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

多基因评分-通过整合来自多个常见DNA位点的信息来量化遗传风险 变化-为临床医学提供量身定做的方法带来了巨大的希望。然而，除了 相当(也是有理由的)热情，我们和其他人强调了一个关键的股票问题-当前 在非欧洲血统中，多基因得分降低了预测能力。通过将一个团队与 在统计遗传学、临床信息学、数据共享和基因组医学方面拥有深厚的专业知识，我们将概述 改进多基因风险的负责任风险建模的函数和精细映射方法 Score(‘FFAIRR-PR’)方法，系统地解决导致业绩下降的关键因素。 为了使NHGRI财团能够在铁砧生态系统内进行分析，我们将贡献遗传和 来自基因&Health和UK的57,136名南亚血统个体的丰富表型数据 来自基因组亚洲第二阶段的5734名南亚人的生物库研究和全基因组测序数据 作为一个血统匹配的参考小组。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%，但 到目前为止，只有1.2%的个人研究和多基因预测工作以及关键的公共卫生需求 用于增强风险分层。铁锤中的个人级别数据将与汇总关联统计数据配对 100,000南亚人和100万其他祖先的个人，这将使增强 精细测绘、SORT权重和跨领域基准活动。 我们的研究网站旨在(1)收集和协调基因分型和表型数据，并提供 从基因分型阵列数据和表型文件开始的可共享和可扩展的端到端分析流水线 并支持多基因评分基准参数的自动输出。(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 On&gt；180功能基因组注释；(Ii)合并跨性状的效应大小之间的相关性；以及 (3)南亚和非南亚地理信息系统数据的整合；和(3)基准FFAIRR-PRS分数 南亚数据集中的27个重要表型，并开发整合了遗传和 非遗传因素。将根据Clingen Complex疾病工作情况对性能进行基准测试 群体推荐，并与欧洲和其他主要祖先群体的个体进行比较。 除了增强多基因评分--了解临床实施的最终目标--我们还将开发一种 针对美国人口校准的综合绝对风险模型框架 大效应、家族史、生活方式和临床风险因素的单基因变异 由co-I Chatterjee开发的个性化一致绝对风险评估(ICARE)工具。

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.