Biologically targeted discovery-replication scan identifies G×G interaction in relation to risk of Barrett’s esophagus and esophageal adenocarcinoma
Li Yan,
Qianchuan He,
Shiv P. Verma,
Xu Zhang,
Ann-Sophie Giel,
Carlo Maj,
Kathryn Graz,
Elnaz Naderi,
Jianhong Chen,
Mourad Wagdy Ali,
Puya Gharahkhani,
Xiang Shu,
Kenneth Offit,
Pari M. Shah,
Hans Gerdes,
Daniela Molena,
Amitabh Srivastava,
Stuart MacGregor,
Claire Palles,
René Thieme,
Michael Vieth,
Ines Gockel,
Thomas L. Vaughan,
Johannes Schumacher,
Matthew F. Buas
Affiliations
Li Yan
Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
Qianchuan He
Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
Shiv P. Verma
Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
Xu Zhang
Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
Ann-Sophie Giel
Center for Human Genetics, University Hospital of Marburg, Marburg, Germany
Carlo Maj
Center for Human Genetics, University Hospital of Marburg, Marburg, Germany
Kathryn Graz
Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
Elnaz Naderi
Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
Jianhong Chen
Department of Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
Mourad Wagdy Ali
Department of Genome Sciences, School of Medicine, University of Virginia, Charlottesville, VA, USA
Puya Gharahkhani
QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
Xiang Shu
Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
Kenneth Offit
Clinical Genetics, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Cancer Biology and Genetics Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
Pari M. Shah
Gastroenterology and Nutrition Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
Hans Gerdes
Gastroenterology and Nutrition Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
Daniela Molena
Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
Amitabh Srivastava
Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
Stuart MacGregor
QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
Claire Palles
Department of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
René Thieme
Department of Visceral, Transplant, Thoracic and Vascular Surgery, University Hospital of Leipzig, Leipzig, Germany
Michael Vieth
Institute of Pathology, Friedrich-Alexander-Universiät Erlangen-Nürnberg, Klinikum Bayreuth, Bayreuth, Germany
Ines Gockel
Department of Visceral, Transplant, Thoracic and Vascular Surgery, University Hospital of Leipzig, Leipzig, Germany
Thomas L. Vaughan
Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA; Department of Epidemiology, University of Washington, School of Public Health, Seattle, WA, USA
Johannes Schumacher
Center for Human Genetics, University Hospital of Marburg, Marburg, Germany
Matthew F. Buas
Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Corresponding author
Summary: Inherited genetics represents an important contributor to risk of esophageal adenocarcinoma (EAC), and its precursor Barrett’s esophagus (BE). Genome-wide association studies have identified ∼30 susceptibility variants for BE/EAC, yet genetic interactions remain unexamined. To address challenges in large-scale G×G scans, we combined knowledge-guided filtering and machine learning approaches, focusing on genes with (1) known/plausible links to BE/EAC pathogenesis (n = 493) or (2) prior evidence of biological interactions (n = 4,196). Approximately 75 × 106 SNP×SNP interactions were screened via hierarchical group lasso (glinternet) using BEACON GWAS data. The top ∼2,000 interactions retained in each scan were prioritized using p values from single logistic models. Identical scans were repeated among males only (78%), with two independent GWAS datasets used for replication. In overall and male-specific primary replications, 11 of 187 and 20 of 191 interactions satisfied p < 0.05, respectively. The strongest evidence for secondary replication was for rs17744726×rs3217992 among males, with consistent directionality across all cohorts (Pmeta = 2.19 × 10−8); rs3217992 “T” was associated with reduced risk only in individuals homozygous for rs17744726 “G.” Rs3217992 maps to the CDKN2B 3′ UTR and reportedly disrupts microRNA-mediated repression. Rs17744726 maps to an intronic enhancer region in BLK. Through in silico prioritization and experimental validation, we identified a nearby proxy variant (rs4841556) as a functional modulator of enhancer activity. Enhancer-gene mapping and eQTLs implicated BLK and FAM167A as targets. The first systematic G×G investigation in BE/EAC, this study uncovers differential risk associations for CDKN2B variation by BLK genotype, suggesting novel biological dependency between two risk loci encoding key mediators of tumor suppression and inflammation.
