Distinct subtypes of polycystic ovary syndrome with novel genetic associations: An unsupervised, phenotypic clustering analysis

Autoři: Matthew Dapas aff001;  Frederick T. J. Lin aff001;  Girish N. Nadkarni aff002;  Ryan Sisk aff001;  Richard S. Legro aff003;  Margrit Urbanek aff001;  M. Geoffrey Hayes aff001;  Andrea Dunaif aff007
Působiště autorů: Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America aff001;  Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America aff002;  Department of Obstetrics and Gynecology, Penn State College of Medicine, Hershey, Pennsylvania, United States of America aff003;  Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America aff004;  Center for Reproductive Science, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America aff005;  Department of Anthropology, Northwestern University, Evanston, Illinois, United States of America aff006;  Division of Endocrinology, Diabetes and Bone Disease, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America aff007
Vyšlo v časopise: Distinct subtypes of polycystic ovary syndrome with novel genetic associations: An unsupervised, phenotypic clustering analysis. PLoS Med 17(6): e1003132. doi:10.1371/journal.pmed.1003132
Kategorie: Research Article
doi: 10.1371/journal.pmed.1003132



Polycystic ovary syndrome (PCOS) is a common, complex genetic disorder affecting up to 15% of reproductive-age women worldwide, depending on the diagnostic criteria applied. These diagnostic criteria are based on expert opinion and have been the subject of considerable controversy. The phenotypic variation observed in PCOS is suggestive of an underlying genetic heterogeneity, but a recent meta-analysis of European ancestry PCOS cases found that the genetic architecture of PCOS defined by different diagnostic criteria was generally similar, suggesting that the criteria do not identify biologically distinct disease subtypes. We performed this study to test the hypothesis that there are biologically relevant subtypes of PCOS.

Methods and findings

Using biochemical and genotype data from a previously published PCOS genome-wide association study (GWAS), we investigated whether there were reproducible phenotypic subtypes of PCOS with subtype-specific genetic associations. Unsupervised hierarchical cluster analysis was performed on quantitative anthropometric, reproductive, and metabolic traits in a genotyped cohort of 893 PCOS cases (median and interquartile range [IQR]: age = 28 [25–32], body mass index [BMI] = 35.4 [28.2–41.5]). The clusters were replicated in an independent, ungenotyped cohort of 263 PCOS cases (median and IQR: age = 28 [24–33], BMI = 35.7 [28.4–42.3]). The clustering revealed 2 distinct PCOS subtypes: a “reproductive” group (21%–23%), characterized by higher luteinizing hormone (LH) and sex hormone binding globulin (SHBG) levels with relatively low BMI and insulin levels, and a “metabolic” group (37%–39%), characterized by higher BMI, glucose, and insulin levels with lower SHBG and LH levels. We performed a GWAS on the genotyped cohort, limiting the cases to either the reproductive or metabolic subtypes. We identified alleles in 4 loci that were associated with the reproductive subtype at genome-wide significance (PRDM2/KAZN, P = 2.2 × 10−10; IQCA1, P = 2.8 × 10−9; BMPR1B/UNC5C, P = 9.7 × 10−9; CDH10, P = 1.2 × 10−8) and one locus that was significantly associated with the metabolic subtype (KCNH7/FIGN, P = 1.0 × 10−8). We developed a predictive model to classify a separate, family-based cohort of 73 women with PCOS (median and IQR: age = 28 [25–33], BMI = 34.3 [27.8–42.3]) and found that the subtypes tended to cluster in families and that carriers of previously reported rare variants in DENND1A, a gene that regulates androgen biosynthesis, were significantly more likely to have the reproductive subtype of PCOS. Limitations of our study were that only PCOS cases of European ancestry diagnosed by National Institutes of Health (NIH) criteria were included, the sample sizes for the subtype GWAS were small, and the GWAS findings were not replicated.


In conclusion, we have found reproducible reproductive and metabolic subtypes of PCOS. Furthermore, these subtypes were associated with novel, to our knowledge, susceptibility loci. Our results suggest that these subtypes are biologically relevant because they appear to have distinct genetic architecture. This study demonstrates how phenotypic subtyping can be used to gain additional insights from GWAS data.

Klíčová slova:

Genetic loci – Genome-wide association studies – Human genetics – Insulin – Molecular genetics – Phenotypes – Polycystic ovary syndrome – Quantitative traits


1. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, et al. Finding the missing heritability of complex diseases. Nature. 2009;461(7265): 747–53. doi: 10.1038/nature08494 19812666

2. Boyle EA, Li YI, Pritchard JK. An expanded view of complex traits: From polygenic to omnigenic. Cell. 2017;169(7): 1177–86. doi: 10.1016/j.cell.2017.05.038 28622505

3. Wray NR, Wijmenga C, Sullivan PF, Yang J, Visscher PM. Common Disease Is More Complex Than Implied by the Core Gene Omnigenic Model. Cell. 2018;173(7): 1573–80. doi: 10.1016/j.cell.2018.05.051 29906445

4. Ringman JM, Goate A, Masters CL, Cairns NJ, Danek A, Graff-Radford N, et al. Genetic heterogeneity in Alzheimer disease and implications for treatment strategies. Curr Neurol Neurosci Rep. 2014;14(11): 499. doi: 10.1007/s11910-014-0499-8 25217249

5. Flint J, Kendler KS. The genetics of major depression. Neuron. 2014;81(3): 484–503. doi: 10.1016/j.neuron.2014.01.027 24507187

6. von Coelln R, Shulman LM. Clinical subtypes and genetic heterogeneity: of lumping and splitting in Parkinson disease. Curr Opin Neurol. 2016;29(6): 727–34. doi: 10.1097/WCO.0000000000000384 27749396

7. Udler MS, Kim J, von Grotthuss M, Bonas-Guarch S, Cole JB, Chiou J, et al. Type 2 diabetes genetic loci informed by multi-trait associations point to disease mechanisms and subtypes: A soft clustering analysis. PLoS Med. 2018;15(9): e1002654. doi: 10.1371/journal.pmed.1002654 30240442

8. Ahlqvist E, Storm P, Karajamaki A, Martinell M, Dorkhan M, Carlsson A, et al. Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. Lancet Diabetes Endocrinol. 2018;6(5): 361–9. doi: 10.1016/S2213-8587(18)30051-2 29503172

9. Saria S, Goldenberg A. Subtyping: What it is and its role in precision medicine. IEEE Intelligent Systems. 2015;30(4): 70–5.

10. Diamanti-Kandarakis E, Dunaif A. Insulin resistance and the polycystic ovary syndrome revisited: an update on mechanisms and implications. Endocr Rev. 2012;33(6): 981–1030. doi: 10.1210/er.2011-1034 23065822

11. Dumesic DA, Oberfield SE, Stener-Victorin E, Marshall JC, Laven JS, Legro RS. Scientific Statement on the Diagnostic Criteria, Epidemiology, Pathophysiology, and Molecular Genetics of Polycystic Ovary Syndrome. Endocr Rev. 2015;36(5): 487–525. doi: 10.1210/er.2015-1018 26426951

12. Witchel SF, Oberfield SE, Pena AS. Polycystic Ovary Syndrome: Pathophysiology, Presentation, and Treatment With Emphasis on Adolescent Girls. J Endocr Soc. 2019;3(8): 1545–73. doi: 10.1210/js.2019-00078 31384717

13. Sanchez-Garrido MA, Tena-Sempere M. Metabolic dysfunction in polycystic ovary syndrome: Pathogenic role of androgen excess and potential therapeutic strategies. Mol Metab. 2020;35: 100937. doi: 10.1016/j.molmet.2020.01.001 32244180

14. Rubin KH, Glintborg D, Nybo M, Abrahamsen B, Andersen M. Development and risk factors of type 2 diabetes in a nationwide population of women with polycystic ovary syndrome. J Clin Endocrinol Metab. 2017;102(10): 3848–57. doi: 10.1210/jc.2017-01354 28938447

15. Dunaif A. Perspectives in polycystic ovary syndrome: From hair to eternity. J Clin Endocrinol Metab. 2016;101(3): 759–68. doi: 10.1210/jc.2015-3780 26908109

16. Zawadski JKD A. Diagnostic criteria for polycystic ovary syndrome; towards a rational approach. In: Dunaif A, Givens JR, Haseltine FP, Merriam GR, editors. Polycystic Ovary Syndrome. Boston, Massachusetts: Blackwell Scientific; 1992. pp. 377–84.

17. Rotterdam EA-SPcwg. Revised 2003 consensus on diagnostic criteria and long-term health risks related to polycystic ovary syndrome (PCOS). Hum Reprod. 2004;19(1): 41–7. doi: 10.1093/humrep/deh098 14688154

18. Rotterdam EA-SPCWG. Revised 2003 consensus on diagnostic criteria and long-term health risks related to polycystic ovary syndrome. Fertil Steril. 2004;81(1): 19–25. doi: 10.1016/j.fertnstert.2003.10.004 14711538

19. Hayes MG, Urbanek M, Ehrmann DA, Armstrong LL, Lee JY, Sisk R, et al. Genome-wide association of polycystic ovary syndrome implicates alterations in gonadotropin secretion in European ancestry populations. Nat Commun. 2015;6: 7502. doi: 10.1038/ncomms8502 26284813

20. Chen ZJ, Zhao H, He L, Shi Y, Qin Y, Shi Y, et al. Genome-wide association study identifies susceptibility loci for polycystic ovary syndrome on chromosome 2p16.3, 2p21 and 9q33.3. Nat Genet. 2011;43(1): 55–9. doi: 10.1038/ng.732 21151128

21. Shi Y, Zhao H, Shi Y, Cao Y, Yang D, Li Z, et al. Genome-wide association study identifies eight new risk loci for polycystic ovary syndrome. Nat Genet. 2012;44(9): 1020–5. doi: 10.1038/ng.2384 22885925

22. Day F, Karaderi T, Jones MR, Meun C, He C, Drong A, et al. Large-scale genome-wide meta-analysis of polycystic ovary syndrome suggests shared genetic architecture for different diagnosis criteria. PLoS Genet. 2018;14(12): e1007813. doi: 10.1371/journal.pgen.1007813 30566500

23. Day FR, Hinds DA, Tung JY, Stolk L, Styrkarsdottir U, Saxena R, et al. Causal mechanisms and balancing selection inferred from genetic associations with polycystic ovary syndrome. Nat Commun. 2015;6: 8464. doi: 10.1038/ncomms9464 26416764

24. Castaldi PJ, Dy J, Ross J, Chang Y, Washko GR, Curran-Everett D, et al. Cluster analysis in the COPDGene study identifies subtypes of smokers with distinct patterns of airway disease and emphysema. Thorax. 2014;69(5): 415–22. doi: 10.1136/thoraxjnl-2013-203601 24563194

25. Li L, Cheng WY, Glicksberg BS, Gottesman O, Tamler R, Chen R, et al. Identification of type 2 diabetes subgroups through topological analysis of patient similarity. Sci Transl Med. 2015;7(311): 311ra174. doi: 10.1126/scitranslmed.aaa9364 26511511

26. Tzeng CR, Chang YC, Chang YC, Wang CW, Chen CH, Hsu MI. Cluster analysis of cardiovascular and metabolic risk factors in women of reproductive age. Fertil Steril. 2014;101(5): 1404–10. doi: 10.1016/j.fertnstert.2014.01.023 24534286

27. Dewailly D, Alebic MS, Duhamel A, Stojanovic N. Using cluster analysis to identify a homogeneous subpopulation of women with polycystic ovarian morphology in a population of non-hyperandrogenic women with regular menstrual cycles. Hum Reprod. 2014;29(11): 2536–43. doi: 10.1093/humrep/deu242 25267785

28. Daan NM, Koster MP, de Wilde MA, Dalmeijer GW, Evelein AM, Fauser BC, et al. Biomarker profiles in women with PCOS and PCOS offspring; A pilot study. PLoS ONE. 2016;11(11): e0165033. doi: 10.1371/journal.pone.0165033 27806063

29. Huang CC, Tien YJ, Chen MJ, Chen CH, Ho HN, Yang YS. Symptom patterns and phenotypic subgrouping of women with polycystic ovary syndrome: association between endocrine characteristics and metabolic aberrations. Hum Reprod. 2015;30(4): 937–46. doi: 10.1093/humrep/dev010 25662806

30. Dapas M, Sisk R, Legro RS, Urbanek M, Dunaif A, Hayes MG. Family-based quantitative trait meta-analysis implicates rare noncoding variants in DENND1A in polycystic ovary syndrome. J Clin Endocrinol Metab. 2019. Forthcoming 2020. doi: 10.1210/jc.2018-02496 31038695

31. Legro RS, Driscoll D, Strauss JF 3rd, Fox J, Dunaif A. Evidence for a genetic basis for hyperandrogenemia in polycystic ovary syndrome. Proc Natl Acad Sci U S A. 1998;95(25): 14956–60. doi: 10.1073/pnas.95.25.14956 9843997

32. Legro RS, Brzyski RG, Diamond MP, Coutifaris C, Schlaff WD, Casson P, et al. Letrozole versus clomiphene for infertility in the polycystic ovary syndrome. N Engl J Med. 2014;371(2): 119–29. doi: 10.1056/NEJMoa1313517 25006718

33. McCarty CA, Chisholm RL, Chute CG, Kullo IJ, Jarvik GP, Larson EB, et al. The eMERGE Network: a consortium of biorepositories linked to electronic medical records data for conducting genomic studies. BMC Med Genomics. 2011;4: 13. doi: 10.1186/1755-8794-4-13 21269473

34. Murtagh F, Legendre P. Ward's Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Ward's Criterion? J Classif. 2014;31(3): 274–95.

35. Strauss T, von Maltitz MJ. Generalising Ward's Method for Use with Manhattan Distances. PLoS ONE. 2017;12(1): e0168288. doi: 10.1371/journal.pone.0168288 28085891

36. Hennig C. Cluster-wise assessment of cluster stability. Comput Stat Data An. 2007;52(1): 258–71.

37. Voight BF, Kang HM, Ding J, Palmer CD, Sidore C, Chines PS, et al. The metabochip, a custom genotyping array for genetic studies of metabolic, cardiovascular, and anthropometric traits. PLoS Genet. 2012;8(8): e1002793. doi: 10.1371/journal.pgen.1002793 22876189

38. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet. 2006;38(8): 904–9. doi: 10.1038/ng1847 16862161

39. O'Connell J, Gurdasani D, Delaneau O, Pirastu N, Ulivi S, Cocca M, et al. A general approach for haplotype phasing across the full spectrum of relatedness. PLoS Genet. 2014;10(4): e1004234. doi: 10.1371/journal.pgen.1004234 24743097

40. Genomes Project C, Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, et al. A global reference for human genetic variation. Nature. 2015;526(7571): 68–74. doi: 10.1038/nature15393 26432245

41. Das S, Forer L, Schonherr S, Sidore C, Locke AE, Kwong A, et al. Next-generation genotype imputation service and methods. Nat Genet. 2016;48(10): 1284–7. doi: 10.1038/ng.3656 27571263

42. Iglesias AI, van der Lee SJ, Bonnemaijer PWM, Hohn R, Nag A, Gharahkhani P, et al. Haplotype reference consortium panel: Practical implications of imputations with large reference panels. Hum Mutat. 2017;38(8): 1025–32. doi: 10.1002/humu.23247 28493391

43. Marchini J, Howie B, Myers S, McVean G, Donnelly P. A new multipoint method for genome-wide association studies by imputation of genotypes. Nat Genet. 2007;39(7): 906–13. doi: 10.1038/ng2088 17572673

44. Willer CJ, Li Y, Abecasis GR. METAL: Fast and efficient meta-analysis of genomewide association scans. Bioinformatics. 2010;26(17): 2190–1. doi: 10.1093/bioinformatics/btq340 20616382

45. Yang D, Jang I, Choi J, Kim MS, Lee AJ, Kim H, et al. 3DIV: A 3D-genome Interaction Viewer and database. Nucleic Acids Res. 2018;46(D1): D52–D7. doi: 10.1093/nar/gkx1017 29106613

46. Shin H, Shi Y, Dai C, Tjong H, Gong K, Alber F, et al. TopDom: an efficient and deterministic method for identifying topological domains in genomes. Nucleic Acids Res. 2016;44(7): e70. doi: 10.1093/nar/gkv1505 26704975

47. Venables WN, Ripley BD. Modern Applied Statistics with S. 4th ed. Härdle WK, editor. New York: Springer-Verlag; 2002.

48. Tuomi T, Santoro N, Caprio S, Cai M, Weng J, Groop L. The many faces of diabetes: a disease with increasing heterogeneity. Lancet. 2014;383(9922): 1084–94. doi: 10.1016/S0140-6736(13)62219-9 24315621

49. Skyler JS, Bakris GL, Bonifacio E, Darsow T, Eckel RH, Groop L, et al. Differentiation of diabetes by pathophysiology, natural history, and prognosis. Diabetes. 2017;66(2): 241–55. doi: 10.2337/db16-0806 27980006

50. Di Zazzo E, De Rosa C, Abbondanza C, Moncharmont B. PRDM proteins: Molecular mechanisms in signal transduction and transcriptional regulation. Biology (Basel). 2013;2(1): 107–41. doi: 10.3390/biology2010107 24832654

51. Consortium GT. The Genotype-Tissue Expression (GTEx) project. Nat Genet. 2013;45(6): 580–5. doi: 10.1038/ng.2653 23715323

52. Consortium F, the RP, Clst, Forrest AR, Kawaji H, Rehli M, et al. A promoter-level mammalian expression atlas. Nature. 2014;507(7493): 462–70. doi: 10.1038/nature13182 24670764

53. Carling T, Kim KC, Yang XH, Gu J, Zhang XK, Huang S. A histone methyltransferase is required for maximal response to female sex hormones. Mol Cell Biol. 2004;24(16): 7032–42. doi: 10.1128/MCB.24.16.7032-7042.2004 15282304

54. Liu L, Shao G, Steele-Perkins G, Huang S. The retinoblastoma interacting zinc finger gene RIZ produces a PR domain-lacking product through an internal promoter. J Biol Chem. 1997;272(5): 2984–91. doi: 10.1074/jbc.272.5.2984 9006946

55. Andreu-Vieyra C, Chen R, Matzuk MM. Conditional deletion of the retinoblastoma (Rb) gene in ovarian granulosa cells leads to premature ovarian failure. Mol Endocrinol. 2008;22(9): 2141–61. doi: 10.1210/me.2008-0033 18599617

56. Yang QE, Nagaoka SI, Gwost I, Hunt PA, Oatley JM. Inactivation of Retinoblastoma Protein (Rb1) in the Oocyte: Evidence That Dysregulated Follicle Growth Drives Ovarian Teratoma Formation in Mice. PLoS Genet. 2015;11(7): e1005355. doi: 10.1371/journal.pgen.1005355 26176933

57. Cimino I, Casoni F, Liu X, Messina A, Parkash J, Jamin SP, et al. Novel role for anti-Mullerian hormone in the regulation of GnRH neuron excitability and hormone secretion. Nat Commun. 2016;7: 10055. doi: 10.1038/ncomms10055 26753790

58. Reader KL, Haydon LJ, Littlejohn RP, Juengel JL, McNatty KP. Booroola BMPR1B mutation alters early follicular development and oocyte ultrastructure in sheep. Reprod Fertil Dev. 2012;24(2): 353–61. doi: 10.1071/RD11095 22281082

59. Shimasaki S, Moore RK, Otsuka F, Erickson GF. The bone morphogenetic protein system in mammalian reproduction. Endocr Rev. 2004;25(1): 72–101. doi: 10.1210/er.2003-0007 14769828

60. Estienne A, Pierre A, di Clemente N, Picard JY, Jarrier P, Mansanet C, et al. Anti-Mullerian hormone regulation by the bone morphogenetic proteins in the sheep ovary: deciphering a direct regulatory pathway. Endocrinology. 2015;156(1): 301–13. doi: 10.1210/en.2014-1551 25322464

61. Yi SE, LaPolt PS, Yoon BS, Chen JY, Lu JK, Lyons KM. The type I BMP receptor BmprIB is essential for female reproductive function. Proc Natl Acad Sci U S A. 2001;98(14): 7994–9. doi: 10.1073/pnas.141002798 11416163

62. Sugiura K, Su YQ, Eppig JJ. Does bone morphogenetic protein 6 (BMP6) affect female fertility in the mouse? Biol Reprod. 2010;83(6): 997–1004. doi: 10.1095/biolreprod.110.086777 20702851

63. Gorsic LK, Kosova G, Werstein B, Sisk R, Legro RS, Hayes MG, et al. Pathogenic anti-Mullerian hormone variants in polycystic ovary syndrome. J Clin Endocrinol Metab. 2017;102(8): 2862–72. doi: 10.1210/jc.2017-00612 28505284

64. Gorsic LK, Dapas M, Legro RS, Hayes MG, Urbanek M. Functional Genetic Variation in the Anti-Mullerian Hormone Pathway in Women With Polycystic Ovary Syndrome. J Clin Endocrinol Metab. 2019;104(7): 2855–74. doi: 10.1210/jc.2018-02178 30786001

65. Pruitt KD, Tatusova T, Maglott DR. NCBI Reference Sequence (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res. 2005;33(Database issue): D501–4. doi: 10.1093/nar/gki025 15608248

66. Shi W, Wymore RS, Wang HS, Pan Z, Cohen IS, McKinnon D, et al. Identification of two nervous system-specific members of the erg potassium channel gene family. J Neurosci. 1997;17(24): 9423–32. doi: 10.1523/JNEUROSCI.17-24-09423.1997 9390998

67. Hardy AB, Fox JE, Giglou PR, Wijesekara N, Bhattacharjee A, Sultan S, et al. Characterization of Erg K+ channels in alpha- and beta-cells of mouse and human islets. J Biol Chem. 2009;284(44): 30441–52. doi: 10.1074/jbc.M109.040659 19690348

68. Muhlbauer E, Bazwinsky I, Wolgast S, Klemenz A, Peschke E. Circadian changes of ether-a-go-go-related-gene (Erg) potassium channel transcripts in the rat pancreas and beta-cell. Cell Mol Life Sci. 2007;64(6): 768–80. doi: 10.1007/s00018-007-6478-3 17322986

69. Wang D, Chu M, Wang F, Zhou A, Ruan M, Chen Y. A Genetic Variant in FIGN Gene Reduces the Risk of Congenital Heart Disease in Han Chinese Populations. Pediatr Cardiol. 2017;38(6): 1169–74. doi: 10.1007/s00246-017-1636-3 28534241

70. Wang D, Wang F, Shi KH, Tao H, Li Y, Zhao R, et al. Lower Circulating Folate Induced by a Fidgetin Intronic Variant Is Associated With Reduced Congenital Heart Disease Susceptibility. Circulation. 2017;135(18): 1733–48. doi: 10.1161/CIRCULATIONAHA.116.025164 28302752

71. Desbuquois B, Carre N, Burnol AF. Regulation of insulin and type 1 insulin-like growth factor signaling and action by the Grb10/14 and SH2B1/B2 adaptor proteins. FEBS J. 2013;280(3): 794–816. doi: 10.1111/febs.12080 23190452

72. Kasus-Jacobi A, Perdereau D, Auzan C, Clauser E, Van Obberghen E, Mauvais-Jarvis F, et al. Identification of the rat adapter Grb14 as an inhibitor of insulin actions. J Biol Chem. 1998;273(40): 26026–35. doi: 10.1074/jbc.273.40.26026 9748281

73. Zhao W, Rasheed A, Tikkanen E, Lee JJ, Butterworth AS, Howson JMM, et al. Identification of new susceptibility loci for type 2 diabetes and shared etiological pathways with coronary heart disease. Nat Genet. 2017;49(10): 1450–7. doi: 10.1038/ng.3943 28869590

74. Brodie A, Azaria JR, Ofran Y. How far from the SNP may the causative genes be? Nucleic Acids Res. 2016;44(13): 6046–54. doi: 10.1093/nar/gkw500 27269582

75. Maher MC, Uricchio LH, Torgerson DG, Hernandez RD. Population genetics of rare variants and complex diseases. Hum Hered. 2012;74(3–4): 118–28. doi: 10.1159/000346826 23594490

76. Park JH, Gail MH, Weinberg CR, Carroll RJ, Chung CC, Wang Z, et al. Distribution of allele frequencies and effect sizes and their interrelationships for common genetic susceptibility variants. Proc Natl Acad Sci U S A. 2011;108(44): 18026–31. doi: 10.1073/pnas.1114759108 22003128

77. Goring HH, Terwilliger JD, Blangero J. Large upward bias in estimation of locus-specific effects from genomewide scans. Am J Hum Genet. 2001;69(6): 1357–69. doi: 10.1086/324471 11593451

78. Kraft P. Curses—winner's and otherwise—in genetic epidemiology. Epidemiology. 2008;19(5): 649–51. doi: 10.1097/EDE.0b013e318181b865 18703928

79. McAllister JM, Modi B, Miller BA, Biegler J, Bruggeman R, Legro RS, et al. Overexpression of a DENND1A isoform produces a polycystic ovary syndrome theca phenotype. Proc Natl Acad Sci U S A. 2014;111(15): E1519–27. doi: 10.1073/pnas.1400574111 24706793

80. Tee MK, Speek M, Legeza B, Modi B, Teves ME, McAllister JM, et al. Alternative splicing of DENND1A, a PCOS candidate gene, generates variant 2. Mol Cell Endocrinol. 2016;434: 25–35. doi: 10.1016/j.mce.2016.06.011 27297658

81. Moran L, Teede H. Metabolic features of the reproductive phenotypes of polycystic ovary syndrome. Hum Reprod Update. 2009;15(4): 477–88. doi: 10.1093/humupd/dmp008 19279045

82. Fauser BC, Tarlatzis BC, Rebar RW, Legro RS, Balen AH, Lobo R, et al. Consensus on women's health aspects of polycystic ovary syndrome (PCOS): the Amsterdam ESHRE/ASRM-Sponsored 3rd PCOS Consensus Workshop Group. Fertil Steril. 2012;97(1): 28–38 e25. doi: 10.1016/j.fertnstert.2011.09.024 22153789

83. Essah PA, Nestler JE, Carmina E. Differences in dyslipidemia between American and Italian women with polycystic ovary syndrome. J Endocrinol Invest. 2008;31(1): 35–41. doi: 10.1007/BF03345564 18296903

84. Carmina E, Koyama T, Chang L, Stanczyk FZ, Lobo RA. Does ethnicity influence the prevalence of adrenal hyperandrogenism and insulin resistance in polycystic ovary syndrome? Am J Obstet Gynecol. 1992;167(6): 1807–12. doi: 10.1016/0002-9378(92)91779-a 1471702

85. Guo M, Chen ZJ, Eijkemans MJ, Goverde AJ, Fauser BC, Macklon NS. Comparison of the phenotype of Chinese versus Dutch Caucasian women presenting with polycystic ovary syndrome and oligo/amenorrhoea. Hum Reprod. 2012;27(5): 1481–8. doi: 10.1093/humrep/des018 22402209

86. Louwers YV, Lao O, Fauser BC, Kayser M, Laven JS. The impact of self-reported ethnicity versus genetic ancestry on phenotypic characteristics of polycystic ovary syndrome (PCOS). J Clin Endocrinol Metab. 2014;99(10): E2107–16. doi: 10.1210/jc.2014-1084 24960542

87. Dunaif A, Sorbara L, Delson R, Green G. Ethnicity and polycystic ovary syndrome are associated with independent and additive decreases in insulin action in Caribbean-Hispanic women. Diabetes. 1993;42(10): 1462–8. doi: 10.2337/diab.42.10.1462 8375585

88. Engmann L, Jin S, Sun F, Legro RS, Polotsky AJ, Hansen KR, et al. Racial and ethnic differences in the polycystic ovary syndrome metabolic phenotype. Am J Obstet Gynecol. 2017;216(5): 493 e1–e13.

89. Zhang W, Collins A, Gibson J, Tapper WJ, Hunt S, Deloukas P, et al. Impact of population structure, effective bottleneck time, and allele frequency on linkage disequilibrium maps. Proc Natl Acad Sci U S A. 2004;101(52): 18075–80. doi: 10.1073/pnas.0408251102 15604137

90. McCarthy MI. The importance of global studies of the genetics of type 2 diabetes. Diabetes Metab J. 2011;35(2): 91–100. doi: 10.4093/dmj.2011.35.2.91 21738890

91. Goyal M, Dawood AS. Debates Regarding Lean Patients with Polycystic Ovary Syndrome: A Narrative Review. J Hum Reprod Sci. 2017;10(3): 154–61. doi: 10.4103/jhrs.JHRS_77_17 29142442

92. Stovall DW, Bailey AP, Pastore LM. Assessment of insulin resistance and impaired glucose tolerance in lean women with polycystic ovary syndrome. J Womens Health (Larchmt). 2011;20(1): 37–43.

93. Caglar GS, Kahyaoglu I, Pabuccu R, Demirtas S, Seker R. Anti-Mullerian hormone and insulin resistance in classic phenotype lean PCOS. Arch Gynecol Obstet. 2013;288(4): 905–10. doi: 10.1007/s00404-013-2833-9 23553200

94. Keskin Kurt R, Okyay AG, Hakverdi AU, Gungoren A, Dolapcioglu KS, Karateke A, et al. The effect of obesity on inflammatory markers in patients with PCOS: a BMI-matched case-control study. Arch Gynecol Obstet. 2014;290(2): 315–9. doi: 10.1007/s00404-014-3199-3 24643802

95. Morciano A, Romani F, Sagnella F, Scarinci E, Palla C, Moro F, et al. Assessment of insulin resistance in lean women with polycystic ovary syndrome. Fertil Steril. 2014;102(1): 250–6 e3. doi: 10.1016/j.fertnstert.2014.04.004 24825420

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PLOS Medicine

2020 Číslo 6
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