Abstract
Objective Oesophageal cancer (EC) is the sixth leading cause of cancer-related deaths. Oesophageal adenocarcinoma (EA), with Barrett's oesophagus (BE) as a precursor lesion, is the most prevalent EC subtype in the Western world. This study aims to contribute to better understand the genetic causes of BE/EA by leveraging genome wide association studies (GWAS), genetic correlation analyses and polygenic risk modelling. Design We combined data from previous GWAS with new cohorts, increasing the sample size to 16 790 BE/EA cases and 32 476 controls. We also carried out a transcriptome wide association study (TWAS) using expression data from disease-relevant tissues to identify BE/EA candidate genes. To investigate the relationship with reported BE/EA risk factors, a linkage disequilibrium score regression (LDSR) analysis was performed. BE/EA risk models were developed combining clinical/lifestyle risk factors with polygenic risk scores (PRS) derived from the GWAS meta-analysis. Results The GWAS meta-analysis identified 27 BE and/or EA risk loci, 11 of which were novel. The TWAS identified promising BE/EA candidate genes at seven GWAS loci and at five additional risk loci. The LDSR analysis led to the identification of novel genetic correlations and pointed to differences in BE and EA aetiology. Gastro-oesophageal reflux disease appeared to contribute stronger to the metaplastic BE transformation than to EA development. Finally, combining PRS with BE/EA risk factors improved the performance of the risk models. Conclusion Our findings provide further insights into BE/EA aetiology and its relationship to risk factors. The results lay the foundation for future follow-up studies to identify underlying disease mechanisms and improving risk prediction.
| Original language | English |
|---|---|
| Journal | Gut |
| Volume | 72 |
| Issue number | 4 |
| Pages (from-to) | 612-623 |
| Number of pages | 12 |
| ISSN | 0017-5749 |
| DOIs | |
| Publication status | Published - 04.2023 |
Funding
This research has been conducted using the UK Biobank Resource under Application Numbers 34390 and 35182. AHi, DHe and JoSc received support for this work from the Else Kröner Fresenius Stiftung (EKFS) (grant number 2013_A118) and the German Federal Ministry of Education and Research (BMBF) (grant number 031L0267A). CPa acknowledges startup funding including a PhD stipend from the University of Birmingham. DEl and AFr were supported by the Deutsche Forschungsgemeinschaft (DFG) Cluster of Excellence 'Precision Medicine in Chronic Inflammation' (EXC2167). We thank all patients and controls for participating in this study. This research has been conducted using the UK Biobank Resource under Application Numbers 34390 and 35182. AHi, DHe and JoSc received support for this work from the Else Kröner Fresenius Stiftung (EKFS) (grant number 2013_A118) and the German Federal Ministry of Education and Research (BMBF) (grant number 031L0267A). CPa acknowledges startup funding including a PhD stipend from the University of Birmingham. DEl and AFr were supported by the Deutsche Forschungsgemeinschaft (DFG) Cluster of Excellence 'Precision Medicine in Chronic Inflammation' (EXC2167). The computations described in this paper were performed using the University of Birmingham's BlueBEAR HPC service, which provides a High Performance Computing service to the University's research community.
Research Areas and Centers
- Research Area: Medical Genetics
- Research Area: Luebeck Integrated Oncology Network (LION)
- Centers: University Cancer Center Schleswig-Holstein (UCCSH)
DFG Research Classification Scheme
- 2.22-14 Hematology, Oncology
