# Genomic predictors of smoking and lung cancer risk

> **NIH NIH U19** · BAYLOR COLLEGE OF MEDICINE · 2021 · $794,912

## Abstract

Abstract – Project 1 
Lung cancer (LC) is the leading cause of cancer. Lung cancer is among the best examples of a disease resulting 
from a complex interaction between environmental exposures and genetic factors. Germline genetic findings make 
an important contribution to the definition of high-risk individuals and provide key insights into LC etiology. LC 
genome wide association studies (GWAS) have identified informative loci that have influenced our approach to 
tobacco control and provided new insights into tumorigenesis. We have identified 24 loci with involved in cancer 
susceptibility. However, the interplay between inherited susceptibility and effects from demographic and 
environmental factors has not been elucidated. We hypothesize that genetic variation influences both smoking 
behaviors and cellular processes that jointly lead to lung cancer development. Our specific aims are: Aim 1 
of this project will characterize the contribution of common genetic variation to lung cancer etiology. We 
will analyze GWAS of lung cancer from 47,506 lung cancer cases and 63,687 to identify factors influencing lung 
cancer risk according to histology and host-characteristics. Mechanisms by which these variants influence cancer 
risk will be explored using eQTL based procedures and through annotation of existing databases. Aim 2 will 
investigate uncommon genetic variants for LC susceptibility. We will analyze exome germline sequencing 
information from over 2,500 lung cancer cases and 2,000 controls use these for imputation and rare variant 
analysis. Aim 3 will identify genetic effects on smoking behavior. For this aim, we will integrate the large-scale 
genetic studies we have conducted with extensive phenotyping performed through the lung cancer cohort 
consortium (LC3) and project 2 to identify the specific impacts of SNPs on smoking behaviors. Aim 4 will 
characterize joint effects of environmental and genetic interactions on lung cancer risk. This extensive data 
and information from cohort studies we have assembled will allow us to model joint effects of smoking and genetic 
factors on lung cancer risk over time. Supported by the biostatistical core we will perform mediation analyses to 
partition risk among multiple smoking phenotypes and genetic factors. We will also use the genetic data to perform 
Mendelian randomization to evaluate the relevance of biomarkers in predicting lung cancer risk, to assist project 2.

## Key facts

- **NIH application ID:** 10135967
- **Project number:** 5U19CA203654-05
- **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:** $794,912
- **Award type:** 5
- **Project period:** 2017-08-01 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10135967

## Citation

> US National Institutes of Health, RePORTER application 10135967, Genomic predictors of smoking and lung cancer risk (5U19CA203654-05). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10135967. Licensed CC0.

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