Herrera-Galeano Jesus Enrique, Hirschberg David L, Mokashi Vishwesh, Solka Jeffrey
Genomics and Bioinformatics, Naval Medical Research Center-Frederick, United States Navy, 8400 Research Plaza, Fort Detrick, Frederick, MD, USA.
BMC Res Notes. 2013 Dec 5;6:511. doi: 10.1186/1756-0500-6-511.
The availability of genetic data has increased dramatically in recent years. The greatest value of this data is its potential for personalized medicine. Many new associations are reported every day from Genome Wide Association Studies (GWAS). However, robust, reproducible associations are elusive for some complex diseases. Ontologies present a potential way to distinguish between spurious associations and those with a potential influence on the phenotype. Such an approach would be based on finding associations of the same genetic variant with closely related, but distinct, phenotypes. This approach can be accomplished with a phenotype ontology that also holds genetic association data.
Here, we report a structured knowledge application to navigate and to facilitate the discovery of relationships between different phenotypes and their genetic associations.
OGA allows users to (1) find the intersecting set of genes for phenotypes of interest, (2) find empirical p values for such observations and (3) OGA outperforms similar applications in number of total concepts and genes mapped.
近年来，基因数据的可用性急剧增加。这些数据的最大价值在于其在个性化医疗方面的潜力。每天都有许多来自全基因组关联研究（GWAS）的新关联被报道。然而，对于某些复杂疾病，强大且可重复的关联难以捉摸。本体提供了一种区分虚假关联和那些可能对表型有影响的关联的潜在方法。这种方法将基于寻找同一基因变异与密切相关但又不同的表型之间的关联。这种方法可以通过一个同时包含基因关联数据的表型本体来实现。
在此，我们报告了一个结构化知识应用，用于导航并促进发现不同表型与其基因关联之间的关系。
OGA允许用户（1）找到感兴趣表型的基因交集，（2）找到此类观察结果的经验p值，并且（3）在映射的总概念和基因数量方面，OGA优于类似应用。
