Add Health: A Genotype and Phenotype Resource for the Global Scientific Community

描述（由申请人提供）：全基因组关联研究（GWAS）是研究基因对人类健康贡献的一种有效且日益负担得起的工具。然而，现有的数据受到样本的同质性、适度的环境数据和缺乏纵向信息的限制。这些限制阻碍了对健康采取动态的、多层次的综合方法，以捕捉双向的生物和环境贡献及其随时间的相互作用。为了解决这一差距，该应用程序建议使用全基因组阵列技术完成大约6000个存档DNA样本的基因分型，并向全球科学界提供前所未有的丰富的国家资源，纵向环境数据，以及来自全国代表性，种族多样化的12,000多名参与者的综合表型和基因型数据。Add Health已将DNA标本存档，以供今后的研究，目前正在对大约6000个GWAS样本进行基因分型。本项目将完成整个样本的基因分型，传播这些数据并进行初步的GWAS。我们有三个具体目标：目标1)使用Illumina Omni1-Quad BeadChip（超过100万个遗传标记），对存档的DNA进行完整的基因分型，产生12,000个样本。目标2)通过与dbGaP合作，为科学界开发一个国家和全球资源，以存储Add Health基因型、表型和环境数据。目标3)进行初步的GWAS并探索基因x环境（GxE）相互作用，以确定在Add Health中定义明确的表型的生物学和环境贡献，并且调查团队具有实质性的专业知识，包括肥胖，物质使用和心血管疾病。Add Health对年轻人生活的多种环境进行了采样，包括家庭、同龄人、学校、邻里、社区和关系，并随时间对这些环境进行了独立和直接的测量。Add Health包含有关健康相关行为的大量纵向信息，包括身体活动的生活史、参与危险行为、物质使用、性行为、公民行为

DESCRIPTION (provided by applicant): Genome-wide association studies (GWAS) are an effective and increasingly affordable tool to investigate genetic contributions to human health. However, existing data are limited by homogeneity of samples, modest environmental data, and absence of longitudinal information. These limitations preclude a dynamic, multilevel integrative approach to health that captures bidirectional biological and contextual contributions and their interactions over time. To address this gap, this application proposes to complete genotyping on approximately 6,000 archived DNA samples using whole genome array technology, and make available to the global scientific community an unprecedented national resource of rich, longitudinal environmental data coupled with comprehensive phenotype and genotype data on more than 12,000 participants in the nationally representative, ethnically diverse Add Health sample. Add Health has archived DNA specimens for future research and is currently genotyping approximately 6,000 samples for GWAS. This project will complete genotyping on the entire sample, disseminate these data and conduct preliminary GWAS. We have three specific aims: Aim 1) Using the Illumina Omni1-Quad BeadChip (more than 1 million genetic markers), complete genotyping on the archived DNA, yielding a sample of 12,000. Aim 2) Develop a national and global resource for the scientific community by collaborating with dbGaP to deposit Add Health genotype, phenotype, and environmental data. Aim 3) Conduct preliminary GWAS and explore gene x environment (GxE) interactions to identify biological and contextual contributions to phenotypes that are well-defined in Add Health and with which the investigative team has substantive expertise, including obesity, substance use, and cardiovascular disease. Add Health sampled the multiple environments in which young people live their lives, including the family, peers, school, neighborhood, community, and relationship dyads, and provides independent and direct measurement of these environments over time. Add Health contains extensive longitudinal information on health-related behavior, including life histories of physical activity, involvement in risk behavior, substance use, sexual behavior, civic engagement, education, and multiple indicators of health status based on self-report (e.g., general health, chronic illness), direct measurement (e.g., overweight status and obesity), and biomarkers (e.g., blood spots). No other data resource with this expanse of genotype and phenotype data on a large nationally representative longitudinal sample with race, ethnic, socioeconomic, and geographic diversity exists.