Chenjie Zeng1, Lisa A Bastarache2, Ran Tao3, Eric Venner4, Scott Hebbring5, Justin D Andujar6,7, Sarah T Bland2, David R Crosslin8, Siddharth Pratap9, Ayorinde Cooley10, Jennifer A Pacheco11, Kurt D Christensen12,13, Emma Perez14, Carrie L Blout Zawatsky14, Leora Witkowski15, Hana Zouk16,17, Chunhua Weng18, Kathleen A Leppig19, Patrick M A Sleiman20,21, Hakon Hakonarson20,21, Marc S Williams22, Yuan Luo23, Gail P Jarvik24,25, Robert C Green26, Wendy K Chung27,28, Ali G Gharavi29,30, Niall J Lennon31, Heidi L Rehm32,33,34, Richard A Gibbs4, Josh F Peterson2, Dan M Roden2,35,36, Georgia L Wiesner6,7, Joshua C Denny1. 1. National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland. 2. Center for Precision Medicine, Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee. 3. Department of Biostatistics, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee. 4. Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas. 5. Center for Human Genetics, Marshfield Clinic Research Institute, Marshfield, Wisconsin. 6. Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee. 7. Clinical and Translational Hereditary Cancer Program, Division of Genetic Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, Tennessee. 8. Department of Biomedical Informatics and Medical Education, University of Washington School of Medicine, Seattle. 9. School of Graduate Studies and Research, Meharry Medical College, Nashville, Tennessee. 10. Department of Microbiology, Immunology and Physiology, Meharry Medical College, Nashville, Tennessee. 11. Center for Genetic Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois. 12. PRecisiOn Medicine Translational Research (PROMoTeR) Center, Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, Massachusetts. 13. Department of Population Medicine, Harvard Medical School, Boston, Massachusetts. 14. Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts. 15. Centre Universitaire de Santé McGill, McGill University Health Centre, Montreal, Quebec, Canada. 16. Laboratory for Molecular Medicine, Partners Healthcare Personalized Medicine, Cambridge, Massachusetts. 17. Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston. 18. Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York. 19. Genetic Services and Kaiser Permanente Washington Health Research Institute, Kaiser Permanente of Washington, Seattle. 20. Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania. 21. Division of Human Genetics, Department of Pediatrics, The University of Pennsylvania Perelman School of Medicine, Philadelphia. 22. Genomic Medicine Institute, Geisinger, Danville, Pennsylvania. 23. Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois. 24. Department of Medicine (Medical Genetics), University of Washington, Seattle. 25. Department of Genome Sciences, University of Washington, Seattle. 26. Brigham and Women's Hospital, Broad Institute, Ariadne Labs and Harvard Medical School, Boston, Massachusetts. 27. Department of Pediatrics, Columbia University, New York, New York. 28. Department of Medicine, Columbia University, New York, New York. 29. Division of Nephrology, Department of Medicine, Columbia University Irving Medical Center, New York, New York. 30. Center for Precision Medicine and Genomics, Department of Medicine, Columbia University Irving Medical Center, New York, New York. 31. Broad Institute of MIT and Harvard, Cambridge, Massachusetts. 32. Medical & Population Genetics Program and Genomics Platform, Broad Institute of MIT and Harvard Cambridge, Cambridge, Massachusetts. 33. Center for Genomic Medicine, Massachusetts General Hospital, Boston. 34. Department of Pathology, Harvard Medical School, Boston, Massachusetts. 35. Divisions of Cardiovascular Medicine and Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee. 36. Department of Pharmacology, Vanderbilt University, Nashville, Tennessee.
Abstract
Importance: Knowledge about the spectrum of diseases associated with hereditary cancer syndromes may improve disease diagnosis and management for patients and help to identify high-risk individuals. Objective: To identify phenotypes associated with hereditary cancer genes through a phenome-wide association study. Design, Setting, and Participants: This phenome-wide association study used health data from participants in 3 cohorts. The Electronic Medical Records and Genomics Sequencing (eMERGEseq) data set recruited predominantly healthy individuals from 10 US medical centers from July 16, 2016, through February 18, 2018, with a mean follow-up through electronic health records (EHRs) of 12.7 (7.4) years. The UK Biobank (UKB) cohort recruited participants from March 15, 2006, through August 1, 2010, with a mean (SD) follow-up of 12.4 (1.0) years. The Hereditary Cancer Registry (HCR) recruited patients undergoing clinical genetic testing at Vanderbilt University Medical Center from May 1, 2012, through December 31, 2019, with a mean (SD) follow-up through EHRs of 8.8 (6.5) years. Exposures: Germline variants in 23 hereditary cancer genes. Pathogenic and likely pathogenic variants for each gene were aggregated for association analyses. Main Outcomes and Measures: Phenotypes in the eMERGEseq and HCR cohorts were derived from the linked EHRs. Phenotypes in UKB were from multiple sources of health-related data. Results: A total of 214 020 participants were identified, including 23 544 in eMERGEseq cohort (mean [SD] age, 47.8 [23.7] years; 12 611 women [53.6%]), 187 234 in the UKB cohort (mean [SD] age, 56.7 [8.1] years; 104 055 [55.6%] women), and 3242 in the HCR cohort (mean [SD] age, 52.5 [15.5] years; 2851 [87.9%] women). All 38 established gene-cancer associations were replicated, and 19 new associations were identified. These included the following 7 associations with neoplasms: CHEK2 with leukemia (odds ratio [OR], 3.81 [95% CI, 2.64-5.48]) and plasma cell neoplasms (OR, 3.12 [95% CI, 1.84-5.28]), ATM with gastric cancer (OR, 4.27 [95% CI, 2.35-7.44]) and pancreatic cancer (OR, 4.44 [95% CI, 2.66-7.40]), MUTYH (biallelic) with kidney cancer (OR, 32.28 [95% CI, 6.40-162.73]), MSH6 with bladder cancer (OR, 5.63 [95% CI, 2.75-11.49]), and APC with benign liver/intrahepatic bile duct tumors (OR, 52.01 [95% CI, 14.29-189.29]). The remaining 12 associations with nonneoplastic diseases included BRCA1/2 with ovarian cysts (OR, 3.15 [95% CI, 2.22-4.46] and 3.12 [95% CI, 2.36-4.12], respectively), MEN1 with acute pancreatitis (OR, 33.45 [95% CI, 9.25-121.02]), APC with gastritis and duodenitis (OR, 4.66 [95% CI, 2.61-8.33]), and PTEN with chronic gastritis (OR, 15.68 [95% CI, 6.01-40.92]). Conclusions and Relevance: The findings of this genetic association study analyzing the EHRs of 3 large cohorts suggest that these new phenotypes associated with hereditary cancer genes may facilitate early detection and better management of cancers. This study highlights the potential benefits of using EHR data in genomic medicine.
Importance: Knowledge about the spectrum of diseases associated with hereditary cancer syndromes may improve disease diagnosis and management for patients and help to identify high-risk individuals. Objective: To identify phenotypes associated with hereditary cancer genes through a phenome-wide association study. Design, Setting, and Participants: This phenome-wide association study used health data from participants in 3 cohorts. The Electronic Medical Records and Genomics Sequencing (eMERGEseq) data set recruited predominantly healthy individuals from 10 US medical centers from July 16, 2016, through February 18, 2018, with a mean follow-up through electronic health records (EHRs) of 12.7 (7.4) years. The UK Biobank (UKB) cohort recruited participants from March 15, 2006, through August 1, 2010, with a mean (SD) follow-up of 12.4 (1.0) years. The Hereditary Cancer Registry (HCR) recruited patients undergoing clinical genetic testing at Vanderbilt University Medical Center from May 1, 2012, through December 31, 2019, with a mean (SD) follow-up through EHRs of 8.8 (6.5) years. Exposures: Germline variants in 23 hereditary cancer genes. Pathogenic and likely pathogenic variants for each gene were aggregated for association analyses. Main Outcomes and Measures: Phenotypes in the eMERGEseq and HCR cohorts were derived from the linked EHRs. Phenotypes in UKB were from multiple sources of health-related data. Results: A total of 214 020 participants were identified, including 23 544 in eMERGEseq cohort (mean [SD] age, 47.8 [23.7] years; 12 611 women [53.6%]), 187 234 in the UKB cohort (mean [SD] age, 56.7 [8.1] years; 104 055 [55.6%] women), and 3242 in the HCR cohort (mean [SD] age, 52.5 [15.5] years; 2851 [87.9%] women). All 38 established gene-cancer associations were replicated, and 19 new associations were identified. These included the following 7 associations with neoplasms: CHEK2 with leukemia (odds ratio [OR], 3.81 [95% CI, 2.64-5.48]) and plasma cell neoplasms (OR, 3.12 [95% CI, 1.84-5.28]), ATM with gastric cancer (OR, 4.27 [95% CI, 2.35-7.44]) and pancreatic cancer (OR, 4.44 [95% CI, 2.66-7.40]), MUTYH (biallelic) with kidney cancer (OR, 32.28 [95% CI, 6.40-162.73]), MSH6 with bladder cancer (OR, 5.63 [95% CI, 2.75-11.49]), and APC with benign liver/intrahepatic bile duct tumors (OR, 52.01 [95% CI, 14.29-189.29]). The remaining 12 associations with nonneoplastic diseases included BRCA1/2 with ovarian cysts (OR, 3.15 [95% CI, 2.22-4.46] and 3.12 [95% CI, 2.36-4.12], respectively), MEN1 with acute pancreatitis (OR, 33.45 [95% CI, 9.25-121.02]), APC with gastritis and duodenitis (OR, 4.66 [95% CI, 2.61-8.33]), and PTEN with chronic gastritis (OR, 15.68 [95% CI, 6.01-40.92]). Conclusions and Relevance: The findings of this genetic association study analyzing the EHRs of 3 large cohorts suggest that these new phenotypes associated with hereditary cancer genes may facilitate early detection and better management of cancers. This study highlights the potential benefits of using EHR data in genomic medicine.
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