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Bioinformatics. 2017 Mar 15;33(6):822-833. doi: 10.1093/bioinformatics/btw762.

SNP interaction pattern identifier (SIPI): an intensive search for SNP-SNP interaction patterns.

Author information

1
Biostatistics Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, USA.
2
Department of Biostatistics and Bioinformatics, Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
3
Computational Intelligence Technology Center, Industrial Technology Research Institute, Hsinchu City, Taiwan.
4
Department of Biometrics, INC Research, LLC, Raleigh, NC, USA.
5
Stanley S. Scott Cancer Center, Louisiana State University Health Sciences Center, New Orleans, USA.
6
Department of Cancer Epidemiology, Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
7
Department of Genitourinary Oncology, Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
8
The Institute of Cancer Research, London, UK.
9
Royal Marsden NHS Foundation Trust, London, UK.
10
Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK.
11
University of Warwick, Coventry, UK.
12
Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Victoria, Australia.
13
Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia.
14
Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden.
15
Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, USA.
16
Department of Medical Biochemistry and Genetics, Institute of Biomedicine, University of Turku, Turku, Finland.
17
Tyks Microbiology and Genetics, Department of Medical Genetics, Turku University Hospital, Turku, Finland.
18
BioMediTech, 30014 University of Tampere, Tampere, Finland.
19
Department of Clinical Biochemistry, Herlev Hospital, Copenhagen University Hospital, Herlev, Denmark.
20
Cancer Epidemiology, Nuffield Department of Population Health University of Oxford, Oxford, UK.
21
Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK.
22
Medical Science, University of Oxford, John Radcliffe Hospital, Oxford, UK.
23
Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK.
24
Department of Applied Health Research, University College London, London, UK.
25
Cambridge Institute of Public Health, University of Cambridge, Cambridge, UK.
26
Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
27
Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA.
28
International Epidemiology Institute, Rockville, MD, USA.
29
Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA.
30
Institute of Human Genetics University Hospital Ulm, Ulm, Germany.
31
Brigham and Women's Hospital/Dana-Farber Cancer Institute, Boston, MA, USA.
32
Washington University, St Louis, MO, USA.
33
International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland.
34
Division of Genetic Epidemiology, Department of Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA.
35
Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.
36
Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany.
37
German Cancer Consortium (DKTK) German Cancer Research Center (DKFZ), Heidelberg, Germany.
38
Molecular Medicine Center and Department of Medical Chemistry and Biochemistry, Medical University - Sofia, Sofia, Bulgaria.
39
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.
40
Department of Genetics, Portuguese Oncology Institute, Porto, Portugal.
41
Biomedical Sciences Institute (ICBAS), Porto University, Porto, Portugal.
42
The University of Surrey, Guildford, Surrey, UK.
43
Centre for Molecular Oncology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, London, UK.

Abstract

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.

PMID:
28039167
PMCID:
PMC5860469
DOI:
10.1093/bioinformatics/btw762
[Indexed for MEDLINE]
Free PMC Article

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