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