000120041 001__ 120041
000120041 005__ 20240228134601.0
000120041 0247_ $$2doi$$a10.1159/000357567
000120041 0247_ $$2pmid$$apmid:24434848
000120041 0247_ $$2pmc$$apmc:PMC4026009
000120041 0247_ $$2ISSN$$a0001-5652
000120041 0247_ $$2ISSN$$a0365-2785
000120041 0247_ $$2ISSN$$a1423-0062
000120041 0247_ $$2altmetric$$aaltmetric:2064391
000120041 037__ $$aDKFZ-2017-00628
000120041 041__ $$aeng
000120041 082__ $$a570
000120041 1001_ $$aFreytag, Saskia$$b0
000120041 245__ $$aA network-based kernel machine test for the identification of risk pathways in genome-wide association studies.
000120041 260__ $$aBasel$$bKarger$$c2013
000120041 3367_ $$2DRIVER$$aarticle
000120041 3367_ $$2DataCite$$aOutput Types/Journal article
000120041 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1490708471_15175
000120041 3367_ $$2BibTeX$$aARTICLE
000120041 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000120041 3367_ $$00$$2EndNote$$aJournal Article
000120041 520__ $$aBiological pathways provide rich information and biological context on the genetic causes of complex diseases. The logistic kernel machine test integrates prior knowledge on pathways in order to analyze data from genome-wide association studies (GWAS). In this study, the kernel converts the genomic information of 2 individuals into a quantitative value reflecting their genetic similarity. With the selection of the kernel, one implicitly chooses a genetic effect model. Like many other pathway methods, none of the available kernels accounts for the topological structure of the pathway or gene-gene interaction types. However, evidence indicates that connectivity and neighborhood of genes are crucial in the context of GWAS, because genes associated with a disease often interact. Thus, we propose a novel kernel that incorporates the topology of pathways and information on interactions. Using simulation studies, we demonstrate that the proposed method maintains the type I error correctly and can be more effective in the identification of pathways associated with a disease than non-network-based methods. We apply our approach to genome-wide association case-control data on lung cancer and rheumatoid arthritis. We identify some promising new pathways associated with these diseases, which may improve our current understanding of the genetic mechanisms.
000120041 536__ $$0G:(DE-HGF)POF3-313$$a313 - Cancer risk factors and prevention (POF3-313)$$cPOF3-313$$fPOF III$$x0
000120041 588__ $$aDataset connected to CrossRef, PubMed,
000120041 7001_ $$aManitz, Juliane$$b1
000120041 7001_ $$aSchlather, Martin$$b2
000120041 7001_ $$aKneib, Thomas$$b3
000120041 7001_ $$aAmos, Christopher I$$b4
000120041 7001_ $$0P:(DE-He78)4981f4ef151aea881f38b33df8e35a21$$aRisch, Angela$$b5$$udkfz
000120041 7001_ $$0P:(DE-He78)c259d6cc99edf5c7bc7ce22c7f87c253$$aChang, Jenny$$b6$$udkfz
000120041 7001_ $$aHeinrich, Joachim$$b7
000120041 7001_ $$aBickeböller, Heike$$b8
000120041 773__ $$0PERI:(DE-600)1482710-4$$a10.1159/000357567$$gVol. 76, no. 2, p. 64 - 75$$n2$$p64 - 75$$tHuman heredity$$v76$$x1423-0062$$y2013
000120041 909CO $$ooai:inrepo02.dkfz.de:120041$$pVDB
000120041 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)4981f4ef151aea881f38b33df8e35a21$$aDeutsches Krebsforschungszentrum$$b5$$kDKFZ
000120041 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)c259d6cc99edf5c7bc7ce22c7f87c253$$aDeutsches Krebsforschungszentrum$$b6$$kDKFZ
000120041 9131_ $$0G:(DE-HGF)POF3-313$$1G:(DE-HGF)POF3-310$$2G:(DE-HGF)POF3-300$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bGesundheit$$lKrebsforschung$$vCancer risk factors and prevention$$x0
000120041 9141_ $$y2013
000120041 915__ $$0StatID:(DE-HGF)0400$$2StatID$$aAllianz-Lizenz / DFG
000120041 915__ $$0StatID:(DE-HGF)0420$$2StatID$$aNationallizenz
000120041 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bHUM HERED : 2015
000120041 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS
000120041 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline
000120041 915__ $$0StatID:(DE-HGF)0310$$2StatID$$aDBCoverage$$bNCBI Molecular Biology Database
000120041 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bThomson Reuters Master Journal List
000120041 915__ $$0StatID:(DE-HGF)0110$$2StatID$$aWoS$$bScience Citation Index
000120041 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection
000120041 915__ $$0StatID:(DE-HGF)0111$$2StatID$$aWoS$$bScience Citation Index Expanded
000120041 915__ $$0StatID:(DE-HGF)1030$$2StatID$$aDBCoverage$$bCurrent Contents - Life Sciences
000120041 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews
000120041 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5
000120041 9201_ $$0I:(DE-He78)C010-20160331$$kC010$$lEpigenomik und Krebsrisikofaktoren$$x0
000120041 9201_ $$0I:(DE-He78)C020-20160331$$kC020$$lEpidemiologie von Krebserkrankungen$$x1
000120041 980__ $$ajournal
000120041 980__ $$aVDB
000120041 980__ $$aI:(DE-He78)C010-20160331
000120041 980__ $$aI:(DE-He78)C020-20160331
000120041 980__ $$aUNRESTRICTED