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。

基本信息

批准号：
10581608
负责人：
Wei Zhou
金额：
$ 15.94万
依托单位：
MASSACHUSETTS GENERAL HOSPITAL
依托单位国家：
美国
项目类别：
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/10581608
关键词：
Address Age related macular degeneration Award Biological Biological Process Calibration Cells Characteristics Chromosome Mapping Clinical Medicine Clinical Treatment Complex Consensus Coronary Arteriosclerosis 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 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 Resolution Resources Running Sampling Schizophrenia Scientist Signal Transduction Statistical Methods Technology Testing Therapeutic Tissues Training Transcript Translating Translations Untranslated RNA Variant Work 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 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

项目摘要

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.

项目摘要摘要全基因组关联研究（GWASS）发现了数十万疾病相关的遗传变异，但GWAS的发现与所需的生物学见解之间存在显着的断开连接用于临床治疗和医学进步。精确指出了在 Gwass因连锁二动能（LD）和无法解释非编码变化而变得复杂。高维分子和细胞数据集（例如转录组学）的系统遗传分析，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间隙的潜力对于相关的生物过程，但提出了未解决的计算和分析挑战。候选提议解决现有方法中的主要差距，以绘制遗传基础分子表型和整合多摩学数据以通过开发套件来改善疾病基因的发现开源统计方法和公开可用的分析资源。候选人将：1）开发新型可伸缩统计方法，用于检测全基因组表达定量性状位置（EQTL）尺度散装或单细胞RNA测序（SCRNA-SEQ）数据具有稀有变体的扩展； 2）组装和分析超过24个可用的批量和SCRNA-SEQ数据集，以备受综合存储库常见和罕见变化的顺式和反式EQTL； 3）开发一种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，以在四个领域的专业知识：1）上进行专业知识。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先的目标至关重要开发和应用统计方法来了解复合物的分子机制的研究者疾病和特征。除了研究培训外，候选人还将参加课程以获得更大的专业知识在转录组学和功能基因组学上，参加常规的半手，参加研讨会和会议，并获得心态和教学经验。所有研究将在分析和翻译中进行马萨诸塞州综合医院和广大研究所的遗传学部门，来自著名的科学家博士。本杰明·尼尔（Benjamin Neale）和马克·戴利（Mark Daly）。领先专家DRS的其他指导。 Xihong Lin，Ramnik Xavier，Kristin Ardlie和Bradley Bernstein将确保出色的指导和支持。总体而言，培训环境是杰出的，导师和顾问是世界一流的，拟议的研究涉及紧急的未满足的需求，该奖项中获得的额外技能将毒害候选人建立独立的候选人利用统计遗传学和大规模多摩斯数据的领导才能解散病因复杂的疾病。