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1.  Identification of shared and unique susceptibility pathways among cancers of the lung, breast, and prostate from genome-wide association studies and tissue-specific protein interactions 
Human Molecular Genetics  2015;24(25):7406-7420.
Results from genome-wide association studies (GWAS) have indicated that strong single-gene effects are the exception, not the rule, for most diseases. We assessed the joint effects of germline genetic variations through a pathway-based approach that considers the tissue-specific contexts of GWAS findings. From GWAS meta-analyses of lung cancer (12 160 cases/16 838 controls), breast cancer (15 748 cases/18 084 controls) and prostate cancer (14 160 cases/12 724 controls) in individuals of European ancestry, we determined the tissue-specific interaction networks of proteins expressed from genes that are likely to be affected by disease-associated variants. Reactome pathways exhibiting enrichment of proteins from each network were compared across the cancers. Our results show that pathways associated with all three cancers tend to be broad cellular processes required for growth and survival. Significant examples include the nerve growth factor (P = 7.86 × 10−33), epidermal growth factor (P = 1.18 × 10−31) and fibroblast growth factor (P = 2.47 × 10−31) signaling pathways. However, within these shared pathways, the genes that influence risk largely differ by cancer. Pathways found to be unique for a single cancer focus on more specific cellular functions, such as interleukin signaling in lung cancer (P = 1.69 × 10−15), apoptosis initiation by Bad in breast cancer (P = 3.14 × 10−9) and cellular responses to hypoxia in prostate cancer (P = 2.14 × 10−9). We present the largest comparative cross-cancer pathway analysis of GWAS to date. Our approach can also be applied to the study of inherited mechanisms underlying risk across multiple diseases in general.
doi:10.1093/hmg/ddv440
PMCID: PMC4664175  PMID: 26483192
2.  Identification of lung cancer histology-specific variants applying Bayesian framework variant prioritization approaches within the TRICL and ILCCO consortia 
Brenner, Darren R. | Amos, Christopher I. | Brhane, Yonathan | Timofeeva, Maria N. | Caporaso, Neil | Wang, Yufei | Christiani, David C. | Bickeböller, Heike | Yang, Ping | Albanes, Demetrius | Stevens, Victoria L. | Gapstur, Susan | McKay, James | Boffetta, Paolo | Zaridze, David | Szeszenia-Dabrowska, Neonilia | Lissowska, Jolanta | Rudnai, Peter | Fabianova, Eleonora | Mates, Dana | Bencko, Vladimir | Foretova, Lenka | Janout, Vladimir | Krokan, Hans E. | Skorpen, Frank | Gabrielsen, Maiken E. | Vatten, Lars | Njølstad, Inger | Chen, Chu | Goodman, Gary | Lathrop, Mark | Vooder, Tõnu | Välk, Kristjan | Nelis, Mari | Metspalu, Andres | Broderick, Peter | Eisen, Timothy | Wu, Xifeng | Zhang, Di | Chen, Wei | Spitz, Margaret R. | Wei, Yongyue | Su, Li | Xie, Dong | She, Jun | Matsuo, Keitaro | Matsuda, Fumihiko | Ito, Hidemi | Risch, Angela | Heinrich, Joachim | Rosenberger, Albert | Muley, Thomas | Dienemann, Hendrik | Field, John K. | Raji, Olaide | Chen, Ying | Gosney, John | Liloglou, Triantafillos | Davies, Michael P.A. | Marcus, Michael | McLaughlin, John | Orlow, Irene | Han, Younghun | Li, Yafang | Zong, Xuchen | Johansson, Mattias | Liu, Geoffrey | Tworoger, Shelley S. | Le Marchand, Loic | Henderson, Brian E. | Wilkens, Lynne R. | Dai, Juncheng | Shen, Hongbing | Houlston, Richard S. | Landi, Maria T. | Brennan, Paul | Hung, Rayjean J.
Carcinogenesis  2015;36(11):1314-1326.
Summary
Using information including variant physical and functional properties, we applied multiple variant prioritization techniques in 13 lung cancer genomic studies. We identified and validated novel regions highlighting the utility of using prioritization analyses to search for robust signals.
Large-scale genome-wide association studies (GWAS) have likely uncovered all common variants at the GWAS significance level. Additional variants within the suggestive range (0.0001> P > 5×10−8) are, however, still of interest for identifying causal associations. This analysis aimed to apply novel variant prioritization approaches to identify additional lung cancer variants that may not reach the GWAS level. Effects were combined across studies with a total of 33456 controls and 6756 adenocarcinoma (AC; 13 studies), 5061 squamous cell carcinoma (SCC; 12 studies) and 2216 small cell lung cancer cases (9 studies). Based on prior information such as variant physical properties and functional significance, we applied stratified false discovery rates, hierarchical modeling and Bayesian false discovery probabilities for variant prioritization. We conducted a fine mapping analysis as validation of our methods by examining top-ranking novel variants in six independent populations with a total of 3128 cases and 2966 controls. Three novel loci in the suggestive range were identified based on our Bayesian framework analyses: KCNIP4 at 4p15.2 (rs6448050, P = 4.6×10−7) and MTMR2 at 11q21 (rs10501831, P = 3.1×10−6) with SCC, as well as GAREM at 18q12.1 (rs11662168, P = 3.4×10−7) with AC. Use of our prioritization methods validated two of the top three loci associated with SCC (P = 1.05×10−4 for KCNIP4, represented by rs9799795) and AC (P = 2.16×10−4 for GAREM, represented by rs3786309) in the independent fine mapping populations. This study highlights the utility of using prior functional data for sequence variants in prioritization analyses to search for robust signals in the suggestive range.
doi:10.1093/carcin/bgv128
PMCID: PMC4635669  PMID: 26363033
3.  Cross Cancer Genomic Investigation of Inflammation Pathway for Five Common Cancers: Lung, Ovary, Prostate, Breast, and Colorectal Cancer 
Background:
Inflammation has been hypothesized to increase the risk of cancer development as an initiator or promoter, yet no large-scale study of inherited variation across cancer sites has been conducted.
Methods:
We conducted a cross-cancer genomic analysis for the inflammation pathway based on 48 genome-wide association studies within the National Cancer Institute GAME-ON Network across five common cancer sites, with a total of 64 591 cancer patients and 74 467 control patients. Subset-based meta-analysis was used to account for possible disease heterogeneity, and hierarchical modeling was employed to estimate the effect of the subcomponents within the inflammation pathway. The network was visualized by enrichment map. All statistical tests were two-sided.
Results:
We identified three pleiotropic loci within the inflammation pathway, including one novel locus in Ch12q24 encoding SH2B3 (rs3184504), which reached GWAS significance with a P value of 1.78 x 10–8, and it showed an association with lung cancer (P = 2.01 x 10–6), colorectal cancer (GECCO P = 6.72x10-6; CORECT P = 3.32x10-5), and breast cancer (P = .009). We also identified five key subpathway components with genetic variants that are relevant for the risk of these five cancer sites: inflammatory response for colorectal cancer (P = .006), inflammation related cell cycle gene for lung cancer (P = 1.35x10-6), and activation of immune response for ovarian cancer (P = .009). In addition, sequence variations in immune system development played a role in breast cancer etiology (P = .001) and innate immune response was involved in the risk of both colorectal (P = .022) and ovarian cancer (P = .003).
Conclusions:
Genetic variations in inflammation and its related subpathway components are keys to the development of lung, colorectal, ovary, and breast cancer, including SH2B3, which is associated with lung, colorectal, and breast cancer.
doi:10.1093/jnci/djv246
PMCID: PMC4675100  PMID: 26319099
4.  A Novel Genetic Variant in Long Non-coding RNA Gene NEXN-AS1 is Associated with Risk of Lung Cancer 
Scientific Reports  2016;6:34234.
Lung cancer etiology is multifactorial, and growing evidence has indicated that long non-coding RNAs (lncRNAs) are important players in lung carcinogenesis. We performed a large-scale meta-analysis of 690,564 SNPs in 15,531 autosomal lncRNAs by using datasets from six previously published genome-wide association studies (GWASs) from the Transdisciplinary Research in Cancer of the Lung (TRICL) consortium in populations of European ancestry. Previously unreported significant SNPs (P value < 1 × 10−7) were further validated in two additional independent lung cancer GWAS datasets from Harvard University and deCODE. In the final meta-analysis of all eight GWAS datasets with 17,153 cases and 239,337 controls, a novel risk SNP rs114020893 in the lncRNA NEXN-AS1 region at 1p31.1 remained statistically significant (odds ratio = 1.17; 95% confidence interval = 1.11–1.24; P = 8.31 × 10−9). In further in silico analysis, rs114020893 was predicted to change the secondary structure of the lncRNA. Our finding indicates that SNP rs114020893 of NEXN-AS1 at 1p31.1 may contribute to lung cancer susceptibility.
doi:10.1038/srep34234
PMCID: PMC5054367  PMID: 27713484
5.  Association between Adult Height and Risk of Colorectal, Lung, and Prostate Cancer: Results from Meta-analyses of Prospective Studies and Mendelian Randomization Analyses 
PLoS Medicine  2016;13(9):e1002118.
Background
Observational studies examining associations between adult height and risk of colorectal, prostate, and lung cancers have generated mixed results. We conducted meta-analyses using data from prospective cohort studies and further carried out Mendelian randomization analyses, using height-associated genetic variants identified in a genome-wide association study (GWAS), to evaluate the association of adult height with these cancers.
Methods and Findings
A systematic review of prospective studies was conducted using the PubMed, Embase, and Web of Science databases. Using meta-analyses, results obtained from 62 studies were summarized for the association of a 10-cm increase in height with cancer risk. Mendelian randomization analyses were conducted using summary statistics obtained for 423 genetic variants identified from a recent GWAS of adult height and from a cancer genetics consortium study of multiple cancers that included 47,800 cases and 81,353 controls. For a 10-cm increase in height, the summary relative risks derived from the meta-analyses of prospective studies were 1.12 (95% CI 1.10, 1.15), 1.07 (95% CI 1.05, 1.10), and 1.06 (95% CI 1.02, 1.11) for colorectal, prostate, and lung cancers, respectively. Mendelian randomization analyses showed increased risks of colorectal (odds ratio [OR] = 1.58, 95% CI 1.14, 2.18) and lung cancer (OR = 1.10, 95% CI 1.00, 1.22) associated with each 10-cm increase in genetically predicted height. No association was observed for prostate cancer (OR = 1.03, 95% CI 0.92, 1.15). Our meta-analysis was limited to published studies. The sample size for the Mendelian randomization analysis of colorectal cancer was relatively small, thus affecting the precision of the point estimate.
Conclusions
Our study provides evidence for a potential causal association of adult height with the risk of colorectal and lung cancers and suggests that certain genetic factors and biological pathways affecting adult height may also affect the risk of these cancers.
In a Mendelian randomisation study Pierce and colleagues show a genetic association between adult height and increased risk of colorectal and lung cancer.
Author Summary
Why Was This Study Done?
Several previous observational studies have examined the association between adult height and risk of cancers of the lung, colon/rectum, and prostate; however, it remains unclear whether adult height is indeed related to the risk of these cancers.
What Did the Researchers Do and Find?
We conducted a systematic review and meta-analysis of prospective cohort studies that examined the association between adult height and the risk of colorectal, lung, and prostate cancers.
To overcome inherent limitations of observational study designs, we conducted Mendelian randomization analyses using genetic data generated from a large multi-center consortium study including 47,800 cases and 81,353 controls.
In the meta-analysis of the prospective observational studies, we found a 12% increased risk of colorectal cancer, a 7% increased risk of prostate cancer, and a 6% increased risk of lung cancer for every ten-centimeter increase in height, and this increased risk was corroborated in the Mendelian randomization analyses for colorectal (58%) and lung cancer (10%).
What Do These Findings Mean?
Our study provides strong evidence for an association between adult height and risk of colorectal and lung cancer, and suggests that certain genetic and biological factors that affect height may also affect the risk of these cancers.
However, our meta-analysis was limited to published studies, and the sample size for the Mendelian randomization analysis for colorectal cancer was relatively small, affecting the precision of the risk estimate.
doi:10.1371/journal.pmed.1002118
PMCID: PMC5012582  PMID: 27598322
6.  Genetic Risk Can Be Decreased: Quitting Smoking Decreases and Delays Lung Cancer for Smokers With High and Low CHRNA5 Risk Genotypes — A Meta-Analysis 
EBioMedicine  2016;11:219-226.
Background
Recent meta-analyses show that individuals with high risk variants in CHRNA5 on chromosome 15q25 are likely to develop lung cancer earlier than those with low-risk genotypes. The same high-risk genetic variants also predict nicotine dependence and delayed smoking cessation. It is unclear whether smoking cessation confers the same benefits in terms of lung cancer risk reduction for those who possess CHRNA5 risk variants versus those who do not.
Methods
Meta-analyses examined the association between smoking cessation and lung cancer risk in 15 studies of individuals with European ancestry who possessed varying rs16969968 genotypes (N = 12,690 ever smokers, including 6988 cases of lung cancer and 5702 controls) in the International Lung Cancer Consortium.
Results
Smoking cessation (former vs. current smokers) was associated with a lower likelihood of lung cancer (OR = 0.48, 95%CI = 0.30–0.75, p = 0.0015). Among lung cancer patients, smoking cessation was associated with a 7-year delay in median age of lung cancer diagnosis (HR = 0.68, 95%CI = 0.61–0.77, p = 4.9 ∗ 10–10). The CHRNA5 rs16969968 risk genotype (AA) was associated with increased risk and earlier diagnosis for lung cancer, but the beneficial effects of smoking cessation were very similar in those with and without the risk genotype.
Conclusion
We demonstrate that quitting smoking is highly beneficial in reducing lung cancer risks for smokers regardless of their CHRNA5 rs16969968 genetic risk status. Smokers with high-risk CHRNA5 genotypes, on average, can largely eliminate their elevated genetic risk for lung cancer by quitting smoking- cutting their risk of lung cancer in half and delaying its onset by 7 years for those who develop it. These results: 1) underscore the potential value of smoking cessation for all smokers, 2) suggest that CHRNA5 rs16969968 genotype affects lung cancer diagnosis through its effects on smoking, and 3) have potential value for framing preventive interventions for those who smoke.
Highlights
•CHRNA5 rs16969968 confers risk for earlier lung cancer diagnosis, but quitting produces benefit regardless of genotype.•Smokers can cut their risk of lung cancer in half and delay its onset by 7 years among those diagnosed.•Precision prevention allows clinicians to provide personalized health benefits of smoking cessation.
This is a report on whether smoking cessation confers the same benefits in terms of lung cancer risk reduction for those who possess CHRNA5 risk variants versus those who do not. We determined that quitting smoking is highly beneficial in reducing lung cancer risk levels for smokers regardless of their CHRNA5 rs16969968 genetic risk status. Although CHRNA5 rs16969968 increases risk for earlier lung cancer by 4 years, quitting produces essentially the same benefit for smokers with either high or low genetic risks. Smokers can cut their risk of lung cancer in half and delay its onset by 7 years among those diagnosed. These results are important for smokers to prevent cancer. On average, smokers at all genetic risk levels can largely eliminate their elevated risk for lung cancer by quitting smoking.
doi:10.1016/j.ebiom.2016.08.012
PMCID: PMC5049934  PMID: 27543155
Smoking cessation; Genetics; Meta-analysis; Lung cancer
7.  CHRNA5 Risk Variant Predicts Delayed Smoking Cessation and Earlier Lung Cancer Diagnosis—A Meta-Analysis 
Background:
Recent meta-analyses show strong evidence of associations among genetic variants in CHRNA5 on chromosome 15q25, smoking quantity, and lung cancer. This meta-analysis tests whether the CHRNA5 variant rs16969968 predicts age of smoking cessation and age of lung cancer diagnosis.
Methods:
Meta-analyses examined associations between rs16969968, age of quitting smoking, and age of lung cancer diagnosis in 24 studies of European ancestry (n = 29 072). In each dataset, we used Cox regression models to evaluate the association between rs16969968 and the two primary phenotypes (age of smoking cessation among ever smokers and age of lung cancer diagnosis among lung cancer case patients) and the secondary phenotype of smoking duration. Heterogeneity across studies was assessed with the Cochran Q test. All statistical tests were two-sided.
Results:
The rs16969968 allele (A) was associated with a lower likelihood of smoking cessation (hazard ratio [HR] = 0.95, 95% confidence interval [CI] = 0.91 to 0.98, P = .0042), and the AA genotype was associated with a four-year delay in median age of quitting compared with the GG genotype. Among smokers with lung cancer diagnoses, the rs16969968 genotype (AA) was associated with a four-year earlier median age of diagnosis compared with the low-risk genotype (GG) (HR = 1.08, 95% CI = 1.04 to 1.12, P = 1.1*10–5).
Conclusion:
These data support the clinical significance of the CHRNA5 variant rs16969968. It predicts delayed smoking cessation and an earlier age of lung cancer diagnosis in this meta-analysis. Given the existing evidence that this CHRNA5 variant predicts favorable response to cessation pharmacotherapy, these findings underscore the potential clinical and public health importance of rs16969968 in CHRNA5 in relation to smoking cessation success and lung cancer risk.
doi:10.1093/jnci/djv100
PMCID: PMC4822525  PMID: 25873736
8.  Hypomethylation of smoking-related genes is associated with future lung cancer in four prospective cohorts 
Nature Communications  2015;6:10192.
DNA hypomethylation in certain genes is associated with tobacco exposure but it is unknown whether these methylation changes translate into increased lung cancer risk. In an epigenome-wide study of DNA from pre-diagnostic blood samples from 132 case–control pairs in the NOWAC cohort, we observe that the most significant associations with lung cancer risk are for cg05575921 in AHRR (OR for 1 s.d.=0.37, 95% CI: 0.31–0.54, P-value=3.3 × 10−11) and cg03636183 in F2RL3 (OR for 1 s.d.=0.40, 95% CI: 0.31–0.56, P-value=3.9 × 10−10), previously shown to be strongly hypomethylated in smokers. These associations remain significant after adjustment for smoking and are confirmed in additional 664 case–control pairs tightly matched for smoking from the MCCS, NSHDS and EPIC HD cohorts. The replication and mediation analyses suggest that residual confounding is unlikely to explain the observed associations and that hypomethylation of these CpG sites may mediate the effect of tobacco on lung cancer risk.
Smoking tobacco is known to alter DNA methylation. Here, the authors show that hypomethylation of smoke-related genes is associated with future increase in lung cancer risk.
doi:10.1038/ncomms10192
PMCID: PMC4682166  PMID: 26667048
9.  META-GSA: Combining Findings from Gene-Set Analyses across Several Genome-Wide Association Studies 
PLoS ONE  2015;10(10):e0140179.
Introduction
Gene-set analysis (GSA) methods are used as complementary approaches to genome-wide association studies (GWASs). The single marker association estimates of a predefined set of genes are either contrasted with those of all remaining genes or with a null non-associated background. To pool the p-values from several GSAs, it is important to take into account the concordance of the observed patterns resulting from single marker association point estimates across any given gene set. Here we propose an enhanced version of Fisher’s inverse χ2-method META-GSA, however weighting each study to account for imperfect correlation between association patterns.
Simulation and Power
We investigated the performance of META-GSA by simulating GWASs with 500 cases and 500 controls at 100 diallelic markers in 20 different scenarios, simulating different relative risks between 1 and 1.5 in gene sets of 10 genes. Wilcoxon’s rank sum test was applied as GSA for each study. We found that META-GSA has greater power to discover truly associated gene sets than simple pooling of the p-values, by e.g. 59% versus 37%, when the true relative risk for 5 of 10 genes was assume to be 1.5. Under the null hypothesis of no difference in the true association pattern between the gene set of interest and the set of remaining genes, the results of both approaches are almost uncorrelated. We recommend not relying on p-values alone when combining the results of independent GSAs.
Application
We applied META-GSA to pool the results of four case-control GWASs of lung cancer risk (Central European Study and Toronto/Lunenfeld-Tanenbaum Research Institute Study; German Lung Cancer Study and MD Anderson Cancer Center Study), which had already been analyzed separately with four different GSA methods (EASE; SLAT, mSUMSTAT and GenGen). This application revealed the pathway GO0015291 “transmembrane transporter activity” as significantly enriched with associated genes (GSA-method: EASE, p = 0.0315 corrected for multiple testing). Similar results were found for GO0015464 “acetylcholine receptor activity” but only when not corrected for multiple testing (all GSA-methods applied; p≈0.02).
doi:10.1371/journal.pone.0140179
PMCID: PMC4621033  PMID: 26501144
10.  Informed Genome-Wide Association Analysis With Family History As a Secondary Phenotype Identifies Novel Loci of Lung Cancer 
Genetic epidemiology  2015;39(3):197-206.
Lung cancer is the leading cause of cancer death worldwide. Although several genetic variants associated with lung cancer have been identified in the past, stringent selection criteria of genome-wide association studies (GWAS) can lead to missed variants. The objective of this study was to uncover missed variants by using the known association between lung cancer and first-degree family history of lung cancer to enrich the variant prioritization for lung cancer susceptibility regions. In this two-stage GWAS study, we first selected a list of variants associated with both lung cancer and family history of lung cancer in four GWAS (3,953 cases, 4,730 controls), then replicated our findings for 30 variants in a meta-analysis of four additional studies (7,510 cases, 7,476 controls). The top ranked genetic variant rs12415204 in chr10q23.33 encoding FFAR4 in the Discovery set was validated in the Replication set with an overall OR of 1.09 (95% CI = 1.04, 1.14, P = 1.63 × 10−4). When combining the two stages of the study, the strongest association was found in rs1158970 at Ch4p15.2 encoding KCNIP4 with an OR of 0.89 (95% CI = 0.85, 0.94, P = 9.64 × 10−6). We performed a stratified analysis of rs12415204 and rs1158970 across all eight studies by age, gender, smoking status, and histology, and found consistent results across strata. Four of the 30 replicated variants act as expression quantitative trait loci (eQTL) sites in 1,111 nontumor lung tissues and meet the genome-wide 10% FDR threshold.
doi:10.1002/gepi.21882
PMCID: PMC4554719  PMID: 25644374
lung cancer; family history; secondary phenotype; genetic susceptibility; genome-wide association studies; eQTL
11.  Genetic determinants of telomere length and risk of common cancers: a Mendelian randomization study 
Human Molecular Genetics  2015;24(18):5356-5366.
Epidemiological studies have reported inconsistent associations between telomere length (TL) and risk for various cancers. These inconsistencies are likely attributable, in part, to biases that arise due to post-diagnostic and post-treatment TL measurement. To avoid such biases, we used a Mendelian randomization approach and estimated associations between nine TL-associated SNPs and risk for five common cancer types (breast, lung, colorectal, ovarian and prostate cancer, including subtypes) using data on 51 725 cases and 62 035 controls. We then used an inverse-variance weighted average of the SNP-specific associations to estimate the association between a genetic score representing long TL and cancer risk. The long TL genetic score was significantly associated with increased risk of lung adenocarcinoma (P = 6.3 × 10−15), even after exclusion of a SNP residing in a known lung cancer susceptibility region (TERT-CLPTM1L) P = 6.6 × 10−6). Under Mendelian randomization assumptions, the association estimate [odds ratio (OR) = 2.78] is interpreted as the OR for lung adenocarcinoma corresponding to a 1000 bp increase in TL. The weighted TL SNP score was not associated with other cancer types or subtypes. Our finding that genetic determinants of long TL increase lung adenocarcinoma risk avoids issues with reverse causality and residual confounding that arise in observational studies of TL and disease risk. Under Mendelian randomization assumptions, our finding suggests that longer TL increases lung adenocarcinoma risk. However, caution regarding this causal interpretation is warranted in light of the potential issue of pleiotropy, and a more general interpretation is that SNPs influencing telomere biology are also implicated in lung adenocarcinoma risk.
doi:10.1093/hmg/ddv252
PMCID: PMC4550826  PMID: 26138067
12.  A Network-Based Kernel Machine Test for the Identification of Risk Pathways in Genome-Wide Association Studies 
Human heredity  2014;76(2):64-75.
Biological pathways provide rich information and biological context on the genetic causes of complex diseases. The logistic kernel machine test integrates prior knowledge on pathways in order to analyze data from genome-wide association studies (GWAS). Here, the kernel converts genomic information of two individuals to a quantitative value reflecting their genetic similarity. With the selection of the kernel one implicitly chooses a genetic effect model. Like many other pathway methods, none of the available kernels accounts for topological structure of the pathway or gene-gene interaction types. However, evidence indicates that connectivity and neighborhood of genes are crucial in the context of GWAS, because genes associated with a disease often interact. Thus, we propose a novel kernel that incorporates the topology of pathways and information on interactions. Using simulation studies, we demonstrate that the proposed method maintains the type I error correctly and can be more effective in the identification of pathways associated with a disease than non-network-based methods. We apply our approach to genome-wide association case control data on lung cancer and rheumatoid arthritis. We identify some promising new pathways associated with these diseases, which may improve our current understanding of the genetic mechanisms.
doi:10.1159/000357567
PMCID: PMC4026009  PMID: 24434848
Kernel Machine Test; Pathways; Networks; Gene-Gene Interactions; Score Test; Generalized Linear Model; Lung Cancer; Rheumatoid Arthritis; Disease Association; Genetic Association Studies
13.  Rare variants of large effect in BRCA2 and CHEK2 affect risk of lung cancer 
Wang, Yufei | McKay, James D. | Rafnar, Thorunn | Wang, Zhaoming | Timofeeva, Maria | Broderick, Peter | Zong, Xuchen | Laplana, Marina | Wei, Yongyue | Han, Younghun | Lloyd, Amy | Delahaye-Sourdeix, Manon | Chubb, Daniel | Gaborieau, Valerie | Wheeler, William | Chatterjee, Nilanjan | Thorleifsson, Gudmar | Sulem, Patrick | Liu, Geoffrey | Kaaks, Rudolf | Henrion, Marc | Kinnersley, Ben | Vallée, Maxime | LeCalvez-Kelm, Florence | Stevens, Victoria L. | Gapstur, Susan M. | Chen, Wei V. | Zaridze, David | Szeszenia-Dabrowska, Neonilia | Lissowska, Jolanta | Rudnai, Peter | Fabianova, Eleonora | Mates, Dana | Bencko, Vladimir | Foretova, Lenka | Janout, Vladimir | Krokan, Hans E. | Gabrielsen, Maiken Elvestad | Skorpen, Frank | Vatten, Lars | Njølstad, Inger | Chen, Chu | Goodman, Gary | Benhamou, Simone | Vooder, Tonu | Valk, Kristjan | Nelis, Mari | Metspalu, Andres | Lener, Marcin | Lubiński, Jan | Johansson, Mattias | Vineis, Paolo | Agudo, Antonio | Clavel-Chapelon, Francoise | Bueno-de-Mesquita, H.Bas | Trichopoulos, Dimitrios | Khaw, Kay-Tee | Johansson, Mikael | Weiderpass, Elisabete | Tjønneland, Anne | Riboli, Elio | Lathrop, Mark | Scelo, Ghislaine | Albanes, Demetrius | Caporaso, Neil E. | Ye, Yuanqing | Gu, Jian | Wu, Xifeng | Spitz, Margaret R. | Dienemann, Hendrik | Rosenberger, Albert | Su, Li | Matakidou, Athena | Eisen, Timothy | Stefansson, Kari | Risch, Angela | Chanock, Stephen J. | Christiani, David C. | Hung, Rayjean J. | Brennan, Paul | Landi, Maria Teresa | Houlston, Richard S. | Amos, Christopher I.
Nature genetics  2014;46(7):736-741.
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.
doi:10.1038/ng.3002
PMCID: PMC4074058  PMID: 24880342
14.  Empirical Hierarchical Bayes Approach to Gene-Environment Interactions: Development and Application to Genome-Wide Association Studies of Lung Cancer in TRICL 
Genetic epidemiology  2013;37(6):551-559.
The analysis of gene-environment (GxE) interactions remains one of the greatest challenges in the post-genome-wide-association-studies (GWAS) era. Recent methods constitute a compromise between the robust but underpowered case-control and powerful case-only methods. Inferences of the latter are biased when the assumption of gene-environment (G-E) independence fails. We propose a novel empirical hierarchical Bayes approach to GxE interaction (EHB-GE), which benefits from greater power while accounting for population-based G-E dependence. Building on Lewinger et al.'s ([2007] Genet Epidemiol 31:871-882) hierarchical Bayes prioritization approach, the method utilizes posterior G-E association estimates in controls based on G-E information across the genome to adjust for it in resulting test statistics. These posteriori estimates are subtracted from the corresponding G-E association coefficients within cases.
We compared EHB-GE with rival methods using simulation. EHB-GE has similar or greater rank power to detect GxE interactions in the presence of large numbers of G-E associations with weak to strong effects or only a low number of such associations with large effect. When there are no or only a few weak G-E associations, Murcray et al.'s method ([2009] Am J Epidemiol 169:219-226) identifies markers with low GxE interaction effects better. We applied EHB-GE and competing methods to four lung cancer case-control GWAS from the TRICL/ILCCO consortium with smoking as environmental factor. Genes identified by the EHB-GE approach are reasonable candidates, suggesting usefulness of the method.
doi:10.1002/gepi.21741
PMCID: PMC4082246  PMID: 23893921
population G-E association; GWAS; rank power; lung cancer
15.  Hierarchical modeling identifies novel lung cancer susceptibility variants in inflammation pathways among 10,140 cases and 11,012 controls 
Human genetics  2013;132(5):579-589.
Recent evidence suggests that inflammation plays a pivotal role in the development of lung cancer. In this study, we used a two-stage approach to investigate associations between genetic variants in inflammation pathways and lung cancer risk based on genome-wide association study (GWAS) data. A total of 7,650 sequence variants from 720 genes relevant to inflammation pathways were identified using keyword and pathway searches from Gene Cards and Gene Ontology databases. In Stage 1, six GWAS datasets from the International Lung Cancer Consortium were pooled (4,441 cases and 5,094 controls of European ancestry), and a hierarchical modeling (HM) approach was used to incorporate prior information for each of the variants into the analysis. The prior matrix was constructed using (1) role of genes in the inflammation and immune pathways; (2) physical properties of the variants including the location of the variants, their conservation scores and amino acid coding; (3) LD with other functional variants and (4) measures of heterogeneity across the studies. HM affected the priority ranking of variants particularly among those having low prior weights, imprecise estimates and/or heterogeneity across studies. In Stage 2, we used an independent NCI lung cancer GWAS study (5,699 cases and 5,818 controls) for in silico replication. We identified one novel variant at the level corrected for multiple comparisons (rs2741354 in EPHX2 at 8q21.1 with p value = 7.4 × 10−6), and confirmed the associations between TERT (rs2736100) and the HLA region and lung cancer risk. HM allows for prior knowledge such as from bioinformatic sources to be incorporated into the analysis systematically, and it represents a complementary analytical approach to the conventional GWAS analysis.
doi:10.1007/s00439-013-1270-y
PMCID: PMC3628758  PMID: 23370545
16.  Pleiotropic Associations of Risk Variants Identified for Other Cancers With Lung Cancer Risk: The PAGE and TRICL Consortia 
Background
Genome-wide association studies have identified hundreds of genetic variants associated with specific cancers. A few of these risk regions have been associated with more than one cancer site; however, a systematic evaluation of the associations between risk variants for other cancers and lung cancer risk has yet to be performed.
Methods
We included 18023 patients with lung cancer and 60543 control subjects from two consortia, Population Architecture using Genomics and Epidemiology (PAGE) and Transdisciplinary Research in Cancer of the Lung (TRICL). We examined 165 single-nucleotide polymorphisms (SNPs) that were previously associated with at least one of 16 non–lung cancer sites. Study-specific logistic regression results underwent meta-analysis, and associations were also examined by race/ethnicity, histological cell type, sex, and smoking status. A Bonferroni-corrected P value of 2.5×10–5 was used to assign statistical significance.
Results
The breast cancer SNP LSP1 rs3817198 was associated with an increased risk of lung cancer (odds ratio [OR] = 1.10; 95% confidence interval [CI] = 1.05 to 1.14; P = 2.8×10–6). This association was strongest for women with adenocarcinoma (P = 1.2×10–4) and not statistically significant in men (P = .14) with this cell type (P het by sex = .10). Two glioma risk variants, TERT rs2853676 and CDKN2BAS1 rs4977756, which are located in regions previously associated with lung cancer, were associated with increased risk of adenocarcinoma (OR = 1.16; 95% CI = 1.10 to 1.22; P = 1.1×10–8) and squamous cell carcinoma (OR = 1.13; CI = 1.07 to 1.19; P = 2.5×10–5), respectively.
Conclusions
Our findings demonstrate a novel pleiotropic association between the breast cancer LSP1 risk region marked by variant rs3817198 and lung cancer risk.
doi:10.1093/jnci/dju061
PMCID: PMC3982896  PMID: 24681604
17.  Epigenetic screen identifies genotype- specific promoter DNA methylation and oncogenic potential of CHRNB4 
Oncogene  2012;32(28):3329-3338.
Genome-wide association studies have highlighted three major lung cancer susceptibility regions at 15q25.1, 5p15.33 and 6p21.33. To gain insight into the possible mechanistic relevance of the genes in these regions, we investigated the regulation of candidate susceptibility gene expression by epigenetic alterations in healthy and lung tumor tissues. For genes up- or downregulated in lung tumors the influence of genetic variants on DNA methylation was investigated and in vitro studies were performed.
We analyzed 394 CpG units within 19 CpG islands in the susceptibility regions in a screening set of 34 patients. Significant findings were validated in an independent patient set (n=50) with available DNA and RNA. The most consistent overall DNA methylation difference between tumor and adjacent normal tissue on 15q25 was tumor hypomethylation in the promoter region of CHRNB4 with a median difference of 8% (p<0.001) which resulted in overexpression of the transcript in tumors (p<0.001). Confirming previous studies we also found hypermethylation in CHRNA3 and TERT with significant expression changes. Decitabine treatment of H1299 cells resulted in reduced methylation levels in gene promoters, elevated transcript levels of CHRNB4 and CHRNA3 and a slight downregulation of TERT demonstrating epigenetic regulation of lung cancer cells. SNPs rs421629 on 5p15.33 and rs1948, rs660652, rs8040868 and rs2036527 on 15q25.1, previously identified as lung cancer risk or nicotine addiction modifiers were associated with tumor DNA methylation levels in the promoters of TERT and CHRNB4 (p<0.001) respectively in two independent sample sets (n=82; n=150). In addition, CHRNB4 knock down in two different cell lines (A549 and H1299) resulted in reduced proliferation (pA549<0.05;pH1299L<0.001) and propensity to form colonies in H1299 cells.
These results suggest epigenetic deregulation of nicotinic acetylcholinereceptor subunit (nAChR) genes which in the case of CHRNB4 is strongly associated with genetic lung cancer susceptibility variants and a functional impact on tumorigenic potential.
doi:10.1038/onc.2012.344
PMCID: PMC3710305  PMID: 22945651
DNA methylation; risk factors; non-small cell lung cancer (NSCLC); CHRNB4; TERT
18.  Methylome Analysis and Epigenetic Changes Associated with Menarcheal Age 
PLoS ONE  2013;8(11):e79391.
Reproductive factors have been linked to both breast cancer and DNA methylation, suggesting methylation as an important mechanism by which reproductive factors impact on disease risk. However, few studies have investigated the link between reproductive factors and DNA methylation in humans. Genome-wide methylation in peripheral blood lymphocytes of 376 healthy women from the prospective EPIC study was investigated using LUminometric Methylation Assay (LUMA). Also, methylation of 458877 CpG sites was additionally investigated in an independent group of 332 participants of the EPIC-Italy sub-cohort, using the Infinium HumanMethylation 450 BeadChip. Multivariate logistic regression and linear models were used to investigate the association between reproductive risk factors and genome wide and CpG-specific DNA methylation, respectively. Menarcheal age was inversely associated with global DNA methylation as measured with LUMA. For each yearly increase in age at menarche, the risk of having genome wide methylation below median level was increased by 32% (OR:1.32, 95%CI:1.14–1.53). When age at menarche was treated as a categorical variable, there was an inverse dose-response relationship with LUMA methylation levels (OR12–14vs.≤11 yrs:1.78, 95%CI:1.01–3.17 and OR≥15vs.≤11 yrs:4.59, 95%CI:2.04–10.33; P for trend<0.0001). However, average levels of global methylation as measured by the Illumina technology were not significantly associated with menarcheal age. In locus by locus comparative analyses, only one CpG site had significantly different methylation depending on the menarcheal age category examined, but this finding was not replicated by pyrosequencing in an independent data set. This study suggests a link between age at menarche and genome wide DNA methylation, and the difference in results between the two arrays suggests that repetitive element methylation has a role in the association. Epigenetic changes may be modulated by menarcheal age, or the association may be a mirror of other important changes in early life that have a detectable effect on both methylation levels and menarcheal age.
doi:10.1371/journal.pone.0079391
PMCID: PMC3835804  PMID: 24278132
19.  Increased Genetic Vulnerability to Smoking at CHRNA5 in Early-Onset Smokers 
Hartz, Sarah M. | Short, Susan E. | Saccone, Nancy L. | Culverhouse, Robert | Chen, LiShiun | Schwantes-An, Tae-Hwi | Coon, Hilary | Han, Younghun | Stephens, Sarah H. | Sun, Juzhong | Chen, Xiangning | Ducci, Francesca | Dueker, Nicole | Franceschini, Nora | Frank, Josef | Geller, Frank | Guđbjartsson, Daniel | Hansel, Nadia N. | Jiang, Chenhui | Keskitalo-Vuokko, Kaisu | Liu, Zhen | Lyytikäinen, Leo-Pekka | Michel, Martha | Rawal, Rajesh | Hum, Sc | Rosenberger, Albert | Scheet, Paul | Shaffer, John R. | Teumer, Alexander | Thompson, John R. | Vink, Jacqueline M. | Vogelzangs, Nicole | Wenzlaff, Angela S. | Wheeler, William | Xiao, Xiangjun | Yang, Bao-Zhu | Aggen, Steven H. | Balmforth, Anthony J. | Baumeister, Sebastian E. | Beaty, Terri | Bennett, Siiri | Bergen, Andrew W. | Boyd, Heather A. | Broms, Ulla | Campbell, Harry | Chatterjee, Nilanjan | Chen, Jingchun | Cheng, Yu-Ching | Cichon, Sven | Couper, David | Cucca, Francesco | Dick, Danielle M. | Foroud, Tatiana | Furberg, Helena | Giegling, Ina | Gu, Fangyi | Hall, Alistair S. | Hällfors, Jenni | Han, Shizhong | Hartmann, Annette M. | Hayward, Caroline | Heikkilä, Kauko | Lic, Phil | Hewitt, John K. | Hottenga, Jouke Jan | Jensen, Majken K. | Jousilahti, Pekka | Kaakinen, Marika | Kittner, Steven J. | Konte, Bettina | Korhonen, Tellervo | Landi, Maria-Teresa | Laatikainen, Tiina | Leppert, Mark | Levy, Steven M. | Mathias, Rasika A. | McNeil, Daniel W. | Medland, Sarah E. | Montgomery, Grant W. | Muley, Thomas | Murray, Tanda | Nauck, Matthias | North, Kari | Pergadia, Michele | Polasek, Ozren | Ramos, Erin M. | Ripatti, Samuli | Risch, Angela | Ruczinski, Ingo | Rudan, Igor | Salomaa, Veikko | Schlessinger, David | Styrkársdóttir, Unnur | Terracciano, Antonio | Uda, Manuela | Willemsen, Gonneke | Wu, Xifeng | Abecasis, Goncalo | Barnes, Kathleen | Bickeböller, Heike | Boerwinkle, Eric | Boomsma, Dorret I. | Caporaso, Neil | Duan, Jubao | Edenberg, Howard J. | Francks, Clyde | Gejman, Pablo V. | Gelernter, Joel | Grabe, Hans Jörgen | Hops, Hyman | Jarvelin, Marjo-Riitta | Viikari, Jorma | Kähönen, Mika | Kendler, Kenneth S. | Lehtimäki, Terho | Levinson, Douglas F. | Marazita, Mary L. | Marchini, Jonathan | Melbye, Mads | Mitchell, Braxton D. | Murray, Jeffrey C. | Nöthen, Markus M. | Penninx, Brenda W. | Raitakari, Olli | Rietschel, Marcella | Rujescu, Dan | Samani, Nilesh J. | Sanders, Alan R. | Schwartz, Ann G. | Shete, Sanjay | Shi, Jianxin | Spitz, Margaret | Stefansson, Kari | Swan, Gary E. | Thorgeirsson, Thorgeir | Völzke, Henry | Wei, Qingyi | Wichmann, H.-Erich | Amos, Christopher I. | Breslau, Naomi | Cannon, Dale S. | Ehringer, Marissa | Grucza, Richard | Hatsukami, Dorothy | Heath, Andrew | Johnson, Eric O. | Kaprio, Jaakko | Madden, Pamela | Martin, Nicholas G. | Stevens, Victoria L. | Stitzel, Jerry A. | Weiss, Robert B. | Kraft, Peter | Bierut, Laura J.
Archives of general psychiatry  2012;69(8):854-860.
Context
Recent studies have shown an association between cigarettes per day (CPD) and a nonsynonymous single-nucleotide polymorphism in CHRNA5, rs16969968.
Objective
To determine whether the association between rs16969968 and smoking is modified by age at onset of regular smoking.
Data Sources
Primary data.
Study Selection
Available genetic studies containing measures of CPD and the genotype of rs16969968 or its proxy.
Data Extraction
Uniform statistical analysis scripts were run locally. Starting with 94 050 ever-smokers from 43 studies, we extracted the heavy smokers (CPD >20) and light smokers (CPD ≤10) with age-at-onset information, reducing the sample size to 33 348. Each study was stratified into early-onset smokers (age at onset ≤16 years) and late-onset smokers (age at onset >16 years), and a logistic regression of heavy vs light smoking with the rs16969968 genotype was computed for each stratum. Meta-analysis was performed within each age-at-onset stratum.
Data Synthesis
Individuals with 1 risk allele at rs16969968 who were early-onset smokers were significantly more likely to be heavy smokers in adulthood (odds ratio [OR]=1.45; 95% CI, 1.36–1.55; n=13 843) than were carriers of the risk allele who were late-onset smokers (OR = 1.27; 95% CI, 1.21–1.33, n = 19 505) (P = .01).
Conclusion
These results highlight an increased genetic vulnerability to smoking in early-onset smokers.
doi:10.1001/archgenpsychiatry.2012.124
PMCID: PMC3482121  PMID: 22868939
20.  Informed Conditioning on Clinical Covariates Increases Power in Case-Control Association Studies 
PLoS Genetics  2012;8(11):e1003032.
Genetic case-control association studies often include data on clinical covariates, such as body mass index (BMI), smoking status, or age, that may modify the underlying genetic risk of case or control samples. For example, in type 2 diabetes, odds ratios for established variants estimated from low–BMI cases are larger than those estimated from high–BMI cases. An unanswered question is how to use this information to maximize statistical power in case-control studies that ascertain individuals on the basis of phenotype (case-control ascertainment) or phenotype and clinical covariates (case-control-covariate ascertainment). While current approaches improve power in studies with random ascertainment, they often lose power under case-control ascertainment and fail to capture available power increases under case-control-covariate ascertainment. We show that an informed conditioning approach, based on the liability threshold model with parameters informed by external epidemiological information, fully accounts for disease prevalence and non-random ascertainment of phenotype as well as covariates and provides a substantial increase in power while maintaining a properly controlled false-positive rate. Our method outperforms standard case-control association tests with or without covariates, tests of gene x covariate interaction, and previously proposed tests for dealing with covariates in ascertained data, with especially large improvements in the case of case-control-covariate ascertainment. We investigate empirical case-control studies of type 2 diabetes, prostate cancer, lung cancer, breast cancer, rheumatoid arthritis, age-related macular degeneration, and end-stage kidney disease over a total of 89,726 samples. In these datasets, informed conditioning outperforms logistic regression for 115 of the 157 known associated variants investigated (P-value = 1×10−9). The improvement varied across diseases with a 16% median increase in χ2 test statistics and a commensurate increase in power. This suggests that applying our method to existing and future association studies of these diseases may identify novel disease loci.
Author Summary
This work describes a new methodology for analyzing genome-wide case-control association studies of diseases with strong correlations to clinical covariates, such as age in prostate cancer and body mass index in type 2 diabetes. Currently, researchers either ignore these clinical covariates or apply approaches that ignore the disease's prevalence and the study's ascertainment strategy. We take an alternative approach, leveraging external prevalence information from the epidemiological literature and constructing a statistic based on the classic liability threshold model of disease. Our approach not only improves the power of studies that ascertain individuals randomly or based on the disease phenotype, but also improves the power of studies that ascertain individuals based on both the disease phenotype and clinical covariates. We apply our statistic to seven datasets over six different diseases and a variety of clinical covariates. We found that there was a substantial improvement in test statistics relative to current approaches at known associated variants. This suggests that novel loci may be identified by applying our method to existing and future association studies of these diseases.
doi:10.1371/journal.pgen.1003032
PMCID: PMC3493452  PMID: 23144628
21.  Influence of common genetic variation on lung cancer risk: meta-analysis of 14 900 cases and 29 485 controls 
Human Molecular Genetics  2012;21(22):4980-4995.
Recent genome-wide association studies (GWASs) have identified common genetic variants at 5p15.33, 6p21–6p22 and 15q25.1 associated with lung cancer risk. Several other genetic regions including variants of CHEK2 (22q12), TP53BP1 (15q15) and RAD52 (12p13) have been demonstrated to influence lung cancer risk in candidate- or pathway-based analyses. To identify novel risk variants for lung cancer, we performed a meta-analysis of 16 GWASs, totaling 14 900 cases and 29 485 controls of European descent. Our data provided increased support for previously identified risk loci at 5p15 (P = 7.2 × 10−16), 6p21 (P = 2.3 × 10−14) and 15q25 (P = 2.2 × 10−63). Furthermore, we demonstrated histology-specific effects for 5p15, 6p21 and 12p13 loci but not for the 15q25 region. Subgroup analysis also identified a novel disease locus for squamous cell carcinoma at 9p21 (CDKN2A/p16INK4A/p14ARF/CDKN2B/p15INK4B/ANRIL; rs1333040, P = 3.0 × 10−7) which was replicated in a series of 5415 Han Chinese (P = 0.03; combined analysis, P = 2.3 × 10−8). This large analysis provides additional evidence for the role of inherited genetic susceptibility to lung cancer and insight into biological differences in the development of the different histological types of lung cancer.
doi:10.1093/hmg/dds334
PMCID: PMC3607485  PMID: 22899653
23.  Comparison of Pathway Analysis Approaches Using Lung Cancer GWAS Data Sets 
PLoS ONE  2012;7(2):e31816.
Pathway analysis has been proposed as a complement to single SNP analyses in GWAS. This study compared pathway analysis methods using two lung cancer GWAS data sets based on four studies: one a combined data set from Central Europe and Toronto (CETO); the other a combined data set from Germany and MD Anderson (GRMD). We searched the literature for pathway analysis methods that were widely used, representative of other methods, and had available software for performing analysis. We selected the programs EASE, which uses a modified Fishers Exact calculation to test for pathway associations, GenGen (a version of Gene Set Enrichment Analysis (GSEA)), which uses a Kolmogorov-Smirnov-like running sum statistic as the test statistic, and SLAT, which uses a p-value combination approach. We also included a modified version of the SUMSTAT method (mSUMSTAT), which tests for association by averaging χ2 statistics from genotype association tests. There were nearly 18000 genes available for analysis, following mapping of more than 300,000 SNPs from each data set. These were mapped to 421 GO level 4 gene sets for pathway analysis. Among the methods designed to be robust to biases related to gene size and pathway SNP correlation (GenGen, mSUMSTAT and SLAT), the mSUMSTAT approach identified the most significant pathways (8 in CETO and 1 in GRMD). This included a highly plausible association for the acetylcholine receptor activity pathway in both CETO (FDR≤0.001) and GRMD (FDR = 0.009), although two strong association signals at a single gene cluster (CHRNA3-CHRNA5-CHRNB4) drive this result, complicating its interpretation. Few other replicated associations were found using any of these methods. Difficulty in replicating associations hindered our comparison, but results suggest mSUMSTAT has advantages over the other approaches, and may be a useful pathway analysis tool to use alongside other methods such as the commonly used GSEA (GenGen) approach.
doi:10.1371/journal.pone.0031816
PMCID: PMC3283683  PMID: 22363742
25.  Replication of Lung Cancer Susceptibility Loci at Chromosomes 15q25, 5p15, and 6p21: A Pooled Analysis From the International Lung Cancer Consortium 
Background
Genome-wide association studies have identified three chromosomal regions at 15q25, 5p15, and 6p21 as being associated with the risk of lung cancer. To confirm these associations in independent studies and investigate heterogeneity of these associations within specific subgroups, we conducted a coordinated genotyping study within the International Lung Cancer Consortium based on independent studies that were not included in previous genome-wide association studies.
Methods
Genotype data for single-nucleotide polymorphisms at chromosomes 15q25 (rs16969968, rs8034191), 5p15 (rs2736100, rs402710), and 6p21 (rs2256543, rs4324798) from 21 case–control studies for 11 645 lung cancer case patients and 14 954 control subjects, of whom 85% were white and 15% were Asian, were pooled. Associations between the variants and the risk of lung cancer were estimated by logistic regression models. All statistical tests were two-sided.
Results
Associations between 15q25 and the risk of lung cancer were replicated in white ever-smokers (rs16969968: odds ratio [OR] = 1.26, 95% confidence interval [CI] = 1.21 to 1.32, Ptrend = 2 × 10−26), and this association was stronger for those diagnosed at younger ages. There was no association in never-smokers or in Asians between either of the 15q25 variants and the risk of lung cancer. For the chromosome 5p15 region, we confirmed statistically significant associations in whites for both rs2736100 (OR = 1.15, 95% CI = 1.10 to 1.20, Ptrend = 1 × 10−10) and rs402710 (OR = 1.14, 95% CI = 1.09 to 1.19, Ptrend = 5 × 10−8) and identified similar associations in Asians (rs2736100: OR = 1.23, 95% CI = 1.12 to 1.35, Ptrend = 2 × 10−5; rs402710: OR = 1.15, 95% CI = 1.04 to 1.27, Ptrend = .007). The associations between the 5p15 variants and lung cancer differed by histology; odds ratios for rs2736100 were highest in adenocarcinoma and for rs402710 were highest in adenocarcinoma and squamous cell carcinomas. This pattern was observed in both ethnic groups. Neither of the two variants on chromosome 6p21 was associated with the risk of lung cancer.
Conclusions
In this international genetic association study of lung cancer, previous associations found in white populations were replicated and new associations were identified in Asian populations. Future genetic studies of lung cancer should include detailed stratification by histology.
doi:10.1093/jnci/djq178
PMCID: PMC2897877  PMID: 20548021

Results 1-25 (32)