Identification and quantification of disease-related gene clusters.
BACKGROUND
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 approximately 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.
D000465:Algorithms; D000818:Animals; D001172:Arthritis, Rheumatoid / Q000235:genetics*; D001483:Base Sequence; D016000:Cluster Analysis; D020869:Gene Expression Profiling / Q000379:methods; D005786:Gene Expression Regulation / Q000235:genetics*; D020022:Genetic Predisposition to Disease / Q000235:genetics; D005820:Genetic Testing / Q000379:methods*; D051379:Mice; D008969:Molecular Sequence Data; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated; D016415:Sequence Alignment / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*