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

Accepted version
Peer-reviewed

Type

Article

Change log

Authors

Lin, Hui-Yi 
Chen, Dung-Tsa 
Huang, Po-Yu 
Liu, Yung-Hsin 
Ochoa, Augusto 

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.

Description

Keywords

Epistasis, Genetic, ErbB Receptors, Genetic Association Studies, Genetic Predisposition to Disease, Humans, Male, Matrix Metalloproteinase 16, Models, Genetic, Polymorphism, Single Nucleotide, Prostatic Neoplasms, Software, Statistics as Topic

Journal Title

Bioinformatics

Conference Name

Journal ISSN

1367-4803
1367-4811

Volume Title

33

Publisher

Oxford University Press (OUP)
Sponsorship
Medical Research Council (MR/N003284/1)
Medical Research Council (G1000143)
Medical Research Council (G0401527)
This study was supported by the National Cancer Institute (R01CA128813, PI: Park, JY and R21CA202417, PI: Lin, HY).