time series analysis in predicting influenza incidence and early warning in Jiangsu province and to provide scientific evidence for the prevention and control of influenza epidemic.
The database was created based on the data collected from monitoring sites in Jiangsu province from October 2005 to February 2010. The ARIMA model was constructed based on the number of weekly influenza-like illness (ILI) cases. Then the achieved ARIMA model was used to predict the number of influenza-like illness cases of March and April in 2010.
The ARIMA model of the influenza-like illness cases was (1 + 0.785B(2))(1-B) ln X(t) = (1 + 0.622B(2))ε(t). Here B stands for back shift operator, t stands for time, X(t) stands for the number of weekly ILI cases and ε(t) stands for random error. The residual error with 16 lags was white noise and the Ljung-Box test statistic for the model was 5.087, giving a P-value of 0.995. The model fitted the data well. True values of influenza-like illness cases from March 2010 to April 2010 were within 95%CI of predicted values obtained from present model.
The ARIMA model fits the trend of influenza-like illness in Jiangsu province.
Zhonghua yu fang yi xue za zhi [Chinese journal of preventive medicine] 12/2011; 45(12):1108-11.
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Rihong Zhai,
Yang Zhao,
Geoffrey Liu,
Monica Ter-Minassian,
I-Chen Wu,
Zhaoxi Wang,
Li Su,
Kofi Asomaning, Feng Chen,
Matthew H Kulke,
Xihong Lin,
Rebecca S Heist,
John C Wain,
David C Christiani
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ABSTRACT: Gastroesophageal reflux disease (GERD), higher body mass index (BMI), smoking, and genetic variants in angiogenic pathway genes have been individually associated with increased risk of esophageal adenocarcinoma. However, how angiogenic gene polymorphisms and environmental factors jointly affect esophageal adenocarcinoma development remains unclear.
By using a case-only design (n = 335), the authors examined interactions between 141 functional/tagging angiogenic single nucleotide polymorphisms (SNPs) and environmental factors (GERD, BMI, smoking) in modulating esophageal adenocarcinoma risk. Gene-environment interactions were assessed by a 2-step approach. First, the authors applied random forest to screen for important SNPs that had either main or interaction effects. Second, they used case-only logistic regression to assess the effects of gene-environment interactions on esophageal adenocarcinoma risk, adjusting for covariates and false-discovery rate.
Random forest analyses identified 3 sets of SNPs (17 SNPs-GERD, 26 SNPs-smoking, and 34 SNPs-BMI) that had the highest importance scores. In subsequent logistic regression analyses, interactions between 2 SNPs (rs2295778 of HIF1AN, rs13337626 of TSC2) and GERD, 2 SNPs (rs2295778 of HIF1AN, rs2296188 of VEGFR1) and smoking, and 7 SNPs (rs2114039 of PDGRFA, rs2296188 of VEGFR1, rs11941492 of VEGFR1, rs17708574 of PDGFRB, rs7324547 of VEGFR1, rs17619601 of VEGFR1, and rs17625898 of VEGFR1) and BMI were significantly associated with esophageal adenocarcinoma development (all false-discovery rates ≤0.10). Moreover, these interactions tended to have SNP dose-response effects for increased esophageal adenocarcinoma risk with increasing number of combined risk genotypes.
These findings suggest that genetic variations in angiogenic genes may modify esophageal adenocarcinoma susceptibility through interactions with environmental factors in an SNP dose-response manner.
Cancer 07/2011; 118(3):804-11. · 4.77 Impact Factor
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Zhibin Hu,
Chen Wu,
Yongyong Shi,
Huan Guo,
Xueying Zhao,
Zhihua Yin,
Lei Yang,
Juncheng Dai,
Lingmin Hu,
Wen Tan, [......],
Zhengdong Zhang, Feng Chen,
Xinru Wang,
Li Jin,
Jiachun Lu,
Baosen Zhou,
Daru Lu,
Tangchun Wu,
Dongxin Lin,
Hongbing Shen
[show abstract]
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ABSTRACT: Lung cancer is the leading cause of cancer-related deaths worldwide. To identify genetic factors that modify the risk of lung cancer in individuals of Chinese ancestry, we performed a genome-wide association scan in 5,408 subjects (2,331 individuals with lung cancer (cases) and 3,077 controls) followed by a two-stage validation among 12,722 subjects (6,313 cases and 6,409 controls). The combined analyses identified six well-replicated SNPs with independent effects and significant lung cancer associations (P < 5.0 × 10(-8)) located in TP63 (rs4488809 at 3q28, P = 7.2 × 10(-26)), TERT-CLPTM1L (rs465498 and rs2736100 at 5p15.33, P = 1.2 × 10(-20) and P = 1.0 × 10(-27), respectively), MIPEP-TNFRSF19 (rs753955 at 13q12.12, P = 1.5 × 10(-12)) and MTMR3-HORMAD2-LIF (rs17728461 and rs36600 at 22q12.2, P = 1.1 × 10(-11) and P = 6.2 × 10(-13), respectively). Two of these loci (13q12.12 and 22q12.2) were newly identified in the Chinese population. These results suggest that genetic variants in 3q28, 5p15.33, 13q12.12 and 22q12.2 may contribute to the susceptibility of lung cancer in Han Chinese.
Nature Genetics 07/2011; 43(8):792-6. · 35.53 Impact Factor
