Yanfei Zhang1, Kevin Ho2, Jacob M Keaton3, Dustin N Hartzel4, Felix Day5, Anne E Justice6, Navya S Josyula6, Sarah A Pendergrass6, Ky'Era Actkins7, Lea K Davis8, Digna R Velez Edwards9, Brody Holohan10, Andrea Ramirez11, Ian B Stanaway12, David R Crosslin12, Gail P Jarvik13, Patrick Sleiman14, Hakon Hakonarson14, Marc S Williams1, Ming Ta Michael Lee15. 1. Genomic Medicine Institute, Geisinger, Danville, PA. 2. Kidney Research Institute, Geisinger, Danville, PA. 3. Division of Epidemiology, Department of Medicine; Institute for Medicine and Public Health; Vanderbilt University Medical Center, Nashville, TN; Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN. 4. Phenomic Analytics and Clinical Data Core, Geisinger, PA. 5. The International PCOS Consortium, Geisinger, Danville, PA. 6. Department of Population Health Sciences, Geisinger, Danville, PA. 7. Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN; Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN; Department of Microbiology, Immunology, and Physiology, Meharry Medical College, Nashville, TN. 8. Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN; Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN; Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN; Department of Biomedical Informatics, Data Sciences Institute, Vanderbilt University Medical Center, Nashville, TN. 9. Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN; Department of Biomedical Informatics, Data Sciences Institute, Vanderbilt University Medical Center, Nashville, TN; Division of Quantitative Science, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, TN. 10. Marshfield Clinic Research Institute, Marshfield, WI. 11. Department of Medicine, Vanderbilt University Medical Center, Nashville, TN. 12. Department of Biomedical Informatics Medical Education, School of Medicine, University of Washington, Seattle, WA. 13. Departments of Medicine (Medical Genetics) and Genome Sciences, School of Medicine, University of Washington, Seattle, WA. 14. Division of Neonatology, Children's Hospital of Philadelphia, Philadelphia, PA. 15. Genomic Medicine Institute, Geisinger, Danville, PA. Electronic address: mlee2@geisinger.edu.
Abstract
BACKGROUND: Polycystic ovary syndrome is the most common endocrine disorder affecting women of reproductive age. A number of criteria have been developed for clinical diagnosis of polycystic ovary syndrome, with the Rotterdam criteria being the most inclusive. Evidence suggests that polycystic ovary syndrome is significantly heritable, and previous studies have identified genetic variants associated with polycystic ovary syndrome diagnosed using different criteria. The widely adopted electronic health record system provides an opportunity to identify patients with polycystic ovary syndrome using the Rotterdam criteria for genetic studies. OBJECTIVE: To identify novel associated genetic variants under the same phenotype definition, we extracted polycystic ovary syndrome cases and unaffected controls based on the Rotterdam criteria from the electronic health records and performed a discovery-validation genome-wide association study. STUDY DESIGN: We developed a polycystic ovary syndrome phenotyping algorithm on the basis of the Rotterdam criteria and applied it to 3 electronic health record-linked biobanks to identify cases and controls for genetic study. In the discovery phase, we performed an individual genome-wide association study using the Geisinger MyCode and the Electronic Medical Records and Genomics cohorts, which were then meta-analyzed. We attempted validation of the significant association loci (P<1×10-6) in the BioVU cohort. All association analyses used logistic regression, assuming an additive genetic model, and adjusted for principal components to control for population stratification. An inverse-variance fixed-effect model was adopted for meta-analysis. In addition, we examined the top variants to evaluate their associations with each criterion in the phenotyping algorithm. We used the STRING database to characterize protein-protein interaction network. RESULTS: Using the same algorithm based on the Rotterdam criteria, we identified 2995 patients with polycystic ovary syndrome and 53,599 population controls in total (2742 cases and 51,438 controls from the discovery phase; 253 cases and 2161 controls in the validation phase). We identified 1 novel genome-wide significant variant rs17186366 (odds ratio [OR]=1.37 [1.23, 1.54], P=2.8×10-8) located near SOD2. In addition, 2 loci with suggestive association were also identified: rs113168128 (OR=1.72 [1.42, 2.10], P=5.2×10-8), an intronic variant of ERBB4 that is independent from the previously published variants, and rs144248326 (OR=2.13 [1.52, 2.86], P=8.45×10-7), a novel intronic variant in WWTR1. In the further association tests of the top 3 single-nucleotide polymorphisms with each criterion in the polycystic ovary syndrome algorithm, we found that rs17186366 (SOD2) was associated with polycystic ovaries and hyperandrogenism, whereas rs11316812 (ERBB4) and rs144248326 (WWTR1) were mainly associated with oligomenorrhea or infertility. We also validated the previously reported association with DENND1A1. Using the STRING database to characterize protein-protein interactions, we found both ERBB4 and WWTR1 can interact with YAP1, which has been previously associated with polycystic ovary syndrome. CONCLUSION: Through a discovery-validation genome-wide association study on polycystic ovary syndrome identified from electronic health records using an algorithm based on Rotterdam criteria, we identified and validated a novel genome-wide significant association with a variant near SOD2. We also identified a novel independent variant within ERBB4 and a suggestive association with WWTR1. With previously identified polycystic ovary syndrome gene YAP1, the ERBB4-YAP1-WWTR1 network suggests involvement of the epidermal growth factor receptor and the Hippo pathway in the multifactorial etiology of polycystic ovary syndrome.
BACKGROUND:Polycystic ovary syndrome is the most common endocrine disorder affecting women of reproductive age. A number of criteria have been developed for clinical diagnosis of polycystic ovary syndrome, with the Rotterdam criteria being the most inclusive. Evidence suggests that polycystic ovary syndrome is significantly heritable, and previous studies have identified genetic variants associated with polycystic ovary syndrome diagnosed using different criteria. The widely adopted electronic health record system provides an opportunity to identify patients with polycystic ovary syndrome using the Rotterdam criteria for genetic studies. OBJECTIVE: To identify novel associated genetic variants under the same phenotype definition, we extracted polycystic ovary syndrome cases and unaffected controls based on the Rotterdam criteria from the electronic health records and performed a discovery-validation genome-wide association study. STUDY DESIGN: We developed a polycystic ovary syndrome phenotyping algorithm on the basis of the Rotterdam criteria and applied it to 3 electronic health record-linked biobanks to identify cases and controls for genetic study. In the discovery phase, we performed an individual genome-wide association study using the Geisinger MyCode and the Electronic Medical Records and Genomics cohorts, which were then meta-analyzed. We attempted validation of the significant association loci (P<1×10-6) in the BioVU cohort. All association analyses used logistic regression, assuming an additive genetic model, and adjusted for principal components to control for population stratification. An inverse-variance fixed-effect model was adopted for meta-analysis. In addition, we examined the top variants to evaluate their associations with each criterion in the phenotyping algorithm. We used the STRING database to characterize protein-protein interaction network. RESULTS: Using the same algorithm based on the Rotterdam criteria, we identified 2995 patients with polycystic ovary syndrome and 53,599 population controls in total (2742 cases and 51,438 controls from the discovery phase; 253 cases and 2161 controls in the validation phase). We identified 1 novel genome-wide significant variant rs17186366 (odds ratio [OR]=1.37 [1.23, 1.54], P=2.8×10-8) located near SOD2. In addition, 2 loci with suggestive association were also identified: rs113168128 (OR=1.72 [1.42, 2.10], P=5.2×10-8), an intronic variant of ERBB4 that is independent from the previously published variants, and rs144248326 (OR=2.13 [1.52, 2.86], P=8.45×10-7), a novel intronic variant in WWTR1. In the further association tests of the top 3 single-nucleotide polymorphisms with each criterion in the polycystic ovary syndrome algorithm, we found that rs17186366 (SOD2) was associated with polycystic ovaries and hyperandrogenism, whereas rs11316812 (ERBB4) and rs144248326 (WWTR1) were mainly associated with oligomenorrhea or infertility. We also validated the previously reported association with DENND1A1. Using the STRING database to characterize protein-protein interactions, we found both ERBB4 and WWTR1 can interact with YAP1, which has been previously associated with polycystic ovary syndrome. CONCLUSION: Through a discovery-validation genome-wide association study on polycystic ovary syndrome identified from electronic health records using an algorithm based on Rotterdam criteria, we identified and validated a novel genome-wide significant association with a variant near SOD2. We also identified a novel independent variant within ERBB4 and a suggestive association with WWTR1. With previously identified polycystic ovary syndrome gene YAP1, the ERBB4-YAP1-WWTR1 network suggests involvement of the epidermal growth factor receptor and the Hippo pathway in the multifactorial etiology of polycystic ovary syndrome.
Authors: Raiane P Crespo; Thais P Rocha; Luciana R Montenegro; Mirian Y Nishi; Alexander A L Jorge; Gustavo A R Maciel; Edmund Baracat; Ana Claudia Latronico; Berenice B Mendonca; Larissa G Gomes Journal: J Endocr Soc Date: 2022-07-05