Population-Based Autism Genetics and Environment Study

基于人群的自闭症遗传学和环境研究

• 批准号: 9918463
• 负责人: Joseph D. Buxbaum
• 金额: $ 64.81万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-01 至 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9918463
• 关键词: Address; Affect; Agreement; Architecture; Biological; Bipolar Disorder; Birth History; Clinical assessments; Cognitive deficits; Cohort Studies; Complex; DNA; DSM-IV; Data; Diagnosis; Dimensions; Disease; Environment; Environmental Risk Factor; Epidemiology; Family; Family Relationship; Family history of; Funding; Genetic; Genetic Load; Genetic Models; Genetic Risk; Genetic Variation; Genotype; Government; Heritability; Individual; Inherited; Joints; Light; Maternal Age; Measures; Medical History; Mental disorders; Methods; Mission; Modeling; Mutation; Nature; Neurodevelopmental Disorder; Nucleotides; Parental Ages; Parents; Paternal Age; Phenotype; Population; Pregnancy; Prevention; Process; Public Health; Recommendation; Recording of previous events; Research; Research Personnel; Resources; Risk; Risk Assessment; Risk Estimate; Risk Factors; SNP array; Sampling; Schizophrenia; Severities; Shapes; Source; Surface; Sweden; United States National Institutes of Health; Variant; Work; analytical tool; autism spectrum disorder; base; case control; clinical care; cognitive function; cohort; database of Genotypes and Phenotypes; delivery complications; density; design; disorder risk; epidemiology study; exome; exome sequencing; family burden; gene discovery; genetic analysis; genetic architecture; genome wide association study; genomic locus; innovation; insight; non-genetic; novel; phenotypic data; population based; psychiatric genomics; rare variant; recruit; risk variant; social communication; social deficits; tool; working group

While enormous progress has been made in elucidating genetic factors underlying autism spectrum disorder, it is largely unknown how genetic and non-genetic risk factors integrate and how they shape severity of social communication and cognitive deficits. This gap can be addressed by developing comprehensive liability models using a population-based epidemiological sample with dense genetic and phenotypic data. To fill this gap, we have developed the Population-based Autism Genetics and Environment Study (PAGES), involving a Swedish epidemiological cohort obtained by ascertaining samples with DSM-IV autistic disorder (AD), which captures more severely affected individuals, chosen from a national, population-based sample of over 7,000 living individuals. Modeling liability in the epidemiological sample of AD has provided accurate estimates of the risk conveyed by common and rare genetic variation. This study has also revealed that ~40% is still unaccounted for. Combining critical environmental variables (paternal and maternal age, gestational history) and phenotyping data (IQ, autism severity, family psychiatric history) with measures of heritability is key to fully understand autism liability. We now propose to strengthen PAGES by pursuing the following specific aims: 1) To recruit, genotype and sequence at least 1,500 additional cases, including 1,350 less severely affected individuals; 2) To study common and rare genetic variation in relation to ASD severity and cognitive function; 3) To determine how other sources of putative risk for ASD are distributed in relation to ASD severity and cognitive function, and, 4) To discover risk genes for ASD by analysis of whole-exome sequence data and identify common risk variation by genome-wide association study (GWAS). We expect to contribute liability models that integrate genetic and environmental risk factors and take into account the phenotypic complexity along two core dimensions: severity of social deficits and cognitive function. In our opinion, this is significant because it allows us to: 1) study rare genetic variation at all scales across phenotypic groups; 2) understand the interplay between polygenic risk and highly penetrant rare variants across phenotypic groups; 3) measure heritability and environmental influences in light of phenotypic variability; and, 4) define the familial burden, both genetic and non-genetic. In our opinion, our study is innovative because it probes specific components of risk, both genetic and non-genetic, in a population-based cohort and introduces phenotypic variability as an additional dimension. It is also innovative because it combines genetic (additive and rare inherited) variation, parental age, and family history of psychiatric disorder to assess the familial burden, while introducing novel tools and approaches to genetic analyses. This radically new way of tackling ASD liability, compared with current studies, will provide novel insights into autism risk factors and their interactions in determining phenotype, thus opening new avenues for clinical assessment of risk, prevention and clinical care.

虽然在阐明自闭症谱系障碍的遗传因素方面取得了巨大进展， 遗传和非遗传风险因素是如何整合的，以及它们如何影响社会风险的严重程度， 沟通和认知缺陷。这一差距可以通过制定全面赔偿责任来解决 模型使用基于人群的流行病学样本与密集的遗传和表型数据。填补这一 差距，我们已经开发了基于人群的自闭症遗传学和环境研究（PAGES），涉及一个 通过确定DSM-IV自闭症（AD）样本获得的瑞典流行病学队列， 从超过7000个国家的人口样本中选出受影响更严重的个人 活着的个体。AD流行病学样本中的建模责任提供了对 常见和罕见遗传变异带来的风险。这项研究还显示，约40%的人仍然是 下落不明结合关键的环境变量（父亲和母亲的年龄，妊娠史） 和表型数据（智商，自闭症严重程度，家族精神病史）与遗传性的措施是关键，充分 理解自闭症的责任。我们现在建议通过实现以下具体目标来加强PAGES：1） 招募、基因分型和测序至少1,500例额外病例，包括1,350例不太严重的病例 2）研究与ASD严重程度和认知功能相关的常见和罕见遗传变异; 3） 确定ASD的其他推定风险来源如何与ASD严重程度相关分布， 认知功能，以及，4）通过分析全外显子组序列数据来发现ASD的风险基因，以及 通过全基因组关联研究（GWAS）确定常见风险变异。我们希望承担责任 整合遗传和环境风险因素并考虑表型复杂性的模型 沿着两个核心维度：社会缺陷的严重程度和认知功能。我们认为，这一点意义重大， 因为它使我们能够：1）研究罕见的遗传变异在所有规模的表型群体; 2）了解 多基因风险和跨表型组的高渗透罕见变异之间的相互作用; 3）测量 根据表型变异性的遗传性和环境影响;以及，4）定义家庭负担， 遗传的和非遗传的。在我们看来，我们的研究是创新的，因为它探讨了特定的组成部分， 风险，包括遗传和非遗传，在一个基于人群的队列，并引入表型变异作为一个 额外的维度。它也是创新的，因为它结合了遗传（添加剂和罕见的遗传）变异， 父母的年龄和精神疾病的家族史，以评估家庭负担，同时引入新的 遗传分析的工具和方法。这种解决ASD责任的全新方式，与 目前的研究，将提供新的见解自闭症风险因素及其相互作用，在确定 表型，从而为风险的临床评估，预防和临床护理开辟了新的途径。