Meta-analysis of genome searches. Export

@article{citeulike:1391758, 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.}, address = {Division of Medical and Molecular Genetics, Guy's, King's and St. Thomas' School of Medicine, Guy's Hospital, London, UK.}, author = {Wise, L. H. and Lanchbury, J. S. and Lewis, C. M.}, citeulike-article-id = {1391758}, citeulike-linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/10738538}, citeulike-linkout-1 = {http://www.hubmed.org/display.cgi?uids=10738538}, issn = {0003-4800}, journal = {Annals of human genetics}, keywords = {gwas, integration, meta-analysis}, month = {May}, number = {Pt 3}, pages = {263--272}, posted-at = {2009-10-26 17:41:52}, priority = {1}, title = {Meta-analysis of genome searches.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/10738538}, volume = {63}, year = {1999} }

TY - JOUR ID - citeulike:1391758 L3 - citeulike-article-id:1391758 N2 - 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. IS - Pt 3 JF - Annals of human genetics SN - 0003-4800 AD - Division of Medical and Molecular Genetics, Guy's, King's and St. Thomas' School of Medicine, Guy's Hospital, London, UK. EP - 272 TI - Meta-analysis of genome searches. VL - 63 SP - 263 KW - gwas KW - integration KW - meta-analysis AU - Wise, LH AU - Lanchbury, JS AU - Lewis, CM PY - 1999/05// UR - http://view.ncbi.nlm.nih.gov/pubmed/10738538 ER -