Fine-mapping causal tissues at disease-associated loci to infer disease subtypes

精细绘制疾病相关位点的致病组织以推断疾病亚型

• 批准号: 10600499
• 负责人: Benjamin Strober
• 金额: $ 6.91万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10600499
• 关键词: Automobile Driving; Biological Assay; Biology; Candidate Disease Gene; Communities; Complex; Computer software; Data; Development; Disease; Drug Targeting; Evaluation; Gene Expression; Genes; Genetic; Genetic Models; Genetic Risk; Genotype; Individual; Knowledge; Link; Maps; Mediating; Metabolic; Methods; Patient Care; Pattern; Phenotype; Physiological; Postdoctoral Fellow; Probability; Regulation; Research; Research Personnel; Statistical Methods; Testing; Tissues; Training; Variant; Work; biological systems; career; causal variant; disease heterogeneity; disorder subtype; genome wide association study; genomic locus; insight; interest; novel; open source; polygenic risk score; skills; statistics; trait; transcriptome

Project Summary (Abstract) Genome-wide association studies (GWAS) have been extremely successful in identifying genetic loci associated with complex diseases, but the causal tissue and functional mechanism underlying the association at each locus is generally unknown. Recent work has shown gene expression across tissues can be used as an intermediate phenotype to fill in these missing links between genotype and disease. An accurate mapping from disease locus to the causal tissue and gene mediating that disease locus would further our understanding of disease biology and provide novel insights into tissue-linked mechanisms of disease. However, accurate inference of the causal tissue and gene driving a disease locus can be obscured by genetic co-regulation (correlation among variant effects on gene expression) across tissues as well as genes. The first aim of this proposal is to develop a new statistical method to accurately infer the causal tissue and gene driving a disease locus, while explicitly modeling genetic co-regulation between tissues and genes. The second aim of this proposal is to infer disease subtypes linked to specific tissues using tissue-specific polygenic risk scores (PRS) generated by annotating disease loci according to their causal tissue type, utilizing the method developed in the first aim. I expect tissue-specific PRS to uncover patterns of tissue-specific genetic risk across individuals that correspond to different subtypes of the disease. If achieved, this work will refine our knowledge of causal tissues and genes underlying disease loci, as well as provide a new paradigm for inferring the disease subtype of each individual in a disease association study. More generally, this work has the potential to refine the set of drug target candidate disease genes and facilitate the development of patient care and treatments specific to disease subtypes. In addition, I propose an in-depth training plan that leverages the research community throughout Harvard to provide me with the training and skills necessary to advance my career from a post- doctoral fellow to an independent investigator.

项目摘要（摘要） 全基因组关联研究（GWAS）在识别遗传位点方面非常成功 与复杂疾病相关，但这种关联背后的因果组织和功能机制 在每一个位点上通常是未知的。最近的研究表明，跨组织的基因表达可以用作 一种中间表型来填补基因型和疾病之间缺失的联系。一个精确的映射 从疾病位点到致病组织和介导该疾病位点的基因将进一步加深我们的理解 疾病生物学和提供新的见解组织相关的疾病机制。然而，准确 致病组织和驱动疾病位点的基因的推断可以被遗传共调节所掩盖 （变异对基因表达的影响之间的相关性）。第一个目标是 一项提案是开发一种新的统计方法，以准确推断导致疾病的组织和基因 基因座，同时明确建模组织和基因之间的遗传共调节。第二个目的是 一项建议是使用组织特异性多基因风险评分（PRS）推断与特定组织相关的疾病亚型 通过根据致病组织类型注释疾病位点产生，利用在 第一个目标。我希望组织特异性PRS能揭示个体间组织特异性遗传风险的模式 对应不同亚型的疾病如果能够实现，这项工作将完善我们对因果关系的认识。 疾病位点的组织和基因，并为推断疾病亚型提供了新的范式 在疾病关联研究中的每一个人。更一般地说，这项工作有可能完善一套 药物靶向候选疾病基因，促进患者护理和治疗的发展， 疾病亚型。此外，我提出了一个深入的培训计划，利用研究界 在整个哈佛，为我提供必要的培训和技能，以促进我的职业生涯从一个后， 博士研究员的独立调查员。