Understanding the functional consequences of genetic variation in the non-coding regions of the human genome remains a challenge. We introduce h ere a computational tool, TURF, to prioritize regulatory variants with tissue-specific function by leveraging evidence from functional genomics experiments, including over 3000 functional genomics datasets from the ENCODE project provided in the RegulomeDB database. TURF is able to generate prediction scores at both organism and tissue/organ-specific levels for any non-coding variant on the genome. We present that TURF has an overall top performance in prediction by using validated variants from MPRA experiments. We also demonstrate how TURF can pick out the regulatory variants with tissue-specific function over a candidate list from associate studies. Furthermore, we found that various GWAS traits showed the enrichment of regulatory variants predicted by TURF scores in the trait-relevant organs, which indicates that these variants can be a valuable source for future studies.

中文翻译：

---

在人类基因组的非编码区域中优先考虑具有组织特异性功能的调节变体

了解人类基因组非编码区遗传变异的功能后果仍然是一个挑战。我们在这里介绍了一个计算工具 TURF，通过利用功能基因组学实验的证据（包括 RegulomeDB 数据库中提供的 ENCODE 项目的 3000 多个功能基因组学数据集）的证据，优先考虑具有组织特异性功能的调节变体。TURF 能够为基因组上的任何非编码变体生成生物体和组织/器官特异性水平的预测分数。我们通过使用来自 MPRA 实验的经过验证的变体，展示了 TURF 在预测方面的整体最佳性能。我们还展示了 TURF 如何从相关研究的候选列表中挑选出具有组织特异性功能的调节变体。此外，