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Abstract：
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.
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 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.
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.
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.
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 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.
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.
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.
摘要：
多囊卵巢综合征是影响育龄妇女最常见的内分泌疾病。多囊卵巢综合征的临床诊断已经制定了许多标准，鹿特丹标准是最具包容性的。证据表明多囊卵巢综合征具有明显的遗传性,以前的研究已经确定了与使用不同标准诊断的多囊卵巢综合征相关的遗传变异。广泛采用的电子健康记录系统提供了使用鹿特丹遗传研究标准识别多囊卵巢综合征患者的机会。
为了在相同的表型定义下识别新的相关遗传变异，我们根据鹿特丹标准从电子健康记录中提取了多囊卵巢综合征病例和未受影响的对照,并进行了发现-验证全基因组关联研究.
我们根据鹿特丹标准开发了一种多囊卵巢综合征表型算法，并将其应用于3个电子健康记录相关生物库，以识别病例和对照进行遗传研究。在发现阶段,我们使用GeisingerMyCode和电子病历和基因组学队列进行了个体全基因组关联研究，然后进行荟萃分析。我们试图验证BioVU队列中的显著关联位点(P<1×10-6)。所有关联分析均使用逻辑回归，假设一个加性遗传模型，并调整主成分以控制人口分层。采用逆方差固定效应模型进行荟萃分析。此外,我们检查了最高变异体,以评估它们与表型算法中每个标准的关联.我们使用STRING数据库来表征蛋白质-蛋白质相互作用网络。
使用基于鹿特丹标准的相同算法，我们总共确定了2,995例多囊卵巢综合征患者和53,599名人群对照(发现期2742例和51,438例对照;验证期253例和2161例对照).我们鉴定出1个新的全基因组显著变异体rs17186366(比值比[OR]=1.37[1.23,1.54],P=2.8×10-8)位于SOD2附近。此外,还确定了2个具有暗示性关联的基因座：rs113168128（OR=1.72[1.42，2.10]，P=5.2×10-8)，ERBB4的内含子变体，独立于先前发表的变体，和rs144248326(OR=2.13[1.52，2.86]，P=8.45×10-7),WWTR1中的一种新型内含子变体。在多囊卵巢综合征算法的前3个单核苷酸多态性与每个标准的进一步关联测试中,我们发现rs17186366（SOD2）与多囊卵巢和高雄激素血症有关，而rs11316812(ERBB4)和rs144248326(WWTR1)主要与月经少发或不孕症相关。我们还验证了先前报道的与DENND1A1的关联。使用STRING数据库来表征蛋白质-蛋白质相互作用，我们发现ERBB4和WWTR1均可与YAP1相互作用,而YAP1先前与多囊卵巢综合征相关.
通过一项基于鹿特丹标准的算法对从电子健康记录中识别出的多囊卵巢综合征进行的发现-验证全基因组关联研究，我们鉴定并验证了一个新的全基因组显著关联与SOD2附近的一个变异体.我们还鉴定了ERBB4中的一个新的独立变异体以及与WWTR1的暗示性关联。通过先前鉴定的多囊卵巢综合征基因YAP1，ERBB4-YAP1-WWTR1网络表明表皮生长因子受体和Hippo途径参与了多囊卵巢综合征的多因素病因。
为了在相同的表型定义下识别新的相关遗传变异，我们根据鹿特丹标准从电子健康记录中提取了多囊卵巢综合征病例和未受影响的对照,并进行了发现-验证全基因组关联研究.
我们根据鹿特丹标准开发了一种多囊卵巢综合征表型算法，并将其应用于3个电子健康记录相关生物库，以识别病例和对照进行遗传研究。在发现阶段,我们使用GeisingerMyCode和电子病历和基因组学队列进行了个体全基因组关联研究，然后进行荟萃分析。我们试图验证BioVU队列中的显著关联位点(P<1×10-6)。所有关联分析均使用逻辑回归，假设一个加性遗传模型，并调整主成分以控制人口分层。采用逆方差固定效应模型进行荟萃分析。此外,我们检查了最高变异体,以评估它们与表型算法中每个标准的关联.我们使用STRING数据库来表征蛋白质-蛋白质相互作用网络。
使用基于鹿特丹标准的相同算法，我们总共确定了2,995例多囊卵巢综合征患者和53,599名人群对照(发现期2742例和51,438例对照;验证期253例和2161例对照).我们鉴定出1个新的全基因组显著变异体rs17186366(比值比[OR]=1.37[1.23,1.54],P=2.8×10-8)位于SOD2附近。此外,还确定了2个具有暗示性关联的基因座：rs113168128（OR=1.72[1.42，2.10]，P=5.2×10-8)，ERBB4的内含子变体，独立于先前发表的变体，和rs144248326(OR=2.13[1.52，2.86]，P=8.45×10-7),WWTR1中的一种新型内含子变体。在多囊卵巢综合征算法的前3个单核苷酸多态性与每个标准的进一步关联测试中,我们发现rs17186366（SOD2）与多囊卵巢和高雄激素血症有关，而rs11316812(ERBB4)和rs144248326(WWTR1)主要与月经少发或不孕症相关。我们还验证了先前报道的与DENND1A1的关联。使用STRING数据库来表征蛋白质-蛋白质相互作用，我们发现ERBB4和WWTR1均可与YAP1相互作用,而YAP1先前与多囊卵巢综合征相关.
通过一项基于鹿特丹标准的算法对从电子健康记录中识别出的多囊卵巢综合征进行的发现-验证全基因组关联研究，我们鉴定并验证了一个新的全基因组显著关联与SOD2附近的一个变异体.我们还鉴定了ERBB4中的一个新的独立变异体以及与WWTR1的暗示性关联。通过先前鉴定的多囊卵巢综合征基因YAP1，ERBB4-YAP1-WWTR1网络表明表皮生长因子受体和Hippo途径参与了多囊卵巢综合征的多因素病因。
