Literature DB >> 31917831

A Polygenic and Phenotypic Risk Prediction for Polycystic Ovary Syndrome Evaluated by Phenome-Wide Association Studies.

Yoonjung Yoonie Joo1, Ky'Era Actkins2, Jennifer A Pacheco3, Anna O Basile4, Robert Carroll5, David R Crosslin6, Felix Day7, Joshua C Denny5, Digna R Velez Edwards5,8, Hakon Hakonarson9,10, John B Harley11,12, Scott J Hebbring13, Kevin Ho14, Gail P Jarvik15, Michelle Jones16, Tugce Karaderi17, Frank D Mentch9, Cindy Meun18, Bahram Namjou11, Sarah Pendergrass14, Marylyn D Ritchie19, Ian B Stanaway6, Margrit Urbanek1, Theresa L Walunas20, Maureen Smith3, Rex L Chisholm3, Abel N Kho20, Lea Davis5, M Geoffrey Hayes1,3,21.   

Abstract

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. © Endocrine Society 2020. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Entities:  

Keywords:  genomic prediction; phenome-wide association study; polycystic ovary syndrome; polygenic risk score

Mesh：

Year:  2020        PMID： 31917831      PMCID： PMC7453038          DOI： 10.1210/clinem/dgz326

Source DB:  PubMed          Journal:  J Clin Endocrinol Metab        ISSN： 0021-972X            Impact factor:   5.958


  46 in total

1.  Polygenic risk score for schizophrenia is more strongly associated with ancestry than with schizophrenia.

Authors:  David Curtis
Journal:  Psychiatr Genet       Date:  2018-10       Impact factor: 2.458

Review 2.  Clinical use of current polygenic risk scores may exacerbate health disparities.

Authors:  Alicia R Martin; Masahiro Kanai; Yoichiro Kamatani; Yukinori Okada; Benjamin M Neale; Mark J Daly
Journal:  Nat Genet       Date:  2019-03-29       Impact factor: 38.330

3.  A twin study of polycystic ovary syndrome and lipids.

Authors:  S Jahanfar; J A Eden; T Nguyen; X L Wang; D E Wilcken
Journal:  Gynecol Endocrinol       Date:  1997-04       Impact factor: 2.260

Review 4.  Diagnosis and challenges of polycystic ovary syndrome in adolescence.

Authors:  Sophia E Agapova; Tamara Cameo; Aviva B Sopher; Sharon E Oberfield
Journal:  Semin Reprod Med       Date:  2014-04-08       Impact factor: 1.303

5.  Electronic medical records for genetic research: results of the eMERGE consortium.

Authors:  Abel N Kho; Jennifer A Pacheco; Peggy L Peissig; Luke Rasmussen; Katherine M Newton; Noah Weston; Paul K Crane; Jyotishman Pathak; Christopher G Chute; Suzette J Bielinski; Iftikhar J Kullo; Rongling Li; Teri A Manolio; Rex L Chisholm; Joshua C Denny
Journal:  Sci Transl Med       Date:  2011-04-20       Impact factor: 17.956

6.  The eMERGE Network: a consortium of biorepositories linked to electronic medical records data for conducting genomic studies.

Authors:  Catherine A McCarty; Rex L Chisholm; Christopher G Chute; Iftikhar J Kullo; Gail P Jarvik; Eric B Larson; Rongling Li; Daniel R Masys; Marylyn D Ritchie; Dan M Roden; Jeffery P Struewing; Wendy A Wolf
Journal:  BMC Med Genomics       Date:  2011-01-26       Impact factor: 3.063

7.  Evidence for chromosome 2p16.3 polycystic ovary syndrome susceptibility locus in affected women of European ancestry.

Authors:  Priscilla Mutharasan; Eugene Galdones; Beatriz Peñalver Bernabé; Obed A Garcia; Nadereh Jafari; Lonnie D Shea; Teresa K Woodruff; Richard S Legro; Andrea Dunaif; Margrit Urbanek
Journal:  J Clin Endocrinol Metab       Date:  2012-11-01       Impact factor: 5.958

8.  Large-scale genome-wide meta-analysis of polycystic ovary syndrome suggests shared genetic architecture for different diagnosis criteria.

Authors:  Felix Day; Tugce Karaderi; Michelle R Jones; Cindy Meun; Chunyan He; Alex Drong; Peter Kraft; Nan Lin; Hongyan Huang; Linda Broer; Reedik Magi; Richa Saxena; Triin Laisk; Margrit Urbanek; M Geoffrey Hayes; Gudmar Thorleifsson; Juan Fernandez-Tajes; Anubha Mahajan; Benjamin H Mullin; Bronwyn G A Stuckey; Timothy D Spector; Scott G Wilson; Mark O Goodarzi; Lea Davis; Barbara Obermayer-Pietsch; André G Uitterlinden; Verneri Anttila; Benjamin M Neale; Marjo-Riitta Jarvelin; Bart Fauser; Irina Kowalska; Jenny A Visser; Marianne Andersen; Ken Ong; Elisabet Stener-Victorin; David Ehrmann; Richard S Legro; Andres Salumets; Mark I McCarthy; Laure Morin-Papunen; Unnur Thorsteinsdottir; Kari Stefansson; Unnur Styrkarsdottir; John R B Perry; Andrea Dunaif; Joop Laven; Steve Franks; Cecilia M Lindgren; Corrine K Welt
Journal:  PLoS Genet       Date:  2018-12-19       Impact factor: 6.020

9.  A reference panel of 64,976 haplotypes for genotype imputation.

Authors:  Shane McCarthy; Sayantan Das; Warren Kretzschmar; Olivier Delaneau; Andrew R Wood; Alexander Teumer; Hyun Min Kang; Christian Fuchsberger; Petr Danecek; Kevin Sharp; Yang Luo; Carlo Sidore; Alan Kwong; Nicholas Timpson; Seppo Koskinen; Scott Vrieze; Laura J Scott; He Zhang; Anubha Mahajan; Jan Veldink; Ulrike Peters; Carlos Pato; Cornelia M van Duijn; Christopher E Gillies; Ilaria Gandin; Massimo Mezzavilla; Arthur Gilly; Massimiliano Cocca; Michela Traglia; Andrea Angius; Jeffrey C Barrett; Dorrett Boomsma; Kari Branham; Gerome Breen; Chad M Brummett; Fabio Busonero; Harry Campbell; Andrew Chan; Sai Chen; Emily Chew; Francis S Collins; Laura J Corbin; George Davey Smith; George Dedoussis; Marcus Dorr; Aliki-Eleni Farmaki; Luigi Ferrucci; Lukas Forer; Ross M Fraser; Stacey Gabriel; Shawn Levy; Leif Groop; Tabitha Harrison; Andrew Hattersley; Oddgeir L Holmen; Kristian Hveem; Matthias Kretzler; James C Lee; Matt McGue; Thomas Meitinger; David Melzer; Josine L Min; Karen L Mohlke; John B Vincent; Matthias Nauck; Deborah Nickerson; Aarno Palotie; Michele Pato; Nicola Pirastu; Melvin McInnis; J Brent Richards; Cinzia Sala; Veikko Salomaa; David Schlessinger; Sebastian Schoenherr; P Eline Slagboom; Kerrin Small; Timothy Spector; Dwight Stambolian; Marcus Tuke; Jaakko Tuomilehto; Leonard H Van den Berg; Wouter Van Rheenen; Uwe Volker; Cisca Wijmenga; Daniela Toniolo; Eleftheria Zeggini; Paolo Gasparini; Matthew G Sampson; James F Wilson; Timothy Frayling; Paul I W de Bakker; Morris A Swertz; Steven McCarroll; Charles Kooperberg; Annelot Dekker; David Altshuler; Cristen Willer; William Iacono; Samuli Ripatti; Nicole Soranzo; Klaudia Walter; Anand Swaroop; Francesco Cucca; Carl A Anderson; Richard M Myers; Michael Boehnke; Mark I McCarthy; Richard Durbin
Journal:  Nat Genet       Date:  2016-08-22       Impact factor: 38.330

10.  The eMERGE genotype set of 83,717 subjects imputed to ~40 million variants genome wide and association with the herpes zoster medical record phenotype.

Authors:  Ian B Stanaway; Taryn O Hall; Elisabeth A Rosenthal; Melody Palmer; Vivek Naranbhai; Rachel Knevel; Bahram Namjou-Khales; Robert J Carroll; Krzysztof Kiryluk; Adam S Gordon; Jodell Linder; Kayla Marie Howell; Brandy M Mapes; Frederick T J Lin; Yoonjung Yoonie Joo; M Geoffrey Hayes; Ali G Gharavi; Sarah A Pendergrass; Marylyn D Ritchie; Mariza de Andrade; Damien C Croteau-Chonka; Soumya Raychaudhuri; Scott T Weiss; Matt Lebo; Sami S Amr; David Carrell; Eric B Larson; Christopher G Chute; Laura Jarmila Rasmussen-Torvik; Megan J Roy-Puckelwartz; Patrick Sleiman; Hakon Hakonarson; Rongling Li; Elizabeth W Karlson; Josh F Peterson; Iftikhar J Kullo; Rex Chisholm; Joshua Charles Denny; Gail P Jarvik; David R Crosslin
Journal:  Genet Epidemiol       Date:  2018-10-08       Impact factor: 2.135
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Review 1.  Insulin resistance and PCOS: chicken or egg?

Authors:  P Moghetti; F Tosi
Journal:  J Endocrinol Invest       Date:  2020-07-09       Impact factor: 4.256

2.  The PCOS GWAS Candidate Gene ZNF217 Influences Theca Cell Expression of DENND1A.V2, CYP17A1, and Androgen Production.

Authors:  Jamaia S Waterbury; Maria E Teves; Alison Gaynor; Angela X Han; Grace Mavodza; Jordan Newell; Jerome F Strauss; Jan M McAllister
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Review 3.  Genetics of Polycystic Ovary Syndrome: What is New?

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Journal:  Endocrinol Metab Clin North Am       Date:  2021-03       Impact factor: 4.741

4.  Fate or coincidence: do COPD and major depression share genetic risk factors?

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Review 5.  Genetic Susceptibility to Joint Occurrence of Polycystic Ovary Syndrome and Hashimoto's Thyroiditis: How Far Is Our Understanding?

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6.  Genomic correlation, shared loci, and causal relationship between obesity and polycystic ovary syndrome: a large-scale genome-wide cross-trait analysis.

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7.  Evidence From Men for Ovary-independent Effects of Genetic Risk Factors for Polycystic Ovary Syndrome.

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8.  The sexual brain, genes, and cognition: A machine-predicted brain sex score explains individual differences in cognitive intelligence and genetic influence in young children.

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Review 9.  Gynecology Meets Big Data in the Disruptive Innovation Medical Era: State-of-Art and Future Prospects.

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10.  Characterizing the Clinical and Genetic Spectrum of Polycystic Ovary Syndrome in Electronic Health Records.

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