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Integrating germline and somatic genetics to identify genes associated with lung cancer
Author(s) -
Pattee Jack,
Zhan Xiaowei,
Xiao Guanghua,
Pan Wei
Publication year - 2020
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.22275
Subject(s) - genome wide association study , biology , germline , genetic association , germline mutation , genetics , somatic cell , computational biology , quantitative trait locus , gene , single nucleotide polymorphism , mutation , genotype
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.