SNP Interaction Pattern Identifier (SIPI): an Intensive Search for SNP-SNP Interaction Patterns

Overview

Journal Bioinformatics

Publisher Oxford University Press

Specialty Biology

Date 2017 Jan 1

PMID 28039167

Citations 9

Authors

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

Borge 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

Affiliations

Soon will be listed here.

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.

Citing Articles

LINC00461 SNPs rs933647 and rs201864123 modify the risk of adenoid hypertrophy susceptibility for children in South China.

Hou C, Luo X, Wan X, Chen K, Xian Z, Xu K Front Genet. 2025; 16:1509053.

PMID: 40061126 PMC: 11885279. DOI: 10.3389/fgene.2025.1509053.

Natural variations in and its neighboring confer yellow seed with elevated oil content in .

Qian L, Yang L, Liu X, Wang T, Kang L, Chen H Proc Natl Acad Sci U S A. 2025; 122(5):e2417264122.

PMID: 39883846 PMC: 11804580. DOI: 10.1073/pnas.2417264122.

Cluster effect for SNP-SNP interaction pairs for predicting complex traits.

Lin H, Mazumder H, Sarkar I, Huang P, Eeles R, Kote-Jarai Z Sci Rep. 2024; 14(1):18677.

PMID: 39134575 PMC: 11319716. DOI: 10.1038/s41598-024-66311-7.

The role of longevity-related genetic variant interactions as predictors of survival after 85 years of age.

Setinc M, Celinscak Z, Bockor L, Zajc Petranovic M, Stojanovic Markovic A, Pericic Salihovic M Mech Ageing Dev. 2024; 219:111926.

PMID: 38484896 PMC: 11166054. DOI: 10.1016/j.mad.2024.111926.

Identification of environment-insensitive genes for oil content by combination of transcriptome and genome-wide association analysis in rapeseed.

Yao M, He D, Li W, Xiong X, He X, Liu Z Biotechnol Biofuels Bioprod. 2024; 17(1):29.

PMID: 38383469 PMC: 10882896. DOI: 10.1186/s13068-024-02480-x.

References

Lin H, Amankwah E, Tseng T, Qu X, Chen D, Park J . SNP-SNP interaction network in angiogenesis genes associated with prostate cancer aggressiveness. PLoS One. 2013; 8(4):e59688. PMC: 3618555. DOI: 10.1371/journal.pone.0059688. View

Ioannidis J, Castaldi P, Evangelou E . A compendium of genome-wide associations for cancer: critical synopsis and reappraisal. J Natl Cancer Inst. 2010; 102(12):846-58. PMC: 2886095. DOI: 10.1093/jnci/djq173. View

Wang Y, Chen J, Li Q, Wang H, Liu G, Jing Q . Identifying novel prostate cancer associated pathways based on integrative microarray data analysis. Comput Biol Chem. 2011; 35(3):151-8. DOI: 10.1016/j.compbiolchem.2011.04.003. View

Su W, Shugart Y, Chang K, Tsang N, Tse K, Chang Y . How genome-wide SNP-SNP interactions relate to nasopharyngeal carcinoma susceptibility. PLoS One. 2013; 8(12):e83034. PMC: 3871583. DOI: 10.1371/journal.pone.0083034. View

Piegorsch W, Weinberg C, Taylor J . Non-hierarchical logistic models and case-only designs for assessing susceptibility in population-based case-control studies. Stat Med. 1994; 13(2):153-62. DOI: 10.1002/sim.4780130206. View

Leotoing L, Manin M, Monte D, Baron S, Communal Y, Lours C . Crosstalk between androgen receptor and epidermal growth factor receptor-signalling pathways: a molecular switch for epithelial cell differentiation. J Mol Endocrinol. 2007; 39(2):151-62. DOI: 10.1677/JME-07-0021. View

Pyonteck S, Gadea B, Wang H, Gocheva V, Hunter K, Tang L . Deficiency of the macrophage growth factor CSF-1 disrupts pancreatic neuroendocrine tumor development. Oncogene. 2011; 31(11):1459-67. DOI: 10.1038/onc.2011.337. View

Motsinger-Reif A . The effect of alternative permutation testing strategies on the performance of multifactor dimensionality reduction. BMC Res Notes. 2009; 1:139. PMC: 2631601. DOI: 10.1186/1756-0500-1-139. View

Moore J, Williams S . New strategies for identifying gene-gene interactions in hypertension. Ann Med. 2002; 34(2):88-95. DOI: 10.1080/07853890252953473. View

10.

Pei B, Sun B, Zhang Y, Wang A, Zhang Z . [Expression of colony-stimulating factor 1 in lung adenocarcinoma and its prognostic implication]. Zhonghua Zhong Liu Za Zhi. 2015; 37(2):113-8. View

11.

Van Meter T, Broaddus W, Rooprai H, Pilkington G, Fillmore H . Induction of membrane-type-1 matrix metalloproteinase by epidermal growth factor-mediated signaling in gliomas. Neuro Oncol. 2004; 6(3):188-99. PMC: 1871990. DOI: 10.1215/S1152851703000486. View

12.

Boulesteix A, Strobl C, Weidinger S, Wichmann H, Wagenpfeil S . Multiple testing for SNP-SNP interactions. Stat Appl Genet Mol Biol. 2008; 6:Article37. DOI: 10.2202/1544-6115.1315. View

13.

Ritchie M, Hahn L, Roodi N, BAILEY L, Dupont W, Parl F . Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am J Hum Genet. 2001; 69(1):138-47. PMC: 1226028. DOI: 10.1086/321276. View

14.

Wan X, Yang C, Yang Q, Xue H, Fan X, Tang N . BOOST: A fast approach to detecting gene-gene interactions in genome-wide case-control studies. Am J Hum Genet. 2010; 87(3):325-40. PMC: 2933337. DOI: 10.1016/j.ajhg.2010.07.021. View

15.

Davidson B, Goldberg I, Gotlieb W, Kopolovic J, Ben-Baruch G, Nesland J . High levels of MMP-2, MMP-9, MT1-MMP and TIMP-2 mRNA correlate with poor survival in ovarian carcinoma. Clin Exp Metastasis. 2000; 17(10):799-808. DOI: 10.1023/a:1006723011835. View

16.

Ritchie M, Hahn L, Moore J . Power of multifactor dimensionality reduction for detecting gene-gene interactions in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity. Genet Epidemiol. 2003; 24(2):150-7. DOI: 10.1002/gepi.10218. View

17.

Cordell H . Detecting gene-gene interactions that underlie human diseases. Nat Rev Genet. 2009; 10(6):392-404. PMC: 2872761. DOI: 10.1038/nrg2579. View

18.

Van den Broeck T, Joniau S, Clinckemalie L, Helsen C, Prekovic S, Spans L . The role of single nucleotide polymorphisms in predicting prostate cancer risk and therapeutic decision making. Biomed Res Int. 2014; 2014:627510. PMC: 3950427. DOI: 10.1155/2014/627510. View

19.

Eeles R, Olama A, Benlloch S, Saunders E, Leongamornlert D, Tymrakiewicz M . Identification of 23 new prostate cancer susceptibility loci using the iCOGS custom genotyping array. Nat Genet. 2013; 45(4):385-91, 391e1-2. PMC: 3832790. DOI: 10.1038/ng.2560. View

20.

Migliaccio A, Castoria G, Di Domenico M, Ciociola A, Lombardi M, de Falco A . Crosstalk between EGFR and extranuclear steroid receptors. Ann N Y Acad Sci. 2007; 1089:194-200. DOI: 10.1196/annals.1386.006. View