TVAR: assessing tissue-specific functional effects of non-coding variants with deep learning.
TVAR: assessing tissue-specific functional effects of non-coding variants with deep learning.
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TVAR：通过深度学习评估非编码变体的组织特异性功能效应。
DOI：
10.1093/bioinformatics/btac608
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Li,Bingshan
中科院分区：
文献类型：
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作者：
Yang,Hai;Chen,Rui;Wang,Quan;Wei,Qiang;Ji,Ying;Zhong,Xue;Li,Bingshan
由于缺乏对非编码变异，特别是罕见变异的准确功能注释，全基因组测序（WGS）的遗传学分析仍然是一个挑战。由于eqtl广泛涉及人类疾病的遗传学，我们假设在WGS中发现的罕见非编码变异体在易感性疾病风险中发挥调节作用。结果利用数以千计的组织和细胞类型特异性表观基因组特征，我们提出了TVAR。这个基于多标签学习的深度神经网络基于GTEx项目中49个人体组织的eqtl，预测了基因组中非编码变异的功能。TVAR学习高维表观基因组学与跨组织的eQTL之间的关系，将组织之间的相关性考虑在内，以了解共享的和组织特异性的eQTL效应。因此，TVAR输出组织特异性注释，这些组织的平均AUROC为0.77。我们评估了TVAR在四种复杂疾病（冠状动脉疾病、乳腺癌、2型糖尿病和精神分裂症）上的表现，使用TVAR的组织特异性注释，并观察到与现有的五种最先进的工具相比，TVAR在预测常见和罕见变异的功能变异方面的优越表现。我们进一步评估了TVAR在ClinVar、精细定位的GWAS基因座、大规模平行报告试验（MPRA）验证的变异上的g评分（一种适用于所有组织的评分方案），并观察到与其他竞争工具相比，TVAR的表现始终更好。可用性和实现TVAR源代码及其在ClinVar目录上的分数，精细映射的GWAS基因座，来自GTEx数据集的高置信度eqtl，以及MPRA验证的功能变体可在https://github.com/haiyang1986/TVAR.Supplementary上获得。
MotivationAnalysis of whole-genome sequencing (WGS) for genetics is still a challenge due to the lack of accurate functional annotation of non-coding variants, especially the rare ones. As eQTLs have been extensively implicated in the genetics of human diseases, we hypothesize that rare non-coding variants discovered in WGS play a regulatory role in predisposing disease risk.ResultsWith thousands of tissue- and cell-type-specific epigenomic features, we propose TVAR. This multi-label learning-based deep neural network predicts the functionality of non-coding variants in the genome based on eQTLs across 49 human tissues in the GTEx project. TVAR learns the relationships between high-dimensional epigenomics and eQTLs across tissues, taking the correlation among tissues into account to understand shared and tissue-specific eQTL effects. As a result, TVAR outputs tissue-specific annotations, with an average AUROC of 0.77 across these tissues. We evaluate TVAR’s performance on four complex diseases (coronary artery disease, breast cancer, Type 2 diabetes and Schizophrenia), using TVAR’s tissue-specific annotations, and observe its superior performance in predicting functional variants for both common and rare variants, compared with five existing state-of-the-art tools. We further evaluate TVAR’s G-score, a scoring scheme across all tissues, on ClinVar, fine-mapped GWAS loci, Massive Parallel Reporter Assay (MPRA) validated variants and observe the consistently better performance of TVAR compared with other competing tools.Availability and implementationThe TVAR source code and its scores on the ClinVar catalog, fine mapped GWAS Loci, high confidence eQTLs from GTEx dataset, and MPRA validated functional variants are available at https://github.com/haiyang1986/TVAR.Supplementary informationSupplementary data are available atBioinformaticsonline.