Mining tissue specificity, gene connectivity and disease association to reveal a set of genes that modify the action of disease causing genes.
Mining tissue specificity, gene connectivity and disease association to reveal a set of genes that modify the action of disease causing genes.
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DOI：
10.1186/1756-0381-1-8
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发表时间：
2008-09-19
期刊：
影响因子：
4.5
通讯作者：
Dalrymple BP
中科院分区：
文献类型：
--
作者：
Reverter A;Ingham A;Dalrymple BP
基因表达的组织特异性与许多重要结果有关，包括表达水平、多态性差异率、演变和疾病相关性。最近的研究也表明了探索差异基因连接和序列保守性在识别疾病相关基因中的重要性。然而，没有研究将基因相互作用与组织特异性和疾病相关性联系起来。我们采用了一种先验的方法，尽可能少的假设来分析基因-基因相互作用与组织特异性及其随后与疾病相关的可能性之间的相互作用。我们挖掘了三个大型数据集，包括从32种组织的大规模平行签名测序中提取的表达数据，描述了7，197个基因的55，606个真阳性相互作用，以及在全身性炎症分析期间生成的微阵列表达结果，其中报告了7，090个基因中的126，543个相互作用。在表达、疾病、连接性和组织特异性之间确定的无数复杂关系中，出现了一些有趣的模式。这些包括管家和疾病相关组织特异性基因的表达和网络连接率升高。我们发现疾病相关基因更有可能表现出组织特异性表达，并且最常与其他疾病基因相互作用。使用这些观察中定义的阈值，我们开发了一种关联内疚算法，并发现了一组112个非疾病注释基因，这些基因主要与疾病相关基因相互作用，影响疾病结局。我们的结论是，参数，如组织特异性和网络连接可以结合使用，以确定一组基因，以前没有确认为致病基因，参与与致病基因的相互作用。我们的内疚协会算法应该是有用的发现额外的遗传疾病的修饰符，更一般地说，关联功能未知的基因簇的基因与定义的功能，允许新的生物推断，可以随后验证的能力。
The tissue specificity of gene expression has been linked to a number of significant outcomes including level of expression, and differential rates of polymorphism, evolution and disease association. Recent studies have also shown the importance of exploring differential gene connectivity and sequence conservation in the identification of disease-associated genes. However, no study relates gene interactions with tissue specificity and disease association. We adopted an a priori approach making as few assumptions as possible to analyse the interplay among gene-gene interactions with tissue specificity and its subsequent likelihood of association with disease. We mined three large datasets comprising expression data drawn from massively parallel signature sequencing across 32 tissues, describing a set of 55,606 true positive interactions for 7,197 genes, and microarray expression results generated during the profiling of systemic inflammation, from which 126,543 interactions among 7,090 genes were reported. Amongst the myriad of complex relationships identified between expression, disease, connectivity and tissue specificity, some interesting patterns emerged. These include elevated rates of expression and network connectivity in housekeeping and disease-associated tissue-specific genes. We found that disease-associated genes are more likely to show tissue specific expression and most frequently interact with other disease genes. Using the thresholds defined in these observations, we develop a guilt-by-association algorithm and discover a group of 112 non-disease annotated genes that predominantly interact with disease-associated genes, impacting on disease outcomes. We conclude that parameters such as tissue specificity and network connectivity can be used in combination to identify a group of genes, not previously confirmed as disease causing, that are involved in interactions with disease causing genes. Our guilt-by-association algorithm should be useful for the discovery of additional modifiers of genetic diseases, and more generally, for the ability to associate genes of unknown function to clusters of genes with defined functions allowing for novel biological inference that can be subsequently validated.
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影响因子：
10.7
作者：
Zhang, LQ;Li, WH
通讯作者：
Li, WH
影响因子：
5.8
作者：
Reverter, A;McWilliam, SM;Dalrymple, BP
通讯作者：
Dalrymple, BP
系统的调查揭示了基因共表达网络中“逐罪”的一般适用性。
影响因子：
3
作者：
Wolfe CJ;Kohane IS;Butte AJ
通讯作者：
Butte AJ
使用中心性在文献挖掘的基因互动网络上识别基因 - 疾病的关联。
DOI：
10.1093/bioinformatics/btn182
发表时间：
2008-07-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Ozgür A;Vu T;Erkan G;Radev DR
通讯作者：
Radev DR
影响因子：
64.8
作者：
Calvano, SE;Xiao, WZ;Lowry, SF
通讯作者：
Lowry, SF