BACKGROUND Identification of functional non-coding variants and their mechanistic interpretation is a major challenge of modern genomics, especially for precision medicine. Transcription factor (TF) binding profiles and epigenomic landscapes in reference samples allow functional annotation of the genome, but do not provide ready answers regarding the effects of non-coding variants on phenotypes. A promising computational approach is to build models that predict TF-DNA binding from sequence, and use such models to score a variant's impact on TF binding strength. Here, we asked if this mechanistic approach to variant interpretation can be combined with information on genotype-phenotype associations to discover transcription factors regulating phenotypic variation among individuals. RESULTS We developed a statistical approach that integrates phenotype, genotype, gene expression, TF ChIP-seq, and Hi-C chromatin interaction data to answer this question. Using drug sensitivity of lymphoblastoid cell lines as the phenotype of interest, we tested if non-coding variants statistically linked to the phenotype are enriched for strong predicted impact on DNA binding strength of a TF and thus identified TFs regulating individual differences in the phenotype. Our approach relies on a new method for predicting variant impact on TF-DNA binding that uses a combination of biophysical modeling and machine learning. We report statistical and literature-based support for many of the TFs discovered here as regulators of drug response variation. We show that the use of mechanistically driven variant impact predictors can identify TF-drug associations that would otherwise be missed. We examined in depth one reported association-that of the transcription factor ELF1 with the drug doxorubicin-and identified several genes that may mediate this regulatory relationship. CONCLUSION Our work represents initial steps in utilizing predictions of variant impact on TF binding sites for discovery of regulatory mechanisms underlying phenotypic variation. Future advances on this topic will be greatly beneficial to the reconstruction of phenotype-associated gene regulatory networks.

中文翻译：

---

用于发现药物反应的转录调节因子的非编码变体的机械解释。

背景技术功能性非编码变体的鉴定及其机理解释是现代基因组学的主要挑战，特别是对于精密医学而言。参考样品中的转录因子（TF）结合谱和表观基因组图谱可对基因组进行功能注释，但不能提供有关非编码变体对表型的影响的现成答案。一种有前途的计算方法是建立可从序列预测TF-DNA结合的模型，并使用此类模型对变体对TF结合强度的影响进行评分。在这里，我们问到这种机制的变体解释方法是否可以与基因型-表型关联的信息相结合，以发现调节个体之间表型变异的转录因子。结果我们开发了一种统计方法，该方法整合了表型，基因型，基因表达，TF ChIP-seq和Hi-C染色质相互作用数据来回答这个问题。使用成淋巴细胞样细胞系的药物敏感性作为目标表型，我们测试了统计上与该表型相关的非编码变异体是否对TF的DNA结合强度具有很强的预测影响，从而确定了调节表型个体差异的TF。我们的方法依靠一种结合了生物物理建模和机器学习的新方法来预测变体对TF-DNA结合的影响。我们报告了许多基于TF的统计和文献支持，这些TF是药物反应变异的调节剂。我们表明，使用机械驱动的变异影响预测因子可以识别否则会被遗忘的TF-药物关联。我们深入研究了一个报道的转录因子ELF1与阿霉素的关联，并确定了几种可能介导这种调节关系的基因。结论我们的工作代表了利用对TF结合位点的变体影响预测来发现表型变异的调控机制的初步步骤。该主题的未来进展将对表型相关基因调控网络的重建大有裨益。结论我们的工作代表了利用对TF结合位点的变体影响预测来发现表型变异的调控机制的初步步骤。该主题的未来进展将对表型相关基因调控网络的重建大有裨益。结论我们的工作代表了利用对TF结合位点的变体影响预测来发现表型变异的调控机制的初步步骤。该主题的未来进展将对表型相关基因调控网络的重建大有裨益。