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 Citations (Scopus)

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

Fingerprint

Single Nucleotide Polymorphism
Testing
Interaction
Statistical methods
Association reactions
Prostate Cancer
Multifactor Dimensionality Reduction
Genetic Association
Binary Outcomes
Genetic Association Studies
Dimensionality Reduction
Direction compound
High Power
Statistical method
Prostatic Neoplasms
Coding
Unstable

ASJC Scopus subject areas

  • Statistics and Probability
  • Medicine(all)
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

Cite this

Lin, H. Y., Chen, D. T., Huang, P. Y., Liu, Y. H., Ochoa, A., Zabaleta, J., ... 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

SNP interaction pattern identifier (SIPI) : An intensive search for SNP-SNP interaction patterns. / Lin, Hui Yi; Chen, Dung Tsa; Huang, Po Yu; Liu, Yung Hsin; Ochoa, Augusto; Zabaleta, Jovanny; Mercante, Donald E.; Fang, Zhide; Sellers, Thomas A.; Pow-Sang, Julio M.; Cheng, Chia Ho; Eeles, Rosalind; Easton, Doug; Kote-Jarai, Zsofia; Al Olama, Ali Amin; Benlloch, Sara; Muir, Kenneth; Giles, Graham G.; Wiklund, Fredrik; Gronberg, Henrik; Haiman, Christopher A.; Schleutker, Johanna; Nordestgaard, Børge G.; Travis, Ruth C.; Hamdy, Freddie; Pashayan, Nora; Khaw, Kay Tee; Stanford, Janet L.; Blot, William J.; Thibodeau, Stephen N; Maier, Christiane; Kibel, Adam S.; Cybulski, Cezary; Cannon-Albright, Lisa; Brenner, Hermann; Kaneva, Radka; Batra, Jyotsna; Teixeira, Manuel R.; Pandha, Hardev; Lu, Yong Jie; Park, Jong Y.; Hancock, John.

In: Bioinformatics, Vol. 33, No. 6, 2017, p. 822-833.

Research output: Contribution to journalArticle

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, Al Olama, AA, 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, Park, JY & Hancock, J 2017, 'SNP interaction pattern identifier (SIPI): An intensive search for SNP-SNP interaction patterns', Bioinformatics, vol. 33, no. 6, pp. 822-833. https://doi.org/10.1093/bioinformatics/btw762
Lin, Hui Yi ; Chen, Dung Tsa ; Huang, Po Yu ; Liu, Yung Hsin ; Ochoa, Augusto ; Zabaleta, Jovanny ; Mercante, Donald E. ; Fang, Zhide ; Sellers, Thomas A. ; Pow-Sang, Julio M. ; Cheng, Chia Ho ; Eeles, Rosalind ; Easton, Doug ; Kote-Jarai, Zsofia ; Al Olama, Ali Amin ; Benlloch, Sara ; Muir, Kenneth ; Giles, Graham G. ; Wiklund, Fredrik ; Gronberg, Henrik ; Haiman, Christopher A. ; Schleutker, Johanna ; Nordestgaard, Børge G. ; Travis, Ruth C. ; Hamdy, Freddie ; Pashayan, Nora ; Khaw, Kay Tee ; Stanford, Janet L. ; Blot, William J. ; Thibodeau, Stephen N ; Maier, Christiane ; Kibel, Adam S. ; Cybulski, Cezary ; Cannon-Albright, Lisa ; Brenner, Hermann ; Kaneva, Radka ; Batra, Jyotsna ; Teixeira, Manuel R. ; Pandha, Hardev ; Lu, Yong Jie ; Park, Jong Y. ; Hancock, John. / SNP interaction pattern identifier (SIPI) : An intensive search for SNP-SNP interaction patterns. In: Bioinformatics. 2017 ; Vol. 33, No. 6. pp. 822-833.
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AU - Lin, Hui Yi

AU - Chen, Dung Tsa

AU - Huang, Po Yu

AU - Liu, Yung Hsin

AU - Ochoa, Augusto

AU - Zabaleta, Jovanny

AU - Mercante, Donald E.

AU - Fang, Zhide

AU - Sellers, Thomas A.

AU - Pow-Sang, Julio M.

AU - Cheng, Chia Ho

AU - Eeles, Rosalind

AU - Easton, Doug

AU - Kote-Jarai, Zsofia

AU - Al Olama, Ali Amin

AU - Benlloch, Sara

AU - Muir, Kenneth

AU - Giles, Graham G.

AU - Wiklund, Fredrik

AU - Gronberg, Henrik

AU - Haiman, Christopher A.

AU - Schleutker, Johanna

AU - Nordestgaard, Børge G.

AU - Travis, Ruth C.

AU - Hamdy, Freddie

AU - Pashayan, Nora

AU - Khaw, Kay Tee

AU - Stanford, Janet L.

AU - Blot, William J.

AU - Thibodeau, Stephen N

AU - Maier, Christiane

AU - Kibel, Adam S.

AU - Cybulski, Cezary

AU - Cannon-Albright, Lisa

AU - Brenner, Hermann

AU - Kaneva, Radka

AU - Batra, Jyotsna

AU - Teixeira, Manuel R.

AU - Pandha, Hardev

AU - Lu, Yong Jie

AU - Park, Jong Y.

AU - Hancock, John

PY - 2017

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N2 - 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.

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