Identification and quantification of disease-related gene clusters

Gábor Firneisz, Idit Zehavi, Csaba Vermes, Anita Hanyecz, Joshua A. Frieman, Tibor T. Glant

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

Motivation: DNA microarray technology and the completion of human and mouse genome sequencing programs are now offering new avenues for the investigation of complex genetic diseases. In particular, this makes possible the study of the spatial distribution of disease-related genes within the genome. We report on the first systematic search for clustering of genes associated with a polygenic autoimmune disease. Results: Using a set of cDNA microarray chip experiments in two mouse models of rheumatoid arthritis, we have identified ∼200 genes based on their expression in inflamed joints and mapped them into the genome. We compute the spatial autocorrelation function of the selected genes and find that they tend to cluster over scales of a few megabase pairs. We then identify significant gene clusters using a friends-of-friends algorithm. This approach should aid in discovering functionally related gene clusters in the mammalian genome.

Original languageEnglish
Pages (from-to)1781-1786
Number of pages6
JournalBioinformatics
Volume19
Issue number14
DOIs
Publication statusPublished - Sep 22 2003

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ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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