New insights into the genetic control of gene expression using a Bayesian multi-tissue approach.
New insights into the genetic control of gene expression using a Bayesian multi-tissue approach.
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DOI：
10.1371/journal.pcbi.1000737
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发表时间：
2010-04-08
影响因子：
4.3
通讯作者：
Richardson S
中科院分区：
文献类型：
--
作者：
Petretto E;Bottolo L;Langley SR;Heinig M;McDermott-Roe C;Sarwar R;Pravenec M;Hübner N;Aitman TJ;Cook SA;Richardson S
大多数表达数量性状基因座 (eQTL) 研究是在单一组织或细胞类型中进行的，使用的方法忽略了跨组织共享的信息。尽管现在可以对多个组织中的 RNA 表达进行全局分析，但迄今为止，还很少开发出用于联合分析跨组织基因表达并同时分析多个遗传变异的综合统计框架。在这里，我们提出了稀疏贝叶斯回归模型，用于在单个组织内以及同时跨组织绘制 eQTL。我们在四种组织中的一组 2,000 个基因上进行了测试，证明我们的方法比传统方法更强大，可以在系统层面揭示 eQTL 景观的真实复杂性。为了强调我们方法的强大功能，我们确定了 Hopx 基因的双 eQTL 模型（顺式/反式），该模型经过实验验证，并且传统方法无法检测到。我们展示了四种组织中约 27% 转录本的基因表达的共同遗传调控，与 5% FDR 水平的单一组织分析相比，eQTL 检测增加了 5 倍以上。这些发现为揭示控制全局基因表达的复杂遗传调控机制提供了新的机会，而我们的建模方法的通用性使其适用于其他模型系统和人类，并广泛应用于多种中间和全身表型的分析。对来自不同组织或细胞类型的全基因组遗传多态性和基因表达谱的综合分析在识别调节动物模型和人类复杂表型的基因方面取得了巨大成功。然而，当前方法的一个重要限制在于它们仅适用于个体组织，从而忽略了不同组织之间共享的信息。为了揭示在整个生物体水平上控制基因表达的复杂遗传调控机制，有必要开发适当的分析方法来同时分析多个组织中的全基因组遗传多态性和基因表达谱。本文提出了一种新颖的、完全集成的贝叶斯方法，用于绘制多个组织内和跨多个组织的基因表达的遗传成分。与传统方法相比，除了增强的功效和增强的作图分辨率之外，我们的模型还直接提供有关转录谱的潜在系统影响以及基因表达共存的局部（顺式）和远程（反式）遗传控制的信息。我们还讨论了扩展我们的方法来分析不同表型和其他研究设计的可能性，从而提供了一个集成的计算工具来探索系统水平上转录调控的遗传控制，超越单一组织分辨率。
The majority of expression quantitative trait locus (eQTL) studies have been carried out in single tissues or cell types, using methods that ignore information shared across tissues. Although global analysis of RNA expression in multiple tissues is now feasible, few integrated statistical frameworks for joint analysis of gene expression across tissues combined with simultaneous analysis of multiple genetic variants have been developed to date. Here, we propose Sparse Bayesian Regression models for mapping eQTLs within individual tissues and simultaneously across tissues. Testing these on a set of 2,000 genes in four tissues, we demonstrate that our methods are more powerful than traditional approaches in revealing the true complexity of the eQTL landscape at the systems-level. Highlighting the power of our method, we identified a two-eQTL model (cis/trans) for the Hopx gene that was experimentally validated and was not detected by conventional approaches. We showed common genetic regulation of gene expression across four tissues for ∼27% of transcripts, providing >5 fold increase in eQTLs detection when compared with single tissue analyses at 5% FDR level. These findings provide a new opportunity to uncover complex genetic regulatory mechanisms controlling global gene expression while the generality of our modelling approach makes it adaptable to other model systems and humans, with broad application to analysis of multiple intermediate and whole-body phenotypes. Integrated analysis of genome-wide genetic polymorphisms and gene expression profiles from different tissues or cell types has been highly successful in identifying genes modulating complex phenotypes in animal models and humans. However, an important limitation of the current approaches consists in their sole application to individual tissues, thus ignoring information shared across different tissues. To uncover complex genetic regulatory mechanisms controlling gene expression at the whole organism's level, it is essential to develop appropriate analytical methods for the analysis of genome-wide genetic polymorphisms and gene expression profiles simultaneously in multiple tissues. This paper presents a novel, fully integrated Bayesian approach for mapping the genetic components of gene expression within and across multiple tissues. In addition to increased power and enhanced mapping resolution when compared with traditional approaches, our model directly provides information on potential systemic effects on transcriptional profiles and co-existing local (cis) and distant (trans) genetic control of gene expression. We also discuss the possibility to extend our approach for the analysis of different phenotypes, and other study designs, thus providing an integrated computational tool to explore the genetic control underlying transcriptional regulation at the systems-level, beyond the single tissue resolution.
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DNA变异和脑区域特异性表达谱图在近交小鼠菌株之间表现出不同的关系：对EQTL映射研究的影响。
影响因子：
12.3
作者：
Hovatta, Iiris;Zapala, Matthew A.;Broide, Ron S.;Schadt, Eric E.;Libiger, Ondrej;Schork, Nicholas J.;Lockhart, David J.;Barlow, Carrolee
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作者：
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影响因子：
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作者：
Bystrykh, L;Weersing, E;de Haan, G
通讯作者：
de Haan, G
影响因子：
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作者：
Chesler, EJ;Lu, L;Williams, RW
通讯作者：
Williams, RW
影响因子：
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作者：
Kendziorski, CM;Chen, M;Attie, AD
通讯作者：
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