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 Gronberg; Christopher 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; The Practical Consortium; Jong Y. Park

doi:10.1093/bioinformatics/btw762

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, The Practical Consortium, Jong Y. Park

^*Corresponding author for this work

Department of Clinical Medicine

5 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 language	English
Journal	Bioinformatics
Volume	33
Issue number	6
Pages (from-to)	822-833
Number of pages	12
ISSN	1367-4803
DOIs	https://doi.org/10.1093/bioinformatics/btw762
Publication status	Published - 2017

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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., ... Park, J. Y. (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

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, The Practical Consortium & Park, JY 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

@article{88e63a4039204642b6e61833bad5d283,

title = "SNP interaction pattern identifier (SIPI): An intensive search for SNP-SNP interaction patterns",

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.",

author = "Lin, {Hui Yi} and Chen, {Dung Tsa} and Huang, {Po Yu} and Liu, {Yung Hsin} and Augusto Ochoa and Jovanny Zabaleta and Mercante, {Donald E.} and Zhide Fang and Sellers, {Thomas A.} and Pow-Sang, {Julio M.} and Cheng, {Chia Ho} and Rosalind Eeles and Doug Easton and Zsofia Kote-Jarai and {Al Olama}, {Ali Amin} and Sara Benlloch and Kenneth Muir and Giles, {Graham G.} and Fredrik Wiklund and Henrik Gronberg and Haiman, {Christopher A.} and Johanna Schleutker and Nordestgaard, {B{\o}rge G.} and Travis, {Ruth C.} and Freddie Hamdy and Nora Pashayan and Khaw, {Kay Tee} and Stanford, {Janet L.} and Blot, {William J.} and Thibodeau, {Stephen N.} and Christiane Maier and Kibel, {Adam S.} and Cezary Cybulski and Lisa Cannon-Albright and Hermann Brenner and Radka Kaneva and Jyotsna Batra and Teixeira, {Manuel R.} and Hardev Pandha and Lu, {Yong Jie} and {The Practical Consortium} and Park, {Jong Y.}",

year = "2017",

doi = "10.1093/bioinformatics/btw762",

language = "English",

volume = "33",

pages = "822--833",

journal = "Bioinformatics",

issn = "1367-4803",

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TY - JOUR

T1 - SNP interaction pattern identifier (SIPI)

T2 - An intensive search for SNP-SNP interaction patterns

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 - The Practical Consortium

AU - Park, Jong Y.

PY - 2017

Y1 - 2017

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.

AB - 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.

U2 - 10.1093/bioinformatics/btw762

DO - 10.1093/bioinformatics/btw762

M3 - Journal article

C2 - 28039167

AN - SCOPUS:85018245832

SN - 1367-4803

VL - 33

SP - 822

EP - 833

JO - Bioinformatics

JF - Bioinformatics

IS - 6

ER -

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

Abstract

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