# Administrative Supplement: Integrative analysis of lung cancer etiology and risk

> **NIH NIH U19** · BAYLOR COLLEGE OF MEDICINE · 2021 · $228,106

## Abstract

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.

## Key facts

- **NIH application ID:** 10260017
- **Project number:** 3U19CA203654-05S1
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** Christopher I. Amos
- **Activity code:** U19 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $228,106
- **Award type:** 3
- **Project period:** 2017-08-01 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10260017

## Citation

> US National Institutes of Health, RePORTER application 10260017, Administrative Supplement: Integrative analysis of lung cancer etiology and risk (3U19CA203654-05S1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10260017. Licensed CC0.

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