转录因子结合位点和增强子区域的多态性与胰腺导管腺癌风险

IF 3.8 3区 医学 Q2 GENETICS & HEREDITY Human Genomics Pub Date : 2024-02-02 DOI:10.1186/s40246-024-00576-x
Pelin Ünal, Ye Lu, Bas Bueno-de-Mesquita, Casper H. J. van Eijck, Renata Talar-Wojnarowska, Andrea Szentesi, Maria Gazouli, Edita Kreivenaite, Francesca Tavano, Ewa Małecka-Wojciesko, Bálint Erőss, Martin Oliverius, Stefania Bunduc, Mateus Nóbrega Aoki, Ludmila Vodickova, Ugo Boggi, Matteo Giaccherini, Jurate Kondrackiene, Roger Chammas, Orazio Palmieri, George E. Theodoropoulos, Maarten F. Bijlsma, Daniela Basso, Beatrice Mohelnikova-Duchonova, Pavel Soucek, Jakob R. Izbicki, Vytautas Kiudelis, Giuseppe Vanella, Paolo Giorgio Arcidiacono, Barbara Włodarczyk, Thilo Hackert, Ben Schöttker, Faik G. Uzunoglu, Franco Bambi, Mara Goetz, Viktor Hlavac, Hermann Brenner, Francesco Perri, Silvia Carrara, Stefano Landi, Péter Hegyi, Frederike Dijk, Evaristo Maiello, Giovanni Capretti, Sabrina Gloria Giulia Testoni, Maria Chiara Petrone, Hannah Stocker, Stefano Ermini, Livia Archibugi, Manuel Gentiluomo, Giulia Martina Cavestro, Raffaele Pezzilli, Gregorio Di Franco, Anna Caterina Milan..
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引用次数: 0

摘要

全基因组关联研究(GWAS)是检测与复杂性状相关的变异的有力工具,有助于胰腺导管腺癌(PDAC)的风险分层和预防策略。然而,由于通常使用严格的显著性阈值,许多真正的风险位点很可能被遗漏。对 GWAS 多态性进行功能注释是一种行之有效的策略,可以发现更多的风险位点。我们的目的是研究调控区域(转录因子结合位点(TFBS)和增强子)中的单核苷酸多态性(SNP),这些多态性可能会改变其作用的多个基因的表达谱,从而改变 PDAC 风险。我们分析了来自 PanScan/PanC4 和东亚 GWAS(发现人群)以及 PANDoRA 联合体(复制人群)的 12,636 例 PDAC 病例和 43,443 例对照。在整个荟萃分析中,我们发现了四种达到全研究统计显著性的关联:rs2472632(A)(增强子变异,OR 1.10,95%CI 1.06,1.13,p = 5.5 × 10-8)、rs17358295(G)(增强子变异,OR 1.16,95%CI 1.10,1.22,p = 6.1 × 10-7)、rs2232079(T)(TFBS 变异,OR 0.88,95%CI 0.83,0.93,p = 6.4 × 10-6)和 rs10025845(A)(TFBS 变异,OR 1.88,95%CI 1.50,1.12,p = 1.32 × 10-5)。具有最显著相关性的 SNP rs2472632 位于一个增强子上,该增强子被预测为靶向含有 34 个盘绕线圈结构域的癌基因。我们的研究结果通过对调控区多态性的集中分析,对 PDAC 的遗传风险因素提供了新的见解,并证明了功能优先化对识别与 PDAC 风险相关的位点的有用性。
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Polymorphisms in transcription factor binding sites and enhancer regions and pancreatic ductal adenocarcinoma risk
Genome-wide association studies (GWAS) are a powerful tool for detecting variants associated with complex traits and can help risk stratification and prevention strategies against pancreatic ductal adenocarcinoma (PDAC). However, the strict significance threshold commonly used makes it likely that many true risk loci are missed. Functional annotation of GWAS polymorphisms is a proven strategy to identify additional risk loci. We aimed to investigate single-nucleotide polymorphisms (SNP) in regulatory regions [transcription factor binding sites (TFBSs) and enhancers] that could change the expression profile of multiple genes they act upon and thereby modify PDAC risk. We analyzed a total of 12,636 PDAC cases and 43,443 controls from PanScan/PanC4 and the East Asian GWAS (discovery populations), and the PANDoRA consortium (replication population). We identified four associations that reached study-wide statistical significance in the overall meta-analysis: rs2472632(A) (enhancer variant, OR 1.10, 95%CI 1.06,1.13, p = 5.5 × 10−8), rs17358295(G) (enhancer variant, OR 1.16, 95%CI 1.10,1.22, p = 6.1 × 10−7), rs2232079(T) (TFBS variant, OR 0.88, 95%CI 0.83,0.93, p = 6.4 × 10−6) and rs10025845(A) (TFBS variant, OR 1.88, 95%CI 1.50,1.12, p = 1.32 × 10−5). The SNP with the most significant association, rs2472632, is located in an enhancer predicted to target the coiled-coil domain containing 34 oncogene. Our results provide new insights into genetic risk factors for PDAC by a focused analysis of polymorphisms in regulatory regions and demonstrating the usefulness of functional prioritization to identify loci associated with PDAC risk.
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来源期刊
Human Genomics
Human Genomics GENETICS & HEREDITY-
CiteScore
6.00
自引率
2.20%
发文量
55
审稿时长
11 weeks
期刊介绍: Human Genomics is a peer-reviewed, open access, online journal that focuses on the application of genomic analysis in all aspects of human health and disease, as well as genomic analysis of drug efficacy and safety, and comparative genomics. Topics covered by the journal include, but are not limited to: pharmacogenomics, genome-wide association studies, genome-wide sequencing, exome sequencing, next-generation deep-sequencing, functional genomics, epigenomics, translational genomics, expression profiling, proteomics, bioinformatics, animal models, statistical genetics, genetic epidemiology, human population genetics and comparative genomics.
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