If genetic information is summarized at the gene level, then each gene in the genome can be assigned a score for each experiment or data set available. This measurement can be quantitative or qualitative. For example, p-values may be assigned to a gene within a quantitative trait locus (QTL) or a linkage region. However, differences in gene-specific p-values within an interval of interest may be misleading since linkage peaks can shift, and variants responsible for the linkage may not be at the peak itself. In contrast, large numbers of genes may show altered expression in microarray studies and represent real changes due to signal cascades affecting entire gene networks. These correlated expression networks, in which a large number of changes are expected, contrast with linkage regions, in which most if not all genes do not actually contain variant(s) linked to the disease. A combined p-value method can be used for quantitative analyses, but this approach may present its own challenges. The individual data sources may not be weighted equally since the relative magnitudes of the p-values can be vastly different
