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

基本信息

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
项目类别：
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

项目摘要

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.

项目摘要在过去的十年中，全基因组关联研究发现了与疾病相关的复杂遗传同时，整个基因组测序研究的变异已经确定了风险等位基因孟德尔和复杂疾病。这些变体有可能阐明人类疾病机制。但是有一些重要的挑战。超过90％的复杂疾病相关变体位于非编码区域内，提出了识别相关细胞类型和细胞状态的挑战，靶基因和调节机制。将这些变体与基因本身联系起来的重要任务可能是具有挑战性的。另外，作为我们识别从头识别的能力和罕见的复合物和孟德尔人的突变疾病正在迅速扩展，定义了这些从头等位基因的功能，这些基因和途径是情感仍然不确定。为了应对这些挑战，我们将预测疾病风险变异的功能影响单个变体，个体基因和阐明疾病生物学的途径。在这个建议的所有目标中，我们将利用IGVF功能基因组数据。在AIM 1中，我们将预测变体的调节潜力至关疾病的细胞类型/状态以单基对分辨率。我们将通过在疾病环境中分析单细胞转录数据集，然后整合单细胞表观遗传学确定这些罕见疾病细胞园的调节景观的数据。这些监管区域在该分析可用于注释潜在功能的变体。最后，了解调节区域中的特定变体，我们使用大规模的整个基因组来量化选择性压力测序数据。在AIM 2中，我们将通过有效将变体与基因联系起来来预测基因的功能影响。定义因果疾病基因至关重要，因为它们对于治疗靶向可能很重要。我们制定使用遗传数据和功能基因组数据来预测下游基因的策略，并评估这些方法具有来自孟德尔表型的一组金标准的休闲基因。在AIM 3中，我们专注于稀有和从头突变具有较大效应大小的突变。在这里，我们认识到预测这些功能等位基因需要了解其影响的途径，将稀有非编码变体连接到基因以及基于种群遗传参数定义变体功能的策略。在AIM 4中，我们开发了一个框架以与IGVF财团协同作用以促进联盟目标，概述了我们集成计划和灵活的程序化框架。该提案代表了Drs之间的合作。 Soumya Raychaudhuri，Alkes Price和Shamil Sunyaev，在功能基因组学，单细胞数据集成和人群中带来了分析专业知识遗传学。这些调查人员有与强大的出版记录成功合作的历史将功能基因组学数据与GWAS和测序研究整合到发现疾病机制。