Lung cancer risk prediction using common SNPs located in GWAS-identified susceptibility regions

Joel L. Weissfeld, Yan Lin, Hui Min Lin, Brenda F. Kurland, David O. Wilson, Carl R. Fuhrman, Arjun Pennathur, Marjorie Romkes, Tomoko Nukui, Jian Min Yuan, Jill M. Siegfried, Brenda Diergaarde

Research output: Contribution to journalArticlepeer-review

24 Scopus citations

Abstract

Introduction: Genome-wide association studies (GWAS) have consistently identified specific lung cancer susceptibility regions. We evaluated the lung cancer-predictive performance of single-nucleotide polymorphisms (SNPs) in these regions. Methods: Lung cancer cases (N = 778) and controls (N = 1166) were genotyped for 77 SNPs located in GWAS-identified lung cancer susceptibility regions. Variable selection and model development used stepwise logistic regression and decision-tree analyses. In a subset nested in the Pittsburgh Lung Screening Study, change in area under the receiver operator characteristic curve and net reclassification improvement were used to compare predictions made by risk factor models with and without genetic variables. Results: Variable selection and model development kept two SNPs in each of three GWAS regions, rs2736100 and rs7727912 in 5p15.33, rs805297 and rs1802127 in 6p21.33, and rs8034191 and rs12440014 in 15q25.1. The ratio of cases to controls was three times higher among subjects with a high-risk genotype in every one as opposed to none of the three GWAS regions (odds ratio, 3.14; 95% confidence interval, 2.02-4.88; adjusted for sex, age, and pack-years). Adding a three-level classified count of GWAS regions with high-risk genotypes to an age and smoking risk factor-only model improved lung cancer prediction by a small amount: area under the receiver operator characteristic curve, 0.725 versus 0.717 (p = 0.056); overall net reclassification improvement was 0.052 across low-, intermediate-, and high- 6-year lung cancer risk categories (<3.0%, 3.0%-4.9%, ≥5.0%). Conclusion: Specifying genotypes for SNPs in three GWAS-identified susceptibility regions improved lung cancer prediction, but probably by an extent too small to affect disease control practice.

Original languageEnglish (US)
Pages (from-to)1538-1545
Number of pages8
JournalJournal of Thoracic Oncology
Volume10
Issue number11
DOIs
StatePublished - Nov 1 2015

Bibliographical note

Funding Information:
This research was supported by the National Institutes of Health grants P50 CA90440 and P30 CA047904 .

Publisher Copyright:
© 2015 by the International Association for the Study of Lung Cancer.

Keywords

  • Lung cancer
  • Risk prediction
  • Single-nucleotide polymorphism

Fingerprint

Dive into the research topics of 'Lung cancer risk prediction using common SNPs located in GWAS-identified susceptibility regions'. Together they form a unique fingerprint.

Cite this