Sohns, Melanie

[["dc.bibliographiccitation.artnumber","S95"],["dc.bibliographiccitation.issue","Suppl 7"],["dc.bibliographiccitation.journal","BMC Proceedings"],["dc.bibliographiccitation.volume","3"],["dc.contributor.author","Sohns, Melanie"],["dc.contributor.author","Rosenberger, Albert"],["dc.contributor.author","Bickeböller, Heike"],["dc.date.accessioned","2016-02-18T17:03:24Z"],["dc.date.accessioned","2021-10-27T13:20:24Z"],["dc.date.available","2016-02-18T17:03:24Z"],["dc.date.available","2021-10-27T13:20:24Z"],["dc.date.issued","2009"],["dc.date.updated","2016-02-18T17:03:24Z"],["dc.description.abstract","Abstract In genome-wide association studies (GWAS) genetic markers are often ranked to select genes for further pursuit. Especially for moderately associated and interrelated genes, information on genes and pathways may improve the selection. We applied and combined two main approaches for data integration to a GWAS for rheumatoid arthritis, gene set enrichment analysis (GSEA) and hierarchical Bayes prioritization (HBP). Many associated genes are located in the HLA region on 6p21. However, the ranking lists of genes and gene sets differ considerably depending on the chosen approach: HBP changes the ranking only slightly and primarily contains HLA genes in the top 100 gene lists. GSEA includes also many non-HLA genes."],["dc.identifier.doi","10.1186/1753-6561-3-S7-S95"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/12881"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/91963"],["dc.language.iso","en"],["dc.notes.intern","Migrated from goescholar"],["dc.relation.orgunit","Universitätsmedizin Göttingen"],["dc.rights","CC BY 2.0"],["dc.rights.holder","Sohns et al; licensee BioMed Central Ltd."],["dc.rights.uri","https://creativecommons.org/licenses/by/2.0"],["dc.title","Integration of a priori gene set information into genome-wide association studies"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","S74"],["dc.bibliographiccitation.journal","Genetic Epidemiology"],["dc.bibliographiccitation.lastpage","S80"],["dc.bibliographiccitation.volume","33"],["dc.contributor.author","Tintle, Nathan"],["dc.contributor.author","Lantieri, Francesca"],["dc.contributor.author","Lebrec, Jeremie"],["dc.contributor.author","Sohns, Melanie"],["dc.contributor.author","Ballard, David"],["dc.contributor.author","Bickeboeller, Heike"],["dc.date.accessioned","2018-11-07T08:34:34Z"],["dc.date.available","2018-11-07T08:34:34Z"],["dc.date.issued","2009"],["dc.description.abstract","Genome-wide association studies (GWAS) continue to gain in popularity To utilize the wealth of data created more effectively, a variety of methods have recently been proposed to include a priori information (e.g., biologically interpretable sets of genes, candidate gene information, or gene expression) in GWAS analysis. Six contributions to Genetic Analysis Workshop 16 Group 11 applied novel or recently proposed methods to GWAS of rheumatoid arthritis and heart disease related phenotypes. The results of these analyses were a variety of novel candidate genes and sets of genes, in addition to the validation of well-known genotype-phenotype associations. However, because many methods are relatively new, they would benefit from further methodological research to ensure that they maintain type I error rates while increasing power to find additional associations. When methods have been adapted from other study types (e.g., gene expression data analysis or linkage analysis), the lessons learned there should be used to guide implementation of techniques. Lastly, many open research questions exist concerning the logistic details of the origin of the a priori information and the way to incorporate it. Overall, our group has demonstrated a strong potential for identifying novel genotype-phenotype relationships by including a priori data in the analysis of GWAS, while also uncovering a series of questions requiring further research. Genet. Epideiniol. 33 (Suppl. 1):S74-S80, 2009. (C) 2009 Wiley-Liss, Inc."],["dc.identifier.doi","10.1002/gepi.20476"],["dc.identifier.isi","000272540700013"],["dc.identifier.pmid","19924705"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/6099"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/17845"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Wiley-liss"],["dc.publisher.place","Hoboken"],["dc.relation.conference","16th Genetic Analysis Workshop/17th Annual Meeting of the International-Genetic-Epidemiology-Society"],["dc.relation.eventlocation","St Louis, MO"],["dc.relation.issn","0741-0395"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","Inclusion of A Priori Information in Genome-Wide Association Analysis"],["dc.type","conference_paper"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","S29"],["dc.bibliographiccitation.issue","Suppl 1"],["dc.bibliographiccitation.journal","BMC Proceedings"],["dc.bibliographiccitation.volume","1"],["dc.contributor.author","Köhler, Karola"],["dc.contributor.author","Sohns, Melanie"],["dc.contributor.author","Bickeböller, Heike"],["dc.date.accessioned","2021-06-01T10:48:01Z"],["dc.date.available","2021-06-01T10:48:01Z"],["dc.date.issued","2007"],["dc.date.updated","2016-02-18T17:03:35Z"],["dc.description.abstract","Abstract Related cases may be included in case-control association studies if correlations between related individuals due to identity-by-descent (IBD) sharing are taken into account. We derived a framework to test for association in a case-control design including affected sibships and unrelated controls. First, a corrected variance for the allele frequency difference between cases and controls was directly calculated or estimated in two ways on the basis of the fixation index F ST and the inbreeding coefficient. Then the correlation-corrected association test including controls and affected sibs was carried out. We applied the three strategies to 20 candidate genes on the Genetic Analysis Workshop 15 rheumatoid arthritis data and to 9187 single-nucleotide polymorphisms of replicate one of the Genetic Analysis Workshop 15 simulated data with knowledge of the \"answers\". The three strategies used to correct for correlation give only minor differences in the variance estimates and yield an almost correct type I error rate for the association tests. Thus, all strategies considered to correct the variance performed quite well."],["dc.identifier.doi","10.1186/1753-6561-1-s1-s29"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/12884"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/85801"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-425"],["dc.relation.issn","1753-6561"],["dc.relation.orgunit","Universitätsmedizin Göttingen"],["dc.rights","CC BY 2.0"],["dc.rights.holder","Köhler et al; licensee BioMed Central Ltd."],["dc.rights.uri","https://creativecommons.org/licenses/by/2.0"],["dc.title","Case-control studies with affected sibships"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","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"]]