The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models
The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models
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DOI：
10.1016/j.cell.2023.02.018
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发表时间：
2023-03-30
期刊：
影响因子：
64.5
通讯作者：
Gerstein, Mark
中科院分区：
文献类型：
--
作者：
Rozowsky, Joel;Gao, Jiahao;Gerstein, Mark
了解遗传变异如何影响分子表型是功能基因组学的一个关键目标，目前由于依赖于单个单倍体参考基因组而受到阻碍。在这里，我们展示了来自四个供体的1，635个开放获取数据集的EN-TEx资源（约30个组织3 - 15个测定）。将数据集映射到具有长读段定相和结构变体的匹配的二倍体基因组，实例化> 100万个等位基因特异性基因座的目录。这些位点沿着单倍型表现出协调的活性，并且比相应的非等位基因特异性位点保守性更低。令人惊讶的是，深度学习Transformer模型可以仅基于局部核苷酸序列背景来预测等位基因特异性活性，突出了对变体特别敏感的转录因子结合基序的重要性。此外，将EN-TEX与现有基因组注释相结合揭示了等位基因特异性和GWAS基因座之间的强关联。它还使得能够将已知的eQTL转移到难以分析的组织（例如，从皮肤到心脏）。总的来说，ENTEx为更准确的个人功能基因组学提供了丰富的数据和可推广的模型。
Understanding how genetic variants impact molecular phenotypes is a key goal of functional genomics, currently hindered by reliance on a single haploid reference genome. Here, we present the EN-TEx resource of 1,635 open-access datasets from four donors (-30 tissues 3 -15 assays). The datasets are mapped to matched, diploid genomes with long-read phasing and structural variants, instantiating a catalog of >1 million allele-specific loci. These loci exhibit coordinated activity along haplotypes and are less conserved than corresponding, non-allele-specific ones. Surprisingly, a deep-learning transformer model can predict the allele specific activity based only on local nucleotide-sequence context, highlighting the importance of transcription-factor-binding motifs particularly sensitive to variants. Furthermore, combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci. It also enables models for transferring known eQTLs to difficult-to-profile tissues (e.g., from skin to heart). Overall, ENTEx provides rich data and generalizable models for more accurate personal functional genomics.