Integrating germline and somatic genetics to identify genes associated with lung cancer

Jack Pattee, Xiaowei Zhan, Guanghua Xiao, Wei Pan

Research output: Contribution to journalArticle

Abstract

Genome-wide association studies (GWAS) have successfully identified many genetic variants associated with complex traits. However, GWAS experience power issues, resulting in the failure to detect certain associated variants. Additionally, GWAS are often unable to parse the biological mechanisms of driving associations. An existing gene-based association test framework, Transcriptome-Wide Association Studies (TWAS), leverages expression quantitative trait loci data to increase the power of association tests and illuminate the biological mechanisms by which genetic variants modulate complex traits. We extend the TWAS methodology to incorporate somatic information from tumors. By integrating germline and somatic data we are able to leverage information from the nuanced somatic landscape of tumors. Thus we can augment the power of TWAS-type tests to detect germline genetic variants associated with cancer phenotypes. We use somatic and germline data on lung adenocarcinomas from The Cancer Genome Atlas in conjunction with a meta-analyzed lung cancer GWAS to identify novel genes associated with lung cancer.

Original languageEnglish (US)
JournalGenetic epidemiology
DOIs
StateAccepted/In press - Jan 1 2019

Fingerprint

Genome-Wide Association Study
Lung Neoplasms
Transcriptome
Genes
Neoplasms
Atlases
Quantitative Trait Loci
Genome
Phenotype

Keywords

  • PrediXcan
  • SSU test
  • TWAS
  • aSPU test
  • eQTL
  • sum test

Cite this

Integrating germline and somatic genetics to identify genes associated with lung cancer. / Pattee, Jack; Zhan, Xiaowei; Xiao, Guanghua; Pan, Wei.

In: Genetic epidemiology, 01.01.2019.

Research output: Contribution to journalArticle

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