Integrating epigenomics with DNA breathing dynamics for human non-coding disease variants

摘要 全基因组关联研究和复杂疾病的全基因组测序揭示了 过多的疾病风险变异，其中大多数存在于DNA的非编码区，难以解释 功能。非编码变异体的主要作用机制是改变染色质对转录的可及性 因子(TF)，从而影响基因表达。预测非编码变体对转铁蛋白结合的影响 因此，大规模的基因表达是重要的，但仍然具有挑战性。可用的计算工具 对于预测调控变异体，很大程度上依赖于TF结合基序模型和/或局部染色质修饰 功能。在这里，我们的目标是开发一种新的计算框架来解决这些问题的两个主要限制 方法：研究方法。首先，考虑到已知的疾病原因非编码变体通常存在于Tf结合之外 基序外，我们如何提高对Tf结合变异体的预测？为此，我们计划 将Tf CHIP-SEQ数据与对Tf绑定很重要但在 以前的方法，特别是DNA呼吸动力学(AIM1)。DNA呼吸反映局部瞬变 由于温度波动，DNA双螺旋打开。我们已经证明，基因变异可以影响 附近(多达几百个碱基对)影响转铁蛋白结合的DNA呼吸动力学。使用TF CHIP-SEQ 数据，我们将训练模型来预测在TF基序内或之外的特定TF结合变体，并结合DNA 呼吸动力学与其他功能，如DNA形状和协同转铁蛋白结合。其次，鉴于 染色质特征仅显示与复合体相关的遗传变异的适度(2倍)丰富 疾病或特征，我们如何才能提高对调控变异的预测？为此，我们将构建一个 计算模型，考虑等位基因特定的染色质可及性(ASCA；即，一个 杂合子个体在染色质可及性分析中显示读取不平衡)作为功能读出 监管变种(AIM2)。我们已经证明，神经性ASCA单核苷酸多态在那些牵连的人中高度丰富 精神分裂症(SZ)GWAS。使用神经元ASCA数据，我们将训练预测变异的模型 监管效果，利用我们的TF特定分类器(来自AIM1)。作为概念的证明，这些模型 将被应用于一个大型的SZ Gwas数据集，以预测假定的因果调控变体。我们将验证 预测的顶级调控SZ变异体对动力良好的HiPSC样本中基因表达的影响 通过结合基于多重CRISPR的SNP编辑和单细胞RNA-SEQ分析(AIM3)。用于播放SNPs 最强的调控作用，我们将进一步使用CRISPR编辑来验证SNP对基因表达的影响 以及与疾病相关的神经元表型。准确预测影响转铁蛋白的非编码变体将使 更好地理解复杂疾病中涉及的大量非编码变异并提供帮助 制定可测试的生物学假说，最终促进靶向治疗的发展。

ABSTRACT Genome-wide association studies (GWAS) and whole genome sequencing of complex diseases have revealed a plethora of disease risk variants, most of which lie in noncoding regions of DNA without easily interpretable function. A main functional mechanism of noncoding variants is to alter chromatin accessibility to transcription factors (TFs), thereby influencing gene expression. Predicting the effects of noncoding variants on TF binding and gene expression on a large scale is thus important but remains challenging. Available computational tools for predicting regulatory variants largely rely on TF-binding motif models and/or local chromatin modification features. Here, we aim to develop a novel computational framework to address two major limitations of these methods. First, given that known disease causal noncoding variants often reside outside of TF binding motifs, how can we improve the prediction of TF binding variants outside of motifs? For this, we plan to integrate TF ChIP-seq data with features that are important for TF binding but have not been considered in previous methods, in particular the DNA breathing dynamics (AIM1). DNA breathing reflects local transient opening of the DNA double helix due to thermal fluctuations. We have shown that genetic variants can affect nearby (up to a few hundred base pairs) DNA breathing dynamics that affect TF binding. Using TF ChIP-seq data, we will train models that predict specific TF binding variants in or outside TF motifs, incorporating DNA breathing dynamics with other features such as DNA shapes and cooperative TF binding. Secondly, given that chromatin features only show modest (<2-fold) enrichment of genetic variants associated with complex diseases or traits, how can we improve the prediction of regulatory variants? For this, we will build a computation model, considering the allele-specific chromatin accessibility (ASCA; i.e., two alleles of a heterozygous individual show read imbalance in chromatin accessibility assays) as a functional readout of a regulatory variant (AIM2). We have shown that neuronal ASCA SNPs are highly enriched for those implicated by schizophrenia (SZ) GWAS. Using neuronal ASCA data, we will train models that predict variants with regulatory effects, taking advantage of our TF-specific classifiers (from AIM1). As a proof of concept, the models will be applied to a large SZ GWAS dataset to predict putative causal regulatory variants. We will validate the effects of the predicted top-ranking regulatory SZ variants on gene expression in a well-powered hiPSC sample by combining multiplex CRISPR-based SNP editing and single-cell RNA-seq analysis (AIM3). For SNPs showing the strongest regulatory effects, we will further use CRISPR editing to verify the SNP effect on gene expression and disease-relevant neuronal phenotypes. Accurately predicting TF-affecting noncoding variants will enable better understanding of the large number of noncoding variants implicated in complex disorders and help formulate testable biological hypotheses, ultimately facilitating the development of targeted therapeutics.