Evolutionary Algorithm for Pathways Detection in GWAS Studies

  1. Fidel Díez Díaz 1
  2. Fernando Sánchez Lasheras 2
  3. Cos Juez, Francisco Javier de 2
  4. Vicente Martín Sánchez 34
  1. 1 CTIC Centro Tecnológico
    info

    CTIC Centro Tecnológico

    Gijón, España

  2. 2 Universidad de Oviedo
    info

    Universidad de Oviedo

    Oviedo, España

    ROR https://ror.org/006gksa02

  3. 3 Instituto de Salud Carlos III
    info

    Instituto de Salud Carlos III

    Madrid, España

    ROR https://ror.org/00ca2c886

  4. 4 Universidad de León
    info

    Universidad de León

    León, España

    ROR https://ror.org/02tzt0b78

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Libro:
Hybrid Artificial Intelligent Systems. 14th International Conference, HAIS 2019: León, Spain, September 4–6, 2019. Proceedings
  1. Hilde Pérez García (coord.)
  2. Lidia Sánchez González (coord.)
  3. Manuel Castejón Limas (coord.)
  4. Héctor Quintián Pardo (coord.)
  5. Emilio Corchado Rodríguez (coord.)

Editorial: Springer Suiza

ISBN: 978-3-030-29859-3,  978-3-030-29858-6

Año de publicación: 2019

Páginas: 111-122

Congreso: Hybrid Artificial Intelligent Systems (14. 2019. León)

Tipo: Aportación congreso

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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.

Fuente de los datos: Dialnet