Administrative Supplement: Integrative analysis of lung cancer etiology and risk

行政补充：肺癌病因和风险的综合分析

• 批准号: 10260017
• 负责人: Christopher I. Amos
• 金额: $ 22.81万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10260017
• 关键词: Address; Administrative Supplement; Affect; African; African American; Alleles; Architecture; Asians; Association Learning; Automobile Driving; Biological Markers; Cancer Etiology; Categories; Cessation of life; Characteristics; Clinical; Clinical Management; Data; Data Set; Death Rate; Decision Making; Development; Diagnosis; Disease; Disease Management; Early Diagnosis; Environmental Risk Factor; Epidemiology; Etiology; European; Evaluation; Event; General Hospitals; Genes; Genetic; Genetic Heterogeneity; Genetic Research; Genetic Risk; Genetic Variation; Genetic study; Genomics; Genotype; Geography; Goals; Health; Health Benefit; Health Personnel; Heritability; Hispanics; Histology; Hospitals; Improve Access; Incidence; Individual; Inherited; Intervention; Interview; Investigation; Knowledge; Linkage Disequilibrium; Machine Learning; Malignant neoplasm of lung; Medical Genetics; Medical center; Medicine; Methods; Modeling; Not Hispanic or Latino; Patient Recruitments; Phenotype; Population; Population Heterogeneity; Predisposition; Prevention; Preventive; Public Health; Recommendation; Research; Resources; Risk; Risk Assessment; Role; Sampling; Screening procedure; Smoking; Smoking Behavior; Structure; Survival Rate; System; Testing; Time; Underserved Population; United States; Universities; Validation; Woman; base; biobank; black men; black women; cancer genomics; cancer health disparity; cancer risk; causal variant; clinical care; college; cost; data resource; disorder risk; disorder subtype; ethnic disparity; ethnic minority population; genetic architecture; genetic variant; genome wide association study; genomic predictors; high risk; improved; insight; mortality; novel; polygenic risk score; predictive modeling; preference; response; risk prediction; risk prediction model; safety net; screening; uptake

RFA: PA-18-842 (NOT-CA-20-006) Title: Administrative Supplement: Integrative analysis of lung cancer etiology and risk Project Summary/Abstract Over the past two decades, many genome-wide association studies (GWAS) have revealed that most common diseases have a polygenic architecture, wherein multiple genetic variants with small genetic effect cumulatively impact disease development. Advances in statistical, epidemiological, and clinical genetics enable to demonstrate the power of polygenic risk profiles in form of polygenic risk scores to define individuals at high- and low-risk of disease. However, most genetic research carried out to date has focused on genetically homogeneous studies from European populations given the limited availability of samples in populations of diverse ancestry and due to confounding from variability in allelic proportions among diverse populations. This implies that GWAS in ethnic disparities are not fully understood. The goals of this proposal are to leverage genetic diversity to develop and refine PRS by addressing the ancestral diversity in African American population that are not well-represented in genetic research; and to elucidate individual and system-level factors affecting disparities in access and participation of African- Americans in lung cancer genomic testing for polygenic risk, engagement with return of results, and uptake of lung cancer polygenic risk score information in clinical care. We postulate that the ancestry-specific and disease subtypes-specific polygenic models can greatly improve risk prediction to identify high- to low-risk individuals of disease development in terms of prevention and management of the disease. The proposal capitalizes on large-scale existing well-genotyped and phenotype dataset using ancestry-specific, disease subset-based, linkage disequilibrium score regression, and machine-learning association analysis to achieve the a “cancer health disparities” principle and to increase prediction accuracy in African American population. In addition, identifying the individual and system –level factors affecting disparities in lung cancer genomic testing for polygenic risk will inform development of more targeted interventions to improve access, participation, and engagement, which could not only enhance model prediction but also have salutary downstream impact by elucidating strategies to improve African-American engagement in cancer genomic testing.

RFA：PA-18-842（非CA-20-006） 标题：行政补充：肺癌病因和风险的综合分析 项目摘要/摘要 在过去的二十年中，许多全基因组关联研究（GWAS）揭示了最常见的 疾病具有多基因结构，其中多种遗传变异具有较小的遗传作用 影响疾病发展。统计，流行病学和临床遗传学的进步使得能够 以多基因风险评分的形式证明多基因风险概况的力量，以定义高度的个体 和低风险疾病。但是，迄今为止进行的大多数遗传研究都集中在基因上 鉴于欧洲人口的同质研究鉴于样本可用性有限 潜水员的血统以及由于潜水员种群中等位基因比例的变化而混淆。这 意味着在种族差异中的GWAS尚未完全理解。 该提案的目标是利用遗传多样性来通过解决 非裔美国人人口中的祖先多样性在遗传研究中没有很好的代表性；然后 阐明影响分布在非洲访问和参与中的个人和系统级别因素 - 美国人在肺癌基因组中进行多基因风险，与结果回报的参与以及吸收 肺癌多基因风险评分信息在临床护理中。我们假设特定于祖先的 疾病亚型特异性多基因模型可以大大改善风险预测，以识别高风险 疾病发展的个体在预防和管理疾病方面。提案 利用祖先特异性疾病的大规模现有的现有良好型和表型数据集 基于子集的链接分裂评分回归和机器学习关联分析以实现 A“癌症健康差异”原则，并提高非裔美国人人口的预测准确性。 此外，识别个体和系统 - 影响肺癌基因组差异的因素 对多基因风险进行测试将为开发更多针对性的干预措施，以改善访问权限， 参与和参与度不仅可以增强模型预测，还可以增强有益的预测 通过阐明改善非裔美国人参与癌症基因组参与的策略来影响下游的影响 测试。