A genome-wide association study of polycystic ovary syndrome identified from electronic health records
Jacob M. Keaton,
Dustin N. Hartzel,
Anne E. Justice,
Navya S Josyula,
Sarah A. Pendergrass,
Lea K. Davis,
Digna R. Velez Edwards,
Ian B. Stanaway,
David R Crosslin,
Gail P Jarvik,
Marc S Williams,
Ming Ta Michael Lee
Posted 15 Dec 2019
medRxiv DOI: 10.1101/2019.12.12.19014761
Posted 15 Dec 2019
BackgroundPolycystic ovary syndrome (PCOS) is the most common endocrine disorder affecting women of reproductive age. Previous studies have identified genetic variants associated with PCOS identified by different diagnostic criteria. The Rotterdam Criteria is the broadest and able to identify the most PCOS cases. ObjectivesTo identify novel associated genetic variants, we extracted PCOS cases and controls from the electronic health records (EHR) based on the Rotterdam Criteria and performed a genome-wide association study (GWAS). Study DesignWe developed a PCOS phenotyping algorithm based on the Rotterdam criteria and applied it to three EHR-linked biobanks to identify cases and controls for genetic study. In discovery phase, we performed individual GWAS using the Geisingers MyCode and the eMERGE cohorts, which were then meta-analyzed. We attempted validation of the significantly association loci (P<1x10-6) 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-analyses. Additionally, we examined the top variants to evaluate their associations with each criterion in the phenotyping algorithm. We used STRING to identify protein-protein interaction network. ResultsWe identified 2,995 PCOS cases and 53,599 controls in total (2,742cases and 51,438 controls from the discovery phase; 253 cases and 2,161 controls in the validation phase). GWAS identified one novel genome-wide significant variant rs17186366 (OR=1.37 [1.23,1.54], P=2.8x10-8) located near SOD2. Additionally, two loci with suggestive association were also identified: rs113168128 (OR=1.72 [1.42,2.10], P=5.2 x10-8), an intronic variant of ERBB4 that is independent from the previously published variants, and rs144248326 (OR=2.13 [1.52,2.86], P=8.45x10-7), a novel intronic variant in WWTR1. In the further association tests of the top 3 SNPs with each criterion in the PCOS algorithm, we found that rs17186366 was associated with polycystic and hyperandrogenism, while rs11316812 and rs144248326 were mainly associated with oligomenorrhea or infertility. Besides ERBB4, we also validated the association with DENND1A1. ConclusionThrough a discovery-validation GWAS on PCOS cases and controls identified from EHR using an algorithm based on Rotterdam criteria, we identified and validated a novel association with variants within ERBB4. We also identified novel associations nearby SOD2 and WWTR1. These results suggest the eGFR and Hippo pathways in the disease etiology. With previously identified PCOS-associated loci YAP1, the ERBB4-YAP1-WWTR1 network implicates the epidermal growth factor receptor and the Hippo pathway in the multifactorial etiology of PCOS.
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