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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

  • Localización: Hybrid Artificial Intelligent Systems. 14th International Conference, HAIS 2019: León, Spain, September 4–6, 2019. Proceedings / coord. por Hilde Pérez García Árbol académico, Lidia Sánchez González Árbol académico, Manuel Castejón Limas Árbol académico, Héctor Quintián Pardo Árbol académico, Emilio Santiago Corchado Rodríguez Árbol académico, 2019, ISBN 978-3-030-29858-6, págs. 111-122
  • Idioma: inglés
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  • Resumen
    • 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.


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