Tissue-specific functional networks for prioritizing phenotype and disease genes.
Tissue-specific functional networks for prioritizing phenotype and disease genes.
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DOI：
10.1371/journal.pcbi.1002694
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发表时间：
2012
影响因子：
4.3
通讯作者：
Troyanskaya OG
中科院分区：
文献类型：
--
作者：
Guan Y;Gorenshteyn D;Burmeister M;Wong AK;Schimenti JC;Handel MA;Bult CJ;Hibbs MA;Troyanskaya OG
功能基因组学数据的综合分析对于识别表型相关基因具有巨大的潜力。组织特异性是许多遗传疾病的一个重要方面，反映了不同细胞系中蛋白质和途径的潜在不同作用。在功能基因组学数据的全球整合中，考虑组织特异性是具有挑战性的，因为“功能”和“功能关系”通常不能解决特定组织类型的问题。我们通过生成组织特异性功能网络来解决这一挑战，该网络可以有效地代表蛋白质功能的多样性，从而更准确地识别实验室小鼠的表型相关基因。具体来说，我们利用组织特异性基因表达模式的知识，通过整合基因组数据，创建了107个组织特异性功能关系网络。跨网络比较显示，与特定组织发育相关的功能丰富的基因发生了显著变化。然后我们利用这些组织特异性网络来预测与不同表型相关的基因。我们的研究结果表明，与全局功能网络相比，使用组织特异性网络可以显著提高预测性能。我们使用睾丸特异性功能关系网络来预测与男性生育能力和精子发生表型相关的基因，并通过实验证实了一个顶级预测，Mbyl1。然后，我们专注于一种不太常见的遗传性疾病，共济失调，并确定了由小脑网络独特预测的候选疾病，这得到了文献和实验证据的支持。我们的系统级、组织特异性方案超越了传统的全球整合和分析，并建立了一个原型，以解决遗传扰动、疾病和药物对组织特异性的影响。组织特异性是许多遗传疾病的一个重要方面，反映了不同细胞系中蛋白质和途径的潜在不同作用。我们提出了一种有效的策略来模拟实验室小鼠的组织特异性功能关系网络。我们整合了大规模基因组学数据集以及低通量组织特异性表达谱，以估计两种蛋白质在研究组织中共同起作用的可能性。这些网络可以准确地反映不同器官和组织区室之间蛋白质功能的多样性。通过计算探索组织特异性网络，我们可以准确预测新的表型相关候选基因。我们通过实验证实了一个顶级候选基因Mybl1可以影响几种基于男性生殖系统特异性网络预测的男性生育表型，我们预测候选基因与一种罕见的遗传性疾病共济失调有关，这些都得到了实验和文献证据的支持。上述结果证明了通过计算方法模拟协同功能的组织特异性动力学的能力。
Integrated analyses of functional genomics data have enormous potential for identifying phenotype-associated genes. Tissue-specificity is an important aspect of many genetic diseases, reflecting the potentially different roles of proteins and pathways in diverse cell lineages. Accounting for tissue specificity in global integration of functional genomics data is challenging, as “functionality” and “functional relationships” are often not resolved for specific tissue types. We address this challenge by generating tissue-specific functional networks, which can effectively represent the diversity of protein function for more accurate identification of phenotype-associated genes in the laboratory mouse. Specifically, we created 107 tissue-specific functional relationship networks through integration of genomic data utilizing knowledge of tissue-specific gene expression patterns. Cross-network comparison revealed significantly changed genes enriched for functions related to specific tissue development. We then utilized these tissue-specific networks to predict genes associated with different phenotypes. Our results demonstrate that prediction performance is significantly improved through using the tissue-specific networks as compared to the global functional network. We used a testis-specific functional relationship network to predict genes associated with male fertility and spermatogenesis phenotypes, and experimentally confirmed one top prediction, Mbyl1. We then focused on a less-common genetic disease, ataxia, and identified candidates uniquely predicted by the cerebellum network, which are supported by both literature and experimental evidence. Our systems-level, tissue-specific scheme advances over traditional global integration and analyses and establishes a prototype to address the tissue-specific effects of genetic perturbations, diseases and drugs. Tissue specificity is an important aspect of many genetic diseases, reflecting the potentially different roles of proteins and pathways in diverse cell lineages. We propose an effective strategy to model tissue-specific functional relationship networks in the laboratory mouse. We integrated large scale genomics datasets as well as low-throughput tissue-specific expression profiles to estimate the probability that two proteins are co-functioning in the tissue under study. These networks can accurately reflect the diversity of protein functions across different organs and tissue compartments. By computationally exploring the tissue-specific networks, we can accurately predict novel phenotype-related gene candidates. We experimentally confirmed a top candidate gene, Mybl1, to affect several male fertility phenotypes, predicted based on male-reproductive system-specific networks and we predicted candidates related to a rare genetic disease ataxia, which are supported by experimental and literature evidence. The above results demonstrate the power of modeling tissue-specific dynamics of co-functionality through computational approaches.
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影响因子：
4.3
作者：
Guan Y;Myers CL;Lu R;Lemischka IR;Bult CJ;Troyanskaya OG
通讯作者：
Troyanskaya OG
影响因子：
14.9
作者：
Blake JA;Bult CJ;Kadin JA;Richardson JE;Eppig JT;Mouse Genome Database Group
通讯作者：
Mouse Genome Database Group
指导实验生物学：线粒体生物发生的案例研究。
影响因子：
4.3
作者：
Hibbs MA;Myers CL;Huttenhower C;Hess DC;Li K;Caudy AA;Troyanskaya OG
通讯作者：
Troyanskaya OG
影响因子：
5.8
作者：
Hibbs, Matthew A.;Hess, David C.;Troyanskaya, Olga G.
通讯作者：
Troyanskaya, Olga G.
影响因子：
--
作者：
Brusco, A;Gellera, C;Taroni, F
通讯作者：
Taroni, F