Enabling improved applicability and transferability of polygenic scores across diverse populations- a focus on South Asians

提高多基因评分在不同人群中的适用性和可转移性——重点关注南亚人

• 批准号: 10212773
• 负责人: Pradeep Natarajan
• 金额: $ 100万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-08 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10212773
• 关键词: Accounting; Address; Adherence; Adoption; Atrial Fibrillation; Automobile Driving; Awareness; Benchmarking; Cholesterol; Clinical; Clinical Data; Clinical Informatics; Clinical Medicine; Complex; Coronary Arteriosclerosis; DNA; Data; Data Linkages; Data Set; Development; Disease; Environmental Exposure; European; Family; Fibrinogen; Gene Frequency; Genes; Genetic; Genomic medicine; Genotype; Goals; Health; Heritability; Individual; Inherited; Life Style; Link; Linkage Disequilibrium; London; Modeling; Mutation; National Human Genome Research Institute; Non-Insulin-Dependent Diabetes Mellitus; Output; Pattern; Performance; Pharmaceutical Preparations; Phase; Phenotype; Population; Population Heterogeneity; Prevalence; Probability; Public Health; Recommendation; Recording of previous events; Records; Research; Risk; Risk Estimate; Risk Factors; Signal Transduction; Site; South Asian; United Kingdom; Variant; Weight; base; biobank; clinical implementation; clinical risk; data ecosystem; data sharing; functional genomics; gene environment interaction; genetic approach; genetic architecture; genetic risk assessment; genome sequencing; genome wide association study; healthy lifestyle; improved; indexing; interest; malignant breast neoplasm; non-genetic; novel strategies; personalized approach; phenotypic data; polygenic risk score; portability; recruit; risk stratification; risk variant; statistics; tool; trait; whole genome; working group

Polygenic scores – which quantify inherited risk by integrating information from many common sites of DNA variation – hold considerable promise for enabling a tailored approach to clinical medicine. However, alongside considerable (and warranted) enthusiasm, we and others have highlighted a crucial equity issue – current polygenic scores have diminished predictive power in non-European ancestries. By assembling a team with deep expertise in statistical genetics, clinical informatics, data sharing, and genomic medicine, we outline the Functional and Fine-Mapping Approach to Improve Responsible Risk-modeling of Polygenic Risk Scores (‘FFAIRR-PRS’) approach to systematically address the key factors driving diminished performance. To enable analysis by the NHGRI consortium within the ANVIL ecosytem, we will contribute genetic and rich phenotype data from >57,136 individuals of South Asian ancestry from the Genes & Health and UK Biobank Studies and whole genome sequencing data from 5,734 South Asians from the GenomeAsia Phase 2 to serve as an ancestry-matched reference panel. South Asian individuals are prioritized based on marked under-representation in genome-wide association studies – accounting for 23% of the global population but only 1.2% of individuals studied – and polygenic prediction efforts to date, as well as a key public health need for enhanced risk stratification. Individual level data in ANVIL will be paired with summary association statistics of >100,000 South Asians and individual >1 million individuals of other ancestries, which will enable enhanced fine-mapping, sore weighting, and transethnic benchmarking activities. Our Study Site aims to (1) Aggregate and harmonize genotyping and phenotype data and deliver a sharable and scalable end-to-end analytic pipeline that starts with genotyping array data and a phenotype file and enables automated output of polygenic score benchmarking parameters.; (2) Develop and share the new ‘FFAIRR-PRS’ statistical genetics framework, leveraging: (i) fine-mapping to assign causal probabilities based on >180 functional genomic annotations; (ii) incorporating correlations between effect sizes across traits; and (iii) integration of South Asian and non-South Asian GWAS data; and (3) Benchmark FFAIRR-PRS scores for 27 important phenotypes in the South Asian datasets, and develop risk models that integrate genetic and nongenetic factors. Performance will be benchmarked in accordance with ClinGen Complex Disease Working Group recommendations and compared against individuals of European and other major ancestry groups. Beyond enhanced polygenic scores – aware of an ultimate aim of clinical implementation – we will develop a framework for integrated absolute risk models calibrated to the U.S. population that account for rare monogenic variants of large effect, family history, lifestyle, and clinical risk factors by adapting the Individualized Coherent Absolute Risk Estimator (iCARE) tool developed by co-I Chatterjee.

多基因评分-通过整合来自DNA许多常见位点的信息来量化遗传风险 变异----具有相当大的希望，能够为临床医学提供量身定制的方法。然而，除了 相当大的（和保证）的热情，我们和其他人强调了一个关键的公平问题-当前 多基因评分降低了非欧洲血统的预测能力。通过组建一个团队， 在统计遗传学，临床信息学，数据共享和基因组医学方面的深厚专业知识，我们概述了 改进多基因风险责任风险模型的功能和精细映射方法 评分（'FFAIRR-PRS'）方法，以系统地解决导致业绩下降的关键因素。 为了使NHGRI联盟能够在ANVIL生态系统中进行分析，我们将提供遗传和 来自Genes & Health和UK的> 57，136名南亚血统个体的丰富表型数据 来自GenomeAsia第2阶段的5，734名南亚人的生物库研究和全基因组测序数据 作为祖先匹配的参考样本南亚人的优先顺序是基于标记 在全基因组关联研究中代表性不足-占全球人口的23%， 迄今为止，只有1.2%的人研究了多基因预测工作，以及关键的公共卫生需求 加强风险分层。ANVIL中的个体水平数据将与汇总关联统计量配对 > 10万南亚人和> 100万其他血统的人，这将使增强 精细映射、疼痛加权和跨种族基准测试活动。 我们的研究中心的目标是（1）聚合和协调基因分型和表型数据，并提供一个 可共享和可扩展的端到端分析管道，从基因分型阵列数据和表型文件开始 并且能够自动输出多基因评分基准参数。(2)开发和共享新的 “FFAIRR-PRS”统计遗传学框架，利用：（i）精细映射来分配基于因果概率 （ii）整合性状间效应大小之间的相关性;以及 (iii)南亚和非南亚GWAS数据的整合;以及（3）FFAIRR-PRS基准得分， 南亚数据集中的27种重要表型，并开发整合遗传和 非遗传因素。将根据ClinGen复杂疾病工作标准对性能进行基准测试 小组建议，并与欧洲和其他主要祖先群体的个人进行比较。 除了增强的多基因评分-意识到临床实施的最终目标-我们将开发一个 综合绝对风险模型的框架校准到美国人口，占罕见的 单基因变异的大效应，家族史，生活方式和临床危险因素，通过调整 个人化一致绝对风险估计（iCARE）工具由co-I Chatterjee开发。