Principled multi-omic analysis reveals gene regulatory mechanisms of phenotype variation.
Principled multi-omic analysis reveals gene regulatory mechanisms of phenotype variation.
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DOI：
10.1101/gr.227066.117
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发表时间：
2018-08
期刊：
影响因子：
7
通讯作者：
Sinha S
中科院分区：
文献类型：
--
作者：
Hanson C;Cairns J;Wang L;Sinha S
最近的研究分析了大规模的基因表达数据集，以确定与从癌症亚型到药物敏感性的表型个体间差异相关的基因，为个性化医学研究开辟了新的途径。然而，单靠基因表达数据揭示表型差异背后的顺式调控机制的能力是有限的。在这项研究中，我们开发了一个新的概率模型，称为pGENMi，它整合了多组数据来研究特定表型-细胞系对细胞毒治疗的反应-个体间差异背后的转录调控机制。特别是，pGENMi同时分析基因型、DNA甲基化、基因表达和转录因子(TF)-DNA结合数据，以及表型测量，以确定转录因子调节表型。它通过结合位于Tf结合位点内的表达数量性状基因座(EQTL)和表达相关甲基化标记(EQTM)的统计信息，以及观察到基因表达与表型变异之间的相关性来做到这一点。将pGENMi应用于用24种药物治疗的一组淋巴母细胞系的数据，结合ENCODE TF芯片数据，产生了许多已知的和新的(TF，药物)关联。TF基因敲除的实验验证确认了41%的预测和测试的关联，相比之下，测试的非关联(对照)的确认率为12%。一项广泛的文献调查也证实了62%的预测关联超过了严格的门槛。此外，仅当结合eQTL和eQTM数据时预测的关联比使用pGENMi进行的仅eQTL或eQTM分析显示出更高的精度，进一步证明了多组整合分析的价值。
Recent studies have analyzed large-scale data sets of gene expression to identify genes associated with interindividual variation in phenotypes ranging from cancer subtypes to drug sensitivity, promising new avenues of research in personalized medicine. However, gene expression data alone is limited in its ability to reveal cis-regulatory mechanisms underlying phenotypic differences. In this study, we develop a new probabilistic model, called pGENMi, that integrates multi-omic data to investigate the transcriptional regulatory mechanisms underlying interindividual variation of a specific phenotype—that of cell line response to cytotoxic treatment. In particular, pGENMi simultaneously analyzes genotype, DNA methylation, gene expression, and transcription factor (TF)-DNA binding data, along with phenotypic measurements, to identify TFs regulating the phenotype. It does so by combining statistical information about expression quantitative trait loci (eQTLs) and expression-correlated methylation marks (eQTMs) located within TF binding sites, as well as observed correlations between gene expression and phenotype variation. Application of pGENMi to data from a panel of lymphoblastoid cell lines treated with 24 drugs, in conjunction with ENCODE TF ChIP data, yielded a number of known as well as novel (TF, Drug) associations. Experimental validations by TF knockdown confirmed 41% of the predicted and tested associations, compared to a 12% confirmation rate of tested nonassociations (controls). An extensive literature survey also corroborated 62% of the predicted associations above a stringent threshold. Moreover, associations predicted only when combining eQTL and eQTM data showed higher precision compared to an eQTL-only or eQTM-only analysis using pGENMi, further demonstrating the value of multi-omic integrative analysis.
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影响因子：
30.8
作者：
Lee D;Gorkin DU;Baker M;Strober BJ;Asoni AL;McCallion AS;Beer MA
通讯作者：
Beer MA
影响因子：
64.5
作者：
Basu A;Bodycombe NE;Cheah JH;Price EV;Liu K;Schaefer GI;Ebright RY;Stewart ML;Ito D;Wang S;Bracha AL;Liefeld T;Wawer M;Gilbert JC;Wilson AJ;Stransky N;Kryukov GV;Dancik V;Barretina J;Garraway LA;Hon CS;Munoz B;Bittker JA;Stockwell BR;Khabele D;Stern AM;Clemons PA;Shamji AF;Schreiber SL
通讯作者：
Schreiber SL
DOI：
10.1186/s13619-015-0015-x
发表时间：
2015
期刊：
Cell regeneration (London, England)
影响因子：
--
作者：
Fontaine F;Overman J;François M
通讯作者：
François M
影响因子：
7
作者：
Battle A;Mostafavi S;Zhu X;Potash JB;Weissman MM;McCormick C;Haudenschild CD;Beckman KB;Shi J;Mei R;Urban AE;Montgomery SB;Levinson DF;Koller D
通讯作者：
Koller D
影响因子：
--
作者：
Gomes AR;Zhao F;Lam EW
通讯作者：
Lam EW