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

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...

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Bibliographic Details
Published in:Mathematics (Basel) 2021-03, Vol.9 (6), p.654
Main Authors: Díez Díaz, Fidel, Sánchez Lasheras, Fernando, Moreno, Víctor, Moratalla-Navarro, Ferran, Molina de la Torre, Antonio José, Martín Sánchez, Vicente
Format: Article
Language:English
Subjects:
Cancer
Classification
Deoxyribonucleic acid
DNA
Genes
Genetic algorithms
genome-wide association studies
Genomes
Machine learning
Mathematics
Methodology
Mutation
Nucleotides
Optimization
pathways analysis
Population
Quantitative genetics
single nucleotide polymorphism
Support vector machines
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Summary: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.
ISSN:2227-7390
2227-7390
DOI:10.3390/math9060654