A novel framework leveraging non-causal associations reveals shared pathways linking inflammation and cancer risk
- 1Imperial College London
Abstract
Confounding is a central challenge in observational studies. Here, we propose a framework for identifying confounders of two non-causally related traits by employing cross-trait pleiotropy analysis to detect genetic loci that affect both traits and multi-trait colocalisation to identify molecular phenotypes mediating these effects. We apply this approach to the analysis of C-reactive protein (CRP) - a non-specific marker of inflammation - and 10 inflammation-related cancers. In UK Biobank, higher pre-diagnostic CRP levels are associated with increased risk of multiple cancers, but bidirectional Mendelian randomization provides little evidence for a causal relationship. Cross-trait genetic analyses identify 92 loci with shared CRP-cancer effects including those with established roles in cancer and 50 novel loci such as RSPO3 (breast cancer) and GCKR (colorectal cancer). Integration with proteomic and single-cell transcriptomic data identified putative molecular mediators at 24 loci including plasma TLR1 levels in breast cancer and CD4+ T cell IRF5 expression in kidney cancer. Notably, 15 candidate effector genes encode targets of approved or investigational medications, including IL6, PDE4D, and CASP8, indicating potential opportunities for their repurposing for cancer prevention. The proposed approach provides a generalisable framework for leveraging non-causal phenotypic relationships to yield insights into disease mechanisms and therapeutic targets for disease prevention.
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BibTeX
@misc{yarmolinsky2026novel,
title = {{A novel framework leveraging non-causal associations reveals shared pathways linking inflammation and cancer risk}},
author = {J. Yarmolinsky and F. R. Cavallo and F. Koskeridis and X. Yu and E. Bouras and G. Richenberg and I. Costantini and D. Ray and B. Woolf and V. Karhunen and L. Ellis and P. C. Haycock and G. Hemani and G. Davey Smith and K. K. Tsilidis and V. Zuber and J. D. McKay and A. Dehghan and I. Tzoulaki},
howpublished = {medRxiv},
year = {2026},
month = aug,
doi = {10.64898/2026.08.30.26361622},
url = {https://www.medrxiv.org/content/10.64898/2026.08.30.26361622v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - A novel framework leveraging non-causal associations reveals shared pathways linking inflammation and cancer risk
AU - J. Yarmolinsky
AU - F. R. Cavallo
AU - F. Koskeridis
AU - X. Yu
AU - E. Bouras
AU - G. Richenberg
AU - I. Costantini
AU - D. Ray
AU - B. Woolf
AU - V. Karhunen
AU - L. Ellis
AU - P. C. Haycock
AU - G. Hemani
AU - G. Davey Smith
AU - K. K. Tsilidis
AU - V. Zuber
AU - J. D. McKay
AU - A. Dehghan
AU - I. Tzoulaki
PY - 2026
DA - 2026/08/30
PB - medRxiv
DO - 10.64898/2026.08.30.26361622
UR - https://www.medrxiv.org/content/10.64898/2026.08.30.26361622v1
AB - Confounding is a central challenge in observational studies. Here, we propose a framework for identifying confounders of two non-causally related traits by employing cross-trait pleiotropy analysis to detect genetic loci that affect both traits and multi-trait colocalisation to identify molecular phenotypes mediating these effects. We apply this approach to the analysis of C-reactive protein (CRP) - a non-specific marker of inflammation - and 10 inflammation-related cancers. In UK Biobank, higher pre-diagnostic CRP levels are associated with increased risk of multiple cancers, but bidirectional Mendelian randomization provides little evidence for a causal relationship. Cross-trait genetic analyses identify 92 loci with shared CRP-cancer effects including those with established roles in cancer and 50 novel loci such as RSPO3 (breast cancer) and GCKR (colorectal cancer). Integration with proteomic and single-cell transcriptomic data identified putative molecular mediators at 24 loci including plasma TLR1 levels in breast cancer and CD4+ T cell IRF5 expression in kidney cancer. Notably, 15 candidate effector genes encode targets of approved or investigational medications, including IL6, PDE4D, and CASP8, indicating potential opportunities for their repurposing for cancer prevention. The proposed approach provides a generalisable framework for leveraging non-causal phenotypic relationships to yield insights into disease mechanisms and therapeutic targets for disease prevention.
ER -