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).

项目摘要 自闭症的遗传力估计> 80％；因此，遗传学应该是一个强大的工具 预测自闭症的风险。使用基因组测序的新生儿筛查是一个可以提供遗传的平台 自闭症症状出现之前的诊断 - 提供早期干预的机会，以改善 自闭症结果。独立于该提议，我们正在进行一项基因组的试点研究（监护人） 排序是在纽约潜水员人口中进行传统新生儿筛查的新平台。在 监护人，父母可以选择至少100个单基因条件 遗传变异对大脑和行为的影响高度渗透，平均约有20％ 具有风险变异的人患有自闭症。具有单基因和自闭症的人通常有 自我伤害行为的挑战，适应性较差，独立性较低，并且有关联 较低的生活质量和更大的家庭负担。目前尚不清楚哪些因素决定了哪些人 单基因风险因素将发展自闭症（包括其他罕见或常见的遗传变异或 其他因素）以及是否可以预测具有这些风险变异的婴儿，他们会发展 自闭症，也许受益于行为干预措施。准确预测遗传学风险的关键是 确定所有风险基因和变体，并精确估计其影响大小。继承，稀有，中等风险 变体和单独效应的常见变体是造成自闭症汇总风险的主要因素， 但是尚未确定这些基因或变体的大多数。随着自闭症的基因组数据的增加 包括火花在内的队列，将有大大改善的能力，以更完全了解 基因组结构并识别新基因和变体，并量化这些变体的自闭症风险。在 项目1，我们建议确定进度的队列：新生儿在一种新的新生中有自闭症的高风险 约克市人口。我们将在《卫报》中筛选大型（约100,000）个基于人口的新生儿队 并确定一群对高渗透神经源具有单基因敏感性的婴儿 增加自闭症风险的状况（IGR，n = 400），并将这些遗传结果归还给父母6之内 生命的几周。在《卫报》中确定的这些婴儿中，将有240名项目2和3。我们将 确定一个没有单基因风险的120名婴儿的比较组（非GR）。使用大型自闭症队列我们 将确定其他基因和遗传变异体，会议会议自闭症和测试遗传模型，包括 高，中和低风险的遗传变异和家族史，以发展综合基因组风险评分和 将其应用于我们在自闭症的遗传风险（IGR）中的新生儿队列中。潜在的 该队列的神经行为发展的评估（项目3）将提供婴儿神经发育 将与复合基因组风险评分相结合以产生综合自闭症风险的轨迹 得分（项目1和3）。