313 research outputs found

    Genetic Variants of PTPN2 are Associated with Lung Cancer Risk: a Re-Analysis of Eight GWASs in the TRICL-ILCCO Consortium

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    The T-cell protein tyrosine phosphatase (TCPTP) pathway consists of signaling events mediated by TCPTP. Mutations and genetic variants of some genes in the TCPTP pathway are associated with lung cancer risk and survival. In the present study, we first investigated associations of 5,162 single nucleotide polymorphisms (SNPs) in 43 genes of this TCPTP pathway with lung cancer risk by using summary data of six published genome-wide association studies (GWAS) of 12,160 cases and 16,838 controls. We identified 11 independent SNPs in eight genes after correction for multiple comparisons by a false discovery rate \u3c 0.20. Then, we performed in silico functional analyses for these 11 SNPs by eQTL analysis, two of which, PTPN2 SNPs rs2847297 and rs2847282, were chosen as tagSNPs. We further included two additional GWAS datasets of Harvard University (984 cases and 970 controls) and deCODE (1,319 cases and 26,380 controls), and the overall effects of these two SNPs among all eight GWAS studies remained significant (OR = 0.95, 95% CI = 0.92–0.98, and P = 0.004 for rs2847297; OR = 0.95, 95% CI = 0.92–0.99, and P = 0.009 for rs2847282). In conclusion, the PTPN2 rs2847297 and rs2847282 may be potential susceptible loci for lung cancer risk

    Aspirin and NSAID use and lung cancer risk: a pooled analysis in the International Lung Cancer Consortium (ILCCO)

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    Purpose: To investigate the hypothesis that non-steroidal anti-inflammatory drugs (NSAIDs) lower lung cancer risk. Methods: We analysed pooled individual-level data from seven case-control and one cohort study in the International Lung Cancer Consortium (ILCCO). Relative risks for lung cancer associated with self-reported history of aspirin and other NSAID use were estimated within individual studies using logistic regression or proportional hazards models, adjusted for packyears of smoking, age, calendar period, ethnicity and education and were combined using random effects meta-analysis. Results: A total of 4,309 lung cancer cases (mean age at diagnosis 65 years, 45% adenocarcinoma and 22% squamous-cell carcinoma) and 58,301 non-cases/controls were included. Amongst controls, 34% had used NSAIDs in the past (81% of them used aspirin). After adjustment for negative confounding by smoking, ever-NSAID use (affirmative answer to the study-specific question on NSAID use) was associated with a 26% reduction (95% confidence interval 8 to 41%) in lung cancer risk in men, but not in women (3% increase (-11% to 30%)). In men, the association was stronger in current and former smokers, and for squamous-cell carcinoma than for adenocarcinomas, but there was no trend with duration of use. No differences were found in the effects on lung cancer risk of aspirin and non-aspirin NSAIDs. Conclusions Evidence from ILCCO suggests that NSAID use in men confers a modest protection for lung cancer, especially amongst ever-smokers. Additional investigation is needed regarding the possible effects of age, duration, dose and type of NSAID and whether effect modification by smoking status or sex exists

    Natural and Orthogonal Interaction framework for modeling gene-environment interactions with application to lung cancer

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    Objectives: We aimed at extending the Natural and Orthogonal Interaction (NOIA) framework, developed for modeling gene-gene interactions in the analysis of quantitative traits, to allow for reduced genetic models, dichotomous traits, and gene-environment interactions. We evaluate the performance of the NOIA statistical models using simulated data and lung cancer data. Methods: The NOIA statistical models are developed for additive, dominant, and recessive genetic models as well as for a binary environmental exposure. Using the Kronecker product rule, a NOIA statistical model is built to model gene-environment interactions. By treating the genotypic values as the logarithm of odds, the NOIA statistical models are extended to the analysis of case-control data. Results: Our simulations showed that power for testing associations while allowing for interaction using the NOIA statistical model is much higher than using functional models for most of the scenarios we simulated. When applied to lung cancer data, much smaller p values were obtained using the NOIA statistical model for either the main effects or the SNP-smoking interactions for some of the SNPs tested. Conclusion: The NOIA statistical models are usually more powerful than the functional models in detecting main effects and interaction effects for both quantitative traits and binary traits. Copyright (C) 2012 S. Karger AG, Base

    A multivariable Mendelian randomization analysis investigating smoking and alcohol consumption in oral and oropharyngeal cancer

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    The independent effects of smoking and alcohol in head and neck cancer are not clear, given the strong association between these risk factors. Their apparent synergistic effect reported in previous observational studies may also underestimate independent effects. Here we report multivariable Mendelian randomization performed in a two-sample approach using summary data on 6,034 oral/oropharyngeal cases and 6,585 controls from a recent genome-wide association study. Our results demonstrate strong evidence for an independent causal effect of smoking on oral/oropharyngeal cancer (IVW OR 2.6, 95% CI = 1.7, 3.9 per standard deviation increase in lifetime smoking behaviour) and an independent causal effect of alcohol consumption when controlling for smoking (IVW OR 2.1, 95% CI = 1.1, 3.8 per standard deviation increase in drinks consumed per week). This suggests the possibility that the causal effect of alcohol may have been underestimated. However, the extent to which alcohol is modified by smoking requires further investigation

    Mendelian randomization study of adiposity-related traits and risk of breast, ovarian, prostate, lung and colorectal cancer

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    Background: Adiposity traits have been associated with risk of many cancers in observational studies, but whether these associations are causal is unclear. Mendelian randomization (MR) uses genetic predictors of risk factors as instrumental variables to eliminate reverse causation and reduce confounding bias. We performed MR analyses to assess the possible causal relationship of birthweight, childhood and adult body mass index (BMI), and waist-hip ratio (WHR) on the risks of breast, ovarian, prostate, colorectal and lung cancers. Methods: We tested the association between genetic risk scores and each trait using summary statistics from published genome-wide association studies (GWAS) and from 51 537 cancer cases and 61 600 controls in the Genetic Associations and Mechanisms in Oncology (GAME-ON) Consortium. Results: We found an inverse association between the genetic score for childhood BMI and risk of breast cancer [odds ratio (OR)=0.71 per standard deviation (s.d.) increase in childhood BMI; 95% confidence interval (CI): 0.60, 0.80; P=6.5×10-5). We also found the genetic score for adult BMI to be inversely associated with breast cancer risk (OR=0.66 per s.d. increase in BMI; 95% CI: 0.57, 0.77; P=2.5×10-7), and positively associated with ovarian cancer (OR=1.35; 95% CI: 1.05, 1.72; P=0.017), lung cancer (OR=1.27; 95% CI: 1.09, 1.49; P=2.9×10-3) and colorectal cancer (OR=1.39; 95% CI: 1.06, 1.82, P=0.016). The inverse association between genetically predicted adult BMI and breast cancer risk remained even after adjusting for directional pleiotropy via MR-Egger regression. Conclusions: Findings from this study provide additional understandings of the complex relationship between adiposity and cancer risks. Our results for breast and lung cancer are particularly interesting, given previous reports of effect heterogeneity by menopausal status and smoking status.</p

    Incorporating multiple sets of eQTL weights into gene-by-environment interaction analysis identifies novel susceptibility loci for pancreatic cancer.

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    It is of great scientific interest to identify interactions between genetic variants and environmental exposures that may modify the risk of complex diseases. However, larger sample sizes are usually required to detect gene-by-environment interaction (G × E) than required to detect genetic main association effects. To boost the statistical power and improve the understanding of the underlying molecular mechanisms, we incorporate functional genomics information, specifically, expression quantitative trait loci (eQTLs), into a data-adaptive G × E test, called aGEw. This test adaptively chooses the best eQTL weights from multiple tissues and provides an extra layer of weighting at the genetic variant level. Extensive simulations show that the aGEw test can control the Type 1 error rate, and the power is resilient to the inclusion of neutral variants and noninformative external weights. We applied the proposed aGEw test to the Pancreatic Cancer Case-Control Consortium (discovery cohort of 3,585 cases and 3,482 controls) and the PanScan II genome-wide association study data (replication cohort of 2,021 cases and 2,105 controls) with smoking as the exposure of interest. Two novel putative smoking-related pancreatic cancer susceptibility genes, TRIP10 and KDM3A, were identified. The aGEw test is implemented in an R package aGE.We thank the two anonymous reviewers for their constructive comments. This research was supported by the National Institutes of Health (NIH) grant R01CA169122; P.W. was supported by NIH grants R01HL116720 and R21HL126032. S.H.O. was supported by NIH grant P30CA008748. R.E.N. and the Queensland Pancreatic Cancer Study were funded by the Australian National Health and Medical Research Council. The authors thank Ms. Jessica Swann and the National Institute of Statistical Sciences writing workshop for editorial assistance and suggestions. The authors acknowledge the Texas Advanced Computing Center at The University of Texas at Austin for providing computing resources. The authors alone are responsible for the views expressed in this article and they do not necessarily represent the views, decisions or policies of the institutions with which they are affiliated. The authors declare that there is no conflict of interest

    Large-scale whole exome sequencing studies identify two genes,CTSL and APOE, associated with lung cancer.

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    Common genetic variants associated with lung cancer have been well studied in the past decade. However, only 12.3% heritability has been explained by these variants. In this study, we investigate the contribution of rare variants (RVs) (minor allele frequency <0.01) to lung cancer through two large whole exome sequencing case-control studies. We first performed gene-based association tests using a novel Bayes Factor statistic in the International Lung Cancer Consortium, the discovery study (European, 1042 cases vs. 881 controls). The top genes identified are further assessed in the UK Biobank (European, 630 cases vs. 172 864 controls), the replication study. After controlling for the false discovery rate, we found two genes, CTSL and APOE, significantly associated with lung cancer in both studies. Single variant tests in UK Biobank identified 4 RVs (3 missense variants) in CTSL and 2 RVs (1 missense variant) in APOE stongly associated with lung cancer (OR between 2.0 and 139.0). The role of these genetic variants in the regulation of CTSL or APOE expression remains unclear. If such a role is established, this could have important therapeutic implications for lung cancer patients
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