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.

项目总结/摘要 对于这个应用程序，“研究不同人群中衰老，行为和社会表型的遗传学，” 我们建议开发工具，以促进衰老，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，多基因性， 环境机制和小效应量），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， 在大型、多样化的数据集中进行全基因组关联研究（GWAS）。当前的GWAS方法需要 将样本限制为近似祖先的样本，这是浪费的， 导致了遗传学研究中的欧洲中心偏见。使用匹配方法，GRMMA可以使用更多的 以一种既能减少偏差又能增加统计功效的方式提供可用数据。我们会委聘 计算效率高的策略，使我们能够实现GRMMA在大的不同样本，如 英国生物银行我们将通过在线公开提供GRMMA工具和教程， 仓库，Github。 ·开发SBayes-Universal（SBayesU），这是一种用于产生多基因评分（PGS）的有效新工具， 最佳组合不同人群中估计的GWAS汇总统计量。的关键特征 SBayesU是它使用的连锁不平衡矩阵的低维本征分解。 这允许SBayesU对更大的SNP集进行建模，对SNP注释进行建模，以解释 不完全的跨祖先遗传相关，以产生不包括在内的群体的PGS 在GWAS汇总统计数据集之间，并允许我们的算法收敛得更多 快速和可靠。我们还将公开提供SBayesU工具和教程。 ·我们将采用最好的方法来生产不同种群的PGS（我们预计 将使用现有的队列，对广泛的衰老、行为和社会表型进行SBayesU）， 新的基因型数据，成为在资助期间可用。我们会做多基因评分 作为社会科学遗传协会联盟多基因的一部分， Index Repository，目前为11个广泛使用的数据集创建多基因评分（但目前 仅针对这些数据集中的欧洲血统个体）。存储库的每个版本都将 并附有明确说明所用方法和基础数据的文件。