Bickeböller, Heike

[["dc.bibliographiccitation.artnumber","e31816"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","PLoS ONE"],["dc.bibliographiccitation.volume","7"],["dc.contributor.author","Fehringer, Gordon"],["dc.contributor.author","Liu, Geoffrey"],["dc.contributor.author","Briollais, Laurent"],["dc.contributor.author","Brennan, P. C."],["dc.contributor.author","Amos, Christopher I."],["dc.contributor.author","Spitz, Margaret R."],["dc.contributor.author","Bickeboeller, Heike"],["dc.contributor.author","Wichmann, Heinz-Erich"],["dc.contributor.author","Risch, Angela"],["dc.contributor.author","Hung, Rayjean J."],["dc.date.accessioned","2018-11-07T09:13:21Z"],["dc.date.available","2018-11-07T09:13:21Z"],["dc.date.issued","2012"],["dc.description.abstract","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 chi(2) 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."],["dc.identifier.doi","10.1371/journal.pone.0031816"],["dc.identifier.isi","000302873700103"],["dc.identifier.pmid","22363742"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/7891"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/27153"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Public Library Science"],["dc.relation.issn","1932-6203"],["dc.rights","CC BY 2.5"],["dc.rights.uri","https://creativecommons.org/licenses/by/2.5"],["dc.title","Comparison of Pathway Analysis Approaches Using Lung Cancer GWAS Data Sets"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","44634"],["dc.bibliographiccitation.journal","Scientific Reports"],["dc.bibliographiccitation.volume","7"],["dc.contributor.author","Pan, Yongchu"],["dc.contributor.author","Liu, Hongliang"],["dc.contributor.author","Wang, Y."],["dc.contributor.author","Kang, Xiaozheng"],["dc.contributor.author","Liu, Zhensheng"],["dc.contributor.author","Owzar, Kouros"],["dc.contributor.author","Han, Younghun"],["dc.contributor.author","Su, L. I."],["dc.contributor.author","Wei, Yongyue"],["dc.contributor.author","Hung, Rayjean J."],["dc.contributor.author","Brhane, Yonathan"],["dc.contributor.author","McLaughlin, John R."],["dc.contributor.author","Brennan, P. C."],["dc.contributor.author","Bickeboeller, Heike"],["dc.contributor.author","Rosenberger, Albert"],["dc.contributor.author","Houlston, Richard S."],["dc.contributor.author","Caporaso, Neil E."],["dc.contributor.author","Landi, Maria Teresa"],["dc.contributor.author","Heinrich, Joachim"],["dc.contributor.author","Risch, Angela"],["dc.contributor.author","Wu, X."],["dc.contributor.author","Ye, Yuanqing"],["dc.contributor.author","Christiani, David C."],["dc.contributor.author","Amos, Christopher I."],["dc.contributor.author","Wei, Qingyi"],["dc.date.accessioned","2018-11-07T10:26:10Z"],["dc.date.available","2018-11-07T10:26:10Z"],["dc.date.issued","2017"],["dc.description.abstract","mRNA splicing is an important mechanism to regulate mRNA expression. Abnormal regulation of this process may lead to lung cancer. Here, we investigated the associations of 11,966 single-nucleotide polymorphisms (SNPs) in 206 mRNA splicing-related genes with lung cancer risk by using the summary data from six published genome-wide association studies (GWASs) of Transdisciplinary Research in Cancer of the Lung (TRICL) (12,160 cases and 16,838 controls) and another two lung cancer GWASs of Harvard University (984 cases and 970 controls) and deCODE (1,319 cases and 26,380 controls). We found that a total of 12 significant SNPs with false discovery rate (FDR) <= 0.05 were mapped to one novel gene PRPF6 and two previously reported genes (DHX16 and LSM2) that were also confirmed in this study. The six novel SNPs in PRPF6 were in high linkage disequilibrium and associated with PRPF6 mRNA expression in lymphoblastoid cells from 373 Europeans in the 1000 Genomes Project. Taken together, our studies shed new light on the role of mRNA splicing genes in the development of lung cancer."],["dc.identifier.doi","10.1038/srep44634"],["dc.identifier.isi","000397001600001"],["dc.identifier.pmid","28304396"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/14434"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/42982"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","PUB_WoS_Import"],["dc.publisher","Nature Publishing Group"],["dc.relation.issn","2045-2322"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Associations between genetic variants in mRNA splicing-related genes and risk of lung cancer: a pathway-based analysis from published GWASs"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","5356"],["dc.bibliographiccitation.issue","18"],["dc.bibliographiccitation.journal","Human Molecular Genetics"],["dc.bibliographiccitation.lastpage","5366"],["dc.bibliographiccitation.volume","24"],["dc.contributor.author","Zhang, C."],["dc.contributor.author","Doherty, Jennifer Anne"],["dc.contributor.author","Burgess, Stephen"],["dc.contributor.author","Hung, Rayjean J."],["dc.contributor.author","Lindstroem, Sara"],["dc.contributor.author","Kraft, Peter"],["dc.contributor.author","Gong, Jian"],["dc.contributor.author","Amos, Christopher I."],["dc.contributor.author","Sellers, Thomas A."],["dc.contributor.author","Monteiro, Alvaro N. A."],["dc.contributor.author","Chenevix-Trench, Georgia"],["dc.contributor.author","Bickeböller, Heike"],["dc.contributor.author","Risch, Angela"],["dc.contributor.author","Brennan, P. C."],["dc.contributor.author","McKay, James D."],["dc.contributor.author","Houlston, Richard S."],["dc.contributor.author","Landi, Maria Teresa"],["dc.contributor.author","Timofeeva, Maria N."],["dc.contributor.author","Wang, Y."],["dc.contributor.author","Heinrich, Joachim"],["dc.contributor.author","Kote-Jarai, Zsofia"],["dc.contributor.author","Eeles, Rosalind A."],["dc.contributor.author","Muir, Ken"],["dc.contributor.author","Wiklund, Fredrik"],["dc.contributor.author","Gronberg, Henrik"],["dc.contributor.author","Berndt, Sonja I."],["dc.contributor.author","Chanock, Stephen J."],["dc.contributor.author","Schumacher, Frederick R."],["dc.contributor.author","Haiman, Christopher A."],["dc.contributor.author","Henderson, Brian E."],["dc.contributor.author","Al Olama, Ali Amin"],["dc.contributor.author","Andrulis, Irene L."],["dc.contributor.author","Hopper, John L."],["dc.contributor.author","Chang-Claude, Jenny"],["dc.contributor.author","John, Esther M."],["dc.contributor.author","Malone, Kathleen E."],["dc.contributor.author","Gammon, Marilie D."],["dc.contributor.author","Ursin, Giske"],["dc.contributor.author","Whittemore, Alice S."],["dc.contributor.author","Hunter, David J."],["dc.contributor.author","Gruber, Stephen B."],["dc.contributor.author","Knight, Julia A."],["dc.contributor.author","Hou, Lifang"],["dc.contributor.author","Le Marchand, Loic"],["dc.contributor.author","Newcomb, Polly A."],["dc.contributor.author","Hudson, Thomas J."],["dc.contributor.author","Chan, Andrew T."],["dc.contributor.author","Li, L. I."],["dc.contributor.author","Woods, Michael O."],["dc.contributor.author","Ahsan, Habibul"],["dc.contributor.author","Pierce, Brandon L."],["dc.date.accessioned","2018-11-07T09:51:38Z"],["dc.date.available","2018-11-07T09:51:38Z"],["dc.date.issued","2015"],["dc.description.abstract","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 x 10(-15)), even after exclusion of a SNP residing in a known lung cancer susceptibility region (TERT-CLPTM1L) P = 6.6 x 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."],["dc.identifier.doi","10.1093/hmg/ddv252"],["dc.identifier.isi","000361317200024"],["dc.identifier.pmid","26138067"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/12147"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/35955"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Oxford Univ Press"],["dc.relation.issn","1460-2083"],["dc.relation.issn","0964-6906"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Genetic determinants of telomere length and risk of common cancers: a Mendelian randomization study"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","e0177875"],["dc.bibliographiccitation.issue","6"],["dc.bibliographiccitation.journal","PloS one"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Carreras-Torres, Robert"],["dc.contributor.author","Johansson, Mattias"],["dc.contributor.author","Haycock, Philip C."],["dc.contributor.author","Wade, Kaitlin H."],["dc.contributor.author","Relton, Caroline L."],["dc.contributor.author","Martin, Richard M."],["dc.contributor.author","Davey Smith, George"],["dc.contributor.author","Albanes, Demetrius"],["dc.contributor.author","Aldrich, Melinda C."],["dc.contributor.author","Andrew, Angeline"],["dc.contributor.author","Arnold, Susanne M."],["dc.contributor.author","Bickeböller, Heike"],["dc.contributor.author","Bojesen, Stig E."],["dc.contributor.author","Brunnström, Hans"],["dc.contributor.author","Manjer, Jonas"],["dc.contributor.author","Brüske, Irene"],["dc.contributor.author","Caporaso, Neil E."],["dc.contributor.author","Chen, Chu"],["dc.contributor.author","Christiani, David C."],["dc.contributor.author","Christian, W. Jay"],["dc.contributor.author","Doherty, Jennifer A."],["dc.contributor.author","Duell, Eric J."],["dc.contributor.author","Field, John K."],["dc.contributor.author","Davies, Michael P. A."],["dc.contributor.author","Marcus, Michael W."],["dc.contributor.author","Goodman, Gary E."],["dc.contributor.author","Grankvist, Kjell"],["dc.contributor.author","Haugen, Aage"],["dc.contributor.author","Hong, Yun-Chul"],["dc.contributor.author","Kiemeney, Lambertus A."],["dc.contributor.author","van der Heijden, Erik H. F. M."],["dc.contributor.author","Kraft, Peter"],["dc.contributor.author","Johansson, Mikael B."],["dc.contributor.author","Lam, Stephen"],["dc.contributor.author","Landi, Maria Teresa"],["dc.contributor.author","Lazarus, Philip"],["dc.contributor.author","Le Marchand, Loïc"],["dc.contributor.author","Liu, Geoffrey"],["dc.contributor.author","Melander, Olle"],["dc.contributor.author","Park, Sungshim L."],["dc.contributor.author","Rennert, Gad"],["dc.contributor.author","Risch, Angela"],["dc.contributor.author","Haura, Eric B."],["dc.contributor.author","Scelo, Ghislaine"],["dc.contributor.author","Zaridze, David"],["dc.contributor.author","Mukeriya, Anush"],["dc.contributor.author","Savić, Milan"],["dc.contributor.author","Lissowska, Jolanta"],["dc.contributor.author","Swiatkowska, Beata"],["dc.contributor.author","Janout, Vladimir"],["dc.contributor.author","Holcatova, Ivana"],["dc.contributor.author","Mates, Dana"],["dc.contributor.author","Schabath, Matthew B."],["dc.contributor.author","Shen, Hongbing"],["dc.contributor.author","Tardon, Adonina"],["dc.contributor.author","Teare, M. Dawn"],["dc.contributor.author","Woll, Penella"],["dc.contributor.author","Tsao, Ming-Sound"],["dc.contributor.author","Wu, Xifeng"],["dc.contributor.author","Yuan, Jian-Min"],["dc.contributor.author","Hung, Rayjean J."],["dc.contributor.author","Amos, Christopher I."],["dc.contributor.author","McKay, James"],["dc.contributor.author","Brennan, Paul"],["dc.date.accessioned","2019-07-09T11:43:32Z"],["dc.date.available","2019-07-09T11:43:32Z"],["dc.date.issued","2017"],["dc.description.abstract","BACKGROUND: Assessing the relationship between lung cancer and metabolic conditions is challenging because of the confounding effect of tobacco. Mendelian randomization (MR), or the use of genetic instrumental variables to assess causality, may help to identify the metabolic drivers of lung cancer. METHODS AND FINDINGS: We identified genetic instruments for potential metabolic risk factors and evaluated these in relation to risk using 29,266 lung cancer cases (including 11,273 adenocarcinomas, 7,426 squamous cell and 2,664 small cell cases) and 56,450 controls. The MR risk analysis suggested a causal effect of body mass index (BMI) on lung cancer risk for two of the three major histological subtypes, with evidence of a risk increase for squamous cell carcinoma (odds ratio (OR) [95% confidence interval (CI)] = 1.20 [1.01-1.43] and for small cell lung cancer (OR [95%CI] = 1.52 [1.15-2.00]) for each standard deviation (SD) increase in BMI [4.6 kg/m2]), but not for adenocarcinoma (OR [95%CI] = 0.93 [0.79-1.08]) (Pheterogeneity = 4.3x10-3). Additional analysis using a genetic instrument for BMI showed that each SD increase in BMI increased cigarette consumption by 1.27 cigarettes per day (P = 2.1x10-3), providing novel evidence that a genetic susceptibility to obesity influences smoking patterns. There was also evidence that low-density lipoprotein cholesterol was inversely associated with lung cancer overall risk (OR [95%CI] = 0.90 [0.84-0.97] per SD of 38 mg/dl), while fasting insulin was positively associated (OR [95%CI] = 1.63 [1.25-2.13] per SD of 44.4 pmol/l). Sensitivity analyses including a weighted-median approach and MR-Egger test did not detect other pleiotropic effects biasing the main results. CONCLUSIONS: Our results are consistent with a causal role of fasting insulin and low-density lipoprotein cholesterol in lung cancer etiology, as well as for BMI in squamous cell and small cell carcinoma. The latter relation may be mediated by a previously unrecognized effect of obesity on smoking behavior."],["dc.identifier.doi","10.1371/journal.pone.0177875"],["dc.identifier.pmid","28594918"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/14558"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/58904"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation.issn","1932-6203"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject.ddc","610"],["dc.title","Obesity, metabolic factors and risk of different histological types of lung cancer: A Mendelian randomization study."],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","6742763"],["dc.bibliographiccitation.journal","Computational and mathematical methods in medicine"],["dc.bibliographiccitation.lastpage","17"],["dc.bibliographiccitation.volume","2017"],["dc.contributor.author","Friedrichs, Stefanie"],["dc.contributor.author","Manitz, Juliane"],["dc.contributor.author","Burger, Patricia"],["dc.contributor.author","Amos, Christopher I."],["dc.contributor.author","Risch, Angela"],["dc.contributor.author","Chang-Claude, Jenny"],["dc.contributor.author","Wichmann, Heinz-Erich"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Bickeböller, Heike"],["dc.contributor.author","Hofner, Benjamin"],["dc.date.accessioned","2018-03-13T14:55:43Z"],["dc.date.available","2018-03-13T14:55:43Z"],["dc.date.issued","2017"],["dc.description.abstract","The analysis of genome-wide association studies (GWAS) benefits from the investigation of biologically meaningful gene sets, such as gene-interaction networks (pathways). We propose an extension to a successful kernel-based pathway analysis approach by integrating kernel functions into a powerful algorithmic framework for variable selection, to enable investigation of multiple pathways simultaneously. We employ genetic similarity kernels from the logistic kernel machine test (LKMT) as base-learners in a boosting algorithm. A model to explain case-control status is created iteratively by selecting pathways that improve its prediction ability. We evaluated our method in simulation studies adopting 50 pathways for different sample sizes and genetic effect strengths. Additionally, we included an exemplary application of kernel boosting to a rheumatoid arthritis and a lung cancer dataset. Simulations indicate that kernel boosting outperforms the LKMT in certain genetic scenarios. Applications to GWAS data on rheumatoid arthritis and lung cancer resulted in sparse models which were based on pathways interpretable in a clinical sense. Kernel boosting is highly flexible in terms of considered variables and overcomes the problem of multiple testing. Additionally, it enables the prediction of clinical outcomes. Thus, kernel boosting constitutes a new, powerful tool in the analysis of GWAS data and towards the understanding of biological processes involved in disease susceptibility."],["dc.identifier.doi","10.1155/2017/6742763"],["dc.identifier.pmid","28785300"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/14774"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/13019"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.relation.eissn","1748-6718"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","14"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","European Journal of Medical Research"],["dc.bibliographiccitation.volume","27"],["dc.contributor.author","Rosenberger, Albert"],["dc.contributor.author","Muttray, Nils"],["dc.contributor.author","Hung, Rayjean J."],["dc.contributor.author","Christiani, David C."],["dc.contributor.author","Caporaso, Neil E."],["dc.contributor.author","Liu, Geoffrey"],["dc.contributor.author","Bojesen, Stig E."],["dc.contributor.author","Le Marchand, Loic"],["dc.contributor.author","Albanes, Demetrios"],["dc.contributor.author","Aldrich, Melinda C."],["dc.contributor.author","Tardon, Adonina"],["dc.contributor.author","Fernández-Tardón, Guillermo"],["dc.contributor.author","Rennert, Gad"],["dc.contributor.author","Field, John K."],["dc.contributor.author","Davies, Michael P. A."],["dc.contributor.author","Liloglou, Triantafillos"],["dc.contributor.author","Kiemeney, Lambertus A."],["dc.contributor.author","Lazarus, Philip"],["dc.contributor.author","Wendel, Bernadette"],["dc.contributor.author","Haugen, Aage"],["dc.contributor.author","Zienolddiny, Shanbeh"],["dc.contributor.author","Lam, Stephen"],["dc.contributor.author","Schabath, Matthew B."],["dc.contributor.author","Andrew, Angeline S."],["dc.contributor.author","Duell, Eric J."],["dc.contributor.author","Arnold, Susanne M."],["dc.contributor.author","Goodman, Gary E."],["dc.contributor.author","Chen, Chu"],["dc.contributor.author","Doherty, Jennifer A."],["dc.contributor.author","Taylor, Fiona"],["dc.contributor.author","Cox, Angela"],["dc.contributor.author","Woll, Penella J."],["dc.contributor.author","Risch, Angela"],["dc.contributor.author","Muley, Thomas R."],["dc.contributor.author","Johansson, Mikael"],["dc.contributor.author","Brennan, Paul"],["dc.contributor.author","Landi, Maria T."],["dc.contributor.author","Shete, Sanjay S."],["dc.contributor.author","Amos, Christopher I."],["dc.contributor.author","Bickeböller, Heike"],["dc.contributor.authorgroup","The INTEGRAL-ILCCO Consortium"],["dc.date.accessioned","2022-04-01T10:03:08Z"],["dc.date.accessioned","2022-08-18T12:40:47Z"],["dc.date.available","2022-04-01T10:03:08Z"],["dc.date.available","2022-08-18T12:40:47Z"],["dc.date.issued","2022-01-31"],["dc.date.updated","2022-07-29T12:18:28Z"],["dc.description.abstract","Background\r\nAberrant Wnt signalling, regulating cell development and stemness, influences the development of many cancer types. The Aryl hydrocarbon receptor (AhR) mediates tumorigenesis of environmental pollutants. Complex interaction patterns of genes assigned to AhR/Wnt-signalling were recently associated with lung cancer susceptibility.\r\n\r\nAim\r\nTo assess the association and predictive ability of AhR/Wnt-genes with lung cancer in cases and controls of European descent.\r\n\r\nMethods\r\nOdds ratios (OR) were estimated for genomic variants assigned to the Wnt agonist and the antagonistic genes DKK2, DKK3, DKK4, FRZB, SFRP4 and Axin2. Logistic regression models with variable selection were trained, validated and tested to predict lung cancer, at which other previously identified SNPs that have been robustly associated with lung cancer risk could also enter the model. Furthermore, decision trees were created to investigate variant × variant interaction. All analyses were performed for overall lung cancer and for subgroups.\r\n\r\nResults\r\nNo genome-wide significant association of AhR/Wnt-genes with overall lung cancer was observed, but within the subgroups of ever smokers (e.g., maker rs2722278 SFRP4; OR  = 1.20; 95% CI 1.13–1.27; p  = 5.6 × 10–10) and never smokers (e.g., maker rs1133683 Axin2; OR  = 1.27; 95% CI 1.19–1.35; p  = 1.0 × 10–12). Although predictability is poor, AhR/Wnt-variants are unexpectedly overrepresented in optimized prediction scores for overall lung cancer and for small cell lung cancer. Remarkably, the score for never-smokers contained solely two AhR/Wnt-variants. The optimal decision tree for never smokers consists of 7 AhR/Wnt-variants and only two lung cancer variants.\r\n\r\nConclusions\r\nThe role of variants belonging to Wnt/AhR-pathways in lung cancer susceptibility may be underrated in main-effects association analysis. Complex interaction patterns in individuals of European descent have moderate predictive capacity for lung cancer or subgroups thereof, especially in never smokers."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2022"],["dc.identifier.citation","European Journal of Medical Research. 2022 Jan 31;27(1):14"],["dc.identifier.doi","10.1186/s40001-022-00638-7"],["dc.identifier.pii","638"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/106090"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/112985"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-530"],["dc.publisher","BioMed Central"],["dc.relation.eissn","2047-783X"],["dc.rights","CC BY 4.0"],["dc.rights.holder","The Author(s)"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject","Susceptibility"],["dc.subject","Association"],["dc.subject","Gene–gene integration"],["dc.subject","Prediction"],["dc.subject","Polygenic risk score"],["dc.subject","Decision trees"],["dc.subject","Never smoker"],["dc.subject","Small cell lung cancer"],["dc.title","Gene–gene interaction of AhRwith and within the Wntcascade affects susceptibility to lung cancer"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","64"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Human Heredity"],["dc.bibliographiccitation.lastpage","75"],["dc.bibliographiccitation.volume","76"],["dc.contributor.author","Freytag, Saskia"],["dc.contributor.author","Manitz, Juliane"],["dc.contributor.author","Schlather, Martin"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Amos, Christopher I."],["dc.contributor.author","Risch, Angela"],["dc.contributor.author","Chang-Claude, Jenny"],["dc.contributor.author","Heinrich, Joachim"],["dc.contributor.author","Bickeböller, Heike"],["dc.date.accessioned","2017-09-07T11:47:18Z"],["dc.date.available","2017-09-07T11:47:18Z"],["dc.date.issued","2014"],["dc.description.abstract","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). In this study, the kernel converts the genomic information of 2 individuals into 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 the 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."],["dc.identifier.doi","10.1159/000357567"],["dc.identifier.gro","3149315"],["dc.identifier.pmid","24434848"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/10822"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/5978"],["dc.language.iso","en"],["dc.notes.intern","Kneib Crossref Import"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.publisher","S. Karger AG"],["dc.relation.eissn","1423-0062"],["dc.relation.issn","0001-5652"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","A Network-Based Kernel Machine Test for the Identification of Risk Pathways in Genome-Wide Association Studies"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","4980"],["dc.bibliographiccitation.issue","22"],["dc.bibliographiccitation.journal","Human Molecular Genetics"],["dc.bibliographiccitation.lastpage","4995"],["dc.bibliographiccitation.volume","21"],["dc.contributor.author","Timofeeva, Maria N."],["dc.contributor.author","Hung, Rayjean J."],["dc.contributor.author","Rafnar, Thorunn"],["dc.contributor.author","Christiani, David C."],["dc.contributor.author","Field, John K."],["dc.contributor.author","Bickeboeller, Heike"],["dc.contributor.author","Risch, Angela"],["dc.contributor.author","McKay, James D."],["dc.contributor.author","Wang, Y."],["dc.contributor.author","Dai, Juncheng"],["dc.contributor.author","Gaborieau, Valerie"],["dc.contributor.author","McLaughlin, John R."],["dc.contributor.author","Brenner, Darren"],["dc.contributor.author","Narod, Steven A."],["dc.contributor.author","Caporaso, Neil E."],["dc.contributor.author","Albanes, Demetrius"],["dc.contributor.author","Thun, Michael"],["dc.contributor.author","Eisen, Timothy"],["dc.contributor.author","Wichmann, Heinz-Erich"],["dc.contributor.author","Rosenberger, Albert"],["dc.contributor.author","Han, Younghun"],["dc.contributor.author","Chen, Wei"],["dc.contributor.author","Zhu, D."],["dc.contributor.author","Spitz, Margaret R."],["dc.contributor.author","Wu, X."],["dc.contributor.author","Pande, Mala"],["dc.contributor.author","Zhao, Yang"],["dc.contributor.author","Zaridze, David"],["dc.contributor.author","Szeszenia-Dabrowska, Neonilia"],["dc.contributor.author","Lissowska, Jolanta"],["dc.contributor.author","Rudnai, Peter"],["dc.contributor.author","Fabianova, Eleonora"],["dc.contributor.author","Mates, Dana"],["dc.contributor.author","Bencko, Vladimir"],["dc.contributor.author","Foretova, Lenka"],["dc.contributor.author","Janout, Vladimir"],["dc.contributor.author","Krokan, Hans E."],["dc.contributor.author","Gabrielsen, Maiken Elvestad"],["dc.contributor.author","Skorpen, Frank"],["dc.contributor.author","Vatten, Lars"],["dc.contributor.author","Njolstad, Inger"],["dc.contributor.author","Chen, Chu"],["dc.contributor.author","Goodman, Gary"],["dc.contributor.author","Lathrop, Mark"],["dc.contributor.author","Benhamou, Simone"],["dc.contributor.author","Vooder, Tonu"],["dc.contributor.author","Vaelk, Kristjan"],["dc.contributor.author","Nelis, Mari"],["dc.contributor.author","Metspalu, Andres"],["dc.contributor.author","Raji, Olaide Y."],["dc.contributor.author","Chen, Ying"],["dc.contributor.author","Gosney, John"],["dc.contributor.author","Liloglou, Triantafillos"],["dc.contributor.author","Muley, Thomas"],["dc.contributor.author","Dienemann, Hendrik"],["dc.contributor.author","Thorleifsson, Gudmar"],["dc.contributor.author","Shen, Hongbing"],["dc.contributor.author","Stefansson, Kari"],["dc.contributor.author","Brennan, P. C."],["dc.contributor.author","Amos, Christopher I."],["dc.contributor.author","Houlston, Richard S."],["dc.contributor.author","Landi, Maria Teresa"],["dc.date.accessioned","2018-11-07T09:03:28Z"],["dc.date.available","2018-11-07T09:03:28Z"],["dc.date.issued","2012"],["dc.description.abstract","Recent genome-wide association studies (GWASs) have identified common genetic variants at 5p15.33, 6p216p22 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/p16(INK4A)/p14(ARF)/CDKN2B/p15(INK4B)/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."],["dc.identifier.doi","10.1093/hmg/dds334"],["dc.identifier.isi","000310369000016"],["dc.identifier.pmid","22899653"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/10648"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/24907"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Oxford Univ Press"],["dc.relation.issn","1460-2083"],["dc.relation.issn","0964-6906"],["dc.rights","CC BY-NC 2.5"],["dc.rights.uri","https://creativecommons.org/licenses/by-nc/2.5"],["dc.title","Influence of common genetic variation on lung cancer risk: meta-analysis of 14 900 cases and 29 485 controls"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","dju061"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","JNCI Journal of the National Cancer Institute"],["dc.bibliographiccitation.volume","106"],["dc.contributor.author","Park, S. L."],["dc.contributor.author","Fesinmeyer, Megan D."],["dc.contributor.author","Timofeeva, Maria N."],["dc.contributor.author","Caberto, Christian P."],["dc.contributor.author","Kocarnik, Jonathan M."],["dc.contributor.author","Han, Younghun"],["dc.contributor.author","Love, Shelly-Ann"],["dc.contributor.author","Young, Alicia"],["dc.contributor.author","Dumitrescu, Logan"],["dc.contributor.author","Lin, Y. I."],["dc.contributor.author","Goodloe, Robert"],["dc.contributor.author","Wilkens, Lynne R."],["dc.contributor.author","Hindorff, Lucia"],["dc.contributor.author","Fowke, Jay H."],["dc.contributor.author","Carty, Cara"],["dc.contributor.author","Buyske, Steven"],["dc.contributor.author","Schumacher, Frederick R."],["dc.contributor.author","Butler, Anne"],["dc.contributor.author","Dilks, Holli"],["dc.contributor.author","Deelman, Ewa"],["dc.contributor.author","Cote, Michele L."],["dc.contributor.author","Chen, Wei"],["dc.contributor.author","Pande, Mala"],["dc.contributor.author","Christiani, David C."],["dc.contributor.author","Field, John K."],["dc.contributor.author","Bickeboeller, Heike"],["dc.contributor.author","Risch, Angela"],["dc.contributor.author","Heinrich, Joachim"],["dc.contributor.author","Brennan, P. C."],["dc.contributor.author","Wang, Y."],["dc.contributor.author","Eisen, Timothy"],["dc.contributor.author","Houlston, Richard S."],["dc.contributor.author","Thun, Michael"],["dc.contributor.author","Albanes, Demetrius"],["dc.contributor.author","Caporaso, Neil E."],["dc.contributor.author","Peters, Ulrike"],["dc.contributor.author","North, Kari E."],["dc.contributor.author","Heiss, Gerardo"],["dc.contributor.author","Crawford, Dana C."],["dc.contributor.author","Bush, William S."],["dc.contributor.author","Haiman, Christopher A."],["dc.contributor.author","Landi, Maria Teresa"],["dc.contributor.author","Hung, Rayjean J."],["dc.contributor.author","Kooperberg, Charles"],["dc.contributor.author","Amos, Christopher I."],["dc.contributor.author","Le Marchand, Loic"],["dc.contributor.author","Cheng, Iona"],["dc.date.accessioned","2018-11-07T09:41:48Z"],["dc.date.available","2018-11-07T09:41:48Z"],["dc.date.issued","2014"],["dc.description.abstract","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 18 023 patients with lung cancer and 60 543 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 x 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 x 10(-6)). This association was strongest for women with adenocarcinoma (P = 1.2 x 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 x 10(-8)) and squamous cell carcinoma (OR = 1.13; CI = 1.07 to 1.19; P = 2.5 x 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."],["dc.identifier.doi","10.1093/jnci/dju061"],["dc.identifier.isi","000334691500032"],["dc.identifier.pmid","24681604"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/10659"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/33814"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Oxford Univ Press Inc"],["dc.relation.issn","1460-2105"],["dc.relation.issn","0027-8874"],["dc.rights","CC BY 3.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/3.0"],["dc.title","Pleiotropic Associations of Risk Variants Identified for Other Cancers With Lung Cancer Risk: The PAGE and TRICL Consortia"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","34234"],["dc.bibliographiccitation.journal","Scientific Reports"],["dc.bibliographiccitation.volume","6"],["dc.contributor.author","Yuan, Hua"],["dc.contributor.author","Liu, Hongliang"],["dc.contributor.author","Liu, Zhensheng"],["dc.contributor.author","Owzar, Kouros"],["dc.contributor.author","Han, Younghun"],["dc.contributor.author","Su, L. I."],["dc.contributor.author","Wei, Yongyue"],["dc.contributor.author","Hung, Rayjean J."],["dc.contributor.author","McLaughlin, John R."],["dc.contributor.author","Brhane, Yonathan"],["dc.contributor.author","Brennan, P. C."],["dc.contributor.author","Bickeboeller, Heike"],["dc.contributor.author","Rosenberger, Albert"],["dc.contributor.author","Houlston, Richard S."],["dc.contributor.author","Caporaso, Neil E."],["dc.contributor.author","Landi, Maria Teresa"],["dc.contributor.author","Heinrich, Joachim"],["dc.contributor.author","Risch, Angela"],["dc.contributor.author","Christiani, David C."],["dc.contributor.author","Gumus, Zeynep H."],["dc.contributor.author","Klein, Robert J."],["dc.contributor.author","Amos, Christopher I."],["dc.contributor.author","Wei, Qingyi"],["dc.date.accessioned","2018-11-07T10:07:09Z"],["dc.date.available","2018-11-07T10:07:09Z"],["dc.date.issued","2016"],["dc.description.abstract","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 x 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 x 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."],["dc.identifier.doi","10.1038/srep34234"],["dc.identifier.isi","000384833100001"],["dc.identifier.pmid","27713484"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/13809"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/39229"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Nature Publishing Group"],["dc.relation.issn","2045-2322"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","A Novel Genetic Variant in Long Non-coding RNA Gene NEXN-AS1 is Associated with Risk of Lung Cancer"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]