Integration of multiomic annotation data to prioritize and characterize inflammation and immune-related risk variants in squamous cell lung cancer

Ryan Sun, Miao Xu, Xihao Li, Sheila Gaynor, Hufeng Zhou, Zilin Li, Yohan Bossé, Stephen Lam, Ming Sound Tsao, Adonina Tardon, Chu Chen, Jennifer Doherty, Gary Goodman, Stig E. Bojesen, Maria T. Landi, Mattias Johansson, John K. Field, Heike Bickeböller, H. Erich Wichmann, Angela RischGadi Rennert, Suzanne Arnold, Xifeng Wu, Olle Melander, Hans Brunnström, Loic Le Marchand, Geoffrey Liu, Angeline Andrew, Eric Duell, Lambertus A. Kiemeney, Hongbing Shen, Aage Haugen, Mikael Johansson, Kjell Grankvist, Neil Caporaso, Penella Woll, M. Dawn Teare, Ghislaine Scelo, Yun Chul Hong, Jian Min Yuan, Philip Lazarus, Matthew B. Schabath, Melinda C. Aldrich, Demetrios Albanes, Raymond Mak, David Barbie, Paul Brennan, Rayjean J. Hung, Christopher I. Amos, David C. Christiani, Xihong Lin

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Clinical trial results have recently demonstrated that inhibiting inflammation by targeting the interleukin-1β pathway can offer a significant reduction in lung cancer incidence and mortality, highlighting a pressing and unmet need to understand the benefits of inflammation-focused lung cancer therapies at the genetic level. While numerous genome-wide association studies (GWAS) have explored the genetic etiology of lung cancer, there remains a large gap between the type of information that may be gleaned from an association study and the depth of understanding necessary to explain and drive translational findings. Thus, in this study we jointly model and integrate extensive multiomics data sources, utilizing a total of 40 genome-wide functional annotations that augment previously published results from the International Lung Cancer Consortium (ILCCO) GWAS, to prioritize and characterize single nucleotide polymorphisms (SNPs) that increase risk of squamous cell lung cancer through the inflammatory and immune responses. Our work bridges the gap between correlative analysis and translational follow-up research, refining GWAS association measures in an interpretable and systematic manner. In particular, reanalysis of the ILCCO data highlights the impact of highly associated SNPs from nuclear factor-κB signaling pathway genes as well as major histocompatibility complex mediated variation in immune responses. One consequence of prioritizing likely functional SNPs is the pruning of variants that might be selected for follow-up work by over an order of magnitude, from potentially tens of thousands to hundreds. The strategies we introduce provide informative and interpretable approaches for incorporating extensive genome-wide annotation data in analysis of genetic association studies.

Original languageEnglish
Pages (from-to)99-114
Number of pages16
JournalGenetic Epidemiology
Issue number1
StatePublished - Feb 2021

Bibliographical note

Publisher Copyright:
© 2020 Wiley Periodicals LLC


  • genome-wide annotation
  • integrative omics
  • lung cancer
  • major histocompatibility complex

ASJC Scopus subject areas

  • Epidemiology
  • Genetics(clinical)


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