Predicting regulatory variants using a dense epigenomic mapped CNN model elucidated the molecular basis of trait-tissue associations.
Predicting regulatory variants using a dense epigenomic mapped CNN model elucidated the molecular basis of trait-tissue associations.
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DOI：
10.1093/nar/gkaa1137
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发表时间：
2021-01-11
影响因子：
14.9
通讯作者：
Jia P
中科院分区：
文献类型：
--
作者：
Pei G;Hu R;Dai Y;Manuel AM;Zhao Z;Jia P
评估人类复杂疾病的病因组织对于确定与性状相关的遗传变异的优先顺序很重要。然而，由于全基因组关联研究中的统计噪声，以及来自全基因组关联研究的90%的遗传变异位于非编码区，与性状相关的变异的生物学基础是极其困难的。在这里，我们从ENCODE和Roadmap联合体收集了最大的人类表观基因组图谱，并实现了一个基于深度学习的卷积神经网络(CNN)模型来预测遗传变体在完整的表观基因组修饰列表中的调节作用。我们的模型名为DeepFun，建立在DNA可及性图、组蛋白修饰标记和转录因子的基础上。DeepFun可以系统地评估具有组织或细胞类型特异性的大多数功能元件中非编码变体的影响，即使是罕见的变体或从头开始的突变。通过应用这个模型，我们优先选择了51个公开可用的GWAS研究中的特征相关基因。我们证明了基于CNN的对密集和高分辨率表观基因组注释的分析可以提炼重要的GWAS关联，以便从背景信号中识别调节基因座，这为更好地理解人类复杂疾病的分子基础提供了新的见解。我们预计我们的方法将成为GWAS下游分析和非编码变体评估的常规方法。
Assessing the causal tissues of human complex diseases is important for the prioritization of trait-associated genetic variants. Yet, the biological underpinnings of trait-associated variants are extremely difficult to infer due to statistical noise in genome-wide association studies (GWAS), and because >90% of genetic variants from GWAS are located in non-coding regions. Here, we collected the largest human epigenomic map from ENCODE and Roadmap consortia and implemented a deep-learning-based convolutional neural network (CNN) model to predict the regulatory roles of genetic variants across a comprehensive list of epigenomic modifications. Our model, called DeepFun, was built on DNA accessibility maps, histone modification marks, and transcription factors. DeepFun can systematically assess the impact of non-coding variants in the most functional elements with tissue or cell-type specificity, even for rare variants or de novo mutations. By applying this model, we prioritized trait-associated loci for 51 publicly-available GWAS studies. We demonstrated that CNN-based analyses on dense and high-resolution epigenomic annotations can refine important GWAS associations in order to identify regulatory loci from background signals, which yield novel insights for better understanding the molecular basis of human complex disease. We anticipate our approaches will become routine in GWAS downstream analysis and non-coding variant evaluation.
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遗传对人体组织基因表达的影响。
影响因子：
64.8
作者：
GTEx Consortium;Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group;Statistical Methods groups—Analysis Working Group;Enhancing GTEx (eGTEx) groups;NIH Common Fund;NIH/NCI;NIH/NHGRI;NIH/NIMH;NIH/NIDA;Biospecimen Collection Source Site—NDRI;Biospecimen Collection Source Site—RPCI;Biospecimen Core Resource—VARI;Brain Bank Repository—University of Miami Brain Endowment Bank;Leidos Biomedical—Project Management;ELSI Study;Genome Browser Data Integration &Visualization—EBI;Genome Browser Data Integration &Visualization—UCSC Genomics Institute, University of California Santa Cruz;Lead analysts:;Laboratory, Data Analysis &Coordinating Center (LDACC):;NIH program management:;Biospecimen collection:;Pathology:;eQTL manuscript working group:;Battle A;Brown CD;Engelhardt BE;Montgomery SB
通讯作者：
Montgomery SB
影响因子：
30.8
作者：
Finucane HK;Reshef YA;Anttila V;Slowikowski K;Gusev A;Byrnes A;Gazal S;Loh PR;Lareau C;Shoresh N;Genovese G;Saunders A;Macosko E;Pollack S;Brainstorm Consortium;Perry JRB;Buenrostro JD;Bernstein BE;Raychaudhuri S;McCarroll S;Neale BM;Price AL
通讯作者：
Price AL
影响因子：
16.6
作者：
Durham TJ;Libbrecht MW;Howbert JJ;Bilmes J;Noble WS
通讯作者：
Noble WS
影响因子：
14.9
作者：
Jia, Peilin;Dai, Yulin;Zhao, Zhongming
通讯作者：
Zhao, Zhongming
影响因子：
12.3
作者：
Chen L;Jin P;Qin ZS
通讯作者：
Qin ZS