A statistical framework for joint eQTL analysis in multiple tissues.
A statistical framework for joint eQTL analysis in multiple tissues.
复制标题
DOI：
10.1371/journal.pgen.1003486
复制
发表时间：
2013-05
期刊：
影响因子：
4.5
通讯作者：
Stephens M
中科院分区：
文献类型：
--
作者：
Flutre T;Wen X;Pritchard J;Stephens M
表达数量性状位点 (eQTL) 作图代表了一种强大且广泛采用的方法，用于识别假定的调控变异并将其与特定基因联系起来。迄今为止，eQTL 研究已在相对狭窄的组织或细胞类型范围内进行。然而，了解生物体表型的生物学将涉及了解多种组织的调控，并且正在进行的研究正在收集数十种细胞类型的 eQTL 数据。在这里，我们提出了一个统计框架，用于有效检测多种组织或细胞类型（或更一般地说，多个亚组）中的 eQTL。该框架明确模拟了每个 eQTL 在某些组织中活跃而在其他组织中失活的潜力。通过对组织之间活性 eQTL 的共享进行建模，与分别检查每个组织的“逐个组织”分析相比，该框架提高了检测多个组织中存在的 eQTL 的能力。相反，通过对某些组织中 eQTL 的不活动进行建模，该框架允许将不同组织之间共享的 eQTL 的比例正式估计为模型的参数，从而解决了在比较逐个组织分析确定的 eQTL 重叠时解释不完全功效的困难。应用我们的框架重新分析来自转化的 B 细胞、T 细胞和成纤维细胞的数据，我们发现与逐个组织分析相比，它大大提高了功效，识别出带有 eQTL 的基因多了 63%（FDR = 0.05）。此外，结果表明，与之前对相同数据的分析相比，这些数据中可检测到的大多数 eQTL 在所有三个组织之间共享。与基因表达相关的遗传变异称为表达数量性状基因座或 eQTL。已经进行了许多研究来识别 eQTL，它们已被证明是识别假定的调控变异并将其与特定基因联系起来的有效工具。到目前为止，大多数研究都是在单一组织或细胞类型中进行的，但未来这种情况正在发生变化，正在进行的研究正在收集旨在绘制数十种组织中的 eQTL 的数据。当前的统计方法无法充分利用此类数据的丰富性，无法考虑组织间 eQTL 的共享和差异。在本文中，我们开发了一个统计框架来解决这个问题，以提高在多个组织之间共享 eQTL 时的检测能力，并允许估计组织之间的差异。将这些方法应用于来自三个组织的数据表明，组织之间共享 eQTL 可能比之前对相同数据的分析中出现的情况要普遍得多。
Mapping expression Quantitative Trait Loci (eQTLs) represents a powerful and widely adopted approach to identifying putative regulatory variants and linking them to specific genes. Up to now eQTL studies have been conducted in a relatively narrow range of tissues or cell types. However, understanding the biology of organismal phenotypes will involve understanding regulation in multiple tissues, and ongoing studies are collecting eQTL data in dozens of cell types. Here we present a statistical framework for powerfully detecting eQTLs in multiple tissues or cell types (or, more generally, multiple subgroups). The framework explicitly models the potential for each eQTL to be active in some tissues and inactive in others. By modeling the sharing of active eQTLs among tissues, this framework increases power to detect eQTLs that are present in more than one tissue compared with “tissue-by-tissue” analyses that examine each tissue separately. Conversely, by modeling the inactivity of eQTLs in some tissues, the framework allows the proportion of eQTLs shared across different tissues to be formally estimated as parameters of a model, addressing the difficulties of accounting for incomplete power when comparing overlaps of eQTLs identified by tissue-by-tissue analyses. Applying our framework to re-analyze data from transformed B cells, T cells, and fibroblasts, we find that it substantially increases power compared with tissue-by-tissue analysis, identifying 63% more genes with eQTLs (at FDR = 0.05). Further, the results suggest that, in contrast to previous analyses of the same data, the majority of eQTLs detectable in these data are shared among all three tissues. Genetic variants that are associated with gene expression are known as expression Quantitative Trait Loci, or eQTLs. Many studies have been conducted to identify eQTLs, and they have proven an effective tool for identifying putative regulatory variants and linking them to specific genes. Up to now most studies have been conducted in a single tissue or cell type, but moving forward this is changing, and ongoing studies are collecting data aimed at mapping eQTLs in dozens of tissues. Current statistical methods are not able to fully exploit the richness of these kinds of data, taking account of both the sharing and differences in eQTLs among tissues. In this paper we develop a statistical framework to address this problem, to improve power to detect eQTLs when they are shared among multiple tissues, and to allow for differences among tissues to be estimated. Applying these methods to data from three tissues suggests that sharing of eQTLs among tissues may be substantially more common than it appeared in previous analyses of the same data.
登录
查看更多内容
影响因子：
4.5
作者：
Gerrits A;Li Y;Tesson BM;Bystrykh LV;Weersing E;Ausema A;Dontje B;Wang X;Breitling R;Jansen RC;de Haan G
通讯作者：
de Haan G
影响因子：
4.3
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
通讯作者：
Richardson S
影响因子：
4.5
作者：
Han B;Eskin E
通讯作者：
Eskin E
影响因子：
9.8
作者：
Bhattacharjee, Samsiddhi;Rajaraman, Preetha;Chatterjee, Nilanjan
通讯作者：
Chatterjee, Nilanjan
影响因子：
14.9
作者：
Hinrichs AS;Karolchik D;Baertsch R;Barber GP;Bejerano G;Clawson H;Diekhans M;Furey TS;Harte RA;Hsu F;Hillman-Jackson J;Kuhn RM;Pedersen JS;Pohl A;Raney BJ;Rosenbloom KR;Siepel A;Smith KE;Sugnet CW;Sultan-Qurraie A;Thomas DJ;Trumbower H;Weber RJ;Weirauch M;Zweig AS;Haussler D;Kent WJ
通讯作者：
Kent WJ