Learning gene networks underlying clinical phenotypes using SNP perturbation.
Learning gene networks underlying clinical phenotypes using SNP perturbation.
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DOI：
10.1371/journal.pcbi.1007940
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发表时间：
2020-10
影响因子：
4.3
通讯作者：
Kim S
中科院分区：
文献类型：
--
作者：
McCarter C;Howrylak J;Kim S
最近的技术进步产生的大型患者队列的基因组序列，分子和临床表型数据的可用性提供了一个机会，在系统水平上剖析复杂疾病的遗传结构。然而，这些数据的先前分析主要集中在与临床和表达性状相关的SNP的共定位上，每个SNP都是从全基因组关联研究和表达数量性状基因座作图中鉴定的。因此，他们对影响临床表型的SNP背后的分子机制的描述仅限于与共定位SNP连锁的单个基因。在这里，我们介绍了PerturbNet，一个用于学习基因网络的统计框架，该基因网络调节遗传变异对表型的影响，使用遗传变异作为生物系统的自然发生的扰动。PerturbNet使用概率图模型直接对从遗传变异到基因网络再到表型网络沿着生物系统每一层的网络的级联扰动进行建模。PerturbNet通过使用高效算法解决单个优化问题来学习整个模型，该算法可以在几个小时内分析人类全基因组数据。PerturbNet推理程序提取了基因网络如何调节表型遗传效应的详细描述。使用模拟和哮喘数据，我们证明PerturbNet提高了检测与疾病相关的SNP的统计能力，并识别了介导SNP对性状影响的基因网络和网络模块，为潜在的分子机制提供了更深入的见解。我们描述了PerturbNet，一个用于学习基因网络的统计框架，该基因网络调节遗传变异对表型的影响，使用遗传变异作为生物系统的自然发生的扰动。PerturbNet直接对从遗传变异到基因网络再到表型网络的干扰级联进行建模，从而将现有的用于eQTL作图、GWAS、eQTL和GWAS变异的共定位分析以及SNP干扰下的基因网络发现的计算工具集成在一个单一的统计框架内。我们证明了PerturbNet提高了检测疾病相关SNP的统计能力，并揭示了介导SNP对性状影响的基因网络，其计算效率允许在几个小时内进行人类数据分析。
Availability of genome sequence, molecular, and clinical phenotype data for large patient cohorts generated by recent technological advances provides an opportunity to dissect the genetic architecture of complex diseases at system level. However, previous analyses of such data have largely focused on the co-localization of SNPs associated with clinical and expression traits, each identified from genome-wide association studies and expression quantitative trait locus mapping. Thus, their description of the molecular mechanisms behind the SNPs influencing clinical phenotypes was limited to the single gene linked to the co-localized SNP. Here we introduce PerturbNet, a statistical framework for learning gene networks that modulate the influence of genetic variants on phenotypes, using genetic variants as naturally occurring perturbation of a biological system. PerturbNet uses a probabilistic graphical model to directly model the cascade of perturbation from genetic variants to the gene network to the phenotype network along with the networks at each layer of the biological system. PerturbNet learns the entire model by solving a single optimization problem with an efficient algorithm that can analyze human genome-wide data within a few hours. PerturbNet inference procedures extract a detailed description of how the gene network modulates the genetic effects on phenotypes. Using simulated and asthma data, we demonstrate that PerturbNet improves statistical power for detecting disease-linked SNPs and identifies gene networks and network modules mediating the SNP effects on traits, providing deeper insights into the underlying molecular mechanisms. We describe PerturbNet, a statistical framework for learning a gene network that modulates the influence of genetic variants on phenotypes, using genetic variants as naturally occurring perturbation of a biological system. PerturbNet directly models the cascade of perturbation from genetic variants to the gene network to the phenotype network, thus integrating the existing computational tools for eQTL mapping, GWAS, co-localization analysis of eQTL and GWAS variants, and gene network discovery under SNP perturbation within a single statistical framework. We demonstrate that PerturbNet improves statistical power for detecting disease-linked SNPs and uncovers gene networks mediating the SNP effects on traits, with computational efficiency that allows for human data analysis within several hours.
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影响因子：
4.5
作者：
Cusanovich DA;Pavlovic B;Pritchard JK;Gilad Y
通讯作者：
Gilad Y
来自1,092个人基因组的遗传变异的综合图。
影响因子：
64.8
作者：
通讯作者：
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影响因子：
7
作者：
Boyle AP;Hong EL;Hariharan M;Cheng Y;Schaub MA;Kasowski M;Karczewski KJ;Park J;Hitz BC;Weng S;Cherry JM;Snyder M
通讯作者：
Snyder M
影响因子：
14.9
作者：
Buniello, Annalisa;MacArthur, Jacqueline A. L.;Parkinson, Helen
通讯作者：
Parkinson, Helen
影响因子：
12.3
作者：
Gibson G;Powell JE;Marigorta UM
通讯作者：
Marigorta UM