Integration of multiomic annotation data to prioritize and characterize inflammation and immune-related risk variants in squamous cell lung cancer
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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 language | English |
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Journal | Genetic Epidemiology |
Volume | 45 |
Issue number | 1 |
Pages (from-to) | 99-114 |
ISSN | 0741-0395 |
DOIs | |
Publication status | Published - 2021 |
Bibliographical note
Funding Information:
We thank the International Lung Cancer Consortium for use of their data and Dr. Song Gao of Sun Yat‐sen University Cancer Center for helpful conversations. We would also like to thank the reviewers for their helpful comments that greatly improved the paper. This study was funded by National Institutes of Health: R35‐CA197449, R01‐HL113338, P42‐ES016454, U01CA209414, U19CA203654, and T32‐ES007142. Christopher I. Amos is a research scholar of the Cancer Prevention Institute of Texas and is partially supported by RR170048.
Funding Information:
We thank the International Lung Cancer Consortium for use of their data and Dr. Song Gao of Sun Yat-sen University Cancer Center for helpful conversations. We would also like to thank the reviewers for their helpful comments that greatly improved the paper. This study was funded by?National Institutes of Health: R35-CA197449, R01-HL113338, P42-ES016454, U01CA209414, U19CA203654, and T32-ES007142. Christopher I. Amos is a research scholar of the Cancer Prevention Institute of Texas and is partially supported by RR170048.
Publisher Copyright:
© 2020 Wiley Periodicals LLC
- genome-wide annotation, integrative omics, lung cancer, major histocompatibility complex
Research areas
ID: 302065461