Publications

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

Author(s): Lin HY,  Chen DT,  Huang PY,  Liu YH,  Ochoa A,  Zabaleta J,  Mercante DE,  Fang Z,  Sellers TA,  Pow-Sang JM,  Cheng CH,  Eeles R,  Easton D,  Kote-Jarai Z,  Amin Al Olama A,  Benlloch S,  Muir K,  Giles GG,  Wiklund F,  Gronberg H,  Haiman CA,  Schleutker J,  Nordestgaard BG,  Travis RC,  Hamdy F,  Pashayan N,  Khaw KT,  Stanford JL,  Blot WJ,  Thibodeau SN,  Maier C,  Kibel AS,  Cybulski C,  Cannon-Albright L,  Brenner H,  Kaneva R,  Batra J,  Teixeira MR,  Pandha H,  Lu YJ,  PRACTICAL Consortium,  Park JY

Journal: Bioinformatics

Date: 2017 Mar 15

Major Program(s) or Research Group(s): PLCO

PubMed ID: 28039167

PMC ID: PMC5860469

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.