Project 1: Identifying and optimizing monogenetic risk prediction for autism in newborns

项目 1：识别和优化新生儿自闭症单基因风险预测

• 批准号: 10698081
• 负责人: Wendy K Chung
• 金额: $ 40.05万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-06 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10698081
• 关键词: Architecture; Behavior; Behavior Therapy; Behavioral; Brain; Categories; Consent; Data; Development; Diagnostic; Disease; Doctor of Philosophy; Down Syndrome; Early Intervention; Education; Evaluation; Family; Family member; Foundations; Genes; Genetic; Genetic Models; Genetic Risk; Genomics; Goals; Heritability; Individual; Infant; Inherited; Intellectual functioning disability; Knowledge; Lead; Life; Methods; Neonatal Screening; New York City; Newborn Infant; Outcome; Parents; Pilot Projects; Population; Predisposition; Quality of life; Recording of previous events; Research; Risk; Risk Factors; Self-Injurious Behavior; Symptoms; Variant; autism spectrum disorder; cohort; comparison group; de novo mutation; exome sequencing; family burden; genetic disorder diagnosis; genetic testing; genetic variant; genome sequencing; genomic data; high risk; improved; improved outcome; individuals with autism spectrum disorder; neurobehavioral; neurogenetics; novel; polygenic risk score; population based; prospective; rare variant; risk prediction; risk variant; tool

PROJECT SUMMARY The heritability of autism has been estimated to be > 80%; therefore, genetics should be a powerful tool to predict risk of autism. Newborn screening using genomic sequencing is a platform that can deliver genetic diagnoses before autism symptoms emerge – providing the opportunity for early intervention which improves autism outcomes. Independent of this proposal, we are conducting a pilot study (GUARDIAN) of genome sequencing as a new platform for traditional newborn screening in a diverse New York City population. In GUARDIAN, parents will have the option to receive results for at least 100 monogenic conditions for which the genetic variants are highly penetrant for some impact on the brain and behavior, and on average ~20% of individuals with the risk variant have autism. The individuals with monogenic conditions and autism often have challenges with self-injurious behavior, have poorer adaptation, are less independent, and have associated lower quality of life and greater family burden. It is unclear what factors determine which of the individuals with the monogenic risk factor will develop autism (including other rare or common inherited genetic variants or other factors) and whether it is possible to predict, among infants with these risk variants, who will develop autism and perhaps benefit from behavioral interventions. The key to accurately predicting risk with genetics is to identify all risk genes and variants and precisely estimate their effect size. Inherited, rare, moderate-risk variants, and common variants of individually small effect are a major contributor to autism risk in aggregation, but the majority of these genes or variants have not been identified. As the genomic data increase in autism cohorts including SPARK, there will be substantially improved power to more completely understand the genomic architecture and identify new genes and variants and quantify the autism risk for these variants. In Project 1, we propose to identify the PROGRESS Cohort: newborns at high risk for autism in a diverse New York City population. We will screen a large (~100,000) population-based cohort of newborns in GUARDIAN and identify an unbiased group of infants with monogenic susceptibility to highly penetrant neurogenetic conditions that increase the risk of autism (IGR, N=400) and return these genetic results to parents within 6 weeks of life. Of these infants identified in GUARDIAN, 240 will be consented for Projects 2 and 3. We will identify a comparison group of 120 infants without monogenic risk (non-IGR). Using large autism cohorts we will identify additional genes and genetic variants that confer risk of autism and test genetic models including high, moderate, and low risk genetic variants and family history to develop a composite genomic risk score and apply it in our PROGRESS cohort of newborns at identified genetic risk (IGR) of autism. The prospective assessment of neurobehavioral development of this cohort (Project 3) will provide infant neurodevelopmental trajectories that will be combined with the composite genomic risk score to generate an integrated autism risk score (Projects 1 and 3).

项目概要 据估计，自闭症的遗传率> 80%，因此，遗传学应该是一个强有力的工具。 使用基因组测序预测新生儿自闭症风险是一个可以提供遗传信息的平台。 在自闭症症状出现之前进行诊断——提供早期干预的机会，从而改善 独立于该提案，我们正在进行基因组试点研究（GUARDIAN）。 测序作为纽约市多元化人群传统新生儿筛查的新平台。 GUARDIAN，家长可以选择接收至少 100 种单基因病症的结果，这些病症的结果 遗传变异对大脑和行为的某些影响具有高度渗透性，平均约 20% 具有风险变异的个体患有自闭症，患有单基因疾病和自闭症的个体通常患有自闭症。 自残行为的挑战，适应能力较差，独立性较差，并且与 目前还不清楚哪些因素决定了哪些人患有这种疾病。 单基因风险因素会发展为自闭症（包括其他罕见或常见的遗传变异或 其他因素）以及是否有可能在具有这些风险变异的婴儿中预测谁将发展为 准确预测自闭症风险的关键是行为干预。 识别所有风险基因和变异并精确估计其影响大小遗传的、罕见的、中等风险的。 个体小效应的变异和常见变异是聚合中自闭症风险的主要贡献者， 但随着自闭症基因组数据的增加，大多数这些基因或变异尚未被识别。 包括 SPARK 在内的队列，将大大提高更全面地理解 基因组结构并识别新的基因和变异，并量化这些变异的自闭症风险。 项目 1，我们建议确定进展队列：在不同的新环境中处于自闭症高风险的新生儿 我们将在 GUARDIAN 中筛查大量（约 100,000）人群的新生儿。 并确定一组对高渗透性神经遗传具有单基因易感性的无偏见婴儿 增加自闭症风险的条件（IGR，N=400），并将这些遗传结果在 6 年内返回给父母 在 GUARDIAN 中确定的这些婴儿中，有 240 名将获得项目 2 和 3 的同意。 我们使用大型自闭症队列确定了一个由 120 名无单基因风险（非 IGR）婴儿组成的对照组。 将识别导致自闭症风险的其他基因和遗传变异，并测试遗传模型，包括 高、中、低风险遗传变异和家族史，以制定综合基因组风险评分 将其应用到我们的 PROGRESS 队列中，该队列由已确定的自闭症遗传风险 (IGR) 新生儿组成。 对该队列的神经行为发育的评估（项目 3）将提供婴儿神经发育 将与综合基因组风险评分相结合以生成综合自闭症风险的轨迹 分数（项目 1 和 3）。