Hui-Yi Lin1, Po-Yu Huang2, Dung-Tsa Chen3, Heng-Yuan Tung1, Thomas A Sellers4, Julio M Pow-Sang5, Rosalind Eeles6,7, Doug Easton8, Zsofia Kote-Jarai6, Ali Amin Al Olama8, Sara Benlloch8, Kenneth Muir9, Graham G Giles10,11, Fredrik Wiklund12, Henrik Gronberg12, Christopher A Haiman13, Johanna Schleutker14,15,16, Børge G Nordestgaard17, Ruth C Travis18, Freddie Hamdy19, David E Neal19,20, Nora Pashayan21,22, Kay-Tee Khaw23, Janet L Stanford24,25, William J Blot26, Stephen N Thibodeau27, Christiane Maier28, Adam S Kibel29,30, Cezary Cybulski31, Lisa Cannon-Albright32, Hermann Brenner33,34,35, Radka Kaneva36, Jyotsna Batra37, Manuel R Teixeira38,39, Hardev Pandha40, Yong-Jie Lu41, Jong Y Park4. 1. Biostatistics Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, LA, USA. 2. Computational Intelligence Technology Center, Industrial Technology Research Institute, Hsinchu City, Taiwan. 3. Department of Biostatistics and Bioinformatics, Moffitt Cancer Center and Research Institute, Tampa, FL, USA. 4. Department of Cancer Epidemiology Moffitt Cancer Center and Research Institute, Tampa, FL, USA. 5. Department of Genitourinary Oncology, Moffitt Cancer Center and Research Institute, Tampa, FL, USA. 6. The Institute of Cancer Research, London, UK. 7. Royal Marsden NHS Foundation Trust, London, UK. 8. Strangeways Research Laboratory, Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Worts Causeway, Cambridge, UK. 9. Institute of Population Health, University of Manchester, Manchester, UK. 10. Division of Cancer Epidemiology and Intelligence, Cancer Council Victoria, Melbourne, VIC, Australia. 11. Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia. 12. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, SE-17177 Stockholm, Sweden. 13. Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, USA. 14. Department of Medical Biochemistry and Genetics, Institute of Biomedicine, University of Turku and Tyks Microbiology and Genetics. 15. Department of Medical Genetics, Turku University Hospital, Turku FI-20014, Finland. 16. BioMediTech, University of Tampere, Tampere, Finland. 17. Department of Clinical Biochemistry, Herlev Hospital, Copenhagen University Hospital, DK-2730 Herlev, Denmark. 18. Cancer Epidemiology, Nuffield Department of Population Health, University of Oxford, Oxford, UK. 19. Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK. 20. Department of Oncology, University of Cambridge, Cambridge, UK. 21. Strangeways Research Laboratory, Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Worts Causeway, Cambridge, UK. 22. Department of Applied Health Research, University College London, London, UK. 23. Cambridge Institute of Public Health, University of Cambridge, Forvie Site, Robinson Way, Cambridge, UK. 24. Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA. 25. Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA. 26. International Epidemiology Institute, Rockville, MD, USA. 27. Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA. 28. Institute of Human Genetics, University Hospital Ulm, Ulm, Germany. 29. Brigham and Women's Hospital/Dana-Farber Cancer Institute, Boston, MA, USA. 30. Washington University, St Louis, MO, USA. 31. Department of Genetics and Pathology, International Hereditary Cancer Center, Pomeranian Medical University, Szczecin, Poland. 32. Division of Genetic Epidemiology, Department of Medicine, University of Utah School of Medicine, UT, USA. 33. Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany. 34. Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany. 35. German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany. 36. Department of Medical Chemistry and Biochemistry, Molecular Medicine Center, Medical University-Sofia, 1431 Sofia, Bulgaria. 37. Australian Prostate Cancer Research Centre-Qld, Institute of Health and Biomedical Innovation and Schools of Life Science and Public Health, Queensland University of Technology, Brisbane, Australia. 38. Department of Genetics, Portuguese Oncology Institute, Porto, Portugal. 39. Biomedical Sciences Institute (ICBAS), Porto University, Porto, Portugal. 40. The University of Surrey, Guildford, Surrey, UK. 41. Centre for Molecular Oncology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, London, UK.
Abstract
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.
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.
Authors: Xiang Wan; Can Yang; Qiang Yang; Hong Xue; Xiaodan Fan; Nelson L S Tang; Weichuan Yu Journal: Am J Hum Genet Date: 2010-09-10 Impact factor: 11.025
Authors: Christine Herold; Michael Steffens; Felix F Brockschmidt; Max P Baur; Tim Becker Journal: Bioinformatics Date: 2009-10-16 Impact factor: 6.937
Authors: Hui-Yi Lin; Dung-Tsa Chen; Po-Yu Huang; Yung-Hsin Liu; Augusto Ochoa; Jovanny Zabaleta; Donald E Mercante; Zhide Fang; Thomas A Sellers; Julio M Pow-Sang; Chia-Ho Cheng; Rosalind Eeles; Doug Easton; Zsofia Kote-Jarai; Ali Amin Al Olama; Sara Benlloch; Kenneth Muir; Graham G Giles; Fredrik Wiklund; Henrik Gronberg; Christopher A Haiman; Johanna Schleutker; Børge G Nordestgaard; Ruth C Travis; Freddie Hamdy; Nora Pashayan; Kay-Tee Khaw; Janet L Stanford; William J Blot; Stephen N Thibodeau; Christiane Maier; Adam S Kibel; Cezary Cybulski; Lisa Cannon-Albright; Hermann Brenner; Radka Kaneva; Jyotsna Batra; Manuel R Teixeira; Hardev Pandha; Yong-Jie Lu; Jong Y Park Journal: Bioinformatics Date: 2017-03-15 Impact factor: 6.937
Authors: Hui-Yi Lin; Ernest K Amankwah; Tung-Sung Tseng; Xiaotao Qu; Dung-Tsa Chen; Jong Y Park Journal: PLoS One Date: 2013-04-03 Impact factor: 3.240
Authors: Hui-Yi Lin; Catherine Y Callan; Zhide Fang; Heng-Yuan Tung; Jong Y Park Journal: Cancer Epidemiol Biomarkers Prev Date: 2019-03-26 Impact factor: 4.254
Authors: Hui-Yi Lin; Po-Yu Huang; Chia-Ho Cheng; Heng-Yuan Tung; Zhide Fang; Anders E Berglund; Ann Chen; Jennifer French-Kwawu; Darian Harris; Julio Pow-Sang; Kosj Yamoah; John L Cleveland; Shivanshu Awasthi; Robert J Rounbehler; Travis Gerke; Jasreman Dhillon; Rosalind Eeles; Zsofia Kote-Jarai; Kenneth Muir; Johanna Schleutker; Nora Pashayan; David E Neal; Sune F Nielsen; Børge G Nordestgaard; Henrik Gronberg; Fredrik Wiklund; Graham G Giles; Christopher A Haiman; Ruth C Travis; Janet L Stanford; Adam S Kibel; Cezary Cybulski; Kay-Tee Khaw; Christiane Maier; Stephen N Thibodeau; Manuel R Teixeira; Lisa Cannon-Albright; Hermann Brenner; Radka Kaneva; Hardev Pandha; Srilakshmi Srinivasan; Judith Clements; Jyotsna Batra; Jong Y Park Journal: Sci Rep Date: 2021-04-29 Impact factor: 4.379