GWAS and drug targets.
GWAS and drug targets.
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DOI：
10.1186/1471-2164-15-s4-s5
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发表时间：
2014
期刊：
影响因子：
4.4
通讯作者：
Moult J
中科院分区：
文献类型：
--
作者：
Cao C;Moult J
全基因组关联研究揭示了基因组变异与复杂疾病之间的大量联系。除了其他好处外，预计这些洞察力将导致新的治疗策略，特别是识别新的药物靶点。在这篇文章中，我们通过检查有多少现有的药物靶点已经被这项技术直接‘重新发现’，以及网络信息可以在多大程度上利用GWAS的结果来发现已知和新的药物靶点，来评估GWAS研究寻找药物靶点的能力。我们发现，在相关的GWAS研究中，只有很小一部分药物靶标被直接检测到。我们调查了这一观察到的两种可能的解释。首先，我们发现了负选择作用于药物靶基因的证据，这是由于与疾病表型强烈耦合的结果，因此减少了与疾病相关的SNPs的发生率。其次，我们发现GWAS基因平均比药物靶标和所有基因都要长得多，这表明GWAS结果中存在与长度相关的偏差。尽管药物靶标与Gwas报告的基因之间的直接相关性很低，但我们发现这两组基因在人类蛋白质网络中紧密耦合。因此，机器学习方法能够根据网络环境和GWAS报告的同一疾病的基因集恢复已知的药物靶点。我们表明，这种方法对于识别药物再利用的机会是潜在的有用的。尽管GWA的研究没有直接确定大多数现有的药物靶点，但有几个理由预计，使用这些数据仍将发现新的靶点。利用网络分析进行药物再利用研究的初步结果令人鼓舞，并为未来的发展提出了方向。
Genome wide association studies (GWAS) have revealed a large number of links between genome variation and complex disease. Among other benefits, it is expected that these insights will lead to new therapeutic strategies, particularly the identification of new drug targets. In this paper, we evaluate the power of GWAS studies to find drug targets by examining how many existing drug targets have been directly 'rediscovered' by this technique, and the extent to which GWAS results may be leveraged by network information to discover known and new drug targets. We find that only a very small fraction of drug targets are directly detected in the relevant GWAS studies. We investigate two possible explanations for this observation. First, we find evidence of negative selection acting on drug target genes as a consequence of strong coupling with the disease phenotype, so reducing the incidence of SNPs linked to the disease. Second, we find that GWAS genes are substantially longer on average than drug targets and than all genes, suggesting there is a length related bias in GWAS results. In spite of the low direct relationship between drug targets and GWAS reported genes, we found these two sets of genes are closely coupled in the human protein network. As a consequence, machine-learning methods are able to recover known drug targets based on network context and the set of GWAS reported genes for the same disease. We show the approach is potentially useful for identifying drug repurposing opportunities. Although GWA studies do not directly identify most existing drug targets, there are several reasons to expect that new targets will nevertheless be discovered using these data. Initial results on drug repurposing studies using network analysis are encouraging and suggest directions for future development.