A New Algorithm for Multivariate Genome Wide Association Studies Based on Differential Evolution and Extreme Learning Machines

Genome-wide association studies (GWAS) are observational studies of a large set of genetic variants, whose aim is to find those that are linked to a certain trait or illness. Due to the multivariate nature of these kinds of studies, machine learning methodologies have been already applied in them, showing good performance. This work presents a new methodology for GWAS that makes use of extreme learning machines and differential evolution. The proposed methodology was tested with the help of the genetic information (370,750 single-nucleotide polymorphisms) of 2049 individuals, 1076 of whom suffer from colorectal cancer. The possible relationship of 10 different pathways with this illness was tested. The results achieved showed that the proposed methodology is suitable for detecting relevant pathways for the trait under analysis with a lower computational cost than other machine learning methodologies previously proposed.

Automated summary

Genome-wide association studies (GWAS) aim to identify genetic variants linked to certain traits or diseases. Machine learning methodologies have shown good performance in these studies. This work introduces a new GWAS methodology using extreme learning machines and differential evolution. The proposed method was tested on individuals with colorectal cancer and successfully detected relevant pathways with lower computational cost than previously proposed machine learning methods.