Discretization of expression quantitative trait loci in association analysis between genotypes and expression data

Masegosa, Andrés R. and Armañanzas Arnedillo, Ruben and Abad Grau, María Mar and Potenciano Enciso, Víctor and Moral Callejón, Serafín and Larrañaga Múgica, Pedro María and Bielza Lozoya, María Concepción and Matesán del Barrio, Fuencisla (2015). Discretization of expression quantitative trait loci in association analysis between genotypes and expression data. "Current Bioinformatics", v. 10 (n. 2); pp. 144-164. ISSN 1574-8936. https://doi.org/10.2174/157489361002150518123918.

Description

Title: Discretization of expression quantitative trait loci in association analysis between genotypes and expression data
Author/s:
  • Masegosa, Andrés R.
  • Armañanzas Arnedillo, Ruben
  • Abad Grau, María Mar
  • Potenciano Enciso, Víctor
  • Moral Callejón, Serafín
  • Larrañaga Múgica, Pedro María
  • Bielza Lozoya, María Concepción
  • Matesán del Barrio, Fuencisla
Item Type: Article
Título de Revista/Publicación: Current Bioinformatics
Date: 2015
ISSN: 1574-8936
Volume: 10
Subjects:
Freetext Keywords: eQTL; Gene expression microarray; Machine learning; RNA-seq; SNP
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Expression quantitative trait loci are used as a tool to identify genetic causes of natural variation in gene expression. Only in a few cases the expression of a gene is controlled by a variant on a single genetic marker. There is a plethora of different complexity levels of interaction effects within markers, within genes and between marker and genes. This complexity challenges biostatisticians and bioinformatitians every day and makes findings difficult to appear. As a way to simplify analysis and better control confounders, we tried a new approach for association analysis between genotypes and expression data. We pursued to understand whether discretization of expression data can be useful in genome-transcriptome association analyses. By discretizing the dependent variable, algorithms for learning classifiers from data as well as performing block selection were used to help understanding the relationship between the expression of a gene and genetic markers. We present the results of using this approach to detect new possible causes of expression variation of DRB5, a gene playing an important role within the immune system. Together with expression of gene DRB5 obtained from the classical microarray technology, we have also measured DRB5 expression by using the more recent next-generation sequencing technology. A supplementary website including a link to the software with the method implemented can be found at http: //bios.ugr.es/DRB5.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainTIN2010-20900-C04-01UnspecifiedUniversidad de GranadaMINERIA DE DATOS CON PGMS: NUEVOS ALGORITMOS Y APLICACIONES. MD-PGMSUGR
Government of SpainTIN2013-46638-C3-2-PUnspecifiedUniversidad de GranadaMODELOS GRAFICOS PROBABILISTICOS PARA ANALITICA ESCALABLE DE DATOS

More information

Item ID: 41016
DC Identifier: http://oa.upm.es/41016/
OAI Identifier: oai:oa.upm.es:41016
DOI: 10.2174/157489361002150518123918
Official URL: http://www.eurekaselect.com/131397/article
Deposited by: Memoria Investigacion
Deposited on: 27 Oct 2016 11:18
Last Modified: 06 Jun 2019 13:23
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