Identification of SNP-SNP interaction for chronic dialysis patients

Analyses of interactions between single nucleotide polymorphisms (SNPs) have reported significant associations between mitochondrial displacement loops (D-loops) and chronic dialysis diseases. However, the method used to detect potential SNP-SNP interaction still requires improvement. This study proposes an effective algorithm named dynamic center particle swarm optimization k-nearest neighbors (DCPSO-KNN) to detect the SNP-SNP interaction. DCPSO-KNN uses dynamic center particle swarm optimization (DCPSO) to generate SNP combinations with a fitness function designed using the KNN method and statistical verification. A total of 77 SNPs in the mitochondrial D-loop were used to detect the SNP-SNP interactions and the search ability was compared against that of other methods. The detected SNP-SNP interactions were statistically evaluated. Experimental results showed that DCPSO-KNN successfully detects SNP-SNP interactions in two-to-seven-order combinations (positive predictive value (PPV)+negative predictive value (NPV)=1.154 to 1.310; odds ratio (OR)=1.859 to 4.015; 95% confidence interval (95% CI)=1.151 to 4.265; p-value <0.001). DCPSO-KNN can improve the detection ability of SNP-SNP associations between mitochondrial D-loops and chronic dialysis diseases, thus facilitating the development of biomedical applications.

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

[2]  Ashfaqur Rahman,et al.  Machine learning approach for pooled DNA sample calibration , 2015, BMC Bioinformatics.

[3]  Li-Yeh Chuang,et al.  Identifying association model for single-nucleotide polymorphisms of ORAI1 gene for breast cancer , 2013, Cancer Cell International.

[4]  Hongzhe Li,et al.  Genetic sharing and heritability of paediatric age of onset autoimmune diseases , 2015, Nature Communications.

[5]  D. Turnbull,et al.  Reanalysis and revision of the Cambridge reference sequence for human mitochondrial DNA , 1999, Nature Genetics.

[6]  James A. Morris,et al.  optiCall: a robust genotype-calling algorithm for rare, low-frequency and common variants , 2012, Bioinform..

[7]  Shiliang Sun,et al.  A survey of multi-view machine learning , 2013, Neural Computing and Applications.

[8]  Li-Yeh Chuang,et al.  MDR-ER: Balancing Functions for Adjusting the Ratio in Risk Classes and Classification Errors for Imbalanced Cases and Controls Using Multifactor-Dimensionality Reduction , 2013, PloS one.

[9]  Li-Yeh Chuang,et al.  Sequence-Based Polymorphisms in the Mitochondrial D-Loop and Potential SNP Predictors for Chronic Dialysis , 2012, PloS one.

[10]  Johanna M Seddon,et al.  Prediction model for prevalence and incidence of advanced age-related macular degeneration based on genetic, demographic, and environmental variables. , 2009, Investigative ophthalmology & visual science.

[11]  Jianguang Wang,et al.  Identification of Differently Expressed Genes with Specific SNP Loci for Breast Cancer by the Integration of SNP and Gene Expression Profiling Analyses , 2015, Pathology & Oncology Research.

[12]  Saurabh Ghosh,et al.  Comprehensive SNP Scan of DNA Repair and DNA Damage Response Genes Reveal Multiple Susceptibility Loci Conferring Risk to Tobacco Associated Leukoplakia and Oral Cancer , 2013, PloS one.

[13]  Yang Yu,et al.  FVGWAS: Fast voxelwise genome wide association analysis of large-scale imaging genetic data , 2015, NeuroImage.

[14]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[15]  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.

[16]  Hong-Zin Lee,et al.  Common Genetic Variants in Wnt Signaling Pathway Genes as Potential Prognostic Biomarkers for Colorectal Cancer , 2013, PloS one.

[17]  Fern,et al.  SNP-SNP Interactions: Focusing on Variable Coding for Complex Models of Epistasis , 2013 .

[18]  L. Chuang,et al.  An efficiency analysis of high-order combinations of gene–gene interactions using multifactor-dimensionality reduction , 2015, BMC Genomics.

[19]  Jason H. Moore,et al.  GAMETES: a fast, direct algorithm for generating pure, strict, epistatic models with random architectures , 2012, BioData Mining.

[20]  Siba K. Udgata,et al.  Particle swarm optimisation with stochastic ranking for constrained numerical and engineering benchmark problems , 2012, Int. J. Bio Inspired Comput..

[21]  Ting Hu,et al.  Functional dyadicity and heterophilicity of gene-gene interactions in statistical epistasis networks , 2015, BioData Mining.

[22]  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.

[23]  Gang Wang,et al.  An efficient diagnosis system for detection of Parkinson's disease using fuzzy k-nearest neighbor approach , 2013, Expert Syst. Appl..

[24]  Li-Yeh Chuang,et al.  Genetic algorithm-generated SNP barcodes of the mitochondrial D-loop for chronic dialysis susceptibility , 2014, Mitochondrial DNA.

[25]  Keinosuke Fukunaga,et al.  A Branch and Bound Algorithm for Computing k-Nearest Neighbors , 1975, IEEE Transactions on Computers.

[26]  Gabor T. Marth,et al.  An integrated map of structural variation in 2,504 human genomes , 2015, Nature.

[27]  Nourhan Zayed,et al.  Discovering Alzheimer Genetic Biomarkers Using Bayesian Networks , 2015, Adv. Bioinformatics.

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

[29]  Li-Yeh Chuang,et al.  The Combinational Polymorphisms of ORAI1 Gene Are Associated with Preventive Models of Breast Cancer in the Taiwanese , 2015, BioMed research international.

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

[31]  William B. Langdon,et al.  Performance of genetic programming optimised Bowtie2 on genome comparison and analytic testing (GCAT) benchmarks , 2015, BioData Mining.

[32]  Juan Wu,et al.  Multiple R&D Projects Scheduling Optimization with Improved Particle Swarm Algorithm , 2014, TheScientificWorldJournal.

[33]  Ammu Prasanna Kumar,et al.  Feature Selection for high Dimensional DNA Microarray data using hybrid approaches , 2013, Bioinformation.

[34]  Kristel Van Steen,et al.  Travelling the world of gene-gene interactions , 2012, Briefings Bioinform..

[35]  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.

[36]  Ikhlas Abdel-Qader,et al.  Embedded Feature Selection using PSO-kNN: Shape-Based Diagnosis of Microcalcification Clusters in Mammography , 2011, J. Ubiquitous Syst. Pervasive Networks.

[37]  B. Liu,et al.  iDNA-Prot|dis: Identifying DNA-Binding Proteins by Incorporating Amino Acid Distance-Pairs and Reduced Alphabet Profile into the General Pseudo Amino Acid Composition , 2014, PloS one.

[38]  Khalid Auhmani,et al.  Gene-expression-based cancer classification through feature selection with KNN and SVM classifiers , 2015, 2015 Intelligent Systems and Computer Vision (ISCV).

[39]  James Kennedy,et al.  Defining a Standard for Particle Swarm Optimization , 2007, 2007 IEEE Swarm Intelligence Symposium.

[40]  Li-Yeh Chuang,et al.  Breast cancer-associated high-order SNP-SNP interaction of CXCL12/CXCR4-related genes by an improved multifactor dimensionality reduction (MDR-ER). , 2016, Oncology reports.

[41]  Cristina Y. González,et al.  Identification of epistatic interactions through genome-wide association studies in sporadic medullary and juvenile papillary thyroid carcinomas , 2015, BMC Medical Genomics.

[42]  Georgios A. Pavlopoulos,et al.  Caipirini: using gene sets to rank literature , 2012, BioData Mining.

[43]  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.

[44]  Li-Yeh Chuang,et al.  Evaluation of Breast Cancer Susceptibility Using Improved Genetic Algorithms to Generate Genotype SNP Barcodes , 2013, IEEE/ACM Transactions on Computational Biology and Bioinformatics.