Predicting the impact of genetic variants, genes and pathways on human Disease

项目摘要 在过去的十年里，全基因组关联研究发现了与疾病相关的复杂基因 变种，同时全基因组测序研究已经确定了风险等位基因 孟德尔式的复杂疾病。这些变种有可能揭示人类疾病。 机械装置。但也有几个重要的挑战。90%以上的复杂疾病与 变异体位于非编码区内，这给识别相关细胞类型和细胞状态带来了挑战， 靶基因和调控机制。将这些变异与基因本身联系起来的重要任务可能是 很有挑战性。此外，由于我们识别复杂和孟德尔基因的从头开始和罕见突变的能力 疾病正在迅速扩大，定义了这些从头开始的等位基因的功能，哪些基因和途径 影响仍不确定。 为了应对这些挑战，我们将在以下水平预测疾病风险变量的功能影响 阐明疾病生物学的个体变异、个体基因和途径。在这项建议的所有目标中，我们 将利用IGVF功能基因组数据。在目标1中，我们将预测变异体在 单个碱基对分辨的疾病关键细胞类型/状态。我们将通过以下方式确定致病细胞状态 分析疾病背景下的单细胞转录数据集，然后整合单细胞表观遗传学 定义这些罕见疾病细胞状态的监管格局的数据。中确定的这些监管区域 这种分析可以用来注释潜在函数的变体。最后，要了解的功能 在调控区域的特定变异中，我们使用大规模的全基因组来量化选择压力 测序数据。在目标2中，我们将通过有效地将变体与基因联系起来来预测基因的功能影响。 定义因果疾病基因是至关重要的，因为它们可能对治疗靶点很重要。我们 制定策略，利用遗传数据和功能基因组数据来预测下游基因，并评估 这些方法使用了一组来自孟德尔表型的黄金标准随意基因。在目标3中，我们将重点放在 效应大的罕见和从头开始的突变。在这里，我们认识到，预测这些因素的功能 等位基因需要了解它们影响的途径，以及将罕见的非编码变体连接到 基因，以及根据群体遗传参数定义变异体功能的策略。在目标4中， 我们开发了一个框架来与IGVF财团协同工作，以推进财团的目标，概述我们的 整合计划和灵活的方案框架。 该提案代表了Soumya Raychaudhuri博士、Alkes Price博士和Shamil博士之间的合作 Sunyaev，带来了跨越功能基因组学、单细胞数据集成和种群的分析专业知识 遗传学。这些调查人员有成功合作的历史，有很强的出版记录 将功能基因组学数据与GWAS和测序研究相结合，以揭示疾病机制。

Project Summary Over the past decade, genome-wide association studies have discovered complex disease-associated genetic variants while at the same time whole genome sequencing studies have been identifying risk alleles for Mendelian and complex diseases. These variants have the potential to shed light on human disease mechanisms. But there are several important challenges. More than 90% of complex disease associated variants lie within non-coding regions, posing a challenge of identifying relevant cell types and cell states, target genes, and regulatory mechanisms. The important task of linking these variants to genes itself can be challenging. In addition, as our ability to identify de novo and rare mutations for complex and Mendelian diseases is rapidly expanding, defining the function of those de novo alleles, which genes and pathways they affect remains uncertain. To address these challenges, we will predict the functional impact of disease risk variants at the level of individual variants, individual genes, and pathways to elucidate disease biology. In all aims of this proposal we will utilize IGVF functional genomic data. In Aim 1, we will predict the regulatory potential of variants in disease-critical cell types/states at a single base-pair resolution. We will identify pathogenic cell-states by analyzing single cell transcriptional data sets in a disease context, and then integrate single-cell epigenetic data to define the regulatory landscape of these rare disease cell-states. These regulatory regions identified in this analysis can be used to annotate variants for potential function. Finally, to understand functionality of specific variants in regulatory regions, we quantify selective pressure using large-scale whole genome sequencing data. In Aim 2, we will predict functional impacts of genes by effectively linking variants to genes. Defining causal diseases genes is critically important since they may be important for therapeutic targeting. We develop strategies to use genetic data and functional genomic data to predict downstream genes, and evaluate these methods with a set of gold-standard casual genes from Mendelian phenotypes. In Aim 3, we focus on rare and de novo mutations with large effect sizes. Here we recognize that predicting the function of these alleles requires an understanding of the pathways they effect, models to connect rare non-coding variants to genes, and strategies to define functionality of the variants based on population genetic parameters. In Aim 4, we develop a framework to synergize with the IGVF consortium to advance consortium goals, outlining our integration plan and flexible programmatic framework. The proposal represents a collaboration between Drs. Soumya Raychaudhuri, Alkes Price, and Shamil Sunyaev, bringing analytical expertise across functional genomics, single-cell data integration, and population genetics. These investigators have a history of successful collaborations with a strong publication records integrating functional genomics data with GWAS and sequencing studies to uncover disease mechanisms.