Large-Scale Genome-Wide Meta-Analysis of Polycystic Ovary Syndrome Suggests Shared Genetic Architecture for Different Diagnosis Criteria

Authors

Felix Day, University of Cambridge, UK
Tugce Karaderi, University of Oxford, UK
Michelle R. Jones, Cedars-Sinai Medical Center
Cindy Meun, University Medical Center Rotterdam, The Netherlands
Chunyan He, University of KentuckyFollow
Alex Drong, University of Oxford, UK
Peter Kraft, Harvard University
Nan Lin, University of KentuckyFollow
Hongyan Huang, Harvard University
Linda Broer, University Medical Center Rotterdam, The Netherlands
Reedik Magi, Universityof Tartu, Estonia
Richa Saxena, Harvard University
Triin Laisk, Universityof Tartu, Estonia
Margrit Urbanek, Northwestern University
M. Geoffrey Hayes, Northwestern University
Gudmar Thorleifsson, deCODE genetics/Amgen, Iceland
Juan Fernandez-Tajes, University of Oxford, UK
Anubha Mahajan, University of Oxford, UK
Benjamin H. Mullin, Sir Charles Gairdner Hospital, Australia
Bronwyn G. A. Stuckey, Sir Charles Gairdner Hospital, Australia
Timothy D. Spector, King’s College London, UK
Scott G. Wilson, Sir Charles Gairdner Hospital, Australia
Mark O. Goodarzi, Cedars-Sinai Medical Center
Lea Davis, Vanderbilt University
Barbara Obermayer-Pietsch
André G. Uitterlinden, Universityof Graz, Austria
Verneri Anttila, Broad Institute of MIT and Harvard
Benjamin M. Neale, Broad Institute of MIT and Harvard
Marjo-Riitta Jarvelin, Imperial College London, UK
Bart Fauser, University Medical Center, The Netherlands

Abstract

Polycystic ovary syndrome (PCOS) is a disorder characterized by hyperandrogenism, ovulatory dysfunction and polycystic ovarian morphology. Affected women frequently have metabolic disturbances including insulin resistance and dysregulation of glucose homeostasis. PCOS is diagnosed with two different sets of diagnostic criteria, resulting in a phenotypic spectrum of PCOS cases. The genetic similarities between cases diagnosed based on the two criteria have been largely unknown. Previous studies in Chinese and European subjects have identified 16 loci associated with risk of PCOS. We report a fixed-effect, inverse-weighted-variance meta-analysis from 10,074 PCOS cases and 103,164 controls of European ancestry and characterisation of PCOS related traits. We identified 3 novel loci (near PLGRKT, ZBTB16 and MAPRE1), and provide replication of 11 previously reported loci. Only one locus differed significantly in its association by diagnostic criteria; otherwise the genetic architecture was similar between PCOS diagnosed by self-report and PCOS diagnosed by NIH or non-NIH Rotterdam criteria across common variants at 13 loci. Identified variants were associated with hyperandrogenism, gonadotropin regulation and testosterone levels in affected women. Linkage disequilibrium score regression analysis revealed genetic correlations with obesity, fasting insulin, type 2 diabetes, lipid levels and coronary artery disease, indicating shared genetic architecture between metabolic traits and PCOS. Mendelian randomization analyses suggested variants associated with body mass index, fasting insulin, menopause timing, depression and male-pattern balding play a causal role in PCOS. The data thus demonstrate 3 novel loci associated with PCOS and similar genetic architecture for all diagnostic criteria. The data also provide the first genetic evidence for a male phenotype for PCOS and a causal link to depression, a previously hypothesized comorbid disease. Thus, the genetics provide a comprehensive view of PCOS that encompasses multiple diagnostic criteria, gender, reproductive potential and mental health.

Document Type

Article

Publication Date

12-19-2018

Notes/Citation Information

Published in PLOS Genetics, v. 14, no. 12, e1007813, p. 1-20.

© 2018 Day et al.

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Due to the large number of authors, only the first 30 and the authors affiliated with the University of Kentucky are listed in the author section above. For the complete list of authors, please download this article or visit: https://doi.org/10.1371/journal.pgen.1007813

Digital Object Identifier (DOI)

https://doi.org/10.1371/journal.pgen.1007813

Funding Information

This work has been supported by MRC grant MC_U106179472 (FD, KO, JRBP), Samuel Oschin Comprehensive Cancer Institute Developmental Funds, Center for Bioinformatics and Functional Genomics and Department of Biomedical Sciences Developmental Funds (MRJ), NCI P30CA177558 (CH), NCI UM1CA186107 (PK), European Regional Development Fund (Project No. 2014-2020.4.01.15-0012) and the European Union’s Horizon 2020 research and innovation program under grant agreements No 692065 (TL, RM, AS) and 692145 (RM), NICHD R01HD065029 (RS), Estonian Ministry of Education and Research (grant IUT34-16 to TL), NICHD R01HD057450 (MU), NICHD P50HD044405 (AD), NICHD R01HD057223 (AD), R01HD085227 (MGH, AD), deCode Genetics (GT, UT, KS, US), Raine Medical Research Foundation Priming Grant (BHM), SCGOPHCG RAC 2015-16/034 (SGW, BGAS), 2016-17/018 (BGAS), NIHR BRC, Wellcome Trust, MRC (TDS), Eris M. Field Chair in Diabetes Research (MOG), NIDDK P30 DK063491 (MOG), NIDDK U01DK094431, U01DK048381 (DE), NICHD U10HD38992 (RL), Estonian Ministry of Education and Research (grant IUT34-16), Enterprise Estonia (grant EU48695); the EU-FP7 Marie Curie Industry-Academia Partnerships and Pathways (IAPP, grant SARM, EU324509 to AS), Wellcome (090532, 098381, 203141); European Commission (ENGAGE: HEALTH-F4-2007-201413 to MIM), MRC G0802782, MR/M012638/1 (SF), Li Ka Shing Foundation, WT-SSI/John Fell Funds, NIHR Biomedical Research Centre, Oxford, Widenlife and NICHD 5P50HD028138-27 (CML), NICHD R01HD065029, ADA 1-10-CT-57, Harvard Clinical and Translational Science Center, from the National Center for Research Resources 1UL1 RR025758 (CKW).

Related Content

Summary statistic GWAS meta-analysis results for the combined dataset excluding 23andMe are available at https://doi.org/10.17863/CAM.27720. The most significant 10,000 SNPs for the meta-analysis including 23andMe are available at https://doi.org/10.17863/CAM.27720.

S1 Data. Supplementary results suggestive evidence of a 15th signal, rs151212108, near ARSDon the X chromosome and literature lookup of genes at PCOS risk loci. https://doi.org/10.1371/journal.pgen.1007813.s001 (DOCX)

S1 Table. Cohorts contributing polycystic ovary syndrome (PCOS) cases, PCOS phenotypes, laboratory data and controls. https://doi.org/10.1371/journal.pgen.1007813.s002 (XLSX)

S2 Table. All PCOS meta-analysis, PCOS meta-analysis without self-report, NIH, non-NIH Rotterdam and self-report meta-analysis results. https://doi.org/10.1371/journal.pgen.1007813.s003 (XLSX)

S3 Table. Heterogeneity analysis for NIH, non-NIH Rotterdam and self-report cohorts. https://doi.org/10.1371/journal.pgen.1007813.s004 (XLSX)

S4 Table. Fine-mapping of PCOS risk loci identified in the meta-analysis to narrow candidate causal variants. https://doi.org/10.1371/journal.pgen.1007813.s005 (XLSX)

S5 Table. Look-up of previously published PCOS risk variants in Han Chinese cohorts with PCOS GWAS meta-analysis and PCOS related traits (HA, OD, PCOM, T, FSH, LH and ovarian volume). https://doi.org/10.1371/journal.pgen.1007813.s006 (XLSX)

S6 Table. All PCOS meta-analysis results and look-up of PCOS GWAS meta-analysis susceptibility variants with PCOS related traits (HA, OD, PCOM, T, FSH, LH and ovarian volume). https://doi.org/10.1371/journal.pgen.1007813.s007 (XLSX)

S7 Table. Number of SNPs removed from each cohort after application of easy QC after application of each filter. https://doi.org/10.1371/journal.pgen.1007813.s008 (XLSX)

S1 Fig. Diagnostic criteria of PCOS results in four distinct PCOS phenotypes. https://doi.org/10.1371/journal.pgen.1007813.s009 (DOCX)

S2 Fig. Cluster plots showing relationships between PCOS loci and related traits. https://doi.org/10.1371/journal.pgen.1007813.s010 (DOCX)

S3 Fig. Weighted genetic risk score to predict odds of PCOS based on either Rotterdam or NIH criteria. https://doi.org/10.1371/journal.pgen.1007813.s011 (DOCX)

S4 Fig. Allele frequency spectrum from each cohort and the combined cohort for meta-analysis. https://doi.org/10.1371/journal.pgen.1007813.s012 (DOCX)

Repository Citation

Day, Felix; Karaderi, Tugce; Jones, Michelle R.; Meun, Cindy; He, Chunyan; Drong, Alex; Kraft, Peter; Lin, Nan; Huang, Hongyan; Broer, Linda; Magi, Reedik; Saxena, Richa; Laisk, Triin; Urbanek, Margrit; Hayes, M. Geoffrey; Thorleifsson, Gudmar; Fernandez-Tajes, Juan; Mahajan, Anubha; Mullin, Benjamin H.; Stuckey, Bronwyn G. A.; Spector, Timothy D.; Wilson, Scott G.; Goodarzi, Mark O.; Davis, Lea; Obermayer-Pietsch, Barbara; Uitterlinden, André G.; Anttila, Verneri; Neale, Benjamin M.; Jarvelin, Marjo-Riitta; and Fauser, Bart, "Large-Scale Genome-Wide Meta-Analysis of Polycystic Ovary Syndrome Suggests Shared Genetic Architecture for Different Diagnosis Criteria" (2018). Internal Medicine Faculty Publications. 166.
https://uknowledge.uky.edu/internalmedicine_facpub/166