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鉴定转录因子活性的遗传决定因素。

Identifying the genetic determinants of transcription factor activity.

机构信息

Department of Biological Sciences, Columbia University, New York, NY 10027, USA.

出版信息

Mol Syst Biol. 2010 Sep 21;6:412. doi: 10.1038/msb.2010.64.

DOI:10.1038/msb.2010.64
PMID:20865005
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2964119/
Abstract

Analysis of parallel genotyping and expression profiling data has shown that mRNA expression levels are highly heritable. Currently, only a tiny fraction of this genetic variance can be mechanistically accounted for. The influence of trans-acting polymorphisms on gene expression traits is often mediated by transcription factors (TFs). We present a method that exploits prior knowledge about the in vitro DNA-binding specificity of a TF in order to map the loci ('aQTLs') whose inheritance modulates its protein-level regulatory activity. Genome-wide regression of differential mRNA expression on predicted promoter affinity is used to estimate segregant-specific TF activity, which is subsequently mapped as a quantitative phenotype. In budding yeast, our method identifies six times as many locus-TF associations and more than twice as many trans-acting loci as all existing methods combined. Application to mouse data from an F2 intercross identified an aQTL on chromosome VII modulating the activity of Zscan4 in liver cells. Our method has greatly improved statistical power over existing methods, is mechanism based, strictly causal, computationally efficient, and generally applicable.

摘要

对平行基因分型和表达谱数据的分析表明,mRNA 表达水平具有高度的遗传性。目前,这种遗传变异只有一小部分可以用机制来解释。转录因子 (TF) 常常介导了对基因表达性状的反式作用多态性的影响。我们提出了一种方法,利用 TF 在体外 DNA 结合特异性方面的先验知识,来映射那些遗传调节其蛋白水平调控活性的基因座(“aQTLs”)。全基因组回归分析差异 mRNA 表达与预测启动子亲和力,以估计分离特异性 TF 活性,然后将其映射为定量表型。在芽殖酵母中,我们的方法鉴定出的基因座-TF 关联数量是现有所有方法的 6 倍,反式作用基因座数量是现有所有方法的两倍多。将该方法应用于来自 F2 杂交的小鼠数据,鉴定出了染色体 VII 上的一个调节肝细胞中 Zscan4 活性的 aQTL。与现有方法相比,我们的方法大大提高了统计能力,具有基于机制、严格因果关系、计算效率高且普遍适用的优点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5781/2964119/e49b54024076/msb201064-f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5781/2964119/d71809022f6c/msb201064-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5781/2964119/e2cf074a9cf6/msb201064-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5781/2964119/29ef9639f421/msb201064-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5781/2964119/d626e0ddfff9/msb201064-f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5781/2964119/7ca31c3c2754/msb201064-f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5781/2964119/e49b54024076/msb201064-f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5781/2964119/d71809022f6c/msb201064-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5781/2964119/e2cf074a9cf6/msb201064-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5781/2964119/29ef9639f421/msb201064-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5781/2964119/d626e0ddfff9/msb201064-f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5781/2964119/7ca31c3c2754/msb201064-f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5781/2964119/e49b54024076/msb201064-f6.jpg

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Learning a prior on regulatory potential from eQTL data.从eQTL数据中学习调控潜力的先验知识。
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Inferring TF activities and activity regulators from gene expression data with constraints from TF perturbation data.从基因表达数据推断 TF 活性和活性调节剂,并从 TF 扰动数据中获取约束条件。
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