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.

项目概要/摘要 提名潜在疾病关键过程的候选风险基因和基因集至关重要 用于开发药物靶点并为 CRISPR 筛选实验提供信息。为此，大规模单细胞 基因组和表观基因组数据（来自 RNA-seq、ATAC-seq、Perturb-seq）可以与全基因组整合 关联研究（GWAS）以增强我们对人类复合体遗传结构的理解 疾病和特征。在这个提案中，我计划开发新的计算方法来集成单 细胞功能基因组和表观基因组数据以及用于复杂疾病和性状的 GWAS 数据来识别 对疾病关键基因和表征功能过程的基因集进行排序，并精确定位 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 哈佛大学 T.H.陈公共卫生学院以及布罗德研究所，在 Alkes 博士的指导下 价格。我培训的关键领域将是开发和评估基因水平和基因集水平的方法 疾病和性状的功能结构以及单细胞以及批量、功能的综合分析 人类疾病遗传学的基因组学数据。我提出的方法将尝试弥合之间的差距 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- 这项研究的长期目标是开发一套计算工具来识别和排名最重要的疾病关键 基因、表征细胞类型或细胞过程的顶级疾病关键基因集以及基因连锁基因组 每种疾病/性状的区域。这些方法将重塑我们对功能架构的理解 在细胞水平上研究人类疾病，并将为未来的药物扰动和 CRISPR 筛选实验提供信息。 该提案的首要目标是开发通过整合来识别和排序疾病关键基因的方法 常见和罕见变异疾病与来自单细胞的基因水平功能信息的关联 基因组学实验。在这里，我将开发、比较和对比不同的多个基因优先级策略 他们如何注释基因的 SNP、如何在基因水平上聚合变异水平关联以及如何 使用功能数据来执行基因优先级排序。该提案的第二个目标是开发新的 评估细胞类型或细胞基础基因组疾病信息的计算策略 组织内或跨细胞类型的活跃过程。该提案的第三个目标是确定并优先考虑 与疾病密切相关或功能相关（例如，作为增强子）的短基因组区域 目标 1 和 2 中的关键基因和基因集。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锁区域，可以推断碱基对分辨率和单细胞的等位基因效应 ATAC-seq 数据。所有疾病关键基因、基因集和基因相关区域以及相关的计算 工具将公开分发给科学界。