Integrating epigenomic maps to predict regulatory functions of genetic variants

描述(申请人提供)：精神疾病是影响人类人口的一些最具破坏性的疾病，给个人、家庭和社会带来巨大负担。全基因组关联研究已经发现了数十种常见的与精神疾病相关的单核苷酸多态(SNPs)，但这些SNPs中的大多数已经被定位到基因间或内含子区域，并且在功能上没有分类。现有的软件或算法只查询多个数据库并生成命中列表，而没有智能集成，并且忽略了许多有价值的监管信息。这项提议的总体目标是将所有可用的遗传、基因组和表观基因组数据整合到 生成关于SNP对大脑中基因表达水平的影响的概率预测。我们以前的研究表明，精神疾病的Gwas信号中富含大脑eQTL SNPs(ESNPs)，这些eSNPs可能具有功能并与疾病易感性有关。我们将使用eQTL中的SNPs锚定一系列证据，包括组蛋白标记、保守序列、转录因子结合位点、DNA甲基化、可访问的染色质、非编码RNA和其他数据。我们将使用机器学习方法，根据这些表观遗传标记与其目标基因之间的已知关系，以及它们在基因组中的不同模式，来预测调节性SNPs。我们还将使用我们的新的无监督去卷积算法从不同种类的脑组织数据中提取细胞类型(即神经元与非神经元)的特定度量，以改进我们的预测。我们将使用统计和实验方法来验证预测。定量PCR和CRISPR-Cas9将用于诱导多能细胞系，以比较预测的功能性SNPs等位基因的基因表达水平。算法和预测的功能变体都将通过网站和独立应用程序公开。这一新的算法将通过揭示与疾病相关的非编码SNPs的基因调控功能，显著提高我们对精神疾病遗传学的理解。

DESCRIPTION (provided by applicant): Mental illnesses are some of the most devastating diseases affecting human populations, placing a huge burden on individuals, families and society. Genome-wide association studies (GWAS) have identified dozens of common single nucleotide polymorphisms (SNPs) that are associated with psychiatric diseases, but a majority of those SNPs have been mapped to intergenic or intronic regions and are functionally unclassified. Existing software or algorithms only query multiple databases and produce lists of hits without intelligent integration and ignore much of the valuable regulatory information. The overall goal of this proposal is to integrate all available genetic, genomic and epigenomic data to generate a probability-based prediction about a SNP's influence on gene expression level in brain. Our previous studies have shown that psychiatric GWAS signals are enriched with brain eQTL SNPs (eSNPs), and these brain eSNPs are likely to be functional and contribute to disease susceptibilities. We will use SNPs in eQTLs to anchor a chain of evidence incorporating histone marks, conserved sequences, transcription factor binding sites, DNA methylation, accessible chromatins, non-coding RNA, and other data. We will use a machine learning method to predict regulatory SNPs based on known relationships between these epigenetic marks and their target genes, as well as their distinct patterns in genome. We will also use our novel unsupervised deconvolution algorithm to extract cell-type (i.e., neuron vs. non-neuron) specific measures from heterogeneous brain tissue data to improve our predictions. We will use both statistical and experimental methods to validate the predictions. Quantitative PCR and CRISPR-cas9 will be used on induced pluripotent cell lines to compare gene expression levels of alleles of predicted functional SNPs. Both algorithm and predicted functional variants will made public via a website and standalone application. The novel algorithm will significantly improve our understanding of psychiatric disease genetics by uncovering the gene-regulatory functions for disease-associated, non-coding SNPs.