Abstract
We have developed a method for meta-analysis of genome scans which allows systematic integration of data from published results. The Genome Search Meta-analysis method (GSMA) uses a non-parametric ranking method to identify genetic regions that show consistently increased sharing statistics or lod scores. The GSMA ranks genetic regions according to the lod score or p-value achieved in each scan. The summed rank across studies is compared to its probability distribution assuming ranks are randomly assigned. The GSMA can confirm evidence for regions highlighted in the original genome scans, and identify novel regions, which did not reach significance in any scan. In this paper, the GSMA was applied to four genome screens in multiple sclerosis and across 11 screens from autoimmune disorders. The GSMA is appropriate for studies with different family ascertainment, markers, and statistical analysis methods. The method increases the power to detect individual linkages in a clinically homogeneous dataset and has the potential to detect susceptibility loci in clinically distinct diseases which show involvement of common pathogenetic pathways.
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