Finding susceptible and protective interaction patterns in large-scale genetic association study

doi:10.1007/s11704-016-5300-5

Front. Comput. Sci.

2017, Vol. 11

Issue (3) : 541-554 https://doi.org/10.1007/s11704-016-5300-5

RESEARCH ARTICLE

Finding susceptible and protective interaction patterns in large-scale genetic association study

Yuan LI^1,², Yuhai ZHAO¹(

), Guoren WANG¹, Xiaofeng ZHU³, Xiang ZHANG², Zhanghui WANG¹, Jun PANG¹

¹. School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
². Department of Electronic Engineering and Computer Science, Case Western Reserve University, Cleveland OH 44106, USA
³. Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland OH 44106, USA

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Abstract

Interaction detection in large-scale genetic association studies has attracted intensive research interest, since many diseases have complex traits. Various approaches have been developed for finding significant genetic interactions. In this article, we propose a novel framework SRMiner to detect interacting susceptible and protective genotype patterns. SRMiner can discover not only probable combination of single nucleotide polymorphisms (SNPs) causing diseases but also the corresponding SNPs suppressing their pathogenic functions, which provides a better prospective to uncover the underlying relevance between genetic variants and complex diseases. We have performed extensive experiments on several real Wellcome Trust Case Control Consortium (WTCCC) datasets. We use the pathway-based and the protein-protein interaction (PPI) network-based evaluation methods to verify the discovered patterns. The results show that SRMiner successfully identifies many disease-related genes verified by the existing work. Furthermore, SRMiner can also infer some uncomfirmed but highly possible disease-related genes.

Keywords genetic association studies genotype pattern mining data mining bioinformatics

Corresponding Author(s): Yuhai ZHAO

Just Accepted Date: 17 June 2016 Online First Date: 14 September 2016 Issue Date: 25 May 2017

Cite this article:

Yuan LI,Yuhai ZHAO,Guoren WANG, et al. Finding susceptible and protective interaction patterns in large-scale genetic association study[J]. Front. Comput. Sci., 2017, 11(3): 541-554.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-016-5300-5
https://academic.hep.com.cn/fcs/EN/Y2017/V11/I3/541

1	LiJ, WangL M, GuoM Z, Zhang R J, DaiQ G , LiuX Y, WangC Y, TengZ, Xuan P, ZhangM M . Mining disease genes using integrated protein-protein interaction and gene-gene co-regulation information. FEBS Open Bio, 2015, 5(1): 251–256 https://doi.org/10.1016/j.fob.2015.03.011
2	CordellH J. Detecting gene-gene interactions that underlie human diseases. Natural Reviews Genetics, 2009, 10(6): 392–404 https://doi.org/10.1038/nrg2579
3	ZengX X, ZhangX, ZouQ. Integrative approaches for predicting microRNA function and prioritizing disease-related microRNA using biological interaction networks. Briefings in Bioinformatics, 2015
4	ZouQ, LiJ J, SongL, Zeng X X, WangG H . Similarity computation strategies in the microRNA-disease network: a survey. Briefings in Functional Genomics, 2016, 15(1): 55–64
5	ZhangL, ChenS C, LiuX J. Detecting differential expression from RNA-seq data with expression measurement uncertainty. Frontiers of Computer Science, 2015, 9(4): 652–663 https://doi.org/10.1007/s11704-015-4308-6
6	ShangJ L, ZhangJ Y, SunY, Liu D, YeD J , YinY L. Performance analysis of novel methods for detecting epistasis. BMC Bioinformatics, 2011, 12(1) https://doi.org/10.1186/1471-2105-12-475
7	WangY, LiuG M, FengM L, Wong L. An empirical comparison of several recent epistatic interaction detection methods. Bioinformatics, 2011, 27(21): 2936–2943 https://doi.org/10.1093/bioinformatics/btr512
8	LiP, GuoM Z, WangC Y, Liu X Y, ZouQ . An overview of SNP interactions in genome-wide association studies. Briefings in Functional Genomics, 2014, 14(3): 129–141
9	LiJ, HuangD L, GuoM Z, Liu X Y, WangC Y , TengZ X, ZhangR J, JiangY S, Lv H C, WangL M . A gene-based information gain method for detecting gene-gene interactions in case-control studies. European Journal of Human Genetics, 2015 https://doi.org/10.1038/ejhg.2015.16
10	PanJ B, HuS C, WangH, Zou Q, JiZ L . PaGeFinder: quantitative identification of spatiotemporal pattern genes. Bioinformatics, 2012, 28(11): 1544–1545 https://doi.org/10.1093/bioinformatics/bts169
11	InfanteJ, SanzC, Fernández-LunaJ L , LlorcaJ, Berciano J, CombarrosO . Gene-gene interaction between interleukin-1A and interleukin-8 increases Alzheimer’s disease risk. Journal of Neurology, 2004, 251(4): 482–483 https://doi.org/10.1007/s00415-004-0375-6
12	CombarrosO, van Duijn C M, HammondN , BelbinO, Arias-Vásquez A, Cortina-BorjaM , LehmannM G, Aulchenko Y S, SchuurM , KölschH. Replication by the Epistasis Project of the interaction between the genes for IL-6 and IL-10 in the risk of Alzheimer’s disease. Journal of Neuroinflammation, 2009, 6(1): 22 https://doi.org/10.1186/1742-2094-6-22
13	BaryshnikovaA, Costanzo M, MyersC L , AndrewsB, BooneC. Genetic interaction networks: toward an understanding of heritability. Annual Review of Genomics and Human Genetics, 2013, 14(1) https://doi.org/10.1146/annurev-genom-082509-141730
14	GoldsteinD B. Common genetic variation and human traits. New England Journal of Medicine, 2009, 360(17): 1696 https://doi.org/10.1056/NEJMp0806284
15	McCarthyM I, Abecasis G R, CardonL R , GoldsteinD B, LittleJ, IoannidisJ P A , HirschhornJ N. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nature Reviews Genetics, 2008, 9(5): 356–369 https://doi.org/10.1038/nrg2344
16	MooreJ H, Gilbert J C, TsaiC T , ChiangF T, HoldenT, BarneyN, White B C. A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility. Journal of Theoretical Biology, 2006, 241(2): 252–261 https://doi.org/10.1016/j.jtbi.2005.11.036
17	WanX, YangC, YangQ, Xue H, FanX D , TangN L S, YuW C. BOOST: a fast approach to detecting gene-gene interactions in genome-wide case-control studies. The American Journal of Human Genetics, 2010, 87(3): 325–340 https://doi.org/10.1016/j.ajhg.2010.07.021
18	WanX, YangCan, YangQ, Xue H, TangN L S , YuW C. Predictive rule inference for epistatic interaction detection in genome-wide association studies. Bioinformatics, 2010, 26(1): 30–37 https://doi.org/10.1093/bioinformatics/btp622
19	ZhangY, LiuJ S. Bayesian inference of epistatic interactions in casecontrol studies. Nature Genetics, 2007, 39(9): 1167–1173 https://doi.org/10.1038/ng2110
20	ZhangX, HuangS P, ZouF, Wang W. TEAM: efficient two-locus epistasis tests in human genome-wide association study. Bioinformatics, 2010, 26(12): i217–i227 https://doi.org/10.1093/bioinformatics/btq186
21	JanssensA C J W, van Duijn C M. Genome-based prediction of common diseases: advances and prospects. Human Molecular Genetics, 2008, 17(R2): R166–R173 https://doi.org/10.1093/hmg/ddn250
22	AbdiH, Williams L J. Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics, 2010, 2(4): 433–459 https://doi.org/10.1002/wics.101
23	ZhaoY H, WangG R, LiY, WangZ H. Finding novel diagnostic gene patterns based on interesting non-redundant contrast sequence rules. In: Proceedings of IEEE International Conference on Data Mining. 2011, 972–981 https://doi.org/10.1109/icdm.2011.68
24	MontgomeryS. Linkage disequilibrium—understanding the evolutionary past and mapping the medical future. Nature Reviews Genetics, 2008, 9(6): 477–485 https://doi.org/10.1038/nrg2361
25	PurcellS, NealeB, Todd-BrownK , ThomasL, Ferreira M A R, BenderD , MallerJ, SklarP, de BakkerP I W , DalyM J, ShamP C. PLINK: A tool set for whole-genome association and population-based linkage analyses. American Journal of Human Genetics, 2007, 81(3): 559–575 https://doi.org/10.1086/519795
26	GoldbergA V. Finding a maximum density subgraph. University of California Berkeley, CA, 1984
27	CharikarM. Greedy approximation algorithms for finding dense components in a graph. Approximation Algorithms for Combinatorial Optimization, 2000, 139–152 https://doi.org/10.1007/3-540-44436-x_10
28	FanW, ZhangK, ChengH, Gao J, YanX F , HanJ W, YuP, VerscheureO . Direct mining of discriminative and essential frequent patterns via model-based search tree. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2008, 230–238 https://doi.org/10.1145/1401890.1401922
29	The Well come Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature, 2007, 447(7145): 661–678 https://doi.org/10.1038/nature05911
30	HanJ W, PeiJ, YinY W. Mining frequent patterns without candidate generation. ACM SIGMOD Record, 2000, 29(2): 1–12 https://doi.org/10.1145/335191.335372
31	PanF, CongG, TungA K H, Yang J, ZakiM J . Carpenter: finding closed patterns in long biological datasets. In: Proceedings of ACM International Conference on Knowledge Discovery and Data Mining. 2003, 637–642
32	SacconeS F, QuanJ X, JonesP L. BioQ: tracing experimental origins in public genomic databases using a novel data provenance model. Bioinformatics, 2012, 28(8): 1189–1191 https://doi.org/10.1093/bioinformatics/bts117
33	Chatr-aryamontriA, Breitkreutz B J, HeinickeS , BoucherL, WinterA, StarkC, Nixon J, RamageL , KolasN, O’Donmell L. The BioGRID interaction database: 2013 update. Nucleic Acids Research, 2013, 41(D1): D816–D823 https://doi.org/10.1093/nar/gks1158
34	WangK, LiM Y, BucanM. Pathway-based approaches for analysis of genomewide association studies. The American Journal of Human Genetics, 2007, 81(6): 1278–1283 https://doi.org/10.1086/522374
35	ChenL S, HutterC M, PotterJ D, Liu Y, PrenticeR L , PetersU, HsuL. Insights into colon cancer etiology via a regularized approach to gene set analysis of gwas data. The American Journal of Human Genetics, 2010, 86(6): 860–871 https://doi.org/10.1016/j.ajhg.2010.04.014
36	LiM X, KwanJ S H, ShamP C. HYST: A hybrid set-based test for genome-wide association studies, with application to protein-protein interaction-based association analysis. The American Journal of Human Genetics, 2012, 91(3): 478–488 https://doi.org/10.1016/j.ajhg.2012.08.004
37	PawsonT, NashP. Protein–protein interactions define specificity in signal transduction. Genes & Development, 2000, 14(9): 1027–1047
38	SharanR, Ulitsky I, ShamirR . Network-based prediction of protein function. Molecular Systems Biology, 2007, 3(1): 88 https://doi.org/10.1038/msb4100129

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