Evolutionary Algorithm for Pathways Detection in GWAS Studies

• Fidel Díez Díaz ^[1] ; Fernando Sánchez Lasheras ^[2] ; Cos Juez, Francisco Javier de ^[2] ; Vicente Martín Sánchez ^{[3] [4]}
  1. [1] CTIC Centro Tecnológico

     CTIC Centro Tecnológico

     Gijón, España

     
  2. [2] Universidad de Oviedo

     Universidad de Oviedo

     Oviedo, España

     
  3. [3] Instituto de Salud Carlos III

     Instituto de Salud Carlos III

     Madrid, España

     
  4. [4] Universidad de León

     Universidad de León

     León, España

     

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• Localización: Hybrid Artificial Intelligent Systems. 14th International Conference, HAIS 2019: León, Spain, September 4–6, 2019. Proceedings / coord. por Hilde Pérez García, Lidia Sánchez González, Manuel Castejón Limas, Héctor Quintián Pardo, Emilio Santiago Corchado Rodríguez, 2019, ISBN 978-3-030-29858-6, págs. 111-122
• Idioma: inglés
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  • In genetics, a genome-wide association study (GWAs) involves an analysis of the single-nucleotide polymorphisms (SNPs) that constitute the genome. This analysis is performed on a large set of individuals usually classified as cases and controls. The study of differences in the SNP chains of both groups is known as pathway analysis. The analysis alluded to allows the researcher to go beyond univariate results like those offered by the p-value analysis and its representation by Manhattan plots. Pathway analysis makes it possible to detect weaker single-variant signals and is also helpful in order to understand molecular mechanisms linked to certain diseases and phenotypes. The present research proposes a new algorithm based on evolutionary computation, capable of finding significant pathways in GWA studies. Its performance has been tested with the help of synthetic data sets created with an ad hoc developed genomic data simulator.