Sohns Melanie, Rosenberger Albert, Bickeböller Heike
Department of Genetic Epidemiology, University Medical Center Göttingen, Humboldtallee 32, 37073 Göttingen, Germany.
BMC Proc. 2009 Dec 15;3 Suppl 7(Suppl 7):S95. doi: 10.1186/1753-6561-3-S7-S95.
In genome-wide association studies (GWAS) genetic markers are often ranked to select genes for further pursuit. Especially for moderately associated and interrelated genes, information on genes and pathways may improve the selection. We applied and combined two main approaches for data integration to a GWAS for rheumatoid arthritis, gene set enrichment analysis (GSEA) and hierarchical Bayes prioritization (HBP). Many associated genes are located in the HLA region on 6p21. However, the ranking lists of genes and gene sets differ considerably depending on the chosen approach: HBP changes the ranking only slightly and primarily contains HLA genes in the top 100 gene lists. GSEA includes also many non-HLA genes.
在全基因组关联研究（GWAS）中，遗传标记常常被排序以选择基因进行进一步研究。特别是对于中度关联和相互关联的基因，基因和通路的信息可能会改善选择。我们将两种主要的数据整合方法——基因集富集分析（GSEA）和层次贝叶斯优先级排序（HBP）应用于类风湿性关节炎的GWAS并进行了结合。许多相关基因位于6号染色体短臂21区的HLA区域。然而，根据所选方法的不同，基因和基因集的排名列表有很大差异：HBP仅略微改变排名，并且在前100个基因列表中主要包含HLA基因。GSEA还包括许多非HLA基因。
