Integration of multiomic annotation data to prioritize and characterize inflammation and immune-related risk variants in squamous cell lung cancer
Sun, Ryan ; Melander, Olle LU
; Brunnström, Hans
LU
and Lin, Xihong
(2021)
In Genetic Epidemiology
45(1).
p.99-114
- Abstract
- 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... (More)
- 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. © 2020 Wiley Periodicals LLC (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/2defccce-7111-4733-ba75-1c6417f621da
- author
- Sun, Ryan
; Melander, Olle
LU
; Brunnström, Hans
LU
and Lin, Xihong
- author collaboration
- organization
- publishing date
- 2021
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- genome-wide annotation, integrative omics, lung cancer, major histocompatibility complex
- in
- Genetic Epidemiology
- volume
- 45
- issue
- 1
- pages
- 99 - 114
- publisher
- John Wiley & Sons Inc.
- external identifiers
-
- scopus:85090970574
- pmid:32924180
- ISSN
- 0741-0395
- DOI
- 10.1002/gepi.22358
- language
- English
- LU publication?
- yes
- id
- 2defccce-7111-4733-ba75-1c6417f621da
- date added to LUP
- 2020-11-23 12:20:47
- date last changed
- 2024-01-17 15:25:17
@article{2defccce-7111-4733-ba75-1c6417f621da,
abstract = {{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. © 2020 Wiley Periodicals LLC}},
author = {{Sun, Ryan and Melander, Olle and Brunnström, Hans and Lin, Xihong}},
issn = {{0741-0395}},
keywords = {{genome-wide annotation; integrative omics; lung cancer; major histocompatibility complex}},
language = {{eng}},
number = {{1}},
pages = {{99--114}},
publisher = {{John Wiley & Sons Inc.}},
series = {{Genetic Epidemiology}},
title = {{Integration of multiomic annotation data to prioritize and characterize inflammation and immune-related risk variants in squamous cell lung cancer}},
url = {{http://dx.doi.org/10.1002/gepi.22358}},
doi = {{10.1002/gepi.22358}},
volume = {{45}},
year = {{2021}},
}