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

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 GronbergChristopher 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, John Hancock

Research output: Contribution to journalArticle

3 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)822-833
Number of pages12
JournalBioinformatics
Volume33
Issue number6
DOIs
StatePublished - 2017

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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    Lin, H. Y., Chen, D. T., Huang, P. Y., Liu, Y. H., Ochoa, A., Zabaleta, J., Mercante, D. E., Fang, Z., Sellers, T. A., Pow-Sang, J. M., Cheng, C. H., Eeles, R., Easton, D., Kote-Jarai, Z., Al Olama, A. A., Benlloch, S., Muir, K., Giles, G. G., Wiklund, F., ... Hancock, J. (2017). SNP interaction pattern identifier (SIPI): An intensive search for SNP-SNP interaction patterns. Bioinformatics, 33(6), 822-833. https://doi.org/10.1093/bioinformatics/btw762