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: 10132395
• 负责人: Joseph D. Buxbaum
• 金额: $ 46.96万
• 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/10132395
• 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; individuals with autism spectrum disorder; 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背后的遗传因素方面已经取得了巨大进展，但 目前尚不清楚遗传和非遗传风险因素如何整合以及它们如何影响社会的严重程度 沟通和认知缺陷。这一差距可以通过制定综合责任来解决 使用具有密集遗传和表型数据的基于人群的流行病学样本进行模型。为了填补这个 差距，我们开发了基于人群的自闭症遗传学和环境研究（PAGES），涉及 瑞典流行病学队列通过确定患有 DSM-IV 自闭症 (AD) 的样本而获得，其中 捕获受影响更严重的个体，这些个体是从超过 7,000 名全国人口样本中选出的 活着的个体。 AD 流行病学样本中的责任建模提供了对 AD 的准确估计 常见和罕见的遗传变异所带来的风险。这项研究还表明，约 40% 的人仍然 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）以及遗传性测量是充分 了解自闭症责任。我们现在建议通过追求以下具体目标来加强 PAGES：1) 招募至少 1,500 例病例，对其进行基因分型和测序，其中包括 1,350 例受影响程度较轻的病例 个人； 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） 确定自闭症谱系障碍 (ASD) 推定风险的其他来源如何与自闭症谱系障碍 (ASD) 严重程度和 认知功能，4) 通过分析全外显子组序列数据来发现自闭症谱系障碍的风险基因 通过全基因组关联研究（GWAS）识别常见的风险变异。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：社交缺陷的严重性和认知功能。我们认为，这意义重大 因为它使我们能够：1）研究跨表型群体的所有尺度的罕见遗传变异； 2）理解 多基因风险与跨表型群体的高渗透性罕见变异之间的相互作用； 3）测量 表型变异的遗传力和环境影响； 4) 定义家庭负担， 分为遗传性和非遗传性。我们认为，我们的研究具有创新性，因为它探讨了 在基于人群的队列中研究遗传和非遗传风险，并引入表型变异作为 附加维度。它也是创新的，因为它结合了遗传（加性和罕见遗传）变异， 父母年龄和精神疾病家族史，以评估家庭负担，同时引入新的 遗传分析的工具和方法。与以往相比，这种解决 ASD 责任的全新方法 当前的研究将为自闭症风险因素及其相互作用提供新的见解，以确定 表型，从而为风险、预防和临床护理的临床评估开辟新途径。