New approaches for leveraging single-cell data to identify disease-critical genes and gene sets

利用单细胞数据识别疾病关键基因和基因集的新方法

• 批准号: 10768004
• 负责人: Kushal Kumar Dey
• 金额: $ 24.9万
• 依托单位: SLOAN-KETTERING INST CAN RESEARCH
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-10 至 2026-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10768004
• 关键词: ATAC-seq; Alleles; Architecture; Area; Base Pairing; Base Sequence; Benchmarking; Biological Assay; Biological Process; CRISPR interference; CRISPR screen; Cell physiology; Cells; Clinical; Clustered Regularly Interspaced Short Palindromic Repeats; Communities; Complex; Data; Disease; Disease Progression; Drug Design; Drug Targeting; Enhancers; Future; Genes; Genetic Diseases; Genome; Genomic Segment; Genomics; Goals; Heritability; Hi-C; Human; Human Genetics; Intervention; Knock-out; Link; Manuscripts; Maps; Mentorship; Methods; Nucleic Acid Regulatory Sequences; Pharmaceutical Preparations; Pharmacotherapy; Phase; Phase Transition; Price; Process; Public Health Schools; Research; Resolution; Shapes; Signal Transduction; Specificity; Statistical Methods; Testing; Time; Tissues; Training; Untranslated RNA; Variant; Work; cell type; complex data; computer framework; computerized tools; deep learning; deep learning model; disorder risk; epigenomics; experimental study; functional genomics; genetic architecture; genetic variant; genome wide association study; genomic data; human disease; insight; machine learning method; method development; novel strategies; rare variant; risk variant; single cell analysis; single-cell RNA sequencing; statistical and machine learning; statistics; tool; trait; transcriptome sequencing; transcriptomics

PROJECT SUMMARY/ABSTRACT Nominating candidate risk genes and gene sets underlying disease-critical processes is of utmost importance for developing drug targets and informing CRISPR screening experiments. To this end, large scale single-cell genomic and epigenomic data (from RNA-seq, ATAC-seq, Perturb-seq) can be integrated with genome wide association studies (GWAS) to enhance our understanding of the genetic architecture of human complex diseases and traits. In this proposal, I plan to develop new computational approaches to integrate single- cell functional genomic and epigenomic data with GWAS data for complex diseases and traits to identify and rank disease-critical genes and gene sets characterizing functional processes, as well as pinpoint short genomic regions linked to these disease-associated genes. My K99 training will be conducted at the Harvard T.H. Chan School of Public Health, as well as the Broad Institute, under the mentorship of Dr. Alkes Price. The key areas of my training will be to develop and evaluate approaches for gene-level and gene set-level functional architecture of diseases and traits and integrative analysis of single-cell, as well as bulk, functional genomics data with human disease genetics. My proposed approaches will attempt to bridge the gap between functional genomics and human genetics and downstream clinical drug/gene intervention experiments. The long- term goal of this research is to produce a set of computational tools that identify and rank top disease-critical genes, top disease-critical gene sets characterizing cell types or cellular processes and gene-linked genomic regions for each disease/trait. These approaches will reshape our understanding of the functional architecture of human diseases at cellular level and will inform future drug perturbation and CRISPR screening experiments. The first aim of this proposal is to develop methods to identify and rank disease-critical genes by integrating common and rare variant disease associations with gene-level functional information derived from single-cell genomics experiments. Here I will develop, compare and contrast multiple gene prioritization strategies that differ in how they annotate SNPs for a gene, how they aggregate variant level associations at gene level and how they use functional data in performing the gene prioritization. The second aim of this proposal is to develop new computational strategies to assess disease information in sets of genes that underlie a cell type or cellular processes active within or across cell types in a tissue. The third aim of this proposal is to pinpoint and prioritize short genomic regions that are either proximally or functionally linked (for example, as an enhancer) to disease- critical genes and gene sets from Aims 1 and 2. Here, I plan to integrate GWAS association signal near these gene-linked regions with deep learning models that can infer allelic effects at base pair resolution and single-cell ATAC-seq data. All disease-critical genes, gene sets and gene-linked regions along with relevant computational tools will be distributed publicly to the scientific community.

项目摘要/摘要 提名候选风险基因和基因集的潜在危害疾病过程至关重要 用于开发药物目标并告知CRISPR筛查实验。为此，大型单细胞 基因组和表观基因组数据（来自RNA-Seq，Atac-Seq，werturb-Seq）可以与基因组宽集成 协会研究（GWAS），以增强我们对人类复合物遗传结构的理解 疾病和特征。在此提案中，我计划开发新的计算方法，以整合单一 细胞功能性基因组和表观基因组数据，具有GWAS数据的复杂疾病和特征，以识别 并等级至关疾病的基因和基因集来表征功能过程，并精确 与这些疾病相关基因相关的短基因组区域。我的K99培训将在 哈佛T.H.在阿尔克斯博士的指导下，陈公共卫生学院以及广大研究所 价格。我的培训的关键领域是开发和评估基因级和基因设定级别的方法 疾病和性状的功能结构以及单细胞的综合分析以及批量功能 具有人类疾病遗传学的基因组学数据。我提出的方法将试图弥合 功能基因组学和人类遗传学以及下游临床药物/基因干预实验。长期 这项研究的术语目标是生产一组计算工具，以识别和对关键性疾病的最高措施进行排名 基因，主要疾病关键基因集，表征细胞类型或细胞过程以及基因连接基因组 每个疾病/性状的区域。这些方法将重塑我们对功能架构的理解 人类疾病在细胞水平上，将为未来的药物扰动和CRISPR筛查实验提供信息。 该提案的第一个目的是开发通过整合识别和对关键疾病基因进行识别和对的方法 与单细胞得出的基因级功能信息的常见和稀有变体疾病关联 基因组学实验。在这里，我将开发，比较和对比多种基因优先级策略 在它们如何注释基因的SNP中，它们如何在基因水平上汇总变异水平关联及其如何 在执行基因优先级时使用功能数据。该提议的第二个目的是开发新的 计算策略以评估细胞类型或细胞基因的基因集中的疾病信息 在组织中或跨细胞类型内或跨越细胞类型的过程。该提案的第三个目的是确定并确定优先级 近端或功能连接（例如，作为增强子）与疾病 - 关键基因和基因集来自目标1和2。在这里，我计划在这些附近整合GWAS关联信号 具有深度学习模型的基因连接区域，可以在基本对分辨率和单细胞下推断等位基因效应 ATAC-SEQ数据。所有关键疾病基因，基因集和基因连接区域以及相关的计算区域 工具将公开分发给科学界。