Friedrichs, Stefanie

[["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.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","47"],["dc.bibliographiccitation.issue","Suppl 9"],["dc.bibliographiccitation.journal","BMC Proceedings"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Yasmeen, Summaira"],["dc.contributor.author","Burger, Patricia"],["dc.contributor.author","Friedrichs, Stefanie"],["dc.contributor.author","Papiol, Sergi"],["dc.contributor.author","Bickeböller, Heike"],["dc.date.accessioned","2019-07-09T11:45:54Z"],["dc.date.available","2019-07-09T11:45:54Z"],["dc.date.issued","2018"],["dc.description.abstract","In GAW20, we investigated the association of specific genetic regions of interest (ROIs) with log-transformed triglyceride (TG) levels following lipid-lowering medication using epigenetic and genetic markers. The goal was to incorporate kernels for cytosine-phosphate-guanine (CpG) markers and compare the kernels to a purely parametric model. Post-treatment TG levels were investigated for post-methylation data at CpG sites and region-specific SNPs and adjusted for pre-treatment TG levels and age, in independent individuals only (real data: n = 150; simulated data, replicate 84: n = 111). In both data sets, our single-CpG-marker results using kernels and linear regression were in good agreement. In the real data, we investigated the introns of the CPT1A gene previously reported as associated with TG levels as separate ROIs, and were able to find hints of an association of cg17058475 and cg00574958 with post-treatment TG levels. In the simulated data, we investigated a total of 10 regions, in which the 5 causal and 5 non-causal markers lie, respectively, with increased methylation variances, yielding plausible results for the 3 window sizes. Overall, this indicates that kernels for CpG markers are feasible. An interaction regression model for the causal SNP with the nearest CpG marker identified an effect for the SNPs with the three greatest heritabilities simulated. The simulation model assumed full SNP effect only for unmethylated regions decreasing to zero in the case of full methylation. Thus, in the context of a clear candidate setting, interaction between epigenetic and genetic data may enhance information, albeit nominally, even with small sample sizes. Relieving the burden of multiple testing, developing kernels further to analyze data from multiple omics jointly is well warranted."],["dc.identifier.doi","10.1186/s12919-018-0154-5"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/15339"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59332"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.publisher","BioMed Central"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Relating drug response to epigenetic and genetic markers using a region-based kernel score test"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","17"],["dc.bibliographiccitation.journal","BMC Proceedings"],["dc.bibliographiccitation.volume","10"],["dc.contributor.author","Malzahn, Dörthe"],["dc.contributor.author","Friedrichs, Stefanie"],["dc.contributor.author","Bickeböller, Heike"],["dc.date.accessioned","2019-07-09T11:42:52Z"],["dc.date.available","2019-07-09T11:42:52Z"],["dc.date.issued","2016"],["dc.description.abstract","Abstract We used our extension of the kernel score test to family data to analyze real and simulated baseline systolic blood pressure in extended pedigrees. We compared the power for different kernels and for different weightings of genetic markers. Moreover, we compared the power of rare and common markers with 3 strategies for joint testing and on marker panels with different densities. Marker weights had much greater influence on power than the kernel chosen. Inverse minor allele frequency weights often increased power on common markers but could decrease power on rare markers. Furthermore, defining the gene region based on linkage disequilibrium blocks often yielded robust power of joint tests of rare and common markers."],["dc.identifier.doi","10.1186/s12919-016-0042-9"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/13923"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/58769"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Comparing strategies for combined testing of rare and common variants in whole sequence and genome-wide genotype data"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["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.issue","7"],["dc.bibliographiccitation.journal","Genetic Epidemiology"],["dc.bibliographiccitation.volume","39"],["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","Brueske, Irene"],["dc.contributor.author","Hunh, Rayjean J."],["dc.contributor.author","Mueller-Nurasyid, Martina"],["dc.contributor.author","Risch, Angela"],["dc.contributor.author","Bickeboeller, Heike"],["dc.date.accessioned","2018-11-07T09:49:48Z"],["dc.date.available","2018-11-07T09:49:48Z"],["dc.date.issued","2015"],["dc.format.extent","576"],["dc.identifier.isi","000363340500129"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/35573"],["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","Meta-analysis of gene-set analyses based on genome wide association studies, method development and application within ILCCO/TRICL consortia"],["dc.type","conference_abstract"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["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"]]

[["dc.bibliographiccitation.firstpage","156"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","European Neuropsychopharmacology : The Journal of the European College of Neuropsychopharmacology"],["dc.bibliographiccitation.lastpage","170"],["dc.bibliographiccitation.volume","29"],["dc.contributor.author","Budde, Monika"],["dc.contributor.author","Friedrichs, Stefanie"],["dc.contributor.author","Alliey-Rodriguez, Ney"],["dc.contributor.author","Ament, Seth"],["dc.contributor.author","Badner, Judith A."],["dc.contributor.author","Berrettini, Wade H."],["dc.contributor.author","Bloss, Cinnamon S."],["dc.contributor.author","Byerley, William"],["dc.contributor.author","Cichon, Sven"],["dc.contributor.author","Comes, Ashley L."],["dc.contributor.author","Coryell, William"],["dc.contributor.author","Craig, David W."],["dc.contributor.author","Degenhardt, Franziska"],["dc.contributor.author","Edenberg, Howard J."],["dc.contributor.author","Foroud, Tatiana"],["dc.contributor.author","Forstner, Andreas J."],["dc.contributor.author","Frank, Josef"],["dc.contributor.author","Gershon, Elliot S."],["dc.contributor.author","Goes, Fernando S."],["dc.contributor.author","Greenwood, Tiffany A."],["dc.contributor.author","Guo, Yiran"],["dc.contributor.author","Hipolito, Maria"],["dc.contributor.author","Hood, Leroy"],["dc.contributor.author","Keating, Brendan J."],["dc.contributor.author","Koller, Daniel L."],["dc.contributor.author","Lawson, William B."],["dc.contributor.author","Liu, Chunyu"],["dc.contributor.author","Mahon, Pamela B."],["dc.contributor.author","McInnis, Melvin G."],["dc.contributor.author","McMahon, Francis J."],["dc.contributor.author","Meier, Sandra M."],["dc.contributor.author","Mühleisen, Thomas W."],["dc.contributor.author","Murray, Sarah S."],["dc.contributor.author","Nievergelt, Caroline M."],["dc.contributor.author","Nurnberger, John I."],["dc.contributor.author","Nwulia, Evaristus A."],["dc.contributor.author","Potash, James B."],["dc.contributor.author","Quarless, Danjuma"],["dc.contributor.author","Rice, John"],["dc.contributor.author","Roach, Jared C."],["dc.contributor.author","Scheftner, William A."],["dc.contributor.author","Schork, Nicholas J."],["dc.contributor.author","Shekhtman, Tatyana"],["dc.contributor.author","Shilling, Paul D."],["dc.contributor.author","Smith, Erin N."],["dc.contributor.author","Streit, Fabian"],["dc.contributor.author","Strohmaier, Jana"],["dc.contributor.author","Szelinger, Szabolcs"],["dc.contributor.author","Treutlein, Jens"],["dc.contributor.author","Witt, Stephanie H."],["dc.contributor.author","Zandi, Peter P."],["dc.contributor.author","Zhang, Peng"],["dc.contributor.author","Zöllner, Sebastian"],["dc.contributor.author","Bickeböller, Heike"],["dc.contributor.author","Falkai, Peter G."],["dc.contributor.author","Kelsoe, John R."],["dc.contributor.author","Nöthen, Markus M."],["dc.contributor.author","Rietschel, Marcella"],["dc.contributor.author","Schulze, Thomas G."],["dc.contributor.author","Malzahn, Dörthe"],["dc.date.accessioned","2019-07-09T11:50:26Z"],["dc.date.available","2019-07-09T11:50:26Z"],["dc.date.issued","2019-"],["dc.description.abstract","Genome-wide association studies of case-control status have advanced the understanding of the genetic basis of psychiatric disorders. Further progress may be gained by increasing sample size but also by new analysis strategies that advance the exploitation of existing data, especially for clinically important quantitative phenotypes. The functionally-informed efficient region-based test strategy (FIERS) introduced herein uses prior knowledge on biological function and dependence of genotypes within a powerful statistical framework with improved sensitivity and specificity for detecting consistent genetic effects across studies. As proof of concept, FIERS was used for the first genome-wide single nucleotide polymorphism (SNP)-based investigation on bipolar disorder (BD) that focuses on an important aspect of disease course, the functional outcome. FIERS identified a significantly associated locus on chromosome 15 (hg38: chr15:48965004 - 49464789 bp) with consistent effect strength between two independent studies (GAIN/TGen: European Americans, BOMA: Germans; n = 1592 BD patients in total). Protective and risk haplotypes were found on the most strongly associated SNPs. They contain a CTCF binding site (rs586758); CTCF sites are known to regulate sets of genes within a chromatin domain. The rs586758 - rs2086256 - rs1904317 haplotype is located in the promoter flanking region of the COPS2 gene, close to microRNA4716, and the EID1, SHC4, DTWD1 genes as plausible biological candidates. While implication with BD is novel, COPS2, EID1, and SHC4 are known to be relevant for neuronal differentiation and function and DTWD1 for psychopharmacological side effects. The test strategy FIERS that enabled this discovery is equally applicable for tag SNPs and sequence data."],["dc.identifier.doi","10.1016/j.euroneuro.2018.10.005"],["dc.identifier.pmid","30503783"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/15939"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59772"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation.issn","1873-7862"],["dc.rights","CC BY-NC-ND 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by-nc-nd/4.0"],["dc.subject.ddc","610"],["dc.title","Efficient region-based test strategy uncovers genetic risk factors for functional outcome in bipolar disorder"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]