Friedrichs, Stefanie

[["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","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"]]

[["dc.bibliographiccitation.artnumber","e0140179"],["dc.bibliographiccitation.issue","10"],["dc.bibliographiccitation.journal","PLoS ONE"],["dc.bibliographiccitation.volume","10"],["dc.contributor.author","Rosenberger, Albert"],["dc.contributor.author","Friedrichs, Stefanie"],["dc.contributor.author","Amos, Christopher I."],["dc.contributor.author","Brennan, P. C."],["dc.contributor.author","Fehringer, Gordon"],["dc.contributor.author","Heinrich, Joachim"],["dc.contributor.author","Hung, Rayjean J."],["dc.contributor.author","Muley, Thomas"],["dc.contributor.author","Mueller-Nurasyid, Martina"],["dc.contributor.author","Risch, Angela"],["dc.contributor.author","Bickeboeller, Heike"],["dc.date.accessioned","2018-11-07T09:50:02Z"],["dc.date.available","2018-11-07T09:50:02Z"],["dc.date.issued","2015"],["dc.description.abstract","Introduction Gene-set analysis (GSA) methods are used as complementary approaches to genome-wide association studies (GWASs). The single marker association estimates of a prede-fined 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 chi(2)-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 approximate to 0.02)."],["dc.description.sponsorship","Open-Access Publikationsfonds 2015"],["dc.identifier.doi","10.1371/journal.pone.0140179"],["dc.identifier.isi","000363799900005"],["dc.identifier.pmid","26501144"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/12563"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/35627"],["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 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","META-GSA: Combining Findings from Gene-Set Analyses across Several Genome-Wide Association Studies"],["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"]]