Leveraging Diversity in Cancer Epidemiology Cohorts and Novel Methods to Improve Polygenic Risk Scores

利用癌症流行病学队列的多样性和新方法来提高多基因风险评分

• 批准号: 10431853
• 负责人: David V Conti
• 金额: $ 98万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-16 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10431853
• 关键词: Address; Admixture; African American; African American population; Age; Aging; Area; Asian Pacific Islander; Cancer Burden; Case-Control Studies; Characteristics; Clinical; Cohort Analysis; Computer software; Data; Data Set; Development; Environmental Risk Factor; Ethnic group; European; Evaluation; Evaluation Methodology; Follow-Up Studies; Genetic; Genetic Research; Genetic Risk; Genomic medicine; Health; Health Professional; High-Risk Cancer; Human; Incidence; Individual; Inherited; Japanese; Joints; Latino Population; Linkage Disequilibrium; Malignant Neoplasms; Malignant neoplasm of liver; Malignant neoplasm of prostate; Measures; Methods; Modeling; Nurses' Health Study; Odds Ratio; Outcome; Patients; Performance; Phenotype; Population; Population Heterogeneity; Predictive Value; Predisposition; Probability; Prospective cohort; Prospective cohort study; Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial; Public Health; ROC Curve; Race; Relative Risks; Reproducibility; Research; Research Design; Resources; Risk; Risk Factors; Sensitivity and Specificity; Signal Transduction; Site; Statistical Methods; Testing; Translations; Variant; Woman; Women' s Health; age group; analysis pipeline; base; cancer epidemiology; cancer risk; cohort; data resource; disorder risk; epidemiology study; ethnic difference; experience; genetic association; genetic resource; genome wide association study; genome-wide; health disparity; high risk; improved; men; mortality; multi-ethnic; novel; polygenic risk score; racial and ethnic; racial diversity; risk prediction; risk variant; screening; social factors; software development; statistics; trait; translational potential

Abstract There are stark differences in the burden of certain cancers across racial/ethnic populations. For example, in comparison to individuals of European ancestry, African American men have a ~67% higher incidence rate of prostate cancer and Asian/Pacific Islander men and women have a 70% and 95% higher incidence rate of liver cancer, respectively. These disparities in the burden of cancer across racial/ethnic groups have been attributed to an interplay of genetic, environmental, and social factors. Despite such disparities, a majority of genetic research has focused on individuals of European ancestry. While genome-wide association studies (GWAS) have successfully identified >1000 risk loci for cancer, they have focused primarily on individuals of European ancestry. The inadequate representation of diverse racial/ethnic populations limits the translational potential of GWAS findings to the world's populations. Applying PRS developed in European ancestry individuals to other populations may result in biased risk prediction, and further exacerbate health disparities due to inaccurate assessment of individuals at high risk of disease. Here, we propose to address the drastic need for appropriate PRS construction and evaluation across multiple race/ethnic groups by applying new PRS approaches to the following six large-scale, longstanding cohorts: the Multiethnic Cohort (MEC); the Kaiser Resource for Genetic Epidemiology Research on Aging (GERA) cohort; the Women's Health Initiative (WHI); the Harvard Nurses Health Studies (NHS); the Harvard Health Professionals Follow-Up Study (HPFS); and the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO). Together, these cohorts include over 300,000 individuals (100,000 non-Europeans) and 91,000 incident cancer cases (24,000 non-Europeans). The individuals in these cohorts are from five racial/ethnic groups: African Americans, Latinos, Japanese, Native Populations, and European ancestry. While focusing on cancer outcomes, we will utilize these unique and extensive resources to develop methods to construct and evaluate PRS, and importantly for translation, estimate absolute and excess relative risk of cancer jointly for PRS and established risk factors in multiethnic populations. To facilitate access to developed pipelines and data resources, we will follow F.A.I.R. analytic principles while participating with the Coordinating Center and other study sites. Ultimately, constructing and evaluating risk models in non-European ancestry populations is essential to broaden the impact of genomic medicine on human health.

抽象的 在种族/族裔人群中，某些癌症的负担存在明显的差异。为了 例如，与欧洲血统的个人相比，非洲裔美国男性高约67％ 前列腺癌的发病率和亚洲/太平洋岛民男性和女性的发病率高70％和95％ 肝癌的发病率分别。种族/族裔的癌症负担中的这些差异 归因于遗传，环境和社会因素的相互作用。尽管存在如此差异，但 大多数遗传研究都集中在欧洲血统的个人上。而全基因组的关联 研究（GWAS）已成功识别出癌症> 1000个风险基因座，他们主要集中于个体 欧洲血统。各种种族/族裔人口的代表不足限制了翻译 GWAS发现对世界人口的潜力。应用在欧洲血统个人中开发的公关 对于其他人群，可能导致风险预测有偏见，并进一步加剧了由于 对处于疾病高风险的个体的评估不准确。在这里，我们建议满足急需的需求 通过应用新的PRS，在多个种族/族裔群体之间进行适当的PRS构建和评估 接近以下六个大规模，长期存在的队列的方法：多民族队列（MEC）；凯撒 遗传流行病学研究资源（GERA）队列；妇女健康计划（WHI）； 哈佛护士健康研究（NHS）；哈佛卫生专业人员后续研究（HPFS）；和 前列腺，肺，结直肠癌和卵巢癌筛查试验（PLCO）。这些队列在一起包括 30万人（100,000个非欧洲人）和91,000例事件癌症病例（24,000个非欧洲人）。这 这些队列中的个人来自五个种族/族裔：非洲裔美国人，拉丁美洲人，日语，本地人 人口和欧洲血统。在专注于癌症结果的同时，我们将利用这些独特的和 广泛的资源来开发构建和评估PR的方法，重要的是翻译，估算 PRS共同癌症的绝对和过量相对风险，以及在多种族人群中建立的危险因素。 为了促进访问开发的管道和数据资源，我们将遵循F.A.I.R.分析原则 参与协调中心和其他研究地点。最终，建造和评估风险 非欧洲血统种群中的模型对于扩大基因组医学对人的影响至关重要 健康。