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: 10342464
• 负责人: Kushal Kumar Dey
• 金额: $ 10.88万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-01 至 2023-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10342464
• 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; Gold; Heritability; Hi-C; Human; Human Genetics; Institutes; Intervention; Knock-out; Link; Manuscripts; Maps; Mentorship; Methods; Nucleic Acid Regulatory Sequences; Pharmaceutical Preparations; Pharmacotherapy; Phase; Phase Transition; Price; Process; Processed Genes; Public Health Schools; Research; Resolution; Signal Transduction; Specificity; Statistical Methods; Testing; Time; Tissues; Training; Untranslated RNA; Variant; Work; base; 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 提名疾病关键过程的候选风险基因和基因集是至关重要的 用于开发药物靶点和通知CRISPR筛查实验。为此，大规模的单细胞 基因组和表观基因组数据(来自rna-seq、atac-seq、perturb-seq)可以与全基因组集成。 联合研究(GWAS)，以加强我们对人类复合体遗传结构的理解 疾病和特征。在这项提案中，我计划开发新的计算方法来整合单个- 细胞功能基因组和表观基因组数据与用于鉴定复杂疾病和性状的GWAS数据 并对疾病关键基因和表征功能过程的基因集进行排序，以及精确定位 与这些疾病相关基因相关联的短基因组区域。我的K99培训将在 哈佛大学公共卫生学院，以及阿尔克斯博士指导下的博德研究所 价格。我的培训的主要领域将是开发和评估基因水平和基因集水平的方法 疾病和特性的功能架构与单细胞和整体功能的综合分析 基因组学数据与人类疾病遗传学。我提出的方法将试图弥合 功能基因组学与人类遗传学及下游临床药物/基因干预实验。长的- 这项研究的术语目标是产生一套计算工具，以识别和排名最关键的疾病 基因、表征细胞类型或细胞过程的顶级疾病关键基因集和与基因连锁的基因组 每种疾病/特征的区域。这些方法将重塑我们对功能体系结构的理解 在细胞水平上研究人类疾病，并将为未来的药物扰动和CRISPR筛查实验提供信息。 这项提议的第一个目标是开发方法来识别和排序疾病关键基因 来自单细胞的基因水平功能信息与常见和罕见变异疾病的关联 基因组学实验。在这里，我将开发、比较和对比多种不同的基因优先排序策略 在他们如何注释一个基因的SNPs，他们如何在基因水平上聚集变异水平的关联，以及他们如何 在执行基因优先排序时使用功能数据。这项提议的第二个目标是开发新的 评估构成细胞类型或细胞的一组基因中的疾病信息的计算策略 在组织中活跃的细胞类型内或跨细胞类型的过程。这项提案的第三个目标是明确并确定优先顺序 与疾病有近端或功能连锁(例如，作为增强子)的短基因组区域- 来自目标1和目标2的关键基因和基因组。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，可以在碱基对分辨率和单细胞水平上推断等位基因效应 ATAC-SEQ数据。所有疾病关键基因、基因组和基因连锁区域以及相关的计算 这些工具将向科学界公开分发。

项目成果

SAIGE-GENE+ improves the efficiency and accuracy of set-based rare variant association tests.

• DOI: 10.1038/s41588-022-01178-w
• 发表时间: 2022-10
• 期刊: NATURE GENETICS
• 影响因子: 30.8
• 作者: Zhou, Wei;Bi, Wenjian;Zhao, Zhangchen;Dey, Kushal K.;Jagadeesh, Karthik A.;Karczewski, Konrad J.;Daly, Mark J.;Neale, Benjamin M.;Lee, Seunggeun
• 通讯作者: Lee, Seunggeun