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

预测遗传变异、基因和途径对人类疾病的影响

• 批准号: 10483152
• 负责人: ALKES L PRICE
• 金额: $ 78.89万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-07 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10483152
• 关键词: ATAC-seq; Address; Affect; Alleles; Base Pairing; Benchmarking; Biological; Biological Assay; Biology; CRISPR screen; Catalogs; Cell physiology; Cells; ChIP-seq; Chromatin; Code; Collaborations; Complex; Computer software; Computing Methodologies; Data; Data Set; Development; Disease; Disease Pathway; Drug Targeting; Epigenetic Process; Gene Expression Regulation; Gene Frequency; Genes; Genetic; Genetic Transcription; Genetic Variation; Genetic study; Genomic approach; Genomics; Genotype; Goals; Gold; Heritability; Heterogeneity; Hi-C; Human Genetics; Individual; Letters; Light; Link; Localized Disease; Mendelian disorder; Methods; Modeling; Molecular; Mutation; Network-based; Nucleic Acid Regulatory Sequences; Parental Ages; Pathogenicity; Pathway interactions; Phenotype; Population Genetics; Price; Publications; Rare Diseases; Recording of previous events; Records; Regulatory Element; Research Personnel; Resolution; Sampling; Source; Testing; Therapeutic; Time; Untranslated RNA; Validation; Variant; base; causal variant; cell type; clinically actionable; cohort; data exchange; data integration; de novo mutation; differential expression; disorder risk; flexibility; functional genomics; genetic variant; genome sequencing; genome wide association study; genome-wide; genomic data; human disease; improved; insight; interest; large scale data; pressure; programs; rare variant; risk variant; single cell analysis; single-cell RNA sequencing; success; therapeutic target; tool; trait; transcription factor; transcriptomics; whole genome

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.

项目概要 在过去的十年中，全基因组关联研究发现了与疾病相关的复杂遗传 变异，同时全基因组测序研究已经确定了风险等位基因 孟德尔疾病和复杂疾病。这些变异有可能揭示人类疾病 机制。但还有几个重要的挑战。 90%以上的复杂疾病相关 变异位于非编码区域内，对识别相关细胞类型和细胞状态提出了挑战， 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以是 具有挑战性的。此外，由于我们有能力识别复杂突变和孟德尔突变的从头突变和罕见突变 疾病正在迅速蔓延，定义了这些从头等位基因的功能，它们所涉及的基因和途径 影响仍不确定。 为了应对这些挑战，我们将预测疾病风险变异的功能影响 个体变异、个体基因和阐明疾病生物学的途径。在本提案的所有目标中，我们 将利用 IGVF 功能基因组数据。在目标 1 中，我们将预测变体的监管潜力 单碱基对分辨率下的疾病关键细胞类型/状态。我们将通过以下方式识别致病细胞状态 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 数据来定义这些罕见疾病细胞状态的监管格局。这些监管区域确定在 该分析可用于注释潜在功能的变体。最后，了解一下功能 针对调节区域中的特定变异，我们使用大规模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异与基因联系起来来预测基因的功能影响。 定义致病基因至关重要，因为它们对于治疗靶向可能很重要。我们 制定策略以使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金标准休闲基因。在目标 3 中，我们重点关注 具有大效应的罕见突变和从头突变。在这里我们认识到预测这些的功能 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的模型 基因，以及根据群体遗传参数定义变体功能的策略。在目标 4 中， 我们制定了一个与 IGVF 联盟协同推进联盟目标的框架，概述了我们的 整合计划和灵活的计划框架。 该提案代表了博士之间的合作。 Soumya Raychaudhuri、阿尔克斯·普莱斯和沙米尔 Sunyaev，带来功能基因组学、单细胞数据集成和群体的分析专业知识 遗传学。这些研究人员有着成功合作的历史和强大的发表记录 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。