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.

尽管在阐明自闭症谱系障碍的遗传因素方面取得了巨大进展，但 在很大程度上未知遗传和非遗传风险因素如何整合以及它们如何塑造社会的严重性 沟通和认知缺陷。这一差距可以通过发展全面的责任来解决 使用基于人群的流行病学样本具有密集的遗传和表型数据的模型。填写这个 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（页），涉及 通过确定患有DSM-IV自闭症（AD）样品获得的瑞典流行病学队列，该样品 捕获更严重的受影响的人，从7,000多个国家的基于人口的样本中选择 活着的人。在AD流行病学样本中建模责任已提供了准确的估计值 常见和罕见的遗传变异传达的风险。这项研究还表明，约有40％仍在 没有责备。结合关键的环境变量（父亲年龄和孕妇年龄，妊娠历史） 以及具有遗传力的衡量标准的表型数据（智商，自闭症严重性，家庭精神史）是完全的关键 了解自闭症责任。现在，我们建议通过追求以下特定目标来加强页面：1） 招募，基因型和序列至少另外1,500例，包括严重影响的1,350例 个人2）研究与ASD严重程度和认知功能有关的常见和罕见的遗传变异； 3） 确定ASD的其他推定风险来源是如何根据ASD严重性分配的 认知函数，以及4）通过分析全外例序列数据的分析来发现ASD的风险基因 通过全基因组关联研究（GWAS）确定常见的风险差异。我们希望缴纳责任 整合遗传和环境风险因素并考虑表型复杂性的模型 沿两个核心维度：社会缺陷的严重性和认知功能。我们认为这很重要 因为它允许我们：1）研究表型群体各个尺度的罕见遗传变异； 2）理解 多基因风险与高度渗透性稀有变体之间的相互作用跨表型组； 3）测量 鉴于表型变异性，遗传力和环境影响； 4）定义家庭负担， 遗传和非遗传学。我们认为，我们的研究具有创新性，因为它探究了特定组成部分 在基于人群的队列中，遗传和非遗传的风险，并引入表型变异性作为一种 附加维度。它也具有创新性，因为它结合了遗传（添加剂和稀有遗传）变化， 父母的年龄和精神障碍的家族史，以评估家族负担，同时引入小说 遗传分析的工具和方法。与 当前的研究将为自闭症风险因素及其在确定方面的相互作用提供新的见解 表型，因此为风险，预防和临床护理的临床评估开辟了新的途径。