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 opportunity to identify patients with polycystic ovary syndrome using the Rotterdam criteria for genetic studies. 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. We developed a polycystic ovary syndrome phenotyping algorithm based on the Rotterdam criteria and applied it to three electronic health records-linked biobanks to identify cases and controls for genetic study. In discovery phase, we performed individual genome-wide association study using the Geisinger's MyCode and the eMERGE cohorts, which were then meta-analyzed. We attempted validation of the significant 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-analysis. Additionally, we examined the top variants to evaluate their associations with each criterion in the phenotyping algorithm. We used STRING to characterize protein-protein interaction network. Using the same algorithm based on the Rotterdam criteria, we identified 2,995 patients with polycystic ovary syndrome and 53,599 population controls in total (2,742 cases and 51,438 controls from the discovery phase; 253 cases and 2,161 controls in the validation phase). We 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 polycystic ovary syndrome algorithm, we found that rs17186366 (SOD2) was associated with polycystic ovaries and hyperandrogenism, while rs11316812 (ERBB4) and rs144248326 (WWTR1) were mainly associated with oligomenorrhea or infertility. We also validated the previously reported association with DENND1A1. Using STRING to characterize protein-protein interactions, we found both ERBB4 and WWTR1 can interact with YAP1 which has been previously associated with polycystic ovary syndrome. 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.

Context As many as 75% of patients with polycystic ovary syndrome (PCOS) are estimated to be unidentified in clinical practice. Objective Utilizing polygenic risk prediction, we aim to identify the phenome-wide comorbidity patterns characteristic of PCOS to improve accurate diagnosis and preventive treatment. Design, Patients, and Methods Leveraging the electronic health records (EHRs) of 124 852 individuals, we developed a PCOS risk prediction algorithm by combining polygenic risk scores (PRS) with PCOS component phenotypes into a polygenic and phenotypic risk score (PPRS). We evaluated its predictive capability across different ancestries and perform a PRS-based phenome-wide association study (PheWAS) to assess the phenomic expression of the heightened risk of PCOS. Results The integrated polygenic prediction improved the average performance (pseudo-R²) for PCOS detection by 0.228 (61.5-fold), 0.224 (58.8-fold), 0.211 (57.0-fold) over the null model across European, African, and multi-ancestry participants respectively. The subsequent PRS-powered PheWAS identified a high level of shared biology between PCOS and a range of metabolic and endocrine outcomes, especially with obesity and diabetes: “morbid obesity”, “type 2 diabetes”, “hypercholesterolemia”, “disorders of lipid metabolism”, “hypertension”, and “sleep apnea” reaching phenome-wide significance. Conclusions Our study has expanded the methodological utility of PRS in patient stratification and risk prediction, especially in a multifactorial condition like PCOS, across different genetic origins. By utilizing the individual genome–phenome data available from the EHR, our approach also demonstrates that polygenic prediction by PRS can provide valuable opportunities to discover the pleiotropic phenomic network associated with PCOS pathogenesis.

Context Polycystic ovary syndrome (PCOS) is one of the leading causes of infertility, yet current diagnostic criteria are ineffective at identifying patients whose symptoms reside outside strict diagnostic criteria. As a result, PCOS is underdiagnosed and its etiology is poorly understood. Objective We aim to characterize the phenotypic spectrum of PCOS clinical features within and across racial and ethnic groups. Methods We developed a strictly defined PCOS algorithm (PCOS_{keyword-strict}) using the International Classification of Diseases, ninth and tenth revisions and keywords mined from clinical notes in electronic health records (EHRs) data. We then systematically relaxed the inclusion criteria to evaluate the change in epidemiological and genetic associations resulting in 3 subsequent algorithms (PCOS_coded-broad, PCOS_coded-strict, and PCOS_{keyword-broad}). We evaluated the performance of each phenotyping approach and characterized prominent clinical features observed in racially and ethnically diverse PCOS patients. Results The best performance came from the PCOS_coded-strict algorithm, with a positive predictive value of 98%. Individuals classified as cases by this algorithm had significantly higher body mass index (BMI), insulin levels, free testosterone values, and genetic risk scores for PCOS, compared to controls. Median BMI was higher in African American females with PCOS compared to White and Hispanic females with PCOS. Conclusions PCOS symptoms are observed across a severity spectrum that parallels the continuous genetic liability to PCOS in the general population. Racial and ethnic group differences exist in PCOS symptomology and metabolic health across different phenotyping strategies.

Polycystic ovary syndrome (PCOS) and its underlying features remain poorly understood. In this genetic study ( n = 544,513), we expand the number of genetic loci from 16 to 29, and additionally identify 31 associated plasma proteins. Many risk-increasing loci were associated with later age at menopause, underscoring the reproductive longevity related to an increased oocyte number and/or availability across the lifespan. Hormonal regulation in the etiology of this condition, through metabolic and reproductive features, was emphasized. The proteomic analysis highlighted metabolic biology known to be related to PCOS. A polygenic risk score (PRS) was associated with adverse cardiometabolic outcomes, with differing relevance of testosterone and body mass index in women and men. Finally, while oligo-anovulation and anovulatory infertility are features of PCOS, we observed no impact of PCOS susceptibility on childlessness. We suggest that PCOS susceptibility confers balanced pleiotropic influences on fertility in women, and life-long adverse metabolic consequences in both sexes.

Lynch syndrome is the most common inherited cancer syndrome that increases the risk of developing colorectal and endometrial cancer. Universal screening guidelines were first recommended by the Centers for Disease Control and Prevention (CDC) in 2009 and are updated annually by multiple societies. Therefore, one would expect genetic testing rates to increase over time. But testing remains underutilized among those with colorectal or endometrial cancer, even though early detection can improve prognosis and survival rates. In this study, we aimed to understand differences in genetic testing uptake among those with colorectal cancer or endometrial cancer from 2005, 2010, 2015, using data from the National Health Interview Survey (NHIS). We examined genetic testing uptake across cancer-type, age (≤50 or ≥51), sex, race, insurance, and education using a χ2 statistical analysis. Despite an upward genetic testing trend in 2010, we found no significant differences in genetic testing uptake over time. In 2010, non-White individuals experienced the highest increase from 2005 in comparison with White individuals. However, genetic testing rates declined for both groups by 2015. Our findings show that genetic testing for colorectal cancer and endometrial cancer did not increase over a 10-year period in spite of guidelines that recommend testing. Prevention Relevance: Genetic testing uptake for colorectal cancer and endometrial cancer has not increased over a 10-year period in spite of universal screening guidelines. More genetic testing education is needed at the provider and patient level to improve screening strategies for cancer patients who are most at risk for Lynch syndrome.

Females with polycystic ovary syndrome (PCOS), the most common endocrine disorder in women, have an increased risk of developing cardiometabolic disorders such as insulin resistance, obesity, and type 2 diabetes (T2D). While only diagnosable in females, males with a family history of PCOS can also exhibit a poor cardiometabolic profile. Therefore, we aimed to elucidate the role of sex in the cardiometabolic comorbidities observed in PCOS by conducting bidirectional genetic risk score analyses in both sexes. We first conducted a phenome-wide association study (PheWAS) using PCOS polygenic risk scores (PCOS_PRS) to identify potential pleiotropic effects of PCOS_PRS across 1,380 medical conditions recorded in the Vanderbilt University Medical Center electronic health record (EHR) database, in females and males. After adjusting for age and genetic ancestry, we found that European (EUR)-ancestry males with higher PCOS_PRS were significantly more likely to develop hypertensive diseases than females at the same level of genetic risk. We performed the same analysis in an African (AFR)-ancestry population, but observed no significant associations, likely due to poor trans-ancestry performance of the PRS. Based on observed significant associations in the EUR-ancestry population, we then tested whether the PRS for comorbid conditions (e.g., T2D, body mass index (BMI), hypertension, etc.) also increased the odds of a PCOS diagnosis. Only BMI_PRS and T2D_PRS were significantly associated with a PCOS diagnosis in EUR-ancestry females. We then further adjusted the T2D_PRS for measured BMI and BMI_residual (regressed on the BMI_PRS and enriched for the environmental contribution to BMI). Results demonstrated that genetically regulated BMI primarily accounted for the relationship between T2D_PRS and PCOS. Overall, our findings show that the genetic architecture of PCOS has distinct sex differences in associations with genetically correlated cardiometabolic traits. It is possible that the cardiometabolic comorbidities observed in PCOS are primarily explained by their shared genetic risk factors, which can be further influenced by biological variables including sex and BMI.