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AA9int: SNP interaction pattern search using non-hierarchical additive model set.
Lin, Hui-Yi; Huang, Po-Yu; Chen, Dung-Tsa; Tung, Heng-Yuan; Sellers, Thomas A; Pow-Sang, Julio M; 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; Neal, David E; 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.
Afiliación
  • Lin HY; Biostatistics Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, LA, USA.
  • Huang PY; Computational Intelligence Technology Center, Industrial Technology Research Institute, Hsinchu City, Taiwan.
  • Chen DT; Department of Biostatistics and Bioinformatics, Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
  • Tung HY; Biostatistics Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, LA, USA.
  • Sellers TA; Department of Cancer Epidemiology Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
  • Pow-Sang JM; Department of Genitourinary Oncology, Moffitt Cancer Center and 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; Strangeways Research Laboratory, Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Worts Causeway, Cambridge, UK.
  • Amin Al Olama A; The Institute of Cancer Research, London, UK.
  • Benlloch S; Strangeways Research Laboratory, Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Worts Causeway, Cambridge, UK.
  • Muir K; Strangeways Research Laboratory, Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Worts Causeway, Cambridge, UK.
  • Giles GG; Institute of Population Health, University of Manchester, Manchester, UK.
  • Wiklund F; Division of Cancer Epidemiology and Intelligence, Cancer Council Victoria, Melbourne, VIC, Australia.
  • Gronberg H; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia.
  • Haiman CA; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, SE-17177 Stockholm, Sweden.
  • Schleutker J; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, SE-17177 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 and Tyks Microbiology and Genetics.
  • Hamdy F; Department of Medical Genetics, Turku University Hospital, Turku FI-20014, Finland.
  • Neal DE; BioMediTech, University of Tampere, Tampere, Finland.
  • Pashayan N; Department of Clinical Biochemistry, Herlev Hospital, Copenhagen University Hospital, DK-2730 Herlev, Denmark.
  • Khaw KT; Cancer Epidemiology, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
  • Stanford JL; Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK.
  • Blot WJ; Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK.
  • Thibodeau SN; Department of Oncology, University of Cambridge, Cambridge, UK.
  • Maier C; Strangeways Research Laboratory, Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Worts Causeway, Cambridge, UK.
  • Kibel AS; Department of Applied Health Research, University College London, London, UK.
  • Cybulski C; Cambridge Institute of Public Health, University of Cambridge, Forvie Site, Robinson Way, 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, UT, USA.
Bioinformatics ; 34(24): 4141-4150, 2018 12 15.
Article en En | MEDLINE | ID: mdl-29878078
Motivation: 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.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Programas Informáticos / Polimorfismo de Nucleótido Simple Tipo de estudio: Prognostic_studies Idioma: En Revista: Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2018 Tipo del documento: Article País de afiliación: Estados Unidos
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Programas Informáticos / Polimorfismo de Nucleótido Simple Tipo de estudio: Prognostic_studies Idioma: En Revista: Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2018 Tipo del documento: Article País de afiliación: Estados Unidos