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GASVeM: A New Machine Learning Methodology for Multi-SNP Analysis of GWAS Data Based on Genetic Algorithms and Support Vector Machines

Author

Listed:
  • Fidel Díez Díaz

    (CTIC Technological Centre, W3C Spain Office Host, Ada Byron 39, 33203 Gijón, Spain)

  • Fernando Sánchez Lasheras

    (Department of Mathematics, Faculty of Sciences, Universidad de Oviedo, 33007 Oviedo, Spain)

  • Víctor Moreno

    (Oncology Data Analytics Program, Catalan Institute of Oncology (ICO), Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, 08908 Barcelona, Spain)

  • Ferran Moratalla-Navarro

    (Oncology Data Analytics Program, Catalan Institute of Oncology (ICO), Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, 08908 Barcelona, Spain)

  • Antonio José Molina de la Torre

    (IBIOMED, University of Leon, Vegazana Campus, 24400 León, Spain)

  • Vicente Martín Sánchez

    (CIBERESP, University of Leon, Vegazana Campus, 24400 León, Spain)

Abstract

Genome-wide association studies (GWAS) are observational studies of a large set of genetic variants in an individual’s sample in order to find if any of these variants are linked to a particular trait. In the last two decades, GWAS have contributed to several new discoveries in the field of genetics. This research presents a novel methodology to which GWAS can be applied to. It is mainly based on two machine learning methodologies, genetic algorithms and support vector machines. The database employed for the study consisted of information about 370,750 single-nucleotide polymorphisms belonging to 1076 cases of colorectal cancer and 973 controls. Ten pathways with different degrees of relationship with the trait under study were tested. The results obtained showed how the proposed methodology is able to detect relevant pathways for a certain trait: in this case, colorectal cancer.

Suggested Citation

  • Fidel Díez Díaz & Fernando Sánchez Lasheras & Víctor Moreno & Ferran Moratalla-Navarro & Antonio José Molina de la Torre & Vicente Martín Sánchez, 2021. "GASVeM: A New Machine Learning Methodology for Multi-SNP Analysis of GWAS Data Based on Genetic Algorithms and Support Vector Machines," Mathematics, MDPI, vol. 9(6), pages 1-19, March.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:6:p:654-:d:519797
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    References listed on IDEAS

    as
    1. J. Vilán Vilán & J. Alonso Fernández & P. García Nieto & F. Sánchez Lasheras & F. de Cos Juez & C. Díaz Muñiz, 2013. "Support Vector Machines and Multilayer Perceptron Networks Used to Evaluate the Cyanotoxins Presence from Experimental Cyanobacteria Concentrations in the Trasona Reservoir (Northern Spain)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(9), pages 3457-3476, July.
    Full references (including those not matched with items on IDEAS)

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