| Title: | Identification of Pancreatic Cancer Germline Risk Variants With Effects That Are Modified by Smoking. |
| Journal: | JCO Precision Oncology |
| Published: | 1 Mar 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38564682/ |
| DOI: | https://doi.org/10.1200/po.23.00355 |
Publication 10153
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Abstract
PURPOSE: Pancreatic cancer (PC) is a deadly disease most often diagnosed in late stages. Identification of high-risk subjects could both contribute to preventative measures and help diagnose the disease at earlier timepoints. However, known risk factors, assessed independently, are currently insufficient for accurately stratifying patients. We use large-scale data from the UK Biobank (UKB) to identify genetic variant-smoking interaction effects and show their importance in risk assessment.</p>
METHODS: We draw data from 15,086,830 genetic variants and 315,512 individuals in the UKB. There are 765 cases of PC. Crucially, robust resampling corrections are used to overcome well-known challenges in hypothesis testing for interactions. Replication analysis is conducted in two independent cohorts totaling 793 cases and 570 controls. Integration of functional annotation data and construction of polygenic risk scores (PRS) demonstrate the additional insight provided by interaction effects.</p>
RESULTS: We identify the genome-wide significant variant rs77196339 on chromosome 2 (per minor allele odds ratio in never-smokers, 2.31 [95% CI, 1.69 to 3.15]; per minor allele odds ratio in ever-smokers, 0.53 [95% CI, 0.30 to 0.91]; P = 3.54 × 10-8) as well as eight other loci with suggestive evidence of interaction effects (P < 5 × 10-6). The rs77196339 region association is validated (P < .05) in the replication sample. PRS incorporating interaction effects show improved discriminatory ability over PRS of main effects alone.</p>
CONCLUSION: This study of genome-wide germline variants identified smoking to modify the effect of rs77196339 on PC risk. Interactions between known risk factors can provide critical information for identifying high-risk subjects, given the relative inadequacy of models considering only main effects, as demonstrated in PRS. Further studies are necessary to advance toward comprehensive risk prediction approaches for PC.</p>
7 Authors
- Huili Zhu
- Jaihee Choi
- Naishu Kui
- Tianzhong Yang
- Peng Wei
- Donghui Li
- Ryan Sun
1 Application
| Application ID | Title |
|---|---|
| 73569 | Development of statistical methods to identify and characterize genetic variants linked to complex diseases under the time-to-event outcome modeling framework. |
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