A Bayesian partition method for detecting pleiotropic and epistatic eQTL modules.
A Bayesian partition method for detecting pleiotropic and epistatic eQTL modules.
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DOI：
10.1371/journal.pcbi.1000642
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发表时间：
2010-01-15
影响因子：
4.3
通讯作者：
Liu JS
中科院分区：
文献类型：
--
作者：
Zhang W;Zhu J;Schadt EE;Liu JS
DNA变异和基因表达变异之间的关系的研究，通常被称为“表达数量性状基因座(EQTL)定位”，已经在许多物种中进行，并产生了许多重要的发现。由于在这类分析中有大量的基因和遗传标记，要发现少数eQTL如何相互作用来影响一组共同调控基因的mRNA表达水平是非常具有挑战性的。我们提出了一种贝叶斯方法来简化这项任务，在该方法中，映射到一组公共标记的共表达基因被视为由潜在指示变量表征的模块。设计了一种马尔可夫链蒙特卡罗算法，用于同时搜索模块基因及其连锁标记。仿真结果表明，与传统的QTL定位方法相比，该方法对eQTL及其靶基因的检测能力更强。我们将该程序应用于包含112个酿酒酵母分离物的基因表达和基因类型的数据集。我们的方法确定了包含映射到先前报道的eQTL热点的基因的模块，并将这些大的eQTL热点解剖为几个模块，这些模块对应于可能不同的生物学功能或对调控扰动的初级和二级反应。此外，我们确定了与eQTL对相关的9个模块，其中2个模块已被报道。我们证明了包含许多子细胞表达基因的新模块之一受AMN1和BPH1的调控。综上所述，同时考虑所有性状和所有标记的贝叶斯分割法在检测多效性和上位性效应方面更有效，基于模拟和经验数据。全基因组关联研究已经发现了多种人类疾病的致病基因。然而，DNA变异如何影响疾病表型的潜在机制在许多情况下还没有被很好地理解。基因表达介于DNA和临床终点之间。将DNA变异和基因表达变异联系起来，通常被称为“表达数量性状基因座(EQTL)定位”，揭示了DNA变异影响表型的机制和途径。由于在这类分析中有大量的基因和遗传标记，要发现少数eQTL如何相互作用来影响一组共同调控基因的mRNA表达水平是非常具有挑战性的。我们提出了一种贝叶斯方法，通过将共表达的基因作为一个模块来识别遗传交互作用和更多的eQTL。我们的方法为研究人类疾病模型中的遗传交互作用提供了一种工具。
Studies of the relationship between DNA variation and gene expression variation, often referred to as “expression quantitative trait loci (eQTL) mapping”, have been conducted in many species and resulted in many significant findings. Because of the large number of genes and genetic markers in such analyses, it is extremely challenging to discover how a small number of eQTLs interact with each other to affect mRNA expression levels for a set of co-regulated genes. We present a Bayesian method to facilitate the task, in which co-expressed genes mapped to a common set of markers are treated as a module characterized by latent indicator variables. A Markov chain Monte Carlo algorithm is designed to search simultaneously for the module genes and their linked markers. We show by simulations that this method is more powerful for detecting true eQTLs and their target genes than traditional QTL mapping methods. We applied the procedure to a data set consisting of gene expression and genotypes for 112 segregants of S. cerevisiae. Our method identified modules containing genes mapped to previously reported eQTL hot spots, and dissected these large eQTL hot spots into several modules corresponding to possibly different biological functions or primary and secondary responses to regulatory perturbations. In addition, we identified nine modules associated with pairs of eQTLs, of which two have been previously reported. We demonstrated that one of the novel modules containing many daughter-cell expressed genes is regulated by AMN1 and BPH1. In conclusion, the Bayesian partition method which simultaneously considers all traits and all markers is more powerful for detecting both pleiotropic and epistatic effects based on both simulated and empirical data. Genome-wide association studies (GWAS) have yielded several causal genes for many human diseases. However, the mechanisms underlying how DNA variations affect disease phenotypes have not been well understood in many cases. Gene expression is intermediate between DNA and clinical endpoints. Linking DNA variation and gene expression variation, often referred to as “expression quantitative trait loci (eQTL) mapping”, has yielded clues of mechanisms and pathways by which DNA variations impact phenotypes. Because of the large number of genes and genetic markers in such analyses, it is extremely challenging to discover how a small number of eQTLs interact with each other to affect mRNA expression levels for a set of co-regulated genes. We present a Bayesian method to identify genetic interactions and more eQTLs by treating co-expressed genes as a module. Our method provides a tool to study genetic interactions in human disease models.
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影响因子：
9.8
作者：
Schadt EE;Molony C;Chudin E;Hao K;Yang X;Lum PY;Kasarskis A;Zhang B;Wang S;Suver C;Zhu J;Millstein J;Sieberts S;Lamb J;GuhaThakurta D;Derry J;Storey JD;Avila-Campillo I;Kruger MJ;Johnson JM;Rohl CA;van Nas A;Mehrabian M;Drake TA;Lusis AJ;Smith RC;Guengerich FP;Strom SC;Schuetz E;Rushmore TH;Ulrich R
通讯作者：
Ulrich R
影响因子：
64.5
作者：
Colman-Lerner, A;Chin, TE;Brent, R
通讯作者：
Brent, R
影响因子：
4.5
作者：
Ronald J;Brem RB;Whittle J;Kruglyak L
通讯作者：
Kruglyak L
影响因子：
9.8
作者：
Storey JD;Akey JM;Kruglyak L
通讯作者：
Kruglyak L
影响因子：
1.9
作者：
Kendziorski, CM;Chen, M;Attie, AD
通讯作者：
Attie, AD