An integrative approach to disease gene discovery combining genetic variation, gene expression, and epigenetics.

结合遗传变异、基因表达和表观遗传学的疾病基因发现的综合方法。

• 批准号: 10349878
• 负责人: Wei Zhou
• 金额: $ 15.97万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-01 至 2024-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10349878
• 关键词: Address; Award; Biological; Biological Process; Cells; Characteristics; Chromosome Mapping; Clinical Medicine; Clinical Treatment; Complex; Consensus; Coronary artery; Data; Data Analyses; Data Set; Development; Disease; Distant; Educational process of instructing; Educational workshop; Ensure; Environment; Epigenetic Process; Etiology; Future; Gene Expression; General Hospitals; Genes; Genetic; Genetic Variation; Genotype-Tissue Expression Project; Goals; Guidelines; Heritability; Heterogeneity; Institutes; Leadership; Linkage Disequilibrium; Maps; Massachusetts; Measures; Medical; Mentors; Mentorship; Meta-Analysis; Methodology; Methods; Modeling; Molecular; Multiomic Data; Participant; Pathway interactions; Phase; Phenotype; Proteomics; Quantitative Trait Loci; Research; Research Personnel; Research Training; Resources; Running; Sampling; Scientist; Signal Transduction; Statistical Methods; Technology; Testing; Therapeutic; Tissue Sample; Tissues; Training; Transcript; Translating; Translations; Untranslated RNA; Variant; Work; age related; base; biobank; cell type; cohort; data tools; design; disorder risk; epigenomics; experience; functional genomics; gene discovery; genetic analysis; genetic association; genetic variant; genome wide association study; genome-wide; genome-wide analysis; genomic variation; high dimensionality; human disease; improved; insight; large scale data; macula; metabolomics; method development; molecular phenotype; novel; open source; rare variant; repository; single-cell RNA sequencing; skills; symposium; tool; trait; transcriptome; transcriptome sequencing; transcriptomics; translational genetics

PROJECT SUMMARY ABSTRACT Genome-wide association studies (GWASs) have uncovered hundreds of thousands of disease-associated genetic variations, but a remarkable disconnect persists between GWAS findings and biological insight required for clinical treatments and medicine advancement. Pinpointing the functional consequences of variants found in GWASs is complicated by linkage disequilibrium (LD) and the inability to interpret non-coding variations. Systematic genetic analysis of high-dimensional molecular and cellular datasets such as transcriptomics, epigenomics, proteomics, and metabolomics, offers the potential to bridge the gap from complex trait association to relevant biological processes yet poses unsolved computational and analytical challenges. The candidate proposes to address major gaps in existing methodologies for mapping the genetic basis of molecular phenotypes and integrating multi-omics data to improve disease gene discovery by developing a suite of open-source statistical methods and publicly available analytical resources. The candidate will: 1) develop a novel scalable statistical method to detect genome-wide expression quantitative trait loci (eQTL) using large- scale bulk or single-cell RNA sequencing (scRNA-seq) data with an extension for rare variants; 2) assemble and analyze more than 24 readily available bulk and scRNA-seq data sets for a comprehensive repository containing cis- and trans-eQTLs of both common and rare variations; 3) develop an integrative method to improve power for disease gene discovery by combining epigenetics, genome-wide eQTLs, and genetic variations. The proposed research and training plan were carefully designed to confer expertise in four domains: 1) transcriptomics and epigenomics, 2) statistical methods development, 3) large-scale data analysis and tools, and 4) professional development. These skills are fundamental to the candidate’s goal of becoming a leading investigator who develops and applies statistical methods to understand molecular mechanisms of complex diseases and traits. In addition to research training, the candidate will take coursework to gain greater expertise in transcriptomics and functional genomics, participate in regular seminars, attend workshops and conferences, and gain mentorship and teaching experience. All research will be conducted in the Analytic and Translational Genetics Unit at Massachusetts General Hospital and the Broad Institute with mentorship from renowned scientists Drs. Benjamin Neale and Mark Daly. Additional guidance from leading experts Drs. Xihong Lin, Ramnik Xavier, Kristin Ardlie, and Bradley Bernstein will ensure exceptional guidance and support. Overall, the training environment is outstanding, the mentors and advisors are world-class, the proposed studies address an urgent unmet need, and the additional skills gained in this award will poise the candidate to establish independent leadership in leveraging statistical genetics and large-scale multi-omics data for disentangling the etiology of complex diseases.

项目概要摘要 全基因组关联研究（GWAS）已经发现了数十万个与疾病相关的 遗传变异，但 GWAS 结果与所需的生物学见解之间仍然存在显着脱节 用于临床治疗和医学进步。查明发现的变异的功能后果 GWAS 因连锁不平衡 (LD) 和无法解释非编码变异而变得复杂。 高维分子和细胞数据集的系统遗传分析，例如转录组学、 表观基因组学、蛋白质组学和代谢组学，提供了弥合复杂性状关联之间差距的潜力 相关生物过程尚未解决的计算和分析挑战。 候选人提议解决现有方法学中的主要差距，以绘制遗传基础图谱 分子表型和整合多组学数据，通过开发套件来改善疾病基因发现 开源统计方法和公开可用的分析资源。候选人将：1）制定 使用大数据检测全基因组表达数量性状位点（eQTL）的新型可扩展统计方法 大规模批量或单细胞 RNA 测序 (scRNA-seq) 数据，并扩展了稀有变异； 2) 组装和 分析超过 24 个现成的批量和 scRNA-seq 数据集，形成一个综合存储库，其中包含 常见和罕见变异的顺式和反式 eQTL； 3）开发综合方法来提高功率 通过结合表观遗传学、全基因组 eQTL 和遗传变异来发现疾病基因。 拟议的研究和培训计划经过精心设计，旨在传授四个领域的专业知识：1) 转录组学和表观基因组学，2）统计方法开发，3）大规模数据分析和工具， 4) 专业发展。这些技能对于候选人成为领导者的目标至关重要 开发并应用统计方法来了解复杂分子机制的研究人员 疾病和特征。除了研究培训外，候选人还将参加课程以获得更多专业知识 在转录组学和功能基因组学领域，参加定期研讨会、研讨会和会议， 并获得指导和教学经验。所有研究都将在分析和转化中进行 马萨诸塞州总医院和布罗德研究所的遗传学部门，得到知名人士的指导 科学家博士。本杰明·尼尔和马克·戴利。来自领先专家 Drs 的额外指导。林熙红，拉姆尼克 泽维尔、克里斯汀·阿德利和布拉德利·伯恩斯坦将确保提供卓越的指导和支持。总体而言，培训 环境优越，导师和顾问都是世界一流的，拟议的研究解决了一个紧迫的问题 未满足的需求，以及在此奖项中获得的额外技能将使候选人能够建立独立的 在利用统计遗传学和大规模多组学数据来解开疾病病因方面发挥着领导作用 复杂的疾病。