Identifying and quantifying genetic effects on neurodevelopmental trajectories in adolescents

识别和量化遗传对青少年神经发育轨迹的影响

• 批准号: 10817321
• 负责人: Chun Chieh Fan
• 金额: $ 33.28万
• 依托单位: LAUREATE INSTITUTE FOR BRAIN RESEARCH
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-30 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10817321
• 关键词: Admixture; Adolescence; Adolescent; Adult; Age; Automobile Driving; Behavior Disorders; Behavioral; Brain; Brain region; Clinical; Communities; Complex; Computer software; Data; Data Analyses; Data Set; Detection; Development; Dimensions; Disease; Disparate; Emotional; Ensure; Epidemiology; Exhibits; Genes; Genetic; Genetic Predisposition to Disease; Genetic Risk; Goals; Human; Image; Individual; Individual Differences; Measurement; Measures; Mental disorders; Methods; Modality; Modeling; Molecular; Multimodal Imaging; National Institute of Mental Health; Neuroanatomy; Neurosciences; Participant; Pattern; Phenotype; Population; Population Genetics; Principal Component Analysis; Process; Research; Research Personnel; Risk; Risk Behaviors; Sampling; Source; Staging; Structure; System; Therapeutic; Time; Variant; cognitive development; cognitive system; cohort; critical period; data archive; data reduction; design; genetic analysis; genome wide association study; genome-wide; genomic locus; high dimensionality; improved; individual variation; innovation; instrument; multimodal neuroimaging; neurodevelopment; neuroimaging; neuropsychiatric disorder; novel; novel strategies; programs; psychiatric symptom; public health intervention; serial imaging; tool; trait

Project Summary The staging and pacing of neurodevelopmental change during adolescence exhibits observable individual differences, yet how much of this between- and within-subject variation is attributable to genetic factors is unknown. Current understanding of the genetic basis for brain structure and function measured by neuroimaging data is based on analyses of (mostly cross-sectional) adult samples. By obtaining results from a developmentally-critical period, i.e. adolescence, we will be able to determine whether the identified genetic components are time-invariant traits or time-sensitive developmental results. Our long-term goal is to determine the causal mechanisms underlying vulnerabilities to risky behavior and psychiatric disorders that emerge during adolescence. To achieve this goal, we aim to search for genetic factors associated with developmental variation measured by multi-modal neuroimaging data. We propose three approaches to perform unbiased genetic analyses of data from the Adolescent Brain Cognitive Development (ABCD) study: 1) Extract patterns of developmental changes across brain regions; 2) Identify associated genetic loci through integrated genome-wide association studies across derived developmental multi-modal imaging phenotypes; 3) Determine the relationship between genetic risk for psychiatric diseases and neurodevelopmental trajectories. We will pursue these aims utilizing a novel combination of methods from population genetics and neuroscience while innovatively tailoring the analytic strategies to avoid potential biases and spurious associations due to admixture and relatedness. The proposed research is significant because the identified genetic mechanisms will provide a potential basis for therapeutics and public health interventions, attending to the impact of the epidemiologically guided sampling of ABCD data. By sharing the tools we develop in this research program and the resulting genetic instruments, we will impact the field immediately, enabling researchers to more deeply investigate neurodevelopmental processes with ABCD data as well as with other accumulating datasets to which they have access.

项目摘要 青少年期神经发育变化的分期和节奏表现出可观察的个体差异， 差异，然而，这种受试者之间和受试者内的差异有多少可归因于遗传因素， 未知目前对大脑结构和功能的遗传基础的理解， 神经成像数据是基于对（大多数是横截面的）成人样本的分析。通过从一个 发展的关键时期，即青春期，我们将能够确定是否确定的遗传 成分是时间不变的性状或时间敏感的发育结果。我们的长期目标是 确定危险行为和精神疾病的潜在脆弱性的因果机制， 出现在青春期。为了实现这一目标，我们的目标是寻找与糖尿病相关的遗传因素。 通过多模态神经成像数据测量的发育变化。我们提出了三种方法， 对青少年大脑认知发展（ABCD）的数据进行无偏见的遗传分析 研究：1）提取大脑区域发育变化的模式; 2）识别相关的遗传位点 通过跨衍生发育多模式成像的全基因组关联研究， 表型; 3）确定精神疾病的遗传风险与 神经发育轨迹我们将利用一种新颖的方法组合来实现这些目标， 群体遗传学和神经科学，同时创新地调整分析策略，以避免潜在的 由于混合和相关性而产生的偏差和虚假关联。所提出的研究是有意义的 因为所确定的遗传机制将为治疗和公共卫生提供潜在的基础， 干预措施，出席流行病学引导ABCD数据抽样的影响。通过共享 我们在这项研究计划中开发的工具和由此产生的遗传仪器，我们将影响该领域 立即，使研究人员能够更深入地研究ABCD数据的神经发育过程 以及与他们可以访问的其他累积数据集。