Core B: Statistical and Computational Analysis Core

核心B：统计和计算分析核心

• 批准号: 10698077
• 负责人: Melanie M Wall
• 金额: $ 21.73万
• 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/10698077
• 关键词: Address; Age Months; Architecture; Area; Autism Diagnosis; Behavioral; Bioinformatics; Biological; Caregiver well-being; Caregivers; Child; Clinical; Collaborations; Complex; Computer Analysis; Data; Data Analyses; Data Analytics; Data Collection; Data Science; Data Storage and Retrieval; Databases; Decision Making; Development; Doctor of Philosophy; Elasticity; Ensure; Equation; Evaluation; Family; Family Characteristics; Gene Frequency; Genetic; Genetic Annotation; Genetic Models; Genetic Risk; Genomics; Goals; Growth; Health; Heterogeneity; Infant; Infrastructure; Lead; Leadership; Life; Machine Learning; Measurement; Measures; Methods; Modality; Modeling; Outcome; Participant; Pattern; Population; Procedures; Process; Psychopathology; Recording of previous events; Reproducibility; Research; Research Personnel; Research Project Grants; Resources; Risk; Risk Factors; Secure; Source; Statistical Data Interpretation; Structural Models; Subgroup; Surveys; System; Testing; Time; Universities; autism spectrum disorder; behavioral outcome; biobehavior; cohort; computerized data processing; computerized tools; data infrastructure; data management; data quality; data sharing; de novo mutation; exome; experience; genetic information; genetic predictors; genetic variant; genome sequencing; genomic data; innovation; insight; meetings; member; multimodality; neurobehavioral; novel; polygenic risk score; population based; predictive modeling; prospective; quality assurance; rare variant; relational database; resilience; risk prediction; risk variant; social; statistical and machine learning; success; whole genome

PROJECT SUMMARY The Statistical and Computational Analysis (SCA) Core will be a source of innovative data analytics and high-quality data management for the new Prospective Genetic Risk Evaluation and Assessment (PROGRESS) in Autism Center at Columbia University, focused on identifying and understanding genomic risk factors, caregiver experiences and decision-making, and early-stage neurodevelopmental patterns in children at identified genetic risk for autism. The SCA Core brings together experienced biostatisticians, computational biologists, bioinformaticians, and genomics experts to deploy rigorous and reproducible data science methods in pursuit of the PROGRESS Center’s aims. Members of the SCA Core have extensive expertise in leading and executing analysis for complex research projects in autism and developmental psychopathology and have deep collaborations with several co-investigators across the Center Projects. The SCA will pursue the following four aims: Aim 1: To perform computational analysis of genomic data to identify new risk genes and develop composite genomic risk scores that predict autism risk based on genetic variants across the entire allele frequency spectrum (Project 1 analyses). Aim 2: To conduct statistical analysis of survey and behavioral outcomes in the PROGRESS cohort to model parental outcomes and effects of genetic risk on children’s longitudinal neurobehavioral development (Project 2 and 3 analyses). Aim 3: To integrate genetic, caregiver, and infant neurobehavioral development data across projects to obtain a complete picture of children’s autism risk signatures (cross-Project analyses). Aim 4: To maximize data quality and availability in all PROGRESS Center research, including through a central data management platform that ensures rigor and reproducibility. By integrating a robust data management and analytic pipeline with population-based, longitudinal data collection, the SCA Core will allow the PROGRESS Center to reveal the implications of early life genetic information, integrating genomic risk with neurodevelopmental trajectories and family impact.

项目摘要 统计和计算分析（SCA）核心将是创新数据分析的来源 新的前瞻性遗传风险评估和评估（进度）的高质量数据管理（进度） 在哥伦比亚大学的自闭症中心，专注于识别和了解基因组风险因素， 照顾者的经验和决策，以及儿童的早期神经发育模式 确定了自闭症的遗传风险。 SCA核心将经验丰富的生物统计学家，计算机汇集在一起 生物学家，生物信息学家和基因组学专家，以部署严格和再现数据科学方法 追求进度中心的目标。 SCA核心的成员在领导和 执行自闭症和发育心理病理学复杂研究项目的分析，并具有深刻的 与整个中心项目的几个共同投资者合作。 SCA将纯化以下四个 目标： 目标1：对基因组数据进行计算分析以识别新的风险基因并发展复合材料 基因组风险评分，可以根据整个等位基因频谱的遗传变异来预测自闭症风险 （项目1分析）。 目的2：在进度队列中对调查和行为结果进行统计分析以建模 父母的结果和遗传风险对儿童纵向神经行为发展的影响（项目2 和3个分析）。 目标3：整合跨项目的遗传，照顾者和婴儿神经行为发展数据 儿童自闭症风险签名（跨项目分析）的完整图片。 目标4：最大化所有进度中心研究中的数据质量和可用性，包括通过中央 确保严格和可重复性的数据管理平台。 通过将强大的数据管理和分析管道与基于人群的纵向数据集成 收集，SCA核心将使进度中心揭示早期生命遗传的含义 信息，将基因组风险与神经发育轨迹和家庭影响整合在一起。