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: 10629437
• 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/10629437
• 关键词: Address; Admixture; African American; African American population; Age; Aging; Area; Asian; Cancer Burden; Case/Control Studies; Characteristics; Clinical; Cohort Analysis; Computer software; Data; Data Set; Development; Disparity; Environmental Risk Factor; Ethnic Origin; Ethnic Population; European ancestry; 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; Pacific Islander; 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 Estimate; Risk Factors; Signal Transduction; Site; Specificity; Statistical Methods; Testing; Translations; Variant; Woman; Women' s Health; age group; analysis pipeline; 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; predictive modeling; racial difference; racial diversity; racial population; 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.

摘要 不同种族/族裔人群中某些癌症的负担存在明显差异。为 例如，与欧洲血统的人相比，非洲裔美国人的平均寿命高出约67%。 前列腺癌的发病率和亚洲/太平洋岛民男性和女性分别高出70%和95% 肝癌的发病率，分别。不同种族/民族之间癌症负担的差异 被归因于遗传、环境和社会因素的相互作用。尽管存在这些差异， 大多数遗传学研究都集中在欧洲血统的个体上。虽然全基因组关联 研究（GWAS）已经成功地确定了>1000个癌症风险位点，它们主要集中在个体上， 欧洲血统。不同种族/民族人口的代表性不足限制了翻译 GWAS发现对世界人口的潜力。应用在欧洲血统个体中开发的PRS 对其他人群的预测可能会导致有偏见的风险预测，并进一步加剧健康差距， 对高危人群的评估不准确。在这里，我们建议解决迫切需要 通过应用新的PRS，在多个种族/族裔群体中构建和评价适当的PRS 以下六个大规模的方法，长期的队列：多种族队列（MEC）;凯撒 老龄问题遗传流行病学研究资源;妇女健康倡议; 哈佛护士健康研究（NHS）;哈佛卫生专业人员随访研究（HPFS）;以及 前列腺癌、肺癌、结直肠癌和卵巢癌筛查试验（PLCO）。总的来说，这些群体包括 30万人（10万非欧洲人）和9.1万例癌症病例（2.4万非欧洲人）。的 这些群体中的个体来自五个种族/民族群体：非裔美国人、拉丁美洲人、日本人、土著人、 人口和欧洲血统。在关注癌症结果的同时，我们将利用这些独特的， 广泛的资源来开发构建和评估PRS的方法，重要的是用于翻译，评估 在多种族人群中，PRS和已确定的风险因素联合的绝对和超额相对癌症风险。 为了方便访问已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中心。最终，构建和评估风险 非欧洲血统人群的模型对于扩大基因组医学对人类的影响至关重要。 健康

项目成果

Combined Effect of a Polygenic Risk Score and Rare Genetic Variants on Prostate Cancer Risk.

• DOI: 10.1016/j.eururo.2021.04.013
• 发表时间: 2021-08
• 期刊: European urology
• 影响因子: 23.4
• 作者: Darst BF;Sheng X;Eeles RA;Kote-Jarai Z;Conti DV;Haiman CA
• 通讯作者: Haiman CA

Testing the generalizability of ancestry-specific polygenic risk scores to predict prostate cancer in sub-Saharan Africa.

• DOI: 10.1186/s13059-022-02766-z
• 发表时间: 2022-09-13
• 期刊: Genome biology
• 影响因子: 12.3