Scientists studying MS are making greater use of data-intensive techniques such as genome-wide screens that use thousands of markers and microarray studies that analyze the expression of thousands of genes. While the results of one study can provide interesting information, combining the results of multiple similar studies in a meta-analysis can be even more valuable, providing greater statistical power to find associations and potentially helping to distinguish true positives from false ones.

MS researchers at UCSF recently took this approach a step further in an analysis that combined the results of several gene expression studies with the results of several genome-wide screens. A total of 55 studies were included in the analysis, which revealed new information about how genes that are differentially expressed in MS are clustered in the genome. The study also found areas in the genome where these expression-based gene clusters overlap regions thought to contain MS susceptibility genes. These areas of overlap may be good places to examine more closely in future MS genetic studies.Entrez PubMed