Evidence map›Paper›PMID 41282795›Full record

ArticlemedRxiv : the preprint server for health sciences2025

Prioritizing context-specific genetic risk mechanisms in 11 solid cancers.

Xueyao Wu, Artem Kim, Charles E Breeze, Tracy A O'Mara, Dhanya Ramachandran, Thilo Dörk, Stella Koutros, Nathaniel Rothman, Ludmila Prokunina-Olsson, Nicholas Mancuso and 2 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors.

Xueyao WuTrans-Divisional Research Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
Artem KimDepartment of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Charles E BreezeOccupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
Tracy A O'MaraCancer Research Program, QIMR Berghofer, Brisbane, Queensland, Australia.
Dhanya RamachandranGynaecology Research Unit, Hannover Medical School, Hannover, Germany.
Thilo DörkGynaecology Research Unit, Hannover Medical School, Hannover, Germany.
Stella KoutrosOccupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
Nathaniel RothmanOccupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
Ludmila Prokunina-OlssonLaboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.ORCID 0000-0002-9622-2091
Nicholas MancusoDepartment of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Sara LindströmDepartment of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA.
Peter KraftTrans-Divisional Research Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.

Funding

Quantifying and Characterizing the shared genetic contribution to common cancersU01CA194393 · NCI · UNIVERSITY OF WASHINGTON · PI KRAFT, PETER, LINDSTROEM, SARA · 2015 to 2023
$3.0M
NCI NIH HHS U01 CA194393
6 · The paper itself

Abstract

Background: While genome-wide association studies (GWAS) have identified hundreds of cancer-associated genetic variants, the specific biological contexts where these variants exert their effects remain largely unknown. We aimed to prioritize context-specific genetic risk mechanisms for 11 solid cancers at both genome-wide and single-variant resolutions. Methods: We integrated cancer GWAS summary statistics from European ancestry samples (avg. n cases=47,856) with ~1,500 context-specific annotations representing candidate Results: Stratified LD score regression analysis identified 52 annotations with significant heritability enrichment (Bonferroni-corrected Conclusion: These findings advance our understanding of genetic susceptibility to different cancers. Future work in larger, more diverse GWAS, coupled with more comprehensive annotation atlases, is essential to expand upon and validate our results.

Identifiers

PMID41282795
PMCPMC12636666

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.