Administrative Supplement: Integrative analysis of lung cancer etiology and risk

RFA：PA-18-842（NOT-CA-20-006） 标题：行政补充：肺癌病因和风险的综合分析 项目总结/摘要 在过去的二十年里，许多全基因组关联研究（GWAS）显示， 疾病具有多基因结构，其中具有小遗传效应的多个遗传变体累积 影响疾病发展。统计学、流行病学和临床遗传学的进展使我们能够 以多基因风险评分的形式证明多基因风险概况的力量，以确定高风险个体， 和低患病风险。然而，迄今为止进行的大多数遗传学研究都集中在遗传学上。 考虑到欧洲人群中样本的可用性有限， 不同的祖先，并由于不同人群中等位基因比例的变异性的混杂。这 这意味着GWAS在种族差异中没有得到充分理解。 该提案的目标是利用遗传多样性，通过解决以下问题，制定和完善减贫战略： 非裔美国人的祖先多样性在遗传研究中没有得到很好的体现;以及 阐明影响非洲人获得和参与机会不平等的个人和系统因素， 美国人在肺癌基因组检测中的多基因风险，参与结果的返回，以及对 肺癌多基因风险评分信息在临床护理中的应用我们假设祖先特异性和 疾病亚型特异性多基因模型可以极大地改善风险预测， 在疾病的预防和管理方面的疾病发展的个人。该提案 利用大规模现有的良好基因分型和表型数据集， 基于子集，连锁不平衡得分回归，和机器学习关联分析实现 “癌症健康差异”原则，并提高非洲裔美国人的预测准确性。 此外，确定影响肺癌基因组差异的个体和系统水平因素， 多基因风险检测将为制定更有针对性的干预措施提供信息， 参与和参与，这不仅可以增强模型预测，而且还具有有益的 通过阐明改善非洲裔美国人参与癌症基因组研究的策略， 试验.

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.