Causal inference of regulator-target pairs by gene mapping of expression phenotypes.
Causal inference of regulator-target pairs by gene mapping of expression phenotypes.
复制标题
DOI：
10.1186/1471-2164-7-125
复制
发表时间：
2006-05-24
期刊：
影响因子：
4.4
通讯作者：
Jagalur M
中科院分区：
文献类型：
--
作者：
Kulp DC;Jagalur M
多态标记与观察到的表型之间的相关性为数量遗传学中的性状定位提供了依据。当表型是基因表达时，理论上可以牵涉到参与调控的基因座。最近从基因和基因表达数据构建基因调控网络的努力表明，通过一种综合的方法可以实现生物相关的网络。在这篇文章中，我们考虑了识别直接或间接、因果、反式作用关系中的个别基因对的问题。受多位点数量性状(QTL)定位的上位性模型的启发，通过将传统的线性模型扩展到同时包括调控基因的基因型和表达及其相互作用，提出了一个统一的表达和基因型模型来识别数量性状基因(QTG)。该模型提供了特定基因的图谱，与标准的连锁方法不同，标准的连锁方法涉及大的QTL区间，通常包含数十个基因。在模拟实验中，我们发现该方法经常可以在数千个性状的背景噪声中检测到弱的反式作用调节因子，并且对包含多个调节基因的转录模型具有很强的鲁棒性。我们重新分析了来自大量酵母配对的几个多效性基因座，并确定了一个可能的替代调节因子，以前没有发表过。然而，我们也发现，由于调控基因上存在顺式作用的QTL，导致基因小邻域之间的紧密连锁，因此许多调控基因并不容易被定位。QTG映射的与ARN1连锁的调控因子-靶点对被组合成一个调控模块，我们观察到该调控模块高度富含铁稳态相关基因，并包含几个因果关系定向的链接，这些链接在该调控模块的其他自动重建中没有被发现。最后，我们也证实了先前发表的令人惊讶的结果，即控制基因表达的调控因子并没有丰富转录因子，但我们确实表明，我们更精确的定位模型揭示了与细胞调控相关的其他几个生物过程的功能丰富。通过结合互作表达和基因类型，我们的QTG作图方法可以识别特定的调控基因，而不是标准的QTL区间作图。我们已经证明，该方法可以恢复具有生物学意义的调节子-靶子对，并且该方法导致了一个通用框架，用于诱导定向和非定向边的调控模块网络拓扑，该拓扑可用于在通路分析中识别导联。
Correlations between polymorphic markers and observed phenotypes provide the basis for mapping traits in quantitative genetics. When the phenotype is gene expression, then loci involved in regulatory control can theoretically be implicated. Recent efforts to construct gene regulatory networks from genotype and gene expression data have shown that biologically relevant networks can be achieved from an integrative approach. In this paper, we consider the problem of identifying individual pairs of genes in a direct or indirect, causal, trans-acting relationship. Inspired by epistatic models of multi-locus quantitative trait (QTL) mapping, we propose a unified model of expression and genotype to identify quantitative trait genes (QTG) by extending the conventional linear model to include both genotype and expression of regulator genes and their interactions. The model provides mapping of specific genes in contrast to standard linkage approaches that implicate large QTL intervals typically containing tens of genes. In simulations, we found that the method can often detect weak trans-acting regulators amid the background noise of thousands of traits and is robust to transcription models containing multiple regulator genes. We reanalyze several pleiotropic loci derived from a large set of yeast matings and identify a likely alternative regulator not previously published. However, we also found that many regulators can not be so easily mapped due to the presence of cis-acting QTLs on the regulators, which induce close linkage among small neighborhoods of genes. QTG mapped regulator-target pairs linked to ARN1 were combined to form a regulatory module, which we observed to be highly enriched in iron homeostasis related genes and contained several causally directed links that had not been identified in other automatic reconstructions of that regulatory module. Finally, we also confirm the surprising, previously published results that regulators controlling gene expression are not enriched for transcription factors, but we do show that our more precise mapping model reveals functional enrichment for several other biological processes related to the regulation of the cell. By incorporating interacting expression and genotype, our QTG mapping method can identify specific regulator genes in contrast to standard QTL interval mapping. We have shown that the method can recover biologically significant regulator-target pairs and the approach leads to a general framework for inducing a regulatory module network topology of directed and undirected edges that can be used to identify leads in pathway analysis.
登录
查看更多内容
影响因子：
64.8
作者：
Steinmetz, LM;Sinha, H;Davis, RW
通讯作者：
Davis, RW
影响因子：
3.3
作者：
Bing, N;Hoeschele, I
通讯作者：
Hoeschele, I
影响因子：
30.8
作者：
Chesler, EJ;Lu, L;Williams, RW
通讯作者：
Williams, RW
影响因子：
3.3
作者：
Spellman, PT;Sherlock, G;Futcher, B
通讯作者：
Futcher, B
影响因子：
5.8
作者：
Peña, JM;Björkegren, J;Tegnér, J
通讯作者：
Tegnér, J