A genome-wide association study of polycystic ovary syndrome identified from electronic health records
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...
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| Veröffentlicht in: | American journal of obstetrics and gynecology 2020-10, Vol.223 (4), p.559.e1-559.e21 |
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| creator | Zhang, Yanfei Ho, Kevin Keaton, Jacob M. Hartzel, Dustin N. Day, Felix Justice, Anne E. Josyula, Navya S. Pendergrass, Sarah A. Actkins, Ky'Era Davis, Lea K. Velez Edwards, Digna R. Holohan, Brody Ramirez, Andrea Stanaway, Ian B. Crosslin, David R. Jarvik, Gail P. Sleiman, Patrick Hakonarson, Hakon Williams, Marc S. Lee, Ming Ta Michael |
| description | 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 |
| doi_str_mv | 10.1016/j.ajog.2020.04.004 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2390149419</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0002937820304282</els_id><sourcerecordid>2390149419</sourcerecordid><originalsourceid>FETCH-LOGICAL-c466t-4ab075c84acfc3328945752cfd2857b5a91250e612faed01ba9d5d0d5e8aadd93</originalsourceid><addsrcrecordid>eNp9kE1LJDEQhoO46Oj6BzxIjl66t5JOfwS8iOyuC4IX9xwySUUz9HTGJKP0v980o3v0FCo870vVQ8glg5oB635sar0JzzUHDjWIGkAckRUD2Vfd0A3HZAUAvJJNP5ySs5Q2y8glPyGnDeeD5AOsiLulzziFLVbv3iLVKQXjdfZhoinv7UyDo7swzmZO2Rsa3nScaZonG0uGlsiUvfNoqSsfFEc0OYapkC-ox_xCI5oQbfpOvjk9Jrz4eM_J318_n-7uq4fH33_ubh8qI7ouV0KvoW_NILRxpmnKkqLtW26c5UPbr1stGW8BO8adRgtsraVtLdgWB62tlc05uT707mJ43WPKauuTwXHUE4Z9UryRwIQUbEH5ATUxpBTRqV3023KeYqAWv2qjFr9q8atAqOK3hK4--vfrLdr_kU-hBbg5AFiufPMYVTIeJ4PWFxVZ2eC_6v8Hw8uONQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2390149419</pqid></control><display><type>article</type><title>A genome-wide association study of polycystic ovary syndrome identified from electronic health records</title><source>MEDLINE</source><source>Elsevier ScienceDirect Journals</source><creator>Zhang, Yanfei ; Ho, Kevin ; Keaton, Jacob M. ; Hartzel, Dustin N. ; Day, Felix ; Justice, Anne E. ; Josyula, Navya S. ; Pendergrass, Sarah A. ; Actkins, Ky'Era ; Davis, Lea K. ; Velez Edwards, Digna R. ; Holohan, Brody ; Ramirez, Andrea ; Stanaway, Ian B. ; Crosslin, David R. ; Jarvik, Gail P. ; Sleiman, Patrick ; Hakonarson, Hakon ; Williams, Marc S. ; Lee, Ming Ta Michael</creator><creatorcontrib>Zhang, Yanfei ; Ho, Kevin ; Keaton, Jacob M. ; Hartzel, Dustin N. ; Day, Felix ; Justice, Anne E. ; Josyula, Navya S. ; Pendergrass, Sarah A. ; Actkins, Ky'Era ; Davis, Lea K. ; Velez Edwards, Digna R. ; Holohan, Brody ; Ramirez, Andrea ; Stanaway, Ian B. ; Crosslin, David R. ; Jarvik, Gail P. ; Sleiman, Patrick ; Hakonarson, Hakon ; Williams, Marc S. ; Lee, Ming Ta Michael</creatorcontrib><description>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.</description><identifier>ISSN: 0002-9378</identifier><identifier>EISSN: 1097-6868</identifier><identifier>DOI: 10.1016/j.ajog.2020.04.004</identifier><identifier>PMID: 32289280</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Adaptor Proteins, Signal Transducing - metabolism ; Adult ; Case-Control Studies ; EGFR pathway ; electronic health record ; Electronic Health Records ; ERBB4 ; Female ; Genome-Wide Association Study ; Hippo pathway ; Humans ; Hyperandrogenism - genetics ; Infertility, Female - genetics ; Middle Aged ; Oligomenorrhea - genetics ; Ovarian Cysts - genetics ; polycystic ovary syndrome ; Polycystic Ovary Syndrome - diagnosis ; Polycystic Ovary Syndrome - genetics ; Polycystic Ovary Syndrome - physiopathology ; Polymorphism, Single Nucleotide ; Receptor, ErbB-4 - genetics ; SOD2 ; Superoxide Dismutase - genetics ; Trans-Activators - genetics ; Transcription Factors - metabolism ; WWTR1</subject><ispartof>American journal of obstetrics and gynecology, 2020-10, Vol.223 (4), p.559.e1-559.e21</ispartof><rights>2020 The Author(s)</rights><rights>Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c466t-4ab075c84acfc3328945752cfd2857b5a91250e612faed01ba9d5d0d5e8aadd93</citedby><cites>FETCH-LOGICAL-c466t-4ab075c84acfc3328945752cfd2857b5a91250e612faed01ba9d5d0d5e8aadd93</cites><orcidid>0000-0002-9222-7725</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0002937820304282$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32289280$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Zhang, Yanfei</creatorcontrib><creatorcontrib>Ho, Kevin</creatorcontrib><creatorcontrib>Keaton, Jacob M.</creatorcontrib><creatorcontrib>Hartzel, Dustin N.</creatorcontrib><creatorcontrib>Day, Felix</creatorcontrib><creatorcontrib>Justice, Anne E.</creatorcontrib><creatorcontrib>Josyula, Navya S.</creatorcontrib><creatorcontrib>Pendergrass, Sarah A.</creatorcontrib><creatorcontrib>Actkins, Ky'Era</creatorcontrib><creatorcontrib>Davis, Lea K.</creatorcontrib><creatorcontrib>Velez Edwards, Digna R.</creatorcontrib><creatorcontrib>Holohan, Brody</creatorcontrib><creatorcontrib>Ramirez, Andrea</creatorcontrib><creatorcontrib>Stanaway, Ian B.</creatorcontrib><creatorcontrib>Crosslin, David R.</creatorcontrib><creatorcontrib>Jarvik, Gail P.</creatorcontrib><creatorcontrib>Sleiman, Patrick</creatorcontrib><creatorcontrib>Hakonarson, Hakon</creatorcontrib><creatorcontrib>Williams, Marc S.</creatorcontrib><creatorcontrib>Lee, Ming Ta Michael</creatorcontrib><title>A genome-wide association study of polycystic ovary syndrome identified from electronic health records</title><title>American journal of obstetrics and gynecology</title><addtitle>Am J Obstet Gynecol</addtitle><description>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.</description><subject>Adaptor Proteins, Signal Transducing - metabolism</subject><subject>Adult</subject><subject>Case-Control Studies</subject><subject>EGFR pathway</subject><subject>electronic health record</subject><subject>Electronic Health Records</subject><subject>ERBB4</subject><subject>Female</subject><subject>Genome-Wide Association Study</subject><subject>Hippo pathway</subject><subject>Humans</subject><subject>Hyperandrogenism - genetics</subject><subject>Infertility, Female - genetics</subject><subject>Middle Aged</subject><subject>Oligomenorrhea - genetics</subject><subject>Ovarian Cysts - genetics</subject><subject>polycystic ovary syndrome</subject><subject>Polycystic Ovary Syndrome - diagnosis</subject><subject>Polycystic Ovary Syndrome - genetics</subject><subject>Polycystic Ovary Syndrome - physiopathology</subject><subject>Polymorphism, Single Nucleotide</subject><subject>Receptor, ErbB-4 - genetics</subject><subject>SOD2</subject><subject>Superoxide Dismutase - genetics</subject><subject>Trans-Activators - genetics</subject><subject>Transcription Factors - metabolism</subject><subject>WWTR1</subject><issn>0002-9378</issn><issn>1097-6868</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kE1LJDEQhoO46Oj6BzxIjl66t5JOfwS8iOyuC4IX9xwySUUz9HTGJKP0v980o3v0FCo870vVQ8glg5oB635sar0JzzUHDjWIGkAckRUD2Vfd0A3HZAUAvJJNP5ySs5Q2y8glPyGnDeeD5AOsiLulzziFLVbv3iLVKQXjdfZhoinv7UyDo7swzmZO2Rsa3nScaZonG0uGlsiUvfNoqSsfFEc0OYapkC-ox_xCI5oQbfpOvjk9Jrz4eM_J318_n-7uq4fH33_ubh8qI7ouV0KvoW_NILRxpmnKkqLtW26c5UPbr1stGW8BO8adRgtsraVtLdgWB62tlc05uT707mJ43WPKauuTwXHUE4Z9UryRwIQUbEH5ATUxpBTRqV3023KeYqAWv2qjFr9q8atAqOK3hK4--vfrLdr_kU-hBbg5AFiufPMYVTIeJ4PWFxVZ2eC_6v8Hw8uONQ</recordid><startdate>202010</startdate><enddate>202010</enddate><creator>Zhang, Yanfei</creator><creator>Ho, Kevin</creator><creator>Keaton, Jacob M.</creator><creator>Hartzel, Dustin N.</creator><creator>Day, Felix</creator><creator>Justice, Anne E.</creator><creator>Josyula, Navya S.</creator><creator>Pendergrass, Sarah A.</creator><creator>Actkins, Ky'Era</creator><creator>Davis, Lea K.</creator><creator>Velez Edwards, Digna R.</creator><creator>Holohan, Brody</creator><creator>Ramirez, Andrea</creator><creator>Stanaway, Ian B.</creator><creator>Crosslin, David R.</creator><creator>Jarvik, Gail P.</creator><creator>Sleiman, Patrick</creator><creator>Hakonarson, Hakon</creator><creator>Williams, Marc S.</creator><creator>Lee, Ming Ta Michael</creator><general>Elsevier Inc</general><scope>6I.</scope><scope>AAFTH</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-9222-7725</orcidid></search><sort><creationdate>202010</creationdate><title>A genome-wide association study of polycystic ovary syndrome identified from electronic health records</title><author>Zhang, Yanfei ; Ho, Kevin ; Keaton, Jacob M. ; Hartzel, Dustin N. ; Day, Felix ; Justice, Anne E. ; Josyula, Navya S. ; Pendergrass, Sarah A. ; Actkins, Ky'Era ; Davis, Lea K. ; Velez Edwards, Digna R. ; Holohan, Brody ; Ramirez, Andrea ; Stanaway, Ian B. ; Crosslin, David R. ; Jarvik, Gail P. ; Sleiman, Patrick ; Hakonarson, Hakon ; Williams, Marc S. ; Lee, Ming Ta Michael</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c466t-4ab075c84acfc3328945752cfd2857b5a91250e612faed01ba9d5d0d5e8aadd93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Adaptor Proteins, Signal Transducing - metabolism</topic><topic>Adult</topic><topic>Case-Control Studies</topic><topic>EGFR pathway</topic><topic>electronic health record</topic><topic>Electronic Health Records</topic><topic>ERBB4</topic><topic>Female</topic><topic>Genome-Wide Association Study</topic><topic>Hippo pathway</topic><topic>Humans</topic><topic>Hyperandrogenism - genetics</topic><topic>Infertility, Female - genetics</topic><topic>Middle Aged</topic><topic>Oligomenorrhea - genetics</topic><topic>Ovarian Cysts - genetics</topic><topic>polycystic ovary syndrome</topic><topic>Polycystic Ovary Syndrome - diagnosis</topic><topic>Polycystic Ovary Syndrome - genetics</topic><topic>Polycystic Ovary Syndrome - physiopathology</topic><topic>Polymorphism, Single Nucleotide</topic><topic>Receptor, ErbB-4 - genetics</topic><topic>SOD2</topic><topic>Superoxide Dismutase - genetics</topic><topic>Trans-Activators - genetics</topic><topic>Transcription Factors - metabolism</topic><topic>WWTR1</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Yanfei</creatorcontrib><creatorcontrib>Ho, Kevin</creatorcontrib><creatorcontrib>Keaton, Jacob M.</creatorcontrib><creatorcontrib>Hartzel, Dustin N.</creatorcontrib><creatorcontrib>Day, Felix</creatorcontrib><creatorcontrib>Justice, Anne E.</creatorcontrib><creatorcontrib>Josyula, Navya S.</creatorcontrib><creatorcontrib>Pendergrass, Sarah A.</creatorcontrib><creatorcontrib>Actkins, Ky'Era</creatorcontrib><creatorcontrib>Davis, Lea K.</creatorcontrib><creatorcontrib>Velez Edwards, Digna R.</creatorcontrib><creatorcontrib>Holohan, Brody</creatorcontrib><creatorcontrib>Ramirez, Andrea</creatorcontrib><creatorcontrib>Stanaway, Ian B.</creatorcontrib><creatorcontrib>Crosslin, David R.</creatorcontrib><creatorcontrib>Jarvik, Gail P.</creatorcontrib><creatorcontrib>Sleiman, Patrick</creatorcontrib><creatorcontrib>Hakonarson, Hakon</creatorcontrib><creatorcontrib>Williams, Marc S.</creatorcontrib><creatorcontrib>Lee, Ming Ta Michael</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>American journal of obstetrics and gynecology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Yanfei</au><au>Ho, Kevin</au><au>Keaton, Jacob M.</au><au>Hartzel, Dustin N.</au><au>Day, Felix</au><au>Justice, Anne E.</au><au>Josyula, Navya S.</au><au>Pendergrass, Sarah A.</au><au>Actkins, Ky'Era</au><au>Davis, Lea K.</au><au>Velez Edwards, Digna R.</au><au>Holohan, Brody</au><au>Ramirez, Andrea</au><au>Stanaway, Ian B.</au><au>Crosslin, David R.</au><au>Jarvik, Gail P.</au><au>Sleiman, Patrick</au><au>Hakonarson, Hakon</au><au>Williams, Marc S.</au><au>Lee, Ming Ta Michael</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A genome-wide association study of polycystic ovary syndrome identified from electronic health records</atitle><jtitle>American journal of obstetrics and gynecology</jtitle><addtitle>Am J Obstet Gynecol</addtitle><date>2020-10</date><risdate>2020</risdate><volume>223</volume><issue>4</issue><spage>559.e1</spage><epage>559.e21</epage><pages>559.e1-559.e21</pages><issn>0002-9378</issn><eissn>1097-6868</eissn><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.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>32289280</pmid><doi>10.1016/j.ajog.2020.04.004</doi><orcidid>https://orcid.org/0000-0002-9222-7725</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0002-9378 |
| ispartof | American journal of obstetrics and gynecology, 2020-10, Vol.223 (4), p.559.e1-559.e21 |
| issn | 0002-9378 1097-6868 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_2390149419 |
| source | MEDLINE; Elsevier ScienceDirect Journals |
| subjects | Adaptor Proteins, Signal Transducing - metabolism Adult Case-Control Studies EGFR pathway electronic health record Electronic Health Records ERBB4 Female Genome-Wide Association Study Hippo pathway Humans Hyperandrogenism - genetics Infertility, Female - genetics Middle Aged Oligomenorrhea - genetics Ovarian Cysts - genetics polycystic ovary syndrome Polycystic Ovary Syndrome - diagnosis Polycystic Ovary Syndrome - genetics Polycystic Ovary Syndrome - physiopathology Polymorphism, Single Nucleotide Receptor, ErbB-4 - genetics SOD2 Superoxide Dismutase - genetics Trans-Activators - genetics Transcription Factors - metabolism WWTR1 |
| title | A genome-wide association study of polycystic ovary syndrome identified from electronic health records |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-31T09%3A38%3A33IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20genome-wide%20association%20study%20of%20polycystic%20ovary%20syndrome%20identified%20from%20electronic%20health%20records&rft.jtitle=American%20journal%20of%20obstetrics%20and%20gynecology&rft.au=Zhang,%20Yanfei&rft.date=2020-10&rft.volume=223&rft.issue=4&rft.spage=559.e1&rft.epage=559.e21&rft.pages=559.e1-559.e21&rft.issn=0002-9378&rft.eissn=1097-6868&rft_id=info:doi/10.1016/j.ajog.2020.04.004&rft_dat=%3Cproquest_cross%3E2390149419%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2390149419&rft_id=info:pmid/32289280&rft_els_id=S0002937820304282&rfr_iscdi=true |