GWAS meta-analysis of 16 790 patients with Barrett’s oesophagus and oesophageal adenocarcinoma identifies 16 novel genetic risk loci and provides insights into disease aetiology beyond the single marker level

全基因组关联研究 遗传关联 连锁不平衡 疾病 病因学 肿瘤科 生物 内科学 生物信息学 遗传学 医学 单核苷酸多态性 基因 基因型
作者
Julia Schröder,Laura Chegwidden,Carlo Maj,Jan Gehlen,Jan Speller,Anne C. Böhmer,Oleg Borisov,Timo Hess,Nicole Kreuser,Marino Venerito,Hakan Alakus,Andrea May,Christian Gerges,Thomas Schmidt,René Thieme,Dominik Heider,Axel M. Hillmer,Julian Reingruber,Orestis Lyros,Arne Dietrich
出处
期刊:Gut [BMJ]
卷期号:72 (4): 612-623 被引量:13
标识
DOI:10.1136/gutjnl-2021-326698
摘要

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.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
科研通AI5应助drughunter采纳,获得10
刚刚
自然的雁芙完成签到 ,获得积分10
1秒前
wp完成签到,获得积分10
1秒前
2秒前
DyLan完成签到,获得积分10
2秒前
huiyou完成签到,获得积分10
3秒前
陈飞飞发布了新的文献求助10
3秒前
MXene应助十里长亭采纳,获得10
3秒前
可耐的乘风应助a31采纳,获得10
4秒前
Ga完成签到,获得积分20
4秒前
CodeCraft应助Nozomi采纳,获得10
4秒前
MaYue完成签到,获得积分10
6秒前
6秒前
红烧茄子完成签到,获得积分10
6秒前
CipherSage应助XY采纳,获得10
7秒前
ZG完成签到,获得积分10
7秒前
旺仔先生完成签到,获得积分0
8秒前
lyh完成签到,获得积分10
8秒前
榴莲牛奶发布了新的文献求助20
8秒前
简单完成签到,获得积分20
9秒前
4645发布了新的文献求助10
9秒前
9秒前
pluto应助曹子轩采纳,获得10
10秒前
斯寜应助偷乐采纳,获得10
10秒前
tcf完成签到,获得积分10
10秒前
花里胡哨的花完成签到 ,获得积分10
10秒前
我是站长才怪给33的求助进行了留言
10秒前
充电宝应助up采纳,获得10
11秒前
Ga发布了新的文献求助30
11秒前
华仔应助阿冷采纳,获得10
12秒前
为你博弈发布了新的文献求助10
12秒前
年轻半雪完成签到,获得积分10
13秒前
liuhui完成签到 ,获得积分10
14秒前
启程完成签到,获得积分10
15秒前
平常难摧发布了新的文献求助10
16秒前
Wlna完成签到,获得积分20
16秒前
17秒前
Rui完成签到 ,获得积分10
18秒前
九思完成签到,获得积分10
18秒前
帕芙芙完成签到,获得积分10
18秒前
高分求助中
All the Birds of the World 4000
Production Logging: Theoretical and Interpretive Elements 3000
Animal Physiology 2000
Les Mantodea de Guyane Insecta, Polyneoptera 2000
Am Rande der Geschichte : mein Leben in China / Ruth Weiss 1500
CENTRAL BOOKS: A BRIEF HISTORY 1939 TO 1999 by Dave Cope 1000
Machine Learning Methods in Geoscience 1000
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 物理 生物化学 纳米技术 计算机科学 化学工程 内科学 复合材料 物理化学 电极 遗传学 量子力学 基因 冶金 催化作用
热门帖子
关注 科研通微信公众号,转发送积分 3736892
求助须知:如何正确求助?哪些是违规求助? 3280826
关于积分的说明 10021216
捐赠科研通 2997475
什么是DOI,文献DOI怎么找? 1644637
邀请新用户注册赠送积分活动 782083
科研通“疑难数据库(出版商)”最低求助积分说明 749705