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SNP interaction pattern identifier (SIPI): an intensive search for SNP-SNP interaction patterns.
Lin, Hui-Yi; Chen, Dung-Tsa; Huang, Po-Yu; Liu, Yung-Hsin; Ochoa, Augusto; Zabaleta, Jovanny; Mercante, Donald E; Fang, Zhide; Sellers, Thomas A; Pow-Sang, Julio M; Cheng, Chia-Ho; Eeles, Rosalind; Easton, Doug; Kote-Jarai, Zsofia; Amin Al Olama, Ali; Benlloch, Sara; Muir, Kenneth; Giles, Graham G; Wiklund, Fredrik; Gronberg, Henrik; Haiman, Christopher A; Schleutker, Johanna; Nordestgaard, Børge G; Travis, Ruth C; Hamdy, Freddie; Pashayan, Nora; Khaw, Kay-Tee; Stanford, Janet L; Blot, William J; Thibodeau, Stephen N; Maier, Christiane; Kibel, Adam S; Cybulski, Cezary; Cannon-Albright, Lisa; Brenner, Hermann; Kaneva, Radka; Batra, Jyotsna; Teixeira, Manuel R; Pandha, Hardev; Lu, Yong-Jie; Park, Jong Y.
Afiliação
  • Lin HY; Biostatistics Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, USA.
  • Chen DT; Department of Biostatistics and Bioinformatics, Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
  • Huang PY; Computational Intelligence Technology Center, Industrial Technology Research Institute, Hsinchu City, Taiwan.
  • Liu YH; Department of Biometrics, INC Research, LLC, Raleigh, NC, USA.
  • Ochoa A; Stanley S. Scott Cancer Center, Louisiana State University Health Sciences Center, New Orleans, USA.
  • Zabaleta J; Stanley S. Scott Cancer Center, Louisiana State University Health Sciences Center, New Orleans, USA.
  • Mercante DE; Biostatistics Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, USA.
  • Fang Z; Biostatistics Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, USA.
  • Sellers TA; Department of Cancer Epidemiology, Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
  • Pow-Sang JM; Department of Genitourinary Oncology, Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
  • Cheng CH; Department of Biostatistics and Bioinformatics, Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
  • Eeles R; The Institute of Cancer Research, London, UK.
  • Easton D; Royal Marsden NHS Foundation Trust, London, UK.
  • Kote-Jarai Z; Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK.
  • Amin Al Olama A; The Institute of Cancer Research, London, UK.
  • Benlloch S; Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK.
  • Muir K; Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK.
  • Giles GG; University of Warwick, Coventry, UK.
  • Wiklund F; Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Victoria, Australia.
  • Gronberg H; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia.
  • Haiman CA; Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden.
  • Schleutker J; Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden.
  • Nordestgaard BG; Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, USA.
  • Travis RC; Department of Medical Biochemistry and Genetics, Institute of Biomedicine, University of Turku, Turku, Finland.
  • Hamdy F; Tyks Microbiology and Genetics, Department of Medical Genetics, Turku University Hospital, Turku, Finland.
  • Pashayan N; BioMediTech, 30014 University of Tampere, Tampere, Finland.
  • Khaw KT; Department of Clinical Biochemistry, Herlev Hospital, Copenhagen University Hospital, Herlev, Denmark.
  • Stanford JL; Cancer Epidemiology, Nuffield Department of Population Health University of Oxford, Oxford, UK.
  • Blot WJ; Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK.
  • Thibodeau SN; Medical Science, University of Oxford, John Radcliffe Hospital, Oxford, UK.
  • Maier C; Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK.
  • Kibel AS; Department of Applied Health Research, University College London, London, UK.
  • Cybulski C; Cambridge Institute of Public Health, University of Cambridge, Cambridge, UK.
  • Cannon-Albright L; Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
  • Brenner H; Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA.
  • Kaneva R; International Epidemiology Institute, Rockville, MD, USA.
  • Batra J; Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA.
  • Teixeira MR; Institute of Human Genetics University Hospital Ulm, Ulm, Germany.
  • Pandha H; Brigham and Women's Hospital/Dana-Farber Cancer Institute, Boston, MA, USA.
  • Lu YJ; Washington University, St Louis, MO, USA.
  • Park JY; Division of Genetic Epidemiology, Department of Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA.
Bioinformatics ; 33(6): 822-833, 2017 03 15.
Article em En | MEDLINE | ID: mdl-28039167
Motivation: 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: hlin1@lsuhsc.edu. Supplementary information: Supplementary data are available at Bioinformatics online.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias da Próstata / Software / Estatística como Assunto / Polimorfismo de Nucleotídeo Único / Epistasia Genética / Estudos de Associação Genética Tipo de estudo: Clinical_trials / Prognostic_studies Limite: Humans / Male Idioma: En Revista: Bioinformatics Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2017 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias da Próstata / Software / Estatística como Assunto / Polimorfismo de Nucleotídeo Único / Epistasia Genética / Estudos de Associação Genética Tipo de estudo: Clinical_trials / Prognostic_studies Limite: Humans / Male Idioma: En Revista: Bioinformatics Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2017 Tipo de documento: Article País de afiliação: Estados Unidos