Studying the Genetics of Aging, Behavioral, and Social Phenotypes in Diverse Populations

研究不同人群的衰老、行为和社会表型的遗传学

• 批准号: 10638152
• 负责人: Patrick Ansel Turley
• 金额: $ 72.82万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-15 至 2028-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10638152
• 关键词: Address; African; Aging; Algorithms; Alzheimer' s Disease; American; Behavioral; Characteristics; China; Communication; Computer software; Data; Data Set; Dedications; Dimensions; Distant; Documentation; East Asian; Education; Environment; Ethics; European; European ancestry; General Population; Genes; Genetic; Genetic Research; Genetic study; Genomics; Genotype; Grant; Health; Human Resources; Individual; Knowledge; Linkage Disequilibrium; Methods; Modeling; Outcome; Paper; Performance; Persons; Phenotype; Population; Population Genetics; Population Heterogeneity; Publications; Publishing; Research; Research Personnel; Sample Size; Sampling; Siblings; Social Sciences; Source; South Asian; Statistical Bias; Weight; Writing; behavioral phenotyping; biobank; cohort; computer studies; computerized tools; cost; design; diverse data; ethical, legal, and social implication; experience; genetic association; genome wide association study; genome-wide; improved; indexing; novel strategies; online repository; prevent; repository; simulation; social; statistics; tool; trait

Project Summary/Abstract For this application, “Studying the Genetics of Aging, Behavioral, and Social Phenotypes in Diverse Populations,” we propose to develop tools to promote genetic research of aging, behavioral, and social phenotypes in diverse populations. These phenotypes have a number of unique characteristics (e.g., polygenicity, environmental mechanisms, and small effect sizes) which require special consideration when developing research tools. In brief, we propose to: • Develop the Genetic-Related-Matrix-Matched Association study (GRMMA) tool for performing genome-wide association studies (GWASs) in large, diverse data sets. Current GWAS methods require restricting samples into approximately homogeneous-ancestry samples, which is wasteful and has resulted in Eurocentric bias in genetics research. Using matching methods, GRMMA can use more of the available data in a way that both reduces bias and increases statistical power. We will employ computationally efficient strategies that allow us to implement GRMMA in large diverse sample such as the UK Biobank. We will make the GRMMA tool and tutorials publicly available through the online repository, Github. • Develop SBayes-Universal (SBayesU), an efficient new tool for producing polygenic scores (PGSs) by optimally combining GWAS summary statistics estimated in different populations. The key feature of SBayesU is that it uses a low-dimensional eigen decomposition of the linkage disequilibrium matrix. This permits SBayesU to model a much larger set of SNPs, to model SNP annotations, to account for imperfect cross-ancestry genetic correlation, to produce PGSs for populations that are not included among the sets of GWAS summary statistics, and to allow our algorithms to converge much more quickly and reliably. We will also make the SBayesU tool and tutorials publicly available. • We will apply the best available method for producing diverse-population PGSs (which we anticipate will be SBayesU) to a wide range of aging, behavioral, and social phenotypes, using existing cohorts and new genotyped data that becomes available during the grant period. We will make the polygenic scores we produce publicly available as part of the Social Science Genetic Association Consortium’s Polygenic Index Repository, which currently creates polygenic scores for 11 widely used datasets (but currently only for the European-ancestry individuals in those datasets). Each release of the Repository will be accompanied by documentation that clearly describes methods used and the underlying data.

项目摘要/摘要 对于这个应用，“研究不同人群中的衰老、行为和社会表型的遗传学，” 我们建议开发工具来促进衰老、行为和社会表型的遗传研究 不同的人群。这些表型具有许多独特的特征(例如，多基因， 环境机制和小的影响规模)，在开发时需要特别考虑 研究工具。简而言之，我们建议： ·开发遗传相关矩阵匹配关联研究(GRMMA)工具，用于执行 全基因组关联研究(GWAS)在大型、多样化的数据集。目前的GWAS方法需要 将样本限制在大致相同的祖先样本中，这是浪费的，而且 导致了遗传学研究中的欧洲中心主义偏见。使用匹配方法，GRMMA可以使用更多的 以一种既能减少偏见又能增强统计能力的方式获得数据。我们将聘用 计算效率高的策略，允许我们在大量不同的样本中实现GRMMA，例如 英国生物库。我们将通过在线提供GRMMA工具和教程 仓库，Github。 ·开发SBayes-Universal(SBayesU)，这是一种通过以下方式生成多基因评分(PGSS)的有效新工具 以最佳方式结合在不同人群中估计的全球气候变化综合统计数据。的主要功能 SBayesU的特点是它使用了链接不平衡矩阵的低维特征分解。 这允许SBayesU对更大的SNP集合进行建模，对SNP注释进行建模，以说明 不完全的交叉祖先遗传相关性，不能为未包括的群体产生PGSS 在GWAS汇总统计集合中，并允许我们的算法收敛更多 又快又可靠。我们还将公开提供SBayesU工具和教程。 ·我们将应用现有最好的方法来生产不同种群的PGSS(我们预计会这样做 将是SBayesU)到广泛的衰老、行为和社会表型，利用现有的队列和 在授权期内提供的新的基因分型数据。我们会让多基因得分 我们生产的产品作为社会科学遗传协会联合会多基因的一部分公开提供 索引存储库，目前为11个广泛使用的数据集创建多基因得分(但目前 仅适用于这些数据集中的欧洲血统个体)。存储库的每个版本都将是 并附有清楚描述所用方法和基础数据的文档。