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dc.contributor.authorLin, Hui-Yi
dc.contributor.authorHuang, Po-Yu
dc.contributor.authorChen, Dung-Tsa
dc.contributor.authorTung, Heng-Yuan
dc.contributor.authorSellers, Thomas A
dc.contributor.authorPow-Sang, Julio M
dc.contributor.authorEeles, Rosalind
dc.contributor.authorEaston, Douglas
dc.contributor.authorKote-Jarai, Zsofia
dc.contributor.authorAmin Al Olama, Ali
dc.contributor.authorBenlloch, Sara
dc.contributor.authorMuir, Kenneth
dc.contributor.authorGiles, Graham G
dc.contributor.authorWiklund, Fredrik
dc.contributor.authorGronberg, Henrik
dc.contributor.authorHaiman, Christopher A
dc.contributor.authorSchleutker, Johanna
dc.contributor.authorNordestgaard, Børge G
dc.contributor.authorTravis, Ruth C
dc.contributor.authorHamdy, Freddie
dc.contributor.authorNeal, David E
dc.contributor.authorPashayan, Nora
dc.contributor.authorKhaw, Kay-Tee
dc.contributor.authorStanford, Janet L
dc.contributor.authorBlot, William J
dc.contributor.authorThibodeau, Stephen N
dc.contributor.authorMaier, Christiane
dc.contributor.authorKibel, Adam S
dc.contributor.authorCybulski, Cezary
dc.contributor.authorCannon-Albright, Lisa
dc.contributor.authorBrenner, Hermann
dc.contributor.authorKaneva, Radka
dc.contributor.authorBatra, Jyotsna
dc.contributor.authorTeixeira, Manuel R
dc.contributor.authorPandha, Hardev
dc.contributor.authorLu, Yong-Jie
dc.contributor.authorPRACTICAL Consortium
dc.contributor.authorPark, Jong Y
dc.date.accessioned2018-09-21T15:22:41Z
dc.date.available2018-09-21T15:22:41Z
dc.date.issued2018-12-15
dc.identifier.issn1367-4803
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/280656
dc.description.abstractMotivation: The use of single nucleotide polymorphism (SNP) interactions to predict complex diseases is getting more attention during the past decade, but related statistical methods are still immature. We previously proposed the SNP Interaction Pattern Identifier (SIPI) approach to evaluate 45 SNP interaction patterns/patterns. SIPI is statistically powerful but suffers from a large computation burden. For large-scale studies, it is necessary to use a powerful and computation-efficient method. The objective of this study is to develop an evidence-based mini-version of SIPI as the screening tool or solitary use and to evaluate the impact of inheritance mode and model structure on detecting SNP-SNP interactions. Results: We tested two candidate approaches: the 'Five-Full' and 'AA9int' method. The Five-Full approach is composed of the five full interaction models considering three inheritance modes (additive, dominant and recessive). The AA9int approach is composed of nine interaction models by considering non-hierarchical model structure and the additive mode. Our simulation results show that AA9int has similar statistical power compared to SIPI and is superior to the Five-Full approach, and the impact of the non-hierarchical model structure is greater than that of the inheritance mode in detecting SNP-SNP interactions. In summary, it is recommended that AA9int is a powerful tool to be used either alone or as the screening stage of a two-stage approach (AA9int+SIPI) for detecting SNP-SNP interactions in large-scale studies. Availability and implementation: The 'AA9int' and 'parAA9int' functions (standard and parallel computing version) are added in the SIPI R package, which is freely available at https://linhuiyi.github.io/LinHY_Software/. Supplementary information: Supplementary data are available at Bioinformatics online.
dc.format.mediumPrint
dc.languageeng
dc.publisherOxford University Press (OUP)
dc.subjectPRACTICAL Consortium
dc.subjectComputational Biology
dc.subjectPolymorphism, Single Nucleotide
dc.subjectAlgorithms
dc.subjectComputer Simulation
dc.subjectSoftware
dc.subjectStatistics as Topic
dc.titleAA9int: SNP interaction pattern search using non-hierarchical additive model set.
dc.typeArticle
prism.endingPage4150
prism.issueIdentifier24
prism.publicationDate2018
prism.publicationNameBioinformatics
prism.startingPage4141
prism.volume34
dc.identifier.doi10.17863/CAM.28022
dcterms.dateAccepted2018-06-05
rioxxterms.versionofrecord10.1093/bioinformatics/bty461
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2018-12
dc.contributor.orcidEaston, Douglas [0000-0003-2444-3247]
dc.contributor.orcidKhaw, Kay-Tee [0000-0002-8802-2903]
dc.identifier.eissn1367-4811
rioxxterms.typeJournal Article/Review
pubs.funder-project-idMedical Research Council (G0401527)
pubs.funder-project-idMedical Research Council (G1000143)
pubs.funder-project-idMedical Research Council (MR/N003284/1)
cam.issuedOnline2018-06-07
rioxxterms.freetoread.startdate2019-06-07


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