Identification of lung cancer histology-specific variants applying Bayesian framework variant prioritization approaches within the TRICL and ILCCO consortia

Darren R. Brenner, Christopher I. Amos, Yonathan Brhane, Maria N. Timofeeva, Neil Caporaso, Yufei Wang, David C. Christiani, Heike Bickeböller, Ping Yang, Demetrius Albanes, Victoria L. Stevens, Susan Gapstur, James McKay, Paolo Boffetta, David Zaridze, Neonilia Szeszenia-Dabrowska, Jolanta Lissowska, Peter Rudnai, Eleonora Fabianova, Dana MatesVladimir Bencko, Lenka Foretova, Vladimir Janout, Hans E. Krokan, Frank Skorpen, Maiken E. Gabrielsen, Lars Vatten, Inger Njølstad, Chu Chen, Gary Goodman, Mark Lathrop, Tõnu Vooder, Kristjan Välk, Mari Nelis, Andres Metspalu, Peter Broderick, Timothy Eisen, Xifeng Wu, Di Zhang, Wei Chen, Margaret R. Spitz, Yongyue Wei, Li Su, Dong Xie, Jun She, Keitaro Matsuo, Fumihiko Matsuda, Hidemi Ito, Angela Risch, Joachim Heinrich, Albert Rosenberger, Thomas Muley, Hendrik Dienemann, John K. Field, Olaide Raji, Ying Chen, John Gosney, Triantafillos Liloglou, Michael P.A. Davies, Michael Marcus, John McLaughlin, Irene Orlow, Younghun Han, Yafang Li, Xuchen Zong, Mattias Johansson, Geoffrey Liu, Shelley S. Tworoger, Loic Le Marchand, Brian E. Henderson, Lynne R. Wilkens, Juncheng Dai, Hongbing Shen, Richard S. Houlston, Maria T. Landi, Paul Brennan, Rayjean J. Hung*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)


Large-scale genome-wide association studies (GWAS) have likely uncovered all common variants at the GWAS significance level. Additional variants within the suggestive range (0.0001> P> 5× 10-8) are, however, still of interest for identifying causal associations. This analysis aimed to apply novel variant prioritization approaches to identify additional lung cancer variants that may not reach the GWAS level. Effects were combined across studies with a total of 33 456 controls and 6756 adenocarcinoma (AC; 13 studies), 5061 squamous cell carcinoma (SCC; 12 studies) and 2216 small cell lung cancer cases (9 studies). Based on prior information such as variant physical properties and functional significance, we applied stratified false discovery rates, hierarchical modeling and Bayesian false discovery probabilities for variant prioritization. We conducted a fine mapping analysis as validation of our methods by examining top-ranking novel variants in six independent populations with a total of 3128 cases and 2966 controls. Three novel loci in the suggestive range were identified based on our Bayesian framework analyses: KCNIP4 at 4p15.2 (rs6448050, P= 4.6× 10-7) and MTMR2 at 11q21 (rs10501831, P= 3.1× 10-6) with SCC, as well as GAREM at 18q12.1 (rs11662168, P= 3.4× 10-7) with AC. Use of our prioritization methods validated two of the top three loci associated with SCC (P= 1.05× 10-4 for KCNIP4, represented by rs9799795) and AC (P= 2.16× 10-4 for GAREM, represented by rs3786309) in the independent fine mapping populations. This study highlights the utility of using prior functional data for sequence variants in prioritization analyses to search for robust signals in the suggestive range.

Original languageEnglish
Pages (from-to)1314-1326
Number of pages13
Issue number11
Publication statusPublished - Nov 2015
Externally publishedYes

Cite this