Sohns, Melanie

[["dc.bibliographiccitation.issue","7"],["dc.bibliographiccitation.journal","Genetic Epidemiology"],["dc.bibliographiccitation.volume","36"],["dc.contributor.author","Sohns, Melanie"],["dc.contributor.author","Viktorova, Elena"],["dc.contributor.author","Fehringer, Gord"],["dc.contributor.author","Gaborieau, Valerie"],["dc.contributor.author","Han, Younghun"],["dc.contributor.author","Chang-Claude, Jenny"],["dc.contributor.author","Heinrich, Joachim"],["dc.contributor.author","Risch, Angela"],["dc.contributor.author","Amos, Christopher I."],["dc.contributor.author","Brennan, P. C."],["dc.contributor.author","Hung, Rayjean J."],["dc.contributor.author","Bickeboeller, Heike"],["dc.date.accessioned","2018-11-07T09:04:08Z"],["dc.date.available","2018-11-07T09:04:08Z"],["dc.date.issued","2012"],["dc.format.extent","756"],["dc.identifier.isi","000309913200133"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/25044"],["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","Stevenson, WA"],["dc.relation.issn","0741-0395"],["dc.title","Application of the Empirical Hierarchical Bayes Approach for Gene-Environment Interaction to TRICL Lung Cancer GWAS"],["dc.type","conference_abstract"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","551"],["dc.bibliographiccitation.issue","6"],["dc.bibliographiccitation.journal","Genetic Epidemiology"],["dc.bibliographiccitation.lastpage","559"],["dc.bibliographiccitation.volume","37"],["dc.contributor.author","Sohns, Melanie"],["dc.contributor.author","Viktorova, Elena"],["dc.contributor.author","Amos, Christopher I."],["dc.contributor.author","Brennan, P. C."],["dc.contributor.author","Fehringer, Gord"],["dc.contributor.author","Gaborieau, Valerie"],["dc.contributor.author","Han, Younghun"],["dc.contributor.author","Heinrich, Joachim"],["dc.contributor.author","Chang-Claude, Jenny"],["dc.contributor.author","Hung, Rayjean J."],["dc.contributor.author","Müller-Nurasyid, Martina"],["dc.contributor.author","Risch, Angela"],["dc.contributor.author","Lewinger, Juan Pablo"],["dc.contributor.author","Thomas, Duncan C."],["dc.contributor.author","Bickeböller, Heike"],["dc.date.accessioned","2018-11-07T09:20:57Z"],["dc.date.available","2018-11-07T09:20:57Z"],["dc.date.issued","2013"],["dc.description.abstract","The analysis of gene-environment (G x E) interactions remains one of the greatest challenges in the postgenome-wide association studies (GWASs) 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 in controls fails. We propose a novel empirical hierarchical Bayes approach to G x E interaction (EHB-GE), which benefits from greater rank power while accounting for population-based G-E correlation. Building on Lewinger et al.'s ([2007] Genet Epidemiol 31:871-882) hierarchical Bayes prioritization approach, the method first obtains posterior G-E correlation estimates in controls for each marker, borrowing strength from G-E information across the genome. These posterior estimates are then subtracted from the corresponding case-only G x E estimates. We compared EHB-GE with rival methods using simulation. EHB-GE has similar or greater rank power to detect G x E interactions in the presence of large numbers of G-E correlations with weak to strong effects or only a low number of such correlations with large effect. When there are no or only a few weak G-E correlations, Murcray et al.'s method ([2009] Am J Epidemiol 169:219-226) identifies markers with low G x E interaction effects better. We applied EHB-GE and competing methods to four lung cancer case-control GWAS from the Interdisciplinary Research in Cancer of the Lung/International Lung Cancer Consortium with smoking as environmental factor. A number of genes worth investigating were identified by the EHB-GE approach."],["dc.description.sponsorship","NIH [TRICL 5U19CA148127-03]; DFG GRK [1034]"],["dc.identifier.doi","10.1002/gepi.21741"],["dc.identifier.isi","000322911100003"],["dc.identifier.pmid","23893921"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/28998"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.relation.issn","0741-0395"],["dc.title","Empirical Hierarchical Bayes Approach to Gene-Environment Interactions: Development and Application to Genome-Wide Association Studies of Lung Cancer in TRICL"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","e0173339"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","PLoS ONE"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Rosenberger, Albert"],["dc.contributor.author","Sohns, Melanie"],["dc.contributor.author","Friedrichs, Stefanie"],["dc.contributor.author","Hung, Rayjean J."],["dc.contributor.author","Fehringer, Gord"],["dc.contributor.author","McLaughlin, John R."],["dc.contributor.author","Amos, Christopher I."],["dc.contributor.author","Brennan, P. C."],["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","Christiani, David C."],["dc.contributor.author","Wei, Yongyue"],["dc.contributor.author","Bickeboeller, Heike"],["dc.date.accessioned","2018-11-07T10:26:17Z"],["dc.date.available","2018-11-07T10:26:17Z"],["dc.date.issued","2017"],["dc.description.abstract","Introduction Gene-set analysis (GSA) is an approach using the results of single-marker genome-wide association studies when investigating pathways as a whole with respect to the genetic basis of a disease. Methods We performed a meta-analysis of seven GSAs for lung cancer, applying the method METAGSA. Overall, the information taken from 11,365 cases and 22,505 controls from within the TRICL/ILCCO consortia was used to investigate a total of 234 pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Results META-GSA reveals the systemic lupus erythematosus KEGG pathway hsa05322, driven by the gene region 6p21-22, as also implicated in lung cancer (p = 0.0306). This gene region is known to be associated with squamous cell lung carcinoma. The most important genes driving the significance of this pathway belong to the genomic areas HIST1-H4L,- 1BN, -2BN, -H2AK, -H4K and C2/C4A/C4B. Within these areas, the markers most significantly associated with LC are rs13194781 (located within HIST12BN) and rs1270942 (located between C2 and C4A). Conclusions We have discovered a pathway currently marked as specific to systemic lupus erythematosus as being significantly implicated in lung cancer. The gene region 6p21-22 in this pathway appears to be more extensively associated with lung cancer than previously assumed. Given wide-stretched linkage disequilibrium to the area APOM/BAG6/MSH5, there is currently simply not enough information or evidence to conclude whether the potential pleiotropy of lung cancer and systemic lupus erythematosus is spurious, biological, or mediated. Further research into this pathway and gene region will be necessary."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2017"],["dc.identifier.doi","10.1371/journal.pone.0173339"],["dc.identifier.isi","000396073700053"],["dc.identifier.pmid","28273134"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/14395"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/43006"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","PUB_WoS_Import"],["dc.publisher","Public Library Science"],["dc.relation.issn","1932-6203"],["dc.rights.access","openAccess"],["dc.title","Gene-set meta-analysis of lung cancer identifies pathway related to systemic lupus erythematosus"],["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"]]