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

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      1 , 2 , 3 , 4 , 5 , 6 , 7 , 2 , 8 , 6 , 7 , 8 , 9 , 10 , 11 , 10 , 12 , 13 , 14 , 13 , 14 , 15 , 16 , 2 , 2 , 17 , 18 , 19 , 18 , 19 , 20 , 21 , 18 , 19 , 21 , 22 , 23 , 24 , 25 , 9 , 26 , 27 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 1 , 36 , 37 , 38 , 12 , 39 , 40 , 41 , 2 , 17 , 42 , 43 , 16 , 44 , 16 , 44 , the 23andMe Research Team, 16 , 1 , 13 , 45 , 5 , 46 , 2 , 11 , 47 , * , 48 , 49 , *
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          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.

          Author summary

          We performed an international meta-analysis of genome-wide association studies combining over 10,000,000 genetic markers in more than 10,000 European women with polycystic ovary syndrome (PCOS) and 100,000 controls. We found three new risk variants associated with PCOS. Our data demonstrate that the genetic architecture does not differ based on the diagnostic criteria used for PCOS. We also demonstrate a genetic pathway shared with male pattern baldness, representing the first evidence for shared disease biology in men, and shared genetics with depression, previously postulated based only on observational studies.

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          Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic

          Background MR-Egger regression has recently been proposed as a method for Mendelian randomization (MR) analyses incorporating summary data estimates of causal effect from multiple individual variants, which is robust to invalid instruments. It can be used to test for directional pleiotropy and provides an estimate of the causal effect adjusted for its presence. MR-Egger regression provides a useful additional sensitivity analysis to the standard inverse variance weighted (IVW) approach that assumes all variants are valid instruments. Both methods use weights that consider the single nucleotide polymorphism (SNP)-exposure associations to be known, rather than estimated. We call this the `NO Measurement Error’ (NOME) assumption. Causal effect estimates from the IVW approach exhibit weak instrument bias whenever the genetic variants utilized violate the NOME assumption, which can be reliably measured using the F-statistic. The effect of NOME violation on MR-Egger regression has yet to be studied. Methods An adaptation of the I 2 statistic from the field of meta-analysis is proposed to quantify the strength of NOME violation for MR-Egger. It lies between 0 and 1, and indicates the expected relative bias (or dilution) of the MR-Egger causal estimate in the two-sample MR context. We call it I G X 2 . The method of simulation extrapolation is also explored to counteract the dilution. Their joint utility is evaluated using simulated data and applied to a real MR example. Results In simulated two-sample MR analyses we show that, when a causal effect exists, the MR-Egger estimate of causal effect is biased towards the null when NOME is violated, and the stronger the violation (as indicated by lower values of I G X 2 ), the stronger the dilution. When additionally all genetic variants are valid instruments, the type I error rate of the MR-Egger test for pleiotropy is inflated and the causal effect underestimated. Simulation extrapolation is shown to substantially mitigate these adverse effects. We demonstrate our proposed approach for a two-sample summary data MR analysis to estimate the causal effect of low-density lipoprotein on heart disease risk. A high value of I G X 2 close to 1 indicates that dilution does not materially affect the standard MR-Egger analyses for these data. Conclusions Care must be taken to assess the NOME assumption via the I G X 2 statistic before implementing standard MR-Egger regression in the two-sample summary data context. If I G X 2 is sufficiently low (less than 90%), inferences from the method should be interpreted with caution and adjustment methods considered.
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            Consensus on women's health aspects of polycystic ovary syndrome (PCOS): the Amsterdam ESHRE/ASRM-Sponsored 3rd PCOS Consensus Workshop Group.

            Polycystic ovary syndrome (PCOS) is the most common endocrine disorder in females, with a high prevalence. The etiology of this heterogeneous condition remains obscure, and its phenotype expression varies. Two widely cited previous ESHRE/ASRM sponsored PCOS consensus workshops focused on diagnosis (published in 2004) and infertility management (published in 2008), respectively. The present third PCOS consensus report summarizes current knowledge and identifies knowledge gaps regarding various women's health aspects of PCOS. Relevant topics addressed-all dealt with in a systematic fashion-include adolescence, hirsutism and acne, contraception, menstrual cycle abnormalities, quality of life, ethnicity, pregnancy complications, long-term metabolic and cardiovascular health, and finally cancer risk. Additional, comprehensive background information is provided separately in an extended online publication. Copyright © 2012 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.
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              Clinical assessment of body hair growth in women.

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                Role: Editor
                Journal
                PLoS Genet
                PLoS Genet
                plos
                plosgen
                PLoS Genetics
                Public Library of Science (San Francisco, CA USA )
                1553-7390
                1553-7404
                19 December 2018
                December 2018
                : 14
                : 12
                : e1007813
                Affiliations
                [1 ] MRC Epidemiology Unit, Cambridge Biomedical Campus, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
                [2 ] The Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
                [3 ] Department of Biological Sciences, Faculty of Arts and Sciences, Eastern Mediterranean University, Famagusta, Cyprus
                [4 ] Center for Bioinformatics & Functional Genomics, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, United States of America
                [5 ] Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynaecology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
                [6 ] Department of Internal Medicine, University of Kentucky College of Medicine, Lexington, Kentucky, United States of America
                [7 ] University of Kentucky Markey Cancer Center, Lexington, Kentucky, United States of America
                [8 ] Departments of Epidemiology and Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
                [9 ] Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
                [10 ] Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
                [11 ] Broad Institute of Harvard and MIT and Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
                [12 ] Department of Obstetrics and Gynaecology, Institute of Clinical Medicine, University of Tartu, Tartu, Estonia
                [13 ] Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
                [14 ] Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
                [15 ] Department of Anthropology, Northwestern University, Evanston, Illinois, United States of America
                [16 ] deCODE genetics/Amgen, Reykjavik, Iceland
                [17 ] Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, United Kingdom
                [18 ] Department of Endocrinology & Diabetes, Sir Charles Gairdner Hospital, Nedlands, Western Australia, Australia
                [19 ] School of Medicine and Pharmacology, University of Western Australia, Crawley, Western Australia, Australia
                [20 ] Keogh Institute for Medical Research, Nedlands, Western Australia, Australia
                [21 ] Department of Twin Research & Genetic Epidemiology, King's College London, London, United Kingdom
                [22 ] Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, United States of America
                [23 ] Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
                [24 ] Vanderbilt Genomics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
                [25 ] Division of Endocrinology and Diabetology, Department of Internal Medicine Medical University of Graz, Graz, Austria
                [26 ] Stanley Center for Psychiatric Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
                [27 ] Analytic and Translational Genetics Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
                [28 ] Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
                [29 ] Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
                [30 ] Biocenter Oulu, University of Oulu, Oulu, Finland
                [31 ] Unit of Primary Care, Oulu University Hospital, Oulu, Finland
                [32 ] Department of Reproductive Medicine and Gynaecology, University Medical Center, Utrecht, The Netherlands
                [33 ] Department of Internal Medicine and Metabolic Diseases, Medical University of Białystok, Białystok, Poland
                [34 ] Department of Internal Medicine, Section of Endocrinology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
                [35 ] Odense University Hospital, University of Southern Denmark, Odense, Denmark
                [36 ] Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
                [37 ] Department of Medicine, Section of Adult and Paediatric Endocrinology, Diabetes, and Metabolism, The University of Chicago, Chicago, Illinois, United States of America
                [38 ] Department of Obstetrics and Gynecology and Public Health Sciences, Penn State University College of Medicine, Hershey, Pennsylvania, United States of America
                [39 ] Competence Centre on Health Technologies, Tartu, Estonia
                [40 ] Institute of Bio- and Translational Medicine, University of Tartu, Tartu, Estonia
                [41 ] Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
                [42 ] Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford, United Kingdom
                [43 ] Department of Obstetrics and Gynecology, University of Oulu and Oulu University Hospital, Medical Research Center, PEDEGO Research Unit, Oulu, Finland
                [44 ] Faculty of Medicine, University of Iceland, Reykjavik, Iceland
                [45 ] Division of Endocrinology, Diabetes and Bone Disease, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
                [46 ] Institute of Reproductive & Developmental Biology, Department of Surgery & Cancer, Imperial College London, London, United Kingdom
                [47 ] Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
                [48 ] Division of Endocrinology, Metabolism and Diabetes, University of Utah, Salt Lake City, Utah, United States of America
                [49 ] Reproductive Endocrine Unit, Massachusetts General Hospital, Boston, Massachusetts, United States of America
                Yale School of Medicine, UNITED STATES
                Author notes

                Members of the 23andMe Research team are employees of and hold stock or stock options in 23andMe, Inc. GT, UT, KS, and US are employees of deCODE genetics/Amgen Inc. MIM serves on advisory panels for Pfizer and NovoNordisk. MIM has received honoraria from Pfizer, NovoNordisk and EliLilly, and has received research funding from Pfizer, NovoNordisk, EliLilly, AstraZeneca, Sanofi Aventis, Boehringer Ingelheim, Merck, Roche, Janssen, Takeda, and Servier. JL has received consultancy fees from Danone, Metagenics inc., Titus Healthcare, Roche and Euroscreen. CW is a consultant for Novartis and has received UptoDate royalties.

                ¶ Authors from 23andMe are provided in the Acknowledgments.

                Author information
                http://orcid.org/0000-0003-3789-7651
                http://orcid.org/0000-0002-6970-0852
                http://orcid.org/0000-0003-1501-9030
                http://orcid.org/0000-0001-5585-3420
                http://orcid.org/0000-0002-0073-4675
                http://orcid.org/0000-0003-1513-6077
                http://orcid.org/0000-0002-2149-0630
                http://orcid.org/0000-0001-7182-3571
                http://orcid.org/0000-0003-4689-7530
                http://orcid.org/0000-0002-3424-1502
                http://orcid.org/0000-0002-1251-8160
                http://orcid.org/0000-0002-7049-2827
                http://orcid.org/0000-0001-8146-8278
                http://orcid.org/0000-0002-8175-4677
                http://orcid.org/0000-0002-8219-5504
                Article
                PGENETICS-D-18-00880
                10.1371/journal.pgen.1007813
                6300389
                30566500
                28a02cea-88a4-48a9-af77-20457e255ad2
                © 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.

                History
                : 30 April 2018
                : 6 November 2018
                Page count
                Figures: 2, Tables: 5, Pages: 20
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100000265, Medical Research Council;
                Award ID: MC_U106179472
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000054, National Cancer Institute;
                Award ID: NCI P30CA177558
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000054, National Cancer Institute;
                Award ID: NCI UM1CA186107
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100008530, European Regional Development Fund;
                Award ID: 2014-2020.4.01.15-0012
                Award Recipient :
                Funded by: European Union Horizon 2020
                Award ID: 692065
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000071, National Institute of Child Health and Human Development;
                Award ID: R01HD065029
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000071, National Institute of Child Health and Human Development;
                Award ID: R01HD057450
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000071, National Institute of Child Health and Human Development;
                Award ID: P50HD044405
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000071, National Institute of Child Health and Human Development;
                Award ID: R01HD065029
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000041, American Diabetes Association;
                Award ID: ADA 1-10-CT-57
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100000265, Medical Research Council;
                Award ID: MRC G0802782
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000062, National Institute of Diabetes and Digestive and Kidney Diseases;
                Award ID: U01DK048381
                Award Recipient :
                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). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Medicine and Health Sciences
                Oncology
                Cancers and Neoplasms
                Gynecological Tumors
                Polycystic Ovary Syndrome
                Biology and Life Sciences
                Genetics
                Genetic Loci
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Statistical Methods
                Metaanalysis
                Physical Sciences
                Mathematics
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                Biology and Life Sciences
                Genetics
                Genetics of Disease
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                Human Genetics
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                Phenotypes
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                Physiological Parameters
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                Medicine and Health Sciences
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                Physiological Parameters
                Body Weight
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                Biology and Life Sciences
                Computational Biology
                Genome Analysis
                Genome-Wide Association Studies
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                Genome Analysis
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                Genome-Wide Association Studies
                Custom metadata
                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.

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