Abstract
Many studies have shown that there is a direct relationship between Single Nucleotide Polymorphisms (SNPs) and the appearance of complex diseases, such as Alzheimer’s or Parkinson’s. However, recent advances in the Study of the Complete Genome Association indicate that the relationship between SNPs and these diseases goes beyond a simple one-to-one relationship, that is, the appearance of multiple SNPs (epistasis) influences the appearance of these diseases. In this sense, this work proposes the application of the NSGA-II multi-objective algorithm for the detection of epistasis of multiple loci in a database with 31,341 SNPs. Moreover, a parallel study has been performed to reduce the execution time of this problem. Our implementation not only achieves a reasonable good parallel performance and scalability, but also its biological significance overcomes other approaches published in the literature.
This work was partially funded by the AEI (State Research Agency, Spain) and the ERDF (European Regional Development Fund, EU), under the contract TIN2016-76259-P (PROTEIN project). Thanks also to the Junta de Extremadura and ERDF for the GR15011 grant provided to the group TIC015. Álvaro Rubio-Largo and Sergio Santander-Jiménez are supported by the Post-Doctoral Fellowships SFRH/BPD/100872/2014 and SFRH/BPD/119220/2016 respectively, granted by the FCT (Fundação para a Ciência e a Tecnologia), Portugal.
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Notes
- 1.
A variation in a single nucleotide that occurs at a specific position in the genome.
- 2.
A locus (plural loci) is the position on a chromosome.
- 3.
Expression of the genetic information that owns a particular organism, or genotype.
References
Auger, A., Bader, J., Brockhoff, D., Zitzler, E.: Theory of the hypervolume indicator: optimal \(\mu \)-distributions and the choice of the reference point. In: Proceedings of the Tenth ACM SIGEVO Workshop on Foundations of Genetic Algorithms, pp. 87–102. ACM (2009)
Cattaert, T., Calle, M., Dudek, S., Hohn, J., Lishout, F., Urrea, V., Ritchie, M., Steel, K.: Model-based multifactor dimensionality reduction for detecting epistasis in case-control data in the presence of noise. Ann. Hum. Genet. 1(75), 78–89 (2011)
Chapman, B., Jost, G., van der Pas, R.: Using OpenMP: Portable Shared Memory Parallel Programming. Scientific and Engineering Computation. The MIT Press, Cambridge (2007)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Tran. Evol. Comput. 6(2), 182–197 (2002)
González-Álvarez, D.L., Vega-Rodríguez, M.A., Rubio-Largo, A.: Finding patterns in protein sequences by using a hybrid multiobjective teaching learning based optimization algorithm. IEEE/ACM Trans. Comput. Biol. Bioinform. 12(3), 656–666 (2015)
González-Domínguez, J., Schmidt, B.: GPU-accelerated exhaustive search for third-order epistatic interactions in case-control studies. J. Comput. Sci. 8, 93–100 (2015)
Goudey, B., Abedini, M., Hopper, J., Inouye, M., Makalic, E., Schmidt, D., Wagner, J., Zhou, Z., Zobel, J., Reumann, M.: High performance computing enabling exhaustive analysis of higher order single nucleotide polymorphism interaction in Genome Wide Association Studies. Health Inf. Sci. Syst. 3(Suppl. 1), S3 (2015)
Han, B., Chen, X., Talebizadeh, Z., Xu, H.: Genetic studies of complex human diseases: characterizing SNP-disease associations using Bayesian networks. BMC Syst. Biol. 6(Suppl. 3), S14 (2012)
Jiang, X., Neapolitan, R.E., Barmada, M.M., Visweswaran, S.: Learning genetic epistasis using bayesian network scoring criteria. BMC Bioinform. 12(1), 89 (2011)
Jing, P., Shen, H.: MACOED: a multi-objective ant colony optimization algorithm for SNP epistasis detection in genome-wide association studies. Bioinformatics 31(5), 634–641 (2015)
Moore, J.H., Asselbergs, F.W., Williams, S.M.: Bioinformatics challenges for genome-wide aassociation studies. Bioinformatics 26(4), 445–455 (2010)
Ritchie, M.D., Hahn, L.W., Roodi, N., Bailey, L.R., Dupont, W.D., Parl, F.F., Moore, J.H.: Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am. J. Hum. Genet. 69, 138–147 (2001)
Rogus, J.J., Poznik, G.D., Pezzolesi, M.G., Smiles, A.M., Dunn, J., Walker, W., Wanic, K., Moczulski, D., Canani, L., Araki, S., Makita, Y., Warram, J.H., Krolewski, A.S.: High-density single nucleotide polymorphism genome-wide linkage scan for susceptibility genes for diabetic nephropathy in type 1 diabetes. Diabetes 57(9), 2519–2526 (2008)
Rubio-Largo, A., Vega-Rodríguez, M.A.: Applying MOEAs to solve the static routing and wavelength assignment problem in optical WDM networks. Eng. Appl. Artif. Intell. 26(5–6), 1602–1619 (2013)
Rubio-Largo, A., Vega-Rodríguez, M.A., González-Álvarez, D.L.: Hybrid multiobjective artificial bee colony for multiple sequence alignment. Appl. Soft Comput. 41, 157–168 (2016)
Rubio-Largo, A., Vega-Rodríguez, M.A., Gonzlez-Álvarez, D.L.: A hybrid multiobjective memetic metaheuristic for multiple sequence alignment. IEEE Trans. Evol. Comput. 20(4), 499–514 (2016)
Tuo, S., Zhang, J., Yuan, X., Zhang, Y., Liu, Z.: FHSA-SED: two-locus model detection for genome-wide association study with harmony search algorithm. PLOS ONE 11(3), 1–27 (2016)
Urbanowicz, R.J., Kiralis, J., Sinnott-Armstrong, N.A., Heberling, T., Fisher, J.M., Moore, J.H.: GAMETES: a fast, direct algorithm for generating pure, strict, epistatic models with random rrchitectures. BioData Min. 5(1), 16 (2012)
Wan, X., Yang, C., Yang, Q., Xue, H., Fan, X., Tang, N.L.S., Yu, W.: BOOST: a fast approach to detecting gene-gene interactions in genome-wide case-control studies. Am. J. Hum. Genet. 87(3), 325–340 (2010)
Wanga, Y., Tanga, B., Yanga, Y., Cuia, Y., Kanga, J., Liua, Z., Lia, K., Suna, Q., Xua, Q., Yana, X., Guo, J.: Relationship between Alzheimer’s disease GWAS-linked top hits and risk of Parkinson’s disease with or without cognitive decline: a Chinese population-based study. Neurobiol. Aging 39, 217.e9–217.e11 (2016)
Wu, T.T., Chen, Y.F., Hastie, T., Sobel, E., Lange, K.: Genome-wide association analysis by lasso penalized logistic regression. Bioinformatics 25(6), 714–721 (2009)
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Gallego-Sánchez, D., Granado-Criado, J.M., Santander-Jiménez, S., Rubio-Largo, Á., Vega-Rodríguez, M.A. (2017). Parallel Multi-objective Optimization for High-Order Epistasis Detection. In: Ibrahim, S., Choo, KK., Yan, Z., Pedrycz, W. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2017. Lecture Notes in Computer Science(), vol 10393. Springer, Cham. https://doi.org/10.1007/978-3-319-65482-9_38
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