Integration of a priori gene set information into genome-wide association studies.
Integration of a priori gene set information into genome-wide association studies.
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DOI：
10.1186/1753-6561-3-s7-s95
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发表时间：
2009-12-15
期刊：
影响因子：
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通讯作者：
Bickeböller H
中科院分区：
文献类型：
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作者：
Sohns M;Rosenberger A;Bickeböller H
在全基因组关联研究（GWAS）中，通常对遗传标记进行排序以选择基因进行进一步研究。特别是对于中度相关和相关的基因，基因和途径的信息可能会改善选择。我们将两种主要的数据集成方法应用于类风湿性关节炎的GWAS，基因集富集分析（GSEA）和分层贝叶斯优先级（HBP）。许多相关基因位于6p21上的HLA区域。然而，基因和基因集的排名列表根据所选择的方法而有很大不同：HBP仅略微改变排名，并且主要包含前100个基因列表中的HLA基因。GSEA还包括许多非HLA基因。
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.