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dc.contributor.authorLin, H-Y
dc.contributor.authorHuang, P-Y
dc.contributor.authorChen, D-T
dc.contributor.authorTung, H-Y
dc.contributor.authorSellers, TA
dc.contributor.authorPow-Sang, JM
dc.contributor.authorEeles, R
dc.contributor.authorEaston, D
dc.contributor.authorKote-Jarai, Z
dc.contributor.authorAmin Al Olama, A
dc.contributor.authorBenlloch, S
dc.contributor.authorMuir, K
dc.contributor.authorGiles, GG
dc.contributor.authorWiklund, F
dc.contributor.authorGronberg, H
dc.contributor.authorHaiman, CA
dc.contributor.authorSchleutker, J
dc.contributor.authorNordestgaard, BG
dc.contributor.authorTravis, RC
dc.contributor.authorHamdy, F
dc.contributor.authorNeal, DE
dc.contributor.authorPashayan, N
dc.contributor.authorKhaw, K-T
dc.contributor.authorStanford, JL
dc.contributor.authorBlot, WJ
dc.contributor.authorThibodeau, SN
dc.contributor.authorMaier, C
dc.contributor.authorKibel, AS
dc.contributor.authorCybulski, C
dc.contributor.authorCannon-Albright, L
dc.contributor.authorBrenner, H
dc.contributor.authorKaneva, R
dc.contributor.authorBatra, J
dc.contributor.authorTeixeira, MR
dc.contributor.authorPandha, H
dc.contributor.authorLu, Y-J
dc.contributor.authorPRACTICAL Consortium
dc.contributor.authorPark, JY
dc.date.accessioned2019-07-17T08:49:18Z
dc.date.issued2018-12
dc.identifier.citationBioinformatics (Oxford, England), 2018, 34 (24), pp. 4141 - 4150
dc.identifier.issn1367-4803
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/3287
dc.identifier.eissn1367-4811
dc.identifier.doi10.1093/bioinformatics/bty461
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.formatPrint
dc.format.extent4141 - 4150
dc.languageeng
dc.language.isoeng
dc.rights.urihttps://www.rioxx.net/licenses/all-rights-reserved
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.typeJournal Article
dcterms.dateAccepted2018-06-05
rioxxterms.versionofrecord10.1093/bioinformatics/bty461
rioxxterms.licenseref.urihttps://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2018-12
rioxxterms.typeJournal Article/Review
dc.relation.isPartOfBioinformatics (Oxford, England)
pubs.issue24
pubs.notesNot known
pubs.organisational-group/ICR
pubs.organisational-group/ICR/Primary Group
pubs.organisational-group/ICR/Primary Group/ICR Divisions
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Genetics and Epidemiology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Genetics and Epidemiology/Oncogenetics
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging/Oncogenetics
pubs.organisational-group/ICR
pubs.organisational-group/ICR/Primary Group
pubs.organisational-group/ICR/Primary Group/ICR Divisions
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Genetics and Epidemiology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Genetics and Epidemiology/Oncogenetics
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging/Oncogenetics
pubs.publication-statusPublished
pubs.volume34
pubs.embargo.termsNot known
pubs.oa-locationhttps://academic.oup.com/bioinformatics/article/34/24/4141/5034431
icr.researchteamOncogeneticsen_US
dc.contributor.icrauthorEeles, Rosalinden
dc.contributor.icrauthorKote-Jarai, Zsofiaen


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