Gene expression changes associated with aging and disease were characterized by metagenes combining sets of genes with significant association with a disease trait and a very strong Pearson correlation with each other. We utilized a procedure of exploring covariance structure of the gene expression data similar to metagene identification [22], factor analysis of gene expression [23], and supervised gene module discovery [21], [24], [25]. Instead of genome-wide search for metagenes followed by analysis of associations between metagenes and disease traits, we used a supervised approach. After selecting genes significantly associated with the disease, we agglomeratively clustered them using Pearson correlation as a distance measure. Especially tight and large clusters in the dendrogram were then assigned to metagenes, i.e., the dendrogram was cut so that several hundred genes in a branch qualified for a metagene and the average of their correlations to the mean (coherence) was not weaker than 0.75. We recognized that some metagenes could have two anti-correlated arms representing opposite trends in the gene expression (e.g., genes that are up- and downregulated with the end point).
