Integrating common and rare genetic variation in autism spectrum disorder

整合自闭症谱系障碍中常见和罕见的遗传变异

• 批准号: 10382806
• 负责人: Daniel Weiner
• 金额: $ 3.9万
• 依托单位: HARVARD MEDICAL SCHOOL
• 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 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10382806
• 关键词: Accounting; Adult; Affect; Biological; Biological Process; Biological Response Modifier Therapy; Biology; Cell Nucleus; Code; Communication; Data Set; Disease; Doctor of Philosophy; Environment; Family; Fellowship; Foundations; Functional disorder; Funding; Gene Expression; Gene Expression Regulation; Gene Targeting; Genes; Genetic; Genetic Variation; Genome; Genomic Segment; Goals; Heritability; Impairment; Institutes; Mediating; Mediation; Mentorship; Methods; Modeling; Neurons; Pathway interactions; Pharmacological Treatment; Physicians; Population; Research Training; Resources; Risk; Risk Factors; Scientist; Symptoms; Test Result; Testing; Tissues; Training; Uncertainty; United States National Institutes of Health; Untranslated RNA; Variant; Work; autism spectrum disorder; brain tissue; career; causal variant; cell type; cohort; exome sequencing; genetic risk factor; genome wide association study; genome-wide; improved; neuropsychiatry; novel; programs; rare variant; relating to nervous system; repetitive behavior; social communication; statistics; therapeutic development; transcriptomics; transmission process

Project Summary Autism spectrum disorder (ASD) is common and often disabling. Currently, there are no pharmacologic treatments for its core symptoms: impairment in social communication and restricted/repetitive behavior. The high heritability of ASD suggests that understanding its genetic risk factors will help define ASD pathophysiology and facilitate therapeutic development. The largest genetic risk factor for ASD on a population level is common polygenic variation, the additive influence of thousands of small effect SNPs estimated with GWAS. However, interpretation of polygenic risk is challenging: most disease-associated SNPs are non- coding, the identity of their target gene(s) is uncertain, and their influence on gene expression is often unknown. Thus, the genes and biological pathways affected by common polygenic risk for ASD remain poorly defined. In contrast, recent studies of rare coding variation have associated >70 genes with ASD (“ASD Genes”), which collectively highlight ASD-relevant biological processes. To define the biology of polygenic risk for ASD, it is critically important to understand the degree to which polygenic risk converges with the genes and pathways identified through rare variant studies. We propose to apply three novel statistical and functional approaches to (a) interpret common polygenic risk for ASD and (b) assess the convergence of common polygenic risk for ASD and ASD Genes. In Aim 1, we introduce the Stratified-polygenic Transmission Disequilibrium Test (S-pTDT), which is powered to examine polygenic risk arising from small regions of the genome. We will use S-pTDT to quantify the concentration of polygenic risk for ASD in and around ASD Genes. In Aim 2, we introduce an approach to understand how polygenic risk influences gene expression, leveraging a large single-nucleus transcriptomic dataset of adult human brain tissue. We will use this approach to test the hypothesis that polygenic risk for ASD decreases expression of ASD Genes, and to nominate candidate causal genes in genomic regions with heightened polygenic relevance in ASD. In Aim 3, we introduce the Abstract Mediation Model (AMM), which quantifies the proportion of common-variant heritability mediated by gene sets while accounting for uncertainty in the gene targets of non-coding SNPs. We will apply AMM to estimate the extent to which polygenic risk for ASD is mediated by ASD Genes and other ASD-relevant gene sets. These Aims will improve our understanding of the genetic underpinnings of ASD, strengthen biological and therapeutic hypotheses, and generate methods applicable to other neuropsychiatric conditions. Finally, this research training will occur in the stimulating environment of the Harvard/MIT MD/PhD Program and Broad Institute of MIT and Harvard. Under the guidance of his sponsors, the fellowship PI will complete five training goals that will create the foundation for a career as an NIH-funded physician-scientist.

项目摘要 自闭症谱系障碍（ASD）是常见的，往往致残。目前，没有药理学 治疗其核心症状：社会沟通障碍和限制/重复行为。的 ASD的高遗传率表明，了解其遗传风险因素将有助于确定ASD 病理生理学和促进治疗发展。人群中ASD的最大遗传风险因素 水平是常见的多基因变异，数千个小效应SNP的加性影响估计与 GWAS。然而，多基因风险的解释是具有挑战性的：大多数疾病相关的SNP是非遗传性的。 编码，其靶基因的身份是不确定的，并且它们对基因表达的影响通常是不确定的。 未知因此，受ASD常见多基因风险影响的基因和生物学途径仍然很差， 定义了相比之下，最近对罕见编码变异的研究已经将>70个基因与ASD（“ASD 基因”），其共同强调ASD相关的生物过程。定义多基因风险的生物学 对于ASD，了解多基因风险与基因的融合程度至关重要 和通过罕见变异研究确定的途径。我们建议应用三种新的统计和 功能性方法（a）解释ASD的常见多基因风险和（B）评估 ASD和ASD基因的常见多基因风险。在目标1中，我们引入分层多基因 传递不平衡检验（S-pTDT），该检验用于检查由小细胞肺癌引起的多基因风险。 基因组的区域。我们将使用S-pTDT来量化ASD的多基因风险浓度， ASD基因的研究在目标2中，我们介绍了一种方法来了解多基因风险如何影响基因 表达，利用成人脑组织的大型单核转录组数据集。我们将使用 这种方法可以检验ASD的多基因风险降低ASD基因表达的假设， 在ASD中具有高度多基因相关性的基因组区域中提名候选致病基因。在目标3中， 我们引入了抽象中介模型（AMM），它量化了共同变量的比例， 基因组介导的遗传性，同时考虑非编码SNP的基因靶点的不确定性。我们 将应用AMM来估计ASD的多基因风险由ASD基因和其他基因介导的程度。 ASD相关基因集这些目标将提高我们对ASD遗传基础的理解， 加强生物和治疗假设，并产生适用于其他神经精神疾病的方法 条件最后，这项研究培训将发生在哈佛/麻省理工学院MD/PhD的刺激环境中。 麻省理工学院和哈佛的项目和广泛的研究所。在他的赞助商的指导下，奖学金PI将 完成五个培训目标，这将为作为NIH资助的医生科学家的职业生涯奠定基础。