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eQTL analysis in mice and rats.

Bruno M Tesson1, Ritsert C Jansen

  • 1Groningen Bioinformatics Center, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Haren, The Netherlands.

Methods in Molecular Biology (Clifton, N.J.)
|September 19, 2009
PubMed
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Genetical genomics integrates genotype and gene expression data to map expression quantitative trait loci (eQTLs). This guide details genome-wide linkage analysis for eQTL mapping using R statistical software.

Area of Science:

  • Genetics
  • Bioinformatics
  • Systems Biology

Background:

  • Genetical genomics has emerged as a powerful strategy since 2001.
  • It leverages microarray technology to combine genotype and gene expression data.
  • This approach has been highly successful in inbred line crosses.

Purpose of the Study:

  • To provide a comprehensive guide for genome-wide linkage analysis in expression quantitative trait loci (eQTL) mapping.
  • To demonstrate the application of the R statistical software framework for eQTL analysis.

Main Methods:

  • Utilizing microarray profiling technologies for gene expression data.
  • Performing genome-wide linkage analysis to identify genetic determinants of gene expression variation.
  • Employing the R statistical software framework for data analysis.

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Main Results:

  • Identification of numerous genomic loci regulating gene expression (eQTLs).
  • Distinction between local and distant eQTLs.
  • Successful application of R for eQTL mapping.

Conclusions:

  • Genetical genomics provides a robust framework for dissecting the genetic basis of gene expression.
  • Genome-wide linkage analysis in R is an effective method for eQTL mapping.
  • This chapter serves as a practical resource for researchers in the field.