Nature-Inspired Multiobjective Epistasis Elucidation from Genome-Wide Association Studies

IEEE/ACM Transactions on Computational Biology and Bioinformatics

Volumn 17, Issue 1, 2020, Pages 226-237

  Li, Xiangtao ^a   Zhang, Shixiong ^b   Wong, Ka Chun ^a  

^a NORTHEAST NORMAL UNIVERSITY   (China)

^b CITY UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

bioinformatics; computational biology; epistatic interaction; Genome wide association studies

Indexed keywords

BIOINFORMATICS; BIOMIMETICS; DISEASES; GENES; GENETIC PROGRAMMING; LINEAR PROGRAMMING; OPTIMIZATION;

COMPUTATIONAL BIOLOGY; COMPUTATIONAL MODEL; EPISTATIC INTERACTIONS; GENOME-WIDE ASSOCIATION STUDIES; GENOMICS;

ASSOCIATION REACTIONS;

ALGORITHM; ARTIFICIAL INTELLIGENCE; BIOLOGICAL MODEL; BIOLOGY; EPISTASIS; GENETICS; GENOME-WIDE ASSOCIATION STUDY; PROCEDURES;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTATIONAL BIOLOGY; EPISTASIS, GENETIC; GENOME-WIDE ASSOCIATION STUDY; MODELS, GENETIC;

EID: 85049106339     PISSN: 15455963     EISSN: 15579964     Source Type: Journal    
DOI: 10.1109/TCBB.2018.2849759     Document Type: Article

References (44)

1. J. H. Moore, F. W. Asselbergs, and S. M. Williams, "Bioinformatics challenges for genome-wide association studies, " Bioinf., vol. 26, no. 4, pp. 445-455, 2010.
2. A. L. Price, N. J. Patterson, R. M. Plenge, M. E. Weinblatt, N. A. Shadick, and D. Reich, "Principal components analysis corrects for stratification in genome-wide association studies, " Nature Genetics, vol. 38, no. 8, 904-909, 2006.
3. G. A. Churchill, et al., "The Collaborative Cross, a community resource for the genetic analysis of complex traits, " Nature Genetics, vol. 36, pp. 1133-1137, 2004.
4. P. R. Burton, et al., "Genome-wide association study of 14, 000 cases of seven common diseases and 3, 000 shared controls, " Nature, vol. 447, pp. 661-678, 2007.
5. R. Jiang, W. Tang, X. Wu, and W. Fu, "A random forest approach to the detection of epistatic interactions in case-control studies, " BMC Bioinf., vol. 10, no. 1, 2009, Art. no. S65.
6. J. Shang, J. Zhang, Y. Sun, D. Liu, D. Ye, and Y. Yin, "Performance analysis of novel methods for detecting epistasis, " BMC Bioinf., vol. 12, no. 1, 2011, Art. no. 475.
7. R. Upstill-Goddard, D. Eccles, J. Fliege, and A. Collins, "Machine learning approaches for the discovery of gene-gene interactions in disease data, " Briefings Bioinf., vol. 14, no. 2, pp. 251-260, 2012.
8. X. Jiang, R. E. Neapolitan, M. M. Barmada, and S. Visweswaran, "Learning genetic epistasis using Bayesian network scoring criteria, " BMC Bioinf., vol. 12, no. 1, 2011, Art. no. 89.
9. B. Han and X. W. Chen, "bNEAT: A Bayesian network method for detecting epistatic interactions in genome-wide association studies, " BMC Genomics, vol. 12, no. 2, 2011, Art. no. S9.
10. X. Wan, C. Yang, Q. Yang, H. Xue, N. L. Tang, and W. Yu, "Predictive rule inference for epistatic interaction detection in genome-wide association studies, " Bioinf., vol. 26, no. 1, pp. 30-37, 2009.
11. P. J. Jing and H.-B. Shen, "MACOED: A multi-objective ant colony optimization algorithm for SNP epistasis detection in genomewide association studies, " Bioinf., vol. 31, no. 5, pp. 634-641, 2014.
12. S. Tuo, J. Zhang, X. Yuan, Y. Zhang, and Z. Liu, "FHSA-SED: Twolocus model detection for genome-wide association study with harmony search algorithm, " PloS One, vol. 11, no. 3, 2016, Art. no. e0150669.
13. J. Shang, J. Zhang, X. Lei, Y. Zhang, and B. Chen, "Incorporating heuristic information into ant colony optimization for epistasis detection, " Genes Genomics, vol. 34, no. 3, pp. 321-327, 2012.
14. E. Sapin, E. Keedwell, and T. Frayling, "Ant colony optimisation of decision tree and contingency table models for the discovery of gene-gene interactions, " IET Syst. Biol., vol. 9, no. 6, pp. 218-225, 2015.
15. X. Li, "A fast and exhaustive method for heterogeneity and epistasis analysis based on multi-objective optimization, " Bioinf., vol. 33, no. 18, pp. 2829-2836, 2017.
16. X. Wang, Q. Peng, and Y. Fan, "Detecting susceptibility to breast cancer with SNP-SNP interaction using BPSOHS and emotional neural networks, " BioMed Res. Int., vol. 2016, 2016, Art. no. 5164347.
17. Y. Wang, X. Liu, K. Robbins, and R. Rekaya, "AntEpiSeeker: Detecting epistatic interactions for case-control studies using a two-stage ant colony optimization algorithm, " BMC Res. Notes, vol. 3, no. 1, 2010, Art. no. 117.
18. Y. Sun, J. Shang, J. X. Liu, S. Li, and C. H. Zheng, "epiACO-A method for identifying epistasis based on ant Colony optimization algorithm, " BioData Mining, vol. 10, no. 1, 2017, Art. no. 23.
19. M. Aflakparast, H. Salimi, A. Gerami, M. P. Dubé, V S. isweswaran, and A. Masoudi-Nejad, "Cuckoo search epistasis: A new method for exploring significant genetic interactions, " Heredity, vol. 112, no. 6, 2014, Art. no. 666.
20. W. Zhang, J. Shang, H. Li, Y. Sun, and J. X. Liu, "SIPSO: Selectively informed particle swarm optimization based on mutual information to determine SNP-SNP interactions, " in Proc. Int. Conf. Intell. Comput., 2016, pp. 112-121.
21. Y. Sun, J. Shang, J. Liu, and S. Li, "An improved ant colony optimization algorithm for the detection of SNP-SNP interactions, " in Proc. Int. Conf. Intell. Comput., 2016, pp. 21-32.
22. D. Karaboga and B. Basturk, "A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm, " J. Global Optimization, vol. 39, no. 3, pp. 459-471, 2007.
23. R. S. Rao, S. V. L. Narasimham, and M. Ramalingaraju, "Optimization of distribution network configuration for loss reduction using artificial bee colony algorithm, " Int. J. Electr. Power Energy Syst. Eng., vol. 1, no. 2, pp. 116-122, 2008.
24. D. Karaboga and B. Basturk, "On the performance of artificial bee colony (ABC) algorithm, " Appl. Soft Comput., vol. 8, no. 1, pp. 687-697, 2008.
25. Q. Zhang and H. Li, "MOEA/D: A multiobjective evolutionary algorithm based on decomposition, " IEEE Trans. Evol. Comput., vol. 11, no. 6, pp. 712-731, Dec. 2007.
26. H. Li and Q. Zhang, "Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II, " IEEE Trans. Evol. Comput., vol. 13, no. 2, pp. 284-302, Apr. 2009.
27. K. Deb, "Multi-objective optimization, " in Search Methodologies, Berlin, Germany: Springer, 2014, pp. 403-449.
28. C. C. Coello, "Evolutionary multi-objective optimization: A historical view of the field, " IEEE Comput. Intell. Mag., vol. 1, no. 1, pp. 28-36, Feb. 2006.
29. G. R. Zavala, A. J. Nebro, F. Luna, and C. A. C. Coello, "A survey of multi-objective metaheuristics applied to structural optimization, " Struct. Multidisciplinary Optimization, vol. 49, no. 4, pp. 537-558, 2014.
30. B. Han, X. W. Chen, Z. Talebizadeh, and H. Xu, "Genetic studies of complex human diseases: Characterizing SNP-disease associations using Bayesian networks, " BMC Syst. Biol., vol. 6, no. 3, 2012, Art. no. S14.
31. L. E. Raileanu and K. Stoffel, "Theoretical comparison between the gini index and information gain criteria, " Ann. Math. Artif. Intell., vol. 41, no. 1, pp. 77-93, 2004.
32. H. Peng, F. Long, and C. Ding, "Feature selection based on mutual information criteria of max-dependency, max-relevance, and minredundancy, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 8, pp. 1226-1238, Aug. 2005.
33. S. Wang, H. H. Jeong, D. Kim, et al., "Integrative information theoretic network analysis for genome-wide association study of aspirin exacerbated respiratory disease in Korean population, " BMC Med. Genomics, vol. 10, no. 1, 2017, Art. no. 31.
34. J. Namkung, K. Kim, S. Yi, W. Chung, M. S. Kwon, and T. Park, "New evaluation measures for multifactor dimensionality reduction classifiers in gene-gene interaction analysis, " Bioinf., vol. 25, no. 3, pp. 338-345, 2009.
35. W. S. Bush, S. M. Dudek, and M. D. Ritchie, "Parallel multifactor dimensionality reduction: A tool for the large-scale analysis of gene-gene interactions, " Bioinf., vol. 22, no. 17, pp. 2173-2174, 2006.
36. S. Jiang and S. Yang, "A steady-state and generational evolutionary algorithm for dynamic multiobjective optimization, " IEEE Trans. Evol. Comput., vol. 21, no. 1, pp. 65-82, Feb. 2017.
37. K. C. Tan, E. F. Khor, and T. H. Lee, Multiobjective Evolutionary Algorithms and Applications. Berlin, Germany: Springer, 2016.
38. X. Li, S. Ma, and K. C. Wong, "Evolving spatial clusters of genomic regions from high-throughput chromatin conformation capture data, " IEEE Trans. Nanobiosci., vol. 16, no. 6, pp. 400-407, Sep. 2017.
39. R. J. Urbanowicz, J. Kiralis, N. A. Sinnott-Armstrong, T. Heberling, J. M. Fisher, and J. H. Moore, "GAMETES: A fast, direct algorithm for generating pure, strict, epistatic models with random architectures, " BioData Mining, vol. 5, no. 1, 2012, Art. no. 16.
40. S. Tuo, J. Zhang, X. Yuan, Z. He, Y. Liu, and Z. Liu, "Niche harmony search algorithm for detecting complex disease associated high-order SNP combinations, " Sci. Rep., vol. 7, no. 1, 2017, Art. no. 11529.
41. P. D. Pharoah, J. Tyrer, A. M. Dunning, D. F. Easton, B. A. Ponder, et al., "Association between common variation in 120 candidate genes and breast cancer risk, " PLoS Genetics, vol. 3, no. 3, 2007, Art. no. e42.
42. C. H. Yang, Y. D. Lin, L. Y. Chaung, and H. W. Chang, "Evaluation of breast cancer susceptibility using improved genetic algorithms to generate genotype SNP barcodes, " IEEE/ACM Trans. Comput. Biol. Bioinf., vol. 10, no. 2, pp. 361-371, Mar. /Apr. 2013.
43. M. J. Shrubsole, W. Lu, Z. Chen, X. O. Shu, Y. Zheng, Q. Dai, Q. Cai, et al., "Drinking green tea modestly reduces breast cancer risk, " J. Nutrition, vol. 139, no. 2, pp. 310-316, 2008.
44. A. M. Dunning, K. Michailidou, K. B. Kuchenbaecker, D. Thompson, J. D. French, et al., "Breast cancer risk variants at 6q25 display different phenotype associations and regulate ESR1, RMND1 and CCDC170, " Nature Genetics, vol. 47, no. 3, 2015, Art. no. 374.