An Improved Opposition-Based Learning Particle Swarm Optimization for the Detection of SNP-SNP Interactions

SNP-SNP interactions have been receiving increasing attention in understanding the mechanism underlying susceptibility to complex diseases. Though many works have been done for the detection of SNP-SNP interactions, the algorithmic development is still ongoing. In this study, an improved opposition-based learning particle swarm optimization (IOBLPSO) is proposed for the detection of SNP-SNP interactions. Highlights of IOBLPSO are the introduction of three strategies, namely, opposition-based learning, dynamic inertia weight, and a postprocedure. Opposition-based learning not only enhances the global explorative ability, but also avoids premature convergence. Dynamic inertia weight allows particles to cover a wider search space when the considered SNP is likely to be a random one and converges on promising regions of the search space while capturing a highly suspected SNP. The postprocedure is used to carry out a deep search in highly suspected SNP sets. Experiments of IOBLPSO are performed on both simulation data sets and a real data set of age-related macular degeneration, results of which demonstrate that IOBLPSO is promising in detecting SNP-SNP interactions. IOBLPSO might be an alternative to existing methods for detecting SNP-SNP interactions.

[1]  Jason H. Moore,et al.  BIOINFORMATICS REVIEW , 2005 .

[2]  Li-Yeh Chuang,et al.  Double-Bottom Chaotic Map Particle Swarm Optimization Based on Chi-Square Test to Determine Gene-Gene Interactions , 2014, BioMed research international.

[3]  Xue-wen Chen,et al.  A Fast Markov Blankets Method for Epistatic Interactions Detection in Genome-wide Association Studies , 2010 .

[4]  Taesung Park,et al.  CUDA-LR: CUDA-accelerated logistic regression analysis tool for gene-gene interaction for genome-wide association study , 2011, 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW).

[5]  Wentian Li,et al.  A Complete Enumeration and Classification of Two-Locus Disease Models , 1999, Human Heredity.

[6]  Jason H. Moore,et al.  Ant Colony Optimization for Genome-Wide Genetic Analysis , 2008, ANTS Conference.

[7]  Saman K. Halgamuge,et al.  Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients , 2004, IEEE Transactions on Evolutionary Computation.

[8]  Hamid R. Tizhoosh,et al.  Opposition-Based Learning: A New Scheme for Machine Intelligence , 2005, International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06).

[9]  Qiang Yang,et al.  BOOST: A fast approach to detecting gene-gene interactions in genome-wide case-control studies , 2010, American journal of human genetics.

[10]  J. Ott,et al.  Complement Factor H Polymorphism in Age-Related Macular Degeneration , 2005, Science.

[11]  E. Tobias,et al.  The TES gene at 7q31.1 is methylated in tumours and encodes a novel growth-suppressing LIM domain protein , 2001, Oncogene.

[12]  Rui Jiang,et al.  A random forest approach to the detection of epistatic interactions in case-control studies , 2009, BMC Bioinformatics.

[13]  Yuanke Zhang,et al.  EpiMiner: A three-stage co-information based method for detecting and visualizing epistatic interactions , 2014, Digit. Signal Process..

[14]  Kelly R. Robbins,et al.  Ant colony algorithm for analysis of gene interaction in high-dimensional association data , 2009 .

[15]  J. H. Moore,et al.  Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. , 2001, American journal of human genetics.

[16]  R. Jiang,et al.  Detecting Epistatic Interactions in Metagenome-Wide Association Studies by metaBOOST , 2014, BioMed research international.

[17]  Li-Yeh Chuang,et al.  Novel generating protective single nucleotide polymorphism barcode for breast cancer using particle swarm optimization. , 2009, Cancer epidemiology.

[18]  Seth Blackshaw,et al.  Mutations in the inosine monophosphate dehydrogenase 1 gene (IMPDH1) cause the RP10 form of autosomal dominant retinitis pigmentosa. , 2002, Human molecular genetics.

[19]  Qiang Yang,et al.  Predictive rule inference for epistatic interaction detection in genome-wide association studies , 2010, Bioinform..

[20]  Taesung Park,et al.  GxG-Viztool: A program for visualizing gene-gene interactions in genetic association analysis , 2012, 2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops.

[21]  Romdhane Rekaya,et al.  AntEpiSeeker: detecting epistatic interactions for case-control studies using a two-stage ant colony optimization algorithm , 2010, BMC Research Notes.

[22]  Xue-wen Chen,et al.  A Markov blanket-based method for detecting causal SNPs in GWAS , 2010, BMC Bioinformatics.

[23]  P. Chanda,et al.  AMBIENCE: A Novel Approach and Efficient Algorithm for Identifying Informative Genetic and Environmental Associations With Complex Phenotypes , 2008, Genetics.

[24]  Xiang Zhang,et al.  TEAM: efficient two-locus epistasis tests in human genome-wide association study , 2010, Bioinform..

[25]  Yi Pan,et al.  Cloud computing for detecting high-order genome-wide epistatic interaction via dynamic clustering , 2014, BMC Bioinformatics.

[26]  Li-Yeh Chuang,et al.  Determination of the SNP-SNP Interaction between Breast Cancer Related Genes to Analyze the Disease Susceptibility , 2014 .

[27]  Hua Xu,et al.  Genetic studies of complex human diseases: Characterizing SNP-disease associations using Bayesian networks , 2012, BMC Systems Biology.

[28]  Li-Yeh Chuang,et al.  Chaotic particle swarm optimization for detecting SNP–SNP interactions for CXCL12-related genes in breast cancer prevention , 2012, European journal of cancer prevention : the official journal of the European Cancer Prevention Organisation.

[29]  Rui Jiang,et al.  Integrating Multiple Genomic Data to Predict Disease-Causing Nonsynonymous Single Nucleotide Variants in Exome Sequencing Studies , 2014, PLoS genetics.

[30]  B. Maher.  Personal genomes: The case of the missing heritability , 2008, Nature.

[31]  Julie A Simpson,et al.  Can genetic associations change with age? CFH and age-related macular degeneration. , 2012, Human molecular genetics.

[32]  Taesung Park,et al.  IGENT: efficient entropy based algorithm for genome-wide gene-gene interaction analysis , 2014, BMC Medical Genomics.

[33]  Junying Zhang,et al.  EpiSIM: simulation of multiple epistasis, linkage disequilibrium patterns and haplotype blocks for genome-wide interaction analysis , 2013, Genes & Genomics.

[34]  B. Maher,et al.  The case of the missing heritability , 2008 .

[35]  Cheng-Hong Yang,et al.  An improved particle swarm optimization with double-bottom chaotic maps for numerical optimization , 2012, Appl. Math. Comput..

[36]  A Masoudi-Nejad,et al.  Cuckoo search epistasis: a new method for exploring significant genetic interactions , 2014, Heredity.

[37]  Jun S. Liu,et al.  Bayesian inference of epistatic interactions in case-control studies , 2007, Nature Genetics.

[38]  N. Schork,et al.  Who's afraid of epistasis? , 1996, Nature Genetics.

[39]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[40]  Li-Yeh Chuang,et al.  Generating SNP barcode to evaluate SNP-SNP interaction of disease by particle swarm optimization , 2009, Comput. Biol. Chem..

[41]  Yang Dai,et al.  A block-based evolutionary optimization strategy to investigate gene-gene interactions in genetic association studies , 2010, 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW).

[42]  Ching Lee Koo,et al.  A Review for Detecting Gene-Gene Interactions Using Machine Learning Methods in Genetic Epidemiology , 2013, BioMed research international.

[43]  Aidong Zhang,et al.  Information-theoretic gene-gene and gene-environment interaction analysis of quantitative traits , 2009, BMC Genomics.

[44]  Jun Ma,et al.  The Gene-Gene Interaction of INSIG-SCAP-SREBP Pathway on the Risk of Obesity in Chinese Children , 2014, BioMed research international.

[45]  N Risch,et al.  The Future of Genetic Studies of Complex Human Diseases , 1996, Science.

[46]  Gregory F Cooper,et al.  A fast algorithm for learning epistatic genomic relationships. , 2010, AMIA ... Annual Symposium proceedings. AMIA Symposium.

[47]  Li-Yeh Chuang,et al.  SNP-SNP Interaction Using Gauss Chaotic Map Particle Swarm Optimization to Detect Susceptibility to Breast Cancer , 2014, 2014 47th Hawaii International Conference on System Sciences.

[48]  R. Jiang,et al.  Epistatic Module Detection for Case-Control Studies: A Bayesian Model with a Gibbs Sampling Strategy , 2009, PLoS genetics.

[49]  Zhongmeng Zhao,et al.  Identifying Interacting Genetic Variations by Fish-Swarm Logic Regression , 2013, BioMed research international.

[50]  Xue-wen Chen,et al.  FEPI-MB: identifying SNPs-disease association using a Markov Blanket-based approach , 2011, BMC Bioinformatics.

[51]  Xue-wen Chen,et al.  DASSO-MB: Detection of Epistatic Interactions in Genome-Wide Association Studies Using Markov Blankets , 2009, 2009 IEEE International Conference on Bioinformatics and Biomedicine.

[52]  Li-Yeh Chuang,et al.  Particle swarm optimization algorithm for analyzing SNP–SNP interaction of renin-angiotensin system genes against hypertension , 2013, Molecular Biology Reports.

[53]  Li-Yeh Chuang,et al.  An Improved PSO Algorithm for Generating Protective SNP Barcodes in Breast Cancer , 2012, PloS one.

[54]  Dan Liu,et al.  Performance analysis of novel methods for detecting epistasis , 2011, BMC Bioinformatics.

[55]  Xiujuan Lei,et al.  Incorporating heuristic information into ant colony optimization for epistasis detection , 2012, Genes & Genomics.

[56]  L. Cardon,et al.  Association study designs for complex diseases , 2001, Nature Reviews Genetics.

[57]  Jason H. Moore,et al.  Optimal Use of Expert Knowledge in Ant Colony Optimization for the Analysis of Epistasis in Human Disease , 2009, EvoBIO.

[58]  Aidong Zhang,et al.  Information-theoretic metrics for visualizing gene-environment interactions. , 2007, American journal of human genetics.

[59]  Aidong Zhang,et al.  The interaction index, a novel information-theoretic metric for prioritizing interacting genetic variations and environmental factors , 2009, European Journal of Human Genetics.