Bickeböller, Heike

[["dc.bibliographiccitation.artnumber","S55"],["dc.bibliographiccitation.journal","BMC Genetics"],["dc.bibliographiccitation.volume","6"],["dc.contributor.author","Rosenberger, Albert"],["dc.contributor.author","Janicke, N."],["dc.contributor.author","Kohler, K."],["dc.contributor.author","Korb, Katrin"],["dc.contributor.author","Kulle, Bettina"],["dc.contributor.author","Bickeboeller, Heike"],["dc.date.accessioned","2018-11-07T10:53:25Z"],["dc.date.available","2018-11-07T10:53:25Z"],["dc.date.issued","2005"],["dc.description.abstract","For the identification of susceptibility loci in complex diseases the choice of the target phenotype is very important. We compared results of genome-wide searches for linkage or for association related to three phenotypes for alcohol use disorder. These are a behavioral score BQ, based on a 12-item questionnaire about drinking behavior and the subject's report of drinking-related health problems, and ERP pattern and ERP magnitude, both derived from the eyes closed resting ERP measures to quantify brain activity. Overall, we were able to identify 11 candidate regions for linkage. Only two regions were found to be related to both BQ and one of the ERP phenotypes. The genome-wide search for association using single-nucleotide polymorphisms did not yield interesting leads."],["dc.identifier.doi","10.1186/1471-2156-6-S1-S55"],["dc.identifier.isi","000236103400055"],["dc.identifier.pmid","16451667"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?goescholar/1371"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/49360"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Biomed Central Ltd"],["dc.publisher.place","London"],["dc.relation.conference","14th Genetic Analysis Workshop"],["dc.relation.eventlocation","Noordwijkerhout, NETHERLANDS"],["dc.relation.issn","1471-2156"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","Surrogate phenotype definition for alcohol use disorders: a genome-wide search for linkage and association"],["dc.type","conference_paper"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","66"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Theoretical Population Biology"],["dc.bibliographiccitation.lastpage","90"],["dc.bibliographiccitation.volume","50"],["dc.contributor.author","Bickeböller, Heike"],["dc.contributor.author","Thompson, Elizabeth A."],["dc.date.accessioned","2022-06-08T07:58:11Z"],["dc.date.available","2022-06-08T07:58:11Z"],["dc.date.issued","1996"],["dc.identifier.doi","10.1006/tpbi.1996.0023"],["dc.identifier.pii","S0040580996900234"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/110335"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-575"],["dc.relation.issn","0040-5809"],["dc.title","Distribution of Genome Shared IBD by Half-Sibs: Approximation by the Poisson Clumping Heuristic"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["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.issue","S7"],["dc.bibliographiccitation.journal","BMC Proceedings"],["dc.bibliographiccitation.volume","3"],["dc.contributor.author","Malzahn, Dörthe"],["dc.contributor.author","Balavarca, Yesilda"],["dc.contributor.author","Lozano, Jingky P"],["dc.contributor.author","Bickeböller, Heike"],["dc.date.accessioned","2021-06-01T10:48:01Z"],["dc.date.available","2021-06-01T10:48:01Z"],["dc.date.issued","2009"],["dc.identifier.doi","10.1186/1753-6561-3-S7-S80"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/12882"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/85802"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-425"],["dc.notes.intern","Merged from goescholar"],["dc.relation.eissn","1753-6561"],["dc.rights","CC BY 2.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/2.0"],["dc.title","Tests for candidate-gene interaction for longitudinal quantitative traits measured in a large cohort"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","7"],["dc.bibliographiccitation.journal","Genetic Epidemiology"],["dc.bibliographiccitation.volume","39"],["dc.contributor.author","Friedrichs, Stefanie"],["dc.contributor.author","Amos, Christopher I."],["dc.contributor.author","Brennan, P. C."],["dc.contributor.author","Christiani, David"],["dc.contributor.author","Hung, Rayjean J."],["dc.contributor.author","Risch, Angela"],["dc.contributor.author","Brueske, Irene"],["dc.contributor.author","Caporaso, Neil E."],["dc.contributor.author","Landi, Maria Teresa"],["dc.contributor.author","Rafnar, Thorunn"],["dc.contributor.author","Bickeboeller, Heike"],["dc.date.accessioned","2018-11-07T09:49:47Z"],["dc.date.available","2018-11-07T09:49:47Z"],["dc.date.issued","2015"],["dc.format.extent","549"],["dc.identifier.isi","000363340500056"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/35571"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Wiley-blackwell"],["dc.publisher.place","Hoboken"],["dc.relation.conference","Annual Meeting of the International-Genetic-Epidemiology-Society (IGES)"],["dc.relation.eventlocation","Baltimore, MD"],["dc.relation.issn","1098-2272"],["dc.relation.issn","0741-0395"],["dc.title","Kernel-based Pathway Meta-Analysis in ILCCO / TRICL Genome-wide Association Studies"],["dc.type","conference_abstract"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["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","98"],["dc.bibliographiccitation.journal","Annals of Human Genetics"],["dc.bibliographiccitation.lastpage","115"],["dc.bibliographiccitation.volume","70"],["dc.contributor.author","Kohler, K."],["dc.contributor.author","Bickeboeller, Heike"],["dc.date.accessioned","2018-11-07T10:29:23Z"],["dc.date.available","2018-11-07T10:29:23Z"],["dc.date.issued","2006"],["dc.description.abstract","In case-control association studies unobserved population stratification may act as a confounder, leading to an increased number of false positive results. Methods accounting for population structure by using additional genetic markers broadly follow one of two concepts: Genomic Control (GC) and Structured Association (SA). While extending existing methods of Structured Association we show that it is necessary to incorporate phenotypic information when inferring population structure, otherwise a systematic bias is introduced. Moreover, for moderate population stratification a Wald test statistic should be preferred as a Structured Association test statistic in comparison to a likelihood ratio test. The introduced extensions are compared to existing methods of Structured Association, as well as to Genomic Control, in a simulation study which is based on realistic situations of large case-control studies with moderate population stratification. A disadvantage of Genomic Control turns out to be the large variation in estimating the variance inflation factor, as well as the power loss if population structure increases. We come to the overall conclusion that Structured Association, if applied correctly, is superior to Genomic Control, at least in the case of simple population structure as simulated here."],["dc.identifier.doi","10.1111/j.1529-8817.2005.00214.x"],["dc.identifier.isi","000234974100008"],["dc.identifier.pmid","16441260"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/43635"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Blackwell Publishing"],["dc.relation.issn","0003-4800"],["dc.title","Case-control association tests correcting for population stratification"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["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","S22"],["dc.bibliographiccitation.journal","Genetic Epidemiology"],["dc.bibliographiccitation.lastpage","S33"],["dc.bibliographiccitation.volume","31"],["dc.contributor.author","Bickebller, Heike"],["dc.contributor.author","Goddard, Katrina A. B."],["dc.contributor.author","Igo, Robert P., Jr."],["dc.contributor.author","Kraft, Peter"],["dc.contributor.author","Lozano, Jingky P."],["dc.contributor.author","Pankratz, Nathan"],["dc.date.accessioned","2018-11-07T11:06:35Z"],["dc.date.available","2018-11-07T11:06:35Z"],["dc.date.issued","2007"],["dc.description.abstract","Genetic association studies have the potential to identify causative genetic variants with small effects in complex diseases, but it is not at all clear which study designs best balance power with sample size, especially when taking into account the difficulty of obtaining a sample of the necessary structure. The 14 contributions from the Genetic Analysis Workshop 15 group 3 used data sets with rheumatoid arthritis as primary phenotype from problem 2 (real data) and Problem 3 (simulated data) to investigate design and analysis problems that arise in candidate-gene, candidate-region, and genome-wide association studies. We identified three major themes that were addressed by multiple groups: (1) comparing family-based and case-control study designs with each other and with hybrid designs incorporating both related and unrelated individuals; (2) exploring and comparing techniques of combining information from multiple, correlated single-nucleotide polymorphisms; and (3) comparing analyses that select the model(s) of best fit with the ultimate aim of detecting the joint effects of several unlinked single-nucleotide polymorphisms. These contributions achieved some success in improving upon existing methods. For example, tests using related cases and unrelated controls can achieve higher power than the tests using unrelated cases and unrelated controls. Aside from these successes, the group 3 contributions highlight some interesting areas for future research."],["dc.description.sponsorship","NHLBI NIH HHS [T32 HL 07567]"],["dc.identifier.doi","10.1002/gepi.20277"],["dc.identifier.isi","000251674000004"],["dc.identifier.pmid","18046763"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/52353"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Wiley-blackwell"],["dc.publisher.place","Hoboken"],["dc.relation.conference","15th Genetic Analysis Workshop/International-Genetic-Epidemiology-Society Meeting"],["dc.relation.eventlocation","St Pete Beach, FL"],["dc.relation.issn","1098-2272"],["dc.relation.issn","0741-0395"],["dc.title","Issues in association mapping with high-density SNP data and diverse family structures"],["dc.type","conference_paper"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]