Computational disease gene identification: a concert of methods prioritizes type 2 diabetes and obesity candidate genes.
Computational disease gene identification: a concert of methods prioritizes type 2 diabetes and obesity candidate genes.
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DOI：
10.1093/nar/gkl381
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发表时间：
2006
影响因子：
14.9
通讯作者：
Hide W
中科院分区：
文献类型：
--
作者：
Tiffin N;Adie E;Turner F;Brunner HG;van Driel MA;Oti M;Lopez-Bigas N;Ouzounis C;Perez-Iratxeta C;Andrade-Navarro MA;Adeyemo A;Patti ME;Semple CA;Hide W
全基因组实验方法来确定疾病基因，如连锁分析和关联研究，产生越来越大的候选基因集，全面的实证分析是不切实际的。计算方法采用来自各种来源的数据来从这些基因集中识别最可能的候选疾病基因。在这里，我们回顾了7个独立的计算疾病基因优先级的方法，然后将它们应用于音乐会的9556 2型糖尿病（T2D）和相关性状肥胖的位置候选基因的分析。我们生成并分析了T2D基因的九个主要候选基因和肥胖症的五个主要候选基因的列表。两个基因，LPL和BCKDHA，是这两组共同的。我们还提出了一组T2D（94个基因）和肥胖症（116个基因）的二级候选人，其中58个基因与这两种疾病相同。
Genome-wide experimental methods to identify disease genes, such as linkage analysis and association studies, generate increasingly large candidate gene sets for which comprehensive empirical analysis is impractical. Computational methods employ data from a variety of sources to identify the most likely candidate disease genes from these gene sets. Here, we review seven independent computational disease gene prioritization methods, and then apply them in concert to the analysis of 9556 positional candidate genes for type 2 diabetes (T2D) and the related trait obesity. We generate and analyse a list of nine primary candidate genes for T2D genes and five for obesity. Two genes, LPL and BCKDHA, are common to these two sets. We also present a set of secondary candidates for T2D (94 genes) and for obesity (116 genes) with 58 genes in common to both diseases.