A sequence-based global map of regulatory activity for deciphering human genetics.
A sequence-based global map of regulatory activity for deciphering human genetics.
复制标题
基于序列的全球调控活动图，用于解读人类遗传学。
DOI：
10.1038/s41588-022-01102-2
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发表时间：
2022-07
期刊：
影响因子：
30.8
通讯作者：
Zhou, Jian
中科院分区：
文献类型：
--
作者：
Chen, Kathleen M.;Wong, Aaron K.;Troyanskaya, Olga G.;Zhou, Jian
表观基因组分析已经能够大规模鉴定调控元件，但我们仍然缺乏从任何序列或变体到调控活动的系统性映射。我们用Sei来应对这一挑战，Sei是一个将人类遗传学数据与序列信息相结合的框架，旨在发现性状和疾病的调控基础。Sei使用深度学习模型学习一个称为序列类的调控活动词汇表，该模型预测了超过1，300个细胞系和组织中的21，907个染色质图谱。序列类别提供了一个全球性的分类和量化的序列和变异的影响的基础上，不同的监管活动，如细胞类型特异性增强子功能。这些预测支持组织特异性表达，表达数量性状基因座和进化约束数据。此外，序列类别使得能够表征复杂性状的组织特异性的调控结构，并生成针对个体调控致病突变的机制假说。我们提供Sei作为阐明人类健康和疾病的监管基础的资源。Sei是一个新的框架，用于整合人类遗传学数据与基于序列的预测调控活动映射，以阐明导致复杂性状和疾病的机制。
Epigenomic profiling has enabled large-scale identification of regulatory elements, yet we still lack a systematic mapping from any sequence or variant to regulatory activities. We address this challenge with Sei, a framework for integrating human genetics data with sequence information to discover the regulatory basis of traits and diseases. Sei learns a vocabulary of regulatory activities, called sequence classes, using a deep learning model that predicts 21,907 chromatin profiles across >1,300 cell lines and tissues. Sequence classes provide a global classification and quantification of sequence and variant effects based on diverse regulatory activities, such as cell type-specific enhancer functions. These predictions are supported by tissue-specific expression, expression quantitative trait loci and evolutionary constraint data. Furthermore, sequence classes enable characterization of the tissue-specific, regulatory architecture of complex traits and generate mechanistic hypotheses for individual regulatory pathogenic mutations. We provide Sei as a resource to elucidate the regulatory basis of human health and disease. Sei is a new framework for integrating human genetics data with a sequence-based mapping of predicted regulatory activities to elucidate mechanisms contributing to complex traits and diseases.
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影响因子：
30.8
作者：
Kerimov N;Hayhurst JD;Peikova K;Manning JR;Walter P;Kolberg L;Samoviča M;Sakthivel MP;Kuzmin I;Trevanion SJ;Burdett T;Jupp S;Parkinson H;Papatheodorou I;Yates AD;Zerbino DR;Alasoo K
通讯作者：
Alasoo K
影响因子：
30.8
作者：
Finucane HK;Bulik-Sullivan B;Gusev A;Trynka G;Reshef Y;Loh PR;Anttila V;Xu H;Zang C;Farh K;Ripke S;Day FR;ReproGen Consortium;Schizophrenia Working Group of the Psychiatric Genomics Consortium;RACI Consortium;Purcell S;Stahl E;Lindstrom S;Perry JR;Okada Y;Raychaudhuri S;Daly MJ;Patterson N;Neale BM;Price AL
通讯作者：
Price AL
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：
7
作者：
Schwammle, Veit;Sidoli, Simone;Jensen, Ole N.
通讯作者：
Jensen, Ole N.
影响因子：
3.5
作者：
Pugacheva, EM;Tiwari, VK;Lobanenkov, VV
通讯作者：
Lobanenkov, VV