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dc.contributor.authorLin, H-Y
dc.contributor.authorChen, D-T
dc.contributor.authorHuang, P-Y
dc.contributor.authorLiu, Y-H
dc.contributor.authorOchoa, A
dc.contributor.authorZabaleta, J
dc.contributor.authorMercante, DE
dc.contributor.authorFang, Z
dc.contributor.authorSellers, TA
dc.contributor.authorPow-Sang, JM
dc.contributor.authorCheng, C-H
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.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.accessioned2018-03-01T09:43:27Z
dc.date.issued2017-03
dc.identifier.citationBioinformatics (Oxford, England), 2017, 33 (6), pp. 822 - 833
dc.identifier.issn1367-4803
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/1462
dc.identifier.eissn1367-4811
dc.identifier.doi10.1093/bioinformatics/btw762
dc.description.abstractMotivation Testing SNP-SNP interactions is considered as a key for overcoming bottlenecks of genetic association studies. However, related statistical methods for testing SNP-SNP interactions are underdeveloped.Results We propose the SNP Interaction Pattern Identifier (SIPI), which tests 45 biologically meaningful interaction patterns for a binary outcome. SIPI takes non-hierarchical models, inheritance modes and mode coding direction into consideration. The simulation results show that SIPI has higher power than MDR (Multifactor Dimensionality Reduction), AA_Full, Geno_Full (full interaction model with additive or genotypic mode) and SNPassoc in detecting interactions. Applying SIPI to the prostate cancer PRACTICAL consortium data with approximately 21 000 patients, the four SNP pairs in EGFR-EGFR , EGFR-MMP16 and EGFR-CSF1 were found to be associated with prostate cancer aggressiveness with the exact or similar pattern in the discovery and validation sets. A similar match for external validation of SNP-SNP interaction studies is suggested. We demonstrated that SIPI not only searches for more meaningful interaction patterns but can also overcome the unstable nature of interaction patterns.Availability and implementation The SIPI software is freely available at http://publichealth.lsuhsc.edu/LinSoftware/ .Contact [email protected] information Supplementary data are available at Bioinformatics online.
dc.formatPrint
dc.format.extent822 - 833
dc.languageeng
dc.language.isoeng
dc.rights.urihttps://www.rioxx.net/licenses/all-rights-reserved
dc.subjectPRACTICAL Consortium
dc.subjectHumans
dc.subjectProstatic Neoplasms
dc.subjectGenetic Predisposition to Disease
dc.subjectEpistasis, Genetic
dc.subjectPolymorphism, Single Nucleotide
dc.subjectModels, Genetic
dc.subjectSoftware
dc.subjectMale
dc.subjectMatrix Metalloproteinase 16
dc.subjectStatistics as Topic
dc.subjectGenetic Association Studies
dc.subjectErbB Receptors
dc.titleSNP interaction pattern identifier (SIPI): an intensive search for SNP-SNP interaction patterns.
dc.typeJournal Article
dcterms.dateAccepted2016-11-28
rioxxterms.versionofrecord10.1093/bioinformatics/btw762
rioxxterms.licenseref.urihttps://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2017-03
rioxxterms.typeJournal Article/Review
dc.relation.isPartOfBioinformatics (Oxford, England)
pubs.issue6
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.volume33en_US
pubs.embargo.termsNot known
icr.researchteamOncogeneticsen_US
dc.contributor.icrauthorEeles, Rosalinden
dc.contributor.icrauthorKote-Jarai, Zsofiaen


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