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

项目摘要 据估计，自闭症的遗传率大于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平台，可以提供遗传 在自闭症症状出现之前进行诊断-提供早期干预的机会， 自闭症的结果。独立于这项建议，我们正在进行一项基因组的试点研究（监护人）， 测序作为一个新的平台，传统的新生儿筛查在一个不同的纽约市人口。在 监护人，父母将有权选择接收至少100个单基因条件的结果， 遗传变异对大脑和行为的影响是高度渗透的，平均约20%的 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 自我伤害行为的挑战，适应性较差，独立性较低， 生活质量下降，家庭负担加重。目前还不清楚是什么因素决定了哪些人患有 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基因变异， 其他因素），以及是否有可能预测，在这些风险变异的婴儿中， 自闭症，也许从行为干预中受益。用遗传学准确预测风险的关键是 识别所有风险基因和变异，并精确估计其效应大小。遗传，罕见，中度风险 变异，以及个体效应较小的常见变异是导致自闭症风险聚集的主要因素， 但这些基因或变体中的大多数尚未被鉴定。随着自闭症基因组数据的增加 包括SPARK在内的队列，将大大提高更全面了解 基因组结构和识别新的基因和变异，并量化这些变异的自闭症风险。在 项目1，我们建议确定进步队列：新生儿在自闭症的高风险，在一个多样化的新的 约克市人口。我们将在GUARDIAN中筛选一个大型（约100，000）基于人群的新生儿队列 并确定一组无偏见的婴儿， 增加自闭症风险的条件（IGR，N=400），并在6天内将这些遗传结果返回给父母 几周的生活。在GUARDIAN中确定的这些婴儿中，将有240名婴儿同意参与项目2和3。我们将 确定120名无单基因风险（非IGR）婴儿的对照组。使用大型自闭症队列， 将确定额外的基因和遗传变异，赋予自闭症的风险和测试遗传模型，包括 高、中、低风险遗传变异和家族史，以开发复合基因组风险评分， 将其应用于我们的自闭症遗传风险（IGR）新生儿进展队列。准 该队列的神经行为发育评估（项目3）将提供婴儿神经发育 这些轨迹将与复合基因组风险评分相结合，以生成综合自闭症风险 评分（项目1和3）。

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).