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

Masegosa, Andrés R.; Armañanzas Arnedillo, Ruben; Abad Grau, María Mar; Potenciano Enciso, Víctor; Moral Callejón, Serafín; Larrañaga Múgica, Pedro María; Bielza Lozoya, María Concepción y 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.

Descripción

Título: Discretization of expression quantitative trait loci in association analysis between genotypes and expression data
Autor/es:
  • 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
Tipo de Documento: Artículo
Título de Revista/Publicación: Current Bioinformatics
Fecha: 2015
Volumen: 10
Materias:
Palabras Clave Informales: eQTL; Gene expression microarray; Machine learning; RNA-seq; SNP
Escuela: E.T.S. de Ingenieros Informáticos (UPM)
Departamento: Inteligencia Artificial
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

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.

Proyectos asociados

TipoCódigoAcrónimoResponsableTítulo
Gobierno de EspañaTIN2010-20900-C04-01Sin especificarUniversidad de GranadaMINERIA DE DATOS CON PGMS: NUEVOS ALGORITMOS Y APLICACIONES. MD-PGMSUGR
Gobierno de EspañaTIN2013-46638-C3-2-PSin especificarUniversidad de GranadaMODELOS GRAFICOS PROBABILISTICOS PARA ANALITICA ESCALABLE DE DATOS

Más información

ID de Registro: 41016
Identificador DC: http://oa.upm.es/41016/
Identificador OAI: oai:oa.upm.es:41016
Identificador DOI: 10.2174/157489361002150518123918
URL Oficial: http://benthamscience.com/journals/current-bioinformatics/volume/10/issue/2/
Depositado por: Memoria Investigacion
Depositado el: 27 Oct 2016 11:18
Ultima Modificación: 27 Oct 2016 11:18
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