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Grant Details

Grant Number: 3U19CA203654-05S1 Interpret this number
Primary Investigator: Amos, Christopher
Organization: Baylor College Of Medicine
Project Title: Administrative Supplement: Integrative Analysis of Lung Cancer Etiology and Risk
Fiscal Year: 2021


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.


None. See parent grant details.

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