A computational system to select candidate genes for complex human traits
A computational system to select candidate genes for complex human traits
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DOI：
10.1093/bioinformatics/btm001
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发表时间：
2007-05-01
期刊：
影响因子：
5.8
通讯作者：
Vision, Todd J.
中科院分区：
文献类型：
--
作者：
Gaulton, Kyle J.;Mohlke, Karen L.;Vision, Todd J.
动机：识别复杂性状背后的遗传变异是具有挑战性的。关于复杂性状的生物学和单个基因功能的丰富信息使信息学辅助方法的发展成为可能，以选择这些试验的候选基因。结果：我们开发了一个名为CAESAR的计算系统，通过使用本体将所有带注释的人类基因排序为复杂性状的候选基因，利用各种以基因为中心的信息源对该性状的自然语言描述进行语义映射。在对其有效性的测试中，凯撒成功地从18个(39%)复杂的人类特征易感基因中选择了全基因组排名前2%的候选基因中的7个，这一子集代表了人类基因组中大约1%的基因，为数百个人类基因的关联研究提供了足够的丰富。这种方法可以应用于存在注释基因集的任何生物体中的任何有充分记录的单因素或多因素特征。
Motivation: Identification of the genetic variation underlying complex traits is challenging. The wealth of information publicly available about the biology of complex traits and the function of individual genes permits the development of informatics-assisted methods for the selection of candidate genes for these traits.Results: We have developed a computational system named CAESAR that ranks all annotated human genes as candidates for a complex trait by using ontologies to semantically map natural language descriptions of the trait with a variety of gene-centric information sources. In a test of its effectiveness, CAESAR successfully selected 7 out of 18 (39%) complex human trait susceptibility genes within the top 2% of ranked candidates genome-wide, a subset that represents roughly 1% of genes in the human genome and provides sufficient enrichment for an association study of several hundred human genes. This approach can be applied to any well-documented mono- or multi-factorial trait in any organism for which an annotated gene set exists.