Studies of related individuals have consistently demonstrated notable familial aggregation of cancer. We aim to estimate the heritability and genetic correlation attributable to the additive effects of common single-nucleotide polymorphisms (SNPs) for cancer at 13 anatomical sites.
Between 2007 and 2014, the US National Cancer Institute has generated data from genome-wide association studies (GWAS) for 49 492 cancer case patients and 34 131 control patients. We apply novel mixed model methodology (GCTA) to this GWAS data to estimate the heritability of individual cancers, as well as the proportion of heritability attributable to cigarette smoking in smoking-related cancers, and the genetic correlation between pairs of cancers.
GWAS heritability was statistically significant at nearly all sites, with the estimates of array-based heritability, hl
2, on the liability threshold (LT) scale ranging from 0.05 to 0.38. Estimating the combined heritability of multiple smoking characteristics, we calculate that at least 24% (95% confidence interval [CI] = 14% to 37%) and 7% (95% CI = 4% to 11%) of the heritability for lung and bladder cancer, respectively, can be attributed to genetic determinants of smoking. Most pairs of cancers studied did not show evidence of strong genetic correlation. We found only four pairs of cancers with marginally statistically significant correlations, specifically kidney and testes (ρ = 0.73, SE = 0.28), diffuse large B-cell lymphoma (DLBCL) and pediatric osteosarcoma (ρ = 0.53, SE = 0.21), DLBCL and chronic lymphocytic leukemia (CLL) (ρ = 0.51, SE =0.18), and bladder and lung (ρ = 0.35, SE = 0.14). Correlation analysis also indicates that the genetic architecture of lung cancer differs between a smoking population of European ancestry and a nonsmoking Asian population, allowing for the possibility that the genetic etiology for the same disease can vary by population and environmental exposures.
Our results provide important insights into the genetic architecture of cancers and suggest new avenues for investigation.
Few studies have demonstrated gene/environment interactions in cancer research. Using data on high-risk occupations for 2258 case patients and 2410 control patients from two bladder cancer studies, we observed that three of 16 known or candidate bladder cancer susceptibility variants displayed statistically significant and consistent evidence of additive interactions; specifically, the GSTM1 deletion polymorphism (P
interaction ≤ .001), rs11892031 (UGT1A, P
interaction = .01), and rs798766 (TMEM129-TACC3-FGFR3, P
interaction = .03). There was limited evidence for multiplicative interactions. When we examined detailed data on a prevalent occupational exposure associated with increased bladder cancer risk, straight metalworking fluids, we also observed statistically significant additive interaction for rs798766 (TMEM129-TACC3-FGFR3, P
interaction = .02), with the interaction more apparent in patients with tumors positive for FGFR3 expression. All statistical tests were two-sided. The interaction we observed for rs798766 (TMEM129-TACC3-FGFR3) with specific exposure to straight metalworking fluids illustrates the value of integrating germline genetic variation, environmental exposures, and tumor marker data to provide insight into the mechanisms of bladder carcinogenesis.
Current methods for detecting genetic associations lack full consideration of the background effects of environmental exposures. Recently proposed methods to account for environmental exposures have focused on logistic regressions with gene-environment interactions. In this report, we developed a test for genetic association, encompassing a broad range of risk models, including linear, logistic and probit, for specifying joint effects of genetic and environmental exposures. We obtained the test statistics by maximizing over a class of score tests, each of which involves modified standard tests of genetic association through a weight function. This weight function reflects the potential heterogeneity of the genetic effects by levels of environmental exposures under a particular model. Simulation studies demonstrate the robust power of these methods for detecting genetic associations under a wide range of scenarios. Applications of these methods are further illustrated using data from genome-wide association studies of type 2 diabetes with body mass index and of lung cancer risk with smoking.
gene-environment interaction; GWAS; score test; SNPs; environmental exposures
Disease classification system increasingly incorporates information on pathogenic mechanisms to predict clinical outcomes and response to therapy and intervention. Technological advancements to interrogate omics (genomics, epigenomics, transcriptomics, proteomics, metabolomics, metagenomics, interactomics, etc.) provide widely-open opportunities in population-based research. Molecular pathological epidemiology (MPE) represents integrative science of molecular pathology and epidemiology. This unified paradigm requires multidisciplinary collaboration between pathology, epidemiology, biostatistics, bioinformatics, and computational biology. Integration of these fields enables better understanding of etiologic heterogeneity, disease continuum, causal inference, and the impact of environment, diet, lifestyle, host factors (including genetics and immunity), and their interactions on disease evolution. Hence, the Second International MPE Meeting was held in Boston in December 2014, with aims to: (1) develop conceptual and practical frameworks; (2) cultivate and expand opportunities; (3) address challenges; and (4) initiate the effort of specifying guidelines for MPE. The meeting mainly consisted of presentations of method developments and recent data in various malignant neoplasms and tumors (breast, prostate, ovarian and colorectal cancers, renal cell carcinoma, lymphoma, and leukemia), followed by open discussion sessions on challenges and future plans. In particular, we recognized need for efforts to further develop statistical methodologies. This meeting provided an unprecedented opportunity for interdisciplinary collaboration, consistent with the purposes of the BD2K (Big Data to Knowledge), GAME-ON (Genetic Associations and Mechanisms in Oncology), and Precision Medicine Initiatives of the U.S.A. National Institute of Health. The MPE Meeting Series can help advance transdisciplinary population science, and optimize training and education systems for 21st century medicine and public health.
epidemiologic method; molecular pathologic epidemiology; personalized medicine; systems biology; translational epidemiology; unique disease principle
Meta-analysis of multiple genome-wide association studies (GWAS) has become an effective approach for detecting single nucleotide polymorphism (SNP) associations with complex traits. However, it is difficult to integrate the readily accessible SNP-level summary statistics from a meta-analysis into more powerful multi-marker testing procedures, which generally require individual-level genetic data. We developed a general procedure called Summary based Adaptive Rank Truncated Product (sARTP) for conducting gene and pathway meta-analysis that uses only SNP-level summary statistics in combination with genotype correlation estimated from a panel of individual-level genetic data. We demonstrated the validity and power advantage of sARTP through empirical and simulated data. We conducted a comprehensive pathway-based meta-analysis with sARTP on type 2 diabetes (T2D) by integrating SNP-level summary statistics from two large studies consisting of 19,809 T2D cases and 111,181 controls with European ancestry. Among 4,713 candidate pathways from which genes in neighborhoods of 170 GWAS established T2D loci were excluded, we detected 43 T2D globally significant pathways (with Bonferroni corrected p-values < 0.05), which included the insulin signaling pathway and T2D pathway defined by KEGG, as well as the pathways defined according to specific gene expression patterns on pancreatic adenocarcinoma, hepatocellular carcinoma, and bladder carcinoma. Using summary data from 8 eastern Asian T2D GWAS with 6,952 cases and 11,865 controls, we showed 7 out of the 43 pathways identified in European populations remained to be significant in eastern Asians at the false discovery rate of 0.1. We created an R package and a web-based tool for sARTP with the capability to analyze pathways with thousands of genes and tens of thousands of SNPs.
As GWAS continue to grow in sample size, it is evident that these studies need to be utilized more effectively for detecting individual susceptibility variants, and more importantly, to provide insight into global genetic architecture of complex traits. Towards this goal, identifying association with respect to a collection of variants in biological pathways can be particularly insightful for understanding how networks of genes might be affecting pathophysiology of diseases. Here we present a new pathway analysis procedure that can be conducted using summary-level association statistics, which have become the main vehicle for performing meta-analysis of individual genetic variants across studies in large consortia. Through simulation studies we showed the proposed method was more powerful than the existing state-of-art method. We carried out a comprehensive pathway analysis of 4,713 candidate pathways on their association with T2D using two large studies with European ancestry and identified 43 T2D-associated pathways. Further examinations of those 43 pathways in 8 Asian studies showed that some pathways were trans-ethnically associated with T2D. This analysis clearly highlights novel T2D-associated pathways beyond what has been known from single-variant association analysis reported from largest GWAS to date. We also identify a novel locus for T2D in the European populations at chromosome 17q21 (rs1058018, p = 3.06 × 10−8).
To investigate large structural clonal mosaicism of chromosome X, we analysed the SNP microarray intensity data of 38,303 women from cancer genome-wide association studies (20,878 cases and 17,425 controls) and detected 124 mosaic X events >2 Mb in 97 (0.25%) women. Here we show rates for X-chromosome mosaicism are four times higher than mean autosomal rates; X mosaic events more often include the entire chromosome and participants with X events more likely harbour autosomal mosaic events. X mosaicism frequency increases with age (0.11% in 50-year olds; 0.45% in 75-year olds), as reported for Y and autosomes. Methylation array analyses of 33 women with X mosaicism indicate events preferentially involve the inactive X chromosome. Our results provide further evidence that the sex chromosomes undergo mosaic events more frequently than autosomes, which could have implications for understanding the underlying mechanisms of mosaic events and their possible contribution to risk for chronic diseases.
It is unclear how often genetic mosaicism of chromosome X arises. Here, the authors examine women with cancer and cancer-free controls and show that X chromosome mosaicism occurs more frequently than on autosomes, especially on the inactive X chromosome, but is not linked to non-haematologic cancer risk
Chronic lymphocytic leukemia (CLL) is a common lymphoid malignancy with strong heritability. To further understand the genetic susceptibility for CLL and identify common loci associated with risk, we conducted a meta-analysis of four genome-wide association studies (GWAS) composed of 3,100 cases and 7,667 controls with follow-up replication in 1,958 cases and 5,530 controls. Here we report three new loci at 3p24.1 (rs9880772, EOMES, P=2.55 × 10−11), 6p25.2 (rs73718779, SERPINB6, P=1.97 × 10−8) and 3q28 (rs9815073, LPP, P=3.62 × 10−8), as well as a new independent SNP at the known 2q13 locus (rs9308731, BCL2L11, P=1.00 × 10−11) in the combined analysis. We find suggestive evidence (P<5 × 10−7) for two additional new loci at 4q24 (rs10028805, BANK1, P=7.19 × 10−8) and 3p22.2 (rs1274963, CSRNP1, P=2.12 × 10−7). Pathway analyses of new and known CLL loci consistently show a strong role for apoptosis, providing further evidence for the importance of this biological pathway in CLL susceptibility.
Chronic lymphocytic leukemia is a highly inheritable cancer. Here the authors conduct a metaanalysis of four genome-wide association studies and identify three novel loci located near EOMES, SERPINB6 and LPP associated with risk of this disease.
We confirmed strong association of rs78378222:A>C (per allele odds ratio [OR] = 3.14; P = 6.48 × 10−11), a germline rare single-nucleotide polymorphism (SNP) in TP53, via imputation of a genome-wide association study of glioma (1,856 cases and 4,955 controls). We subsequently performed integrative analyses on the Cancer Genome Atlas (TCGA) data for GBM (glioblastoma multiforme) and LUAD (lung adenocarcinoma). Based on SNP data, we imputed genotypes for rs78378222 and selected individuals carrying rare risk allele (C). Using RNA sequencing data, we observed aberrant transcripts with ~3 kb longer than normal for those individuals. Using exome sequencing data, we further showed that loss of haplotype carrying common protective allele (A) occurred somatically in GBM but not in LUAD. Our bioinformatic analysis suggests rare risk allele (C) disrupts mRNA termination, and an allelic loss of a genomic region harboring common protective allele (A) occurs during tumor initiation or progression for glioma.
glioma; TP53; rare SNP; TCGA
Recent evidence from several relatively small nested case-control studies in prospective cohorts shows an association between longer telomere length measured phenotypically in peripheral white blood cell (WBC) DNA and increased lung cancer risk. We sought to further explore this relationship by examining a panel of 7 telomere-length associated genetic variants in a large study of 5,457 never-smoking female Asian lung cancer cases and 4,493 never-smoking female Asian controls using data from a previously reported genome-wide association study. Using a group of 1,536 individuals with phenotypically measured telomere length in WBCs in the prospective Shanghai Women’s Health study, we demonstrated the utility of a genetic risk score (GRS) of 7 telomere-length associated variants to predict telomere length in an Asian population. We then found that GRSs used as instrumental variables to predict longer telomere length were associated with increased lung cancer risk (OR = 1.51 (95% CI=1.34–1.69) for upper vs. lower quartile of the weighted GRS, P-value=4.54×10−14) even after removing rs2736100 (P-value=4.81×10−3), a SNP in the TERT locus robustly associated with lung cancer risk in prior association studies. Stratified analyses suggested the effect of the telomere-associated GRS is strongest among younger individuals. We found no difference in GRS effect between adenocarcinoma and squamous cell subtypes. Our results indicate that a genetic background that favors longer telomere length may increase lung cancer risk, which is consistent with earlier prospective studies relating longer telomere length with increased lung cancer risk.
association study; genetics; lung cancer; telomere length; genetic risk score
Risk-prediction models need careful calibration to ensure they produce unbiased estimates of risk for subjects in the underlying population given their risk-factor profiles. As subjects with extreme high or low risk may be the most affected by knowledge of their risk estimates, checking the adequacy of risk models at the extremes of risk is very important for clinical applications. We propose a new approach to test model calibration targeted toward extremes of disease risk distribution where standard goodness-of-fit tests may lack power due to sparseness of data. We construct a test statistic based on model residuals summed over only those individuals who pass high and/or low risk thresholds and then maximize the test statistic over different risk thresholds. We derive an asymptotic distribution for the max-test statistic based on analytic derivation of the variance–covariance function of the underlying Gaussian process. The method is applied to a large case–control study of breast cancer to examine joint effects of common single nucleotide polymorphisms (SNPs) discovered through recent genome-wide association studies. The analysis clearly indicates a non-additive effect of the SNPs on the scale of absolute risk, but an excellent fit for the linear-logistic model even at the extremes of risks.
Case–control studies; Gene–gene and gene–environment interactions; Genome-wide association studies; Goodness-of-fit tests; Polygenic score; Risk stratification
Genome-wide association studies (GWAS) have mapped risk alleles for at least 10 distinct cancers to a small region of 63 000 bp on chromosome 5p15.33. This region harbors the TERT and CLPTM1L genes; the former encodes the catalytic subunit of telomerase reverse transcriptase and the latter may play a role in apoptosis. To investigate further the genetic architecture of common susceptibility alleles in this region, we conducted an agnostic subset-based meta-analysis (association analysis based on subsets) across six distinct cancers in 34 248 cases and 45 036 controls. Based on sequential conditional analysis, we identified as many as six independent risk loci marked by common single-nucleotide polymorphisms: five in the TERT gene (Region 1: rs7726159, P = 2.10 × 10−39; Region 3: rs2853677, P = 3.30 × 10−36 and PConditional = 2.36 × 10−8; Region 4: rs2736098, P = 3.87 × 10−12 and PConditional = 5.19 × 10−6, Region 5: rs13172201, P = 0.041 and PConditional = 2.04 × 10−6; and Region 6: rs10069690, P = 7.49 × 10−15 and PConditional = 5.35 × 10−7) and one in the neighboring CLPTM1L gene (Region 2: rs451360; P = 1.90 × 10−18 and PConditional = 7.06 × 10−16). Between three and five cancers mapped to each independent locus with both risk-enhancing and protective effects. Allele-specific effects on DNA methylation were seen for a subset of risk loci, indicating that methylation and subsequent effects on gene expression may contribute to the biology of risk variants on 5p15.33. Our results provide strong support for extensive pleiotropy across this region of 5p15.33, to an extent not previously observed in other cancer susceptibility loci.
We systematically examined common genetic variants in the 9p21 region and risk of eight cancers, based on GWAS data deposited in dbGaP. A number of SNPs were associated with multiple cancers, which are not confined to the CDKN2/MTAP cluster.
The chromosome 9p21 region has been implicated in the pathogenesis of multiple cancers. We analyzed 9p21 single nucleotide polymorphisms (SNPs) from eight genome-wide association studies (GWAS) with data deposited in dbGaP, including studies of esophageal squamous cell carcinoma (ESCC), gastric cancer (GC), pancreatic cancer, renal cell carcinoma (RCC), lung cancer (LC), breast cancer (BrC), bladder cancer (BC) and prostate cancer (PrC). The number of subjects ranged from 2252 (PrC) to 7619 (LC). SNP-level analyses for each cancer were conducted by logistic regression or random-effects meta-analysis. A subset-based statistical approach (ASSET) was performed to combine SNP-level P values across multiple cancers. We calculated gene-level P values using the adaptive rank truncated product method. We identified that rs1063192 and rs2157719 in the CDKN2A/2B region were significantly associated with ESCC and rs2764736 (3′ of TUSC1) was associated with BC (P ≤ 2.59 × 10−6). ASSET analyses identified four SNPs significantly associated with multiple cancers: rs3731239 (CDKN2A intronic) with ESCC, GC and BC (P = 3.96 × 10−
4); rs10811474 (3′ of IFNW1) with RCC and BrC (P = 0.001); rs12683422 (LINGO2 intronic) with RCC and BC (P = 5.93 × 10−
4) and rs10511729 (3′ of ELAVL2) with LC and BrC (P = 8.63 × 10−
4). At gene level, CDKN2B, CDKN2A and CDKN2B-AS1 were significantly associated with ESCC (P ≤ 4.70 × 10−
5). Rs10511729 and rs10811474 were associated with cis-expression of 9p21 genes in corresponding cancer tissues in the expression quantitative trait loci analysis. In conclusion, we identified several genetic variants in the 9p21 region associated with the risk of multiple cancers, suggesting that this region may contribute to a shared susceptibility across different cancer types.
Genome-wide association studies (GWAS) have identified hundreds of genetic susceptibility loci for cancers and other complex diseases. However, the public health and clinical relevance of these discoveries is unclear. Evaluating the combined associations of genetic and environmental risk factors, particularly those that can be modified, will be critical in assessing the utility of genetic information for risk stratified prevention. In this commentary, using breast cancer as a model, we show that genetic information in combination with other risk factors can provide levels of risk stratification that could be useful for individual decision-making or population-based prevention programs. Our projections are theoretical and rely on a number of assumptions, including multiplicative models for the combined associations of the different risk factors, which need confirmation. Thus, analyses of epidemiological studies with high-quality risk factor information, as well as prevention trials, are needed to empirically assess the impact of genetics in risk stratified prevention.
We report a set of tools to estimate the number of susceptibility loci and the distribution of their effect sizes for a trait on the basis of discoveries from existing genome-wide association studies (GWASs). We propose statistical power calculations for future GWASs using estimated distributions of effect sizes. Using reported GWAS findings for height, Crohn’s disease and breast, prostate and colorectal (BPC) cancers, we determine that each of these traits is likely to harbor additional loci within the spectrum of low-penetrance common variants. These loci, which can be identified from sufficiently powerful GWASs, together could explain at least 15–20% of the known heritability of these traits. However, for BPC cancers, which have modest familial aggregation, our analysis suggests that risk models based on common variants alone will have modest discriminatory power (63.5% area under curve), even with new discoveries.
Alcohol consumption is an established risk factor for breast cancer. Whether associations vary by specific tumor characteristics independent of other characteristics is unclear. We evaluated the association between alcohol consumption and breast cancer risk in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial cohort (54,562 women aged 55–74 years recruited at 10 US screening centers between 1993 and 2001; median follow-up, 8.9 years; 1,905 invasive breast cancer cases). Hazard ratios and 95% confidence intervals for subtypes defined by histological type and estrogen receptor (ER)/progesterone receptor (PR) status were calculated with standard Cox models. A novel 2-stage Cox model assessed heterogeneity in risk for individual tumor characteristics while adjusting for others. Significant trends across categories of alcohol consumption were observed, with hazard ratios for those consuming 7 or more drinks per week versus never drinkers as follows: for estrogen receptor–positive (ER+) cancer, 1.48 (95% confidence interval (CI): 1.19, 1.83); for progesterone receptor–positive (PR+) cancer, 1.64 (95% CI: 1.31, 2.06); for ER+/PR+ cancer, 1.63 (95% CI: 1.30, 2.05); and for mixed ductal/lobular cancer, 2.51 (95% CI: 1.20, 5.24). For ER+ and PR+ cancers, trends were significant for ductal and mixed ductal/lobular types. PR status explained the positive association with ER status (for ER status, Pheterogeneity = 0.70 after adjustment for PR status). Alcohol consumption was not associated with all breast cancer subtypes. Future work should emphasize large collaborative studies, precise definition of subtypes, and adjustment for correlated tumor characteristics.
alcohol; breast cancer; ductal carcinoma; estrogen receptor; lobular carcinoma; progesterone receptor
With the advent of modern genomic methods to adjust for population stratification, the use of external or publicly available controls has become an attractive option for reducing the cost of large-scale case-control genetic association studies. In this article, we study the estimation of joint effects of genetic and environmental exposures from a case-control study where data on genome-wide markers are available on the cases and a set of external controls while data on environmental exposures are available on the cases and a set of internal controls. We show that under such a design, one can exploit an assumption of gene-environment independence in the underlying population to estimate the gene-environment joint effects, after adjustment for population stratification. We develop a semiparametric profile likelihood method and related pseudolikelihood and working likelihood methods that are easy to implement in practice. We propose variance estimators for the methods based on asymptotic theory. Simulation is used to study the performance of the methods, and data from a multi-centre genome-wide association study of bladder cancer is further used to illustrate their application.
Case-control study; Gene-environment interaction; Genetic epidemiology; Genome-wide association study; Logistic regression; Population stratification; Profile likelihood; Retrospective study; Semiparametric method
Metabolic syndrome (MetS) refers to the clustering of cardio-metabolic risk factors including dyslipidemia, central adiposity, hypertension and hyperglycemia in individuals. Identification of pleiotropic genetic factors associated with MetS traits may shed light on key pathways or mediators underlying MetS.
Methods and Results
Using the Metabochip array in 15,148 African Americans (AA) from the PAGE Study, we identify susceptibility loci and investigate pleiotropy among genetic variants using a subset-based meta-analysis method, ASsociation-analysis-based-on-subSETs (ASSET). Unlike conventional models which lack power when associations for MetS components are null or have opposite effects, ASSET uses one-sided tests to detect positive and negative associations for components separately and combines tests accounting for correlations among components. With ASSET, we identify 27 SNPs in 1 glucose and 4 lipids loci (TCF7L2, LPL, APOA5, CETP, LPL, APOC1/APOE/TOMM40) significantly associated with MetS components overall, all P< 2.5e-7, the Bonferroni adjusted P-value. Three loci replicate in a Hispanic population, n=5172. A novel AA-specific variant, rs12721054/APOC1, and rs10096633/LPL are associated with ≥3 MetS components. We find additional evidence of pleiotropy for APOE, TOMM40, TCF7L2 and CETP variants, many with opposing effects; e.g. the same rs7901695/TCF7L2 allele is associated with increased odds of high glucose and decreased odds of central adiposity.
We highlight a method to increase power in large-scale genomic association analyses, and report a novel variant associated with all MetS components in AA. We also identify pleiotropic associations that may be clinically useful in patient risk profiling and for informing translational research of potential gene targets and medications.
metabolic syndrome; population studies; high-density lipoprotein cholesterol; genetic variation; hyperglycemia; ASSET; PAGE Study; African Americans; cardio-metabolic traits; Metabochip
Our GWAS of smoking and bladder cancer risk based on data from 5,424 cases and 10,162 controls suggest that exploring additive and multiplicative gene–environment interactions can identify novel susceptibility loci that are associated with risk for different subgroups.
Bladder cancer is a complex disease with known environmental and genetic risk factors. We performed a genome-wide interaction study (GWAS) of smoking and bladder cancer risk based on primary scan data from 3002 cases and 4411 controls from the National Cancer Institute Bladder Cancer GWAS. Alternative methods were used to evaluate both additive and multiplicative interactions between individual single nucleotide polymorphisms (SNPs) and smoking exposure. SNPs with interaction P values < 5 × 10−
5 were evaluated further in an independent dataset of 2422 bladder cancer cases and 5751 controls. We identified 10 SNPs that showed association in a consistent manner with the initial dataset and in the combined dataset, providing evidence of interaction with tobacco use. Further, two of these novel SNPs showed strong evidence of association with bladder cancer in tobacco use subgroups that approached genome-wide significance. Specifically, rs1711973 (FOXF2) on 6p25.3 was a susceptibility SNP for never smokers [combined odds ratio (OR) = 1.34, 95% confidence interval (CI) = 1.20–1.50, P value = 5.18 × 10−
7]; and rs12216499 (RSPH3-TAGAP-EZR) on 6q25.3 was a susceptibility SNP for ever smokers (combined OR = 0.75, 95% CI = 0.67–0.84, P value = 6.35 × 10−
7). In our analysis of smoking and bladder cancer, the tests for multiplicative interaction seemed to more commonly identify susceptibility loci with associations in never smokers, whereas the additive interaction analysis identified more loci with associations among smokers—including the known smoking and NAT2 acetylation interaction. Our findings provide additional evidence of gene–environment interactions for tobacco and bladder cancer.
A genome-wide association study (GWAS) of bladder cancer identified a genetic marker rs8102137 within the 19q12 region as a novel susceptibility variant. This marker is located upstream of the CCNE1 gene, which encodes cyclin E, a cell cycle protein. We performed genetic fine mapping analysis of the CCNE1 region using data from two bladder cancer GWAS (5,942 cases and 10,857 controls). We found that the original GWAS marker rs8102137 represents a group of 47 linked SNPs (with r2≥0.7) associated with increased bladder cancer risk. From this group we selected a functional promoter variant rs7257330, which showed strong allele-specific binding of nuclear proteins in several cell lines. In both GWAS, rs7257330 was associated only with aggressive bladder cancer, with a combined per-allele odds ratio (OR) =1.18 (95%CI=1.09-1.27, p=4.67×10−5 vs. OR =1.01 (95%CI=0.93-1.10, p=0.79) for non-aggressive disease, with p=0.0015 for case-only analysis. Cyclin E protein expression analyzed in 265 bladder tumors was increased in aggressive tumors (p=0.013) and, independently, with each rs7257330-A risk allele (ptrend=0.024). Over-expression of recombinant cyclin E in cell lines caused significant acceleration of cell cycle. In conclusion, we defined the 19q12 signal as the first GWAS signal specific for aggressive bladder cancer. Molecular mechanisms of this genetic association may be related to cyclin E over-expression and alteration of cell cycle in carriers of CCNE1 risk variants. In combination with established bladder cancer risk factors and other somatic and germline genetic markers, the CCNE1 variants could be useful for inclusion into bladder cancer risk prediction models.
Aggressive bladder cancer; cyclin E; cell cycle; single nucleotide polymorphism; GWAS
Candidate gene and genome-wide association studies (GWAS) have identified 11 independent susceptibility loci associated with bladder cancer risk. To discover additional risk variants, we conducted a new GWAS of 2422 bladder cancer cases and 5751 controls, followed by a meta-analysis with two independently published bladder cancer GWAS, resulting in a combined analysis of 6911 cases and 11 814 controls of European descent. TaqMan genotyping of 13 promising single nucleotide polymorphisms with P < 1 × 10−5 was pursued in a follow-up set of 801 cases and 1307 controls. Two new loci achieved genome-wide statistical significance: rs10936599 on 3q26.2 (P = 4.53 × 10−9) and rs907611 on 11p15.5 (P = 4.11 × 10−8). Two notable loci were also identified that approached genome-wide statistical significance: rs6104690 on 20p12.2 (P = 7.13 × 10−7) and rs4510656 on 6p22.3 (P = 6.98 × 10−7); these require further studies for confirmation. In conclusion, our study has identified new susceptibility alleles for bladder cancer risk that require fine-mapping and laboratory investigation, which could further understanding into the biological underpinnings of bladder carcinogenesis.
Brain glioma is a relatively rare and fatal malignancy in adulthood with few known risk factors. Some observational studies have reported inverse associations between diabetes and subsequent glioma risk, but possible mechanisms are unclear.
We conducted a pooled analysis of original data from five nested case-control studies and two case-control studies from the U.S. and China that included 962 glioma cases and 2,195 controls. We examined self-reported diabetes history in relation to glioma risk, as well as effect modification by seven glioma risk-associated single-nucleotide polymorphisms (SNPs). We also examined the associations between 13 diabetes risk-associated SNPs, identified from genome-wide association studies, and glioma risk. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using multivariable-adjusted logistic regression models.
We observed a 42% reduced risk of glioma for individuals with a history of diabetes (OR=0.58, 95% CI: 0.40–0.84). The association did not differ by sex, study design, or after restricting to glioblastoma, the most common histological sub-type. We did not observe any significant per-allele trends among the 13 diabetes-related SNPs examined in relation to glioma risk.
These results support an inverse association between diabetes history and glioma risk. The role of genetic susceptibility to diabetes cannot be excluded, and should be pursued in future studies together with other factors that might be responsible for the diabetes-glioma association.
These data suggest the need for studies that can evaluate, separately, the association between type 1 and type 2 diabetes and subsequent risk of adult glioma.
diabetes mellitus; brain cancer; glioma; cancer; epidemiology
We conducted imputation to the 1000 Genomes Project of four genome-wide association studies of lung cancer in populations of European ancestry (11,348 cases and 15,861 controls) and genotyped an additional 10,246 cases and 38,295 controls for follow-up. We identified large-effect genome-wide associations for squamous lung cancer with the rare variants of BRCA2-K3326X (rs11571833; odds ratio [OR]=2.47, P=4.74×10−20) and of CHEK2-I157T (rs17879961; OR=0.38 P=1.27×10−13). We also showed an association between common variation at 3q28 (TP63; rs13314271; OR=1.13, P=7.22×10−10) and lung adenocarcinoma previously only reported in Asians. These findings provide further evidence for inherited genetic susceptibility to lung cancer and its biological basis. Additionally, our analysis demonstrates that imputation can identify rare disease-causing variants having substantive effects on cancer risk from pre-existing GWAS data.
Cancer risk is determined by a complex interplay of genetic and environmental factors. Genome-wide association studies (GWAS) have identified hundreds of common (minor allele frequency [MAF]>0.05) and less common (0.01
Gene-environment interactions; complex phenotypes; genetic epidemiology
To identify common genetic variants that contribute to lung cancer susceptibility, we conducted a multistage genome-wide association study of lung cancer in Asian women who never smoked. We scanned 5,510 never-smoking female lung cancer cases and 4,544 controls drawn from 14 studies from mainland China, South Korea, Japan, Singapore, Taiwan, and Hong Kong. We genotyped the most promising variants (associated at P < 5 × 10-6) in an additional 1,099 cases and 2,913 controls. We identified three new susceptibility loci at 10q25.2 (rs7086803, P = 3.54 × 10-18), 6q22.2 (rs9387478, P = 4.14 × 10-10) and 6p21.32 (rs2395185, P = 9.51 × 10-9). We also confirmed associations reported for loci at 5p15.33 and 3q28 and a recently reported finding at 17q24.3. We observed no evidence of association for lung cancer at 15q25 in never-smoking women in Asia, providing strong evidence that this locus is not associated with lung cancer independent of smoking.
Results 1-25 (130)
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