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Pathway Analysis Based on a Genome-Wide Association Study of Polycystic Ovary Syndrome

  • Unjin Shim,

    Affiliation Department of Internal Medicine, Seoul Seonam Hospital, Ewha Womans University Medical Center, Seoul, Korea

  • Han-Na Kim,

    Affiliation Department of Biochemistry, Ewha Womans University School of Medicine, Seoul, Korea

  • Hyejin Lee,

    Affiliation Department of Internal Medicine, Ewha Womans University School of Medicine, Seoul, Korea

  • Jee-Young Oh,

    Affiliation Department of Internal Medicine, Ewha Womans University School of Medicine, Seoul, Korea

  • Yeon-Ah Sung ,

    Contributed equally to this work with: Yeon-Ah Sung, Hyung-Lae Kim (YAS); (HLK)

    Affiliation Department of Internal Medicine, Ewha Womans University School of Medicine, Seoul, Korea

  • Hyung-Lae Kim

    Contributed equally to this work with: Yeon-Ah Sung, Hyung-Lae Kim (YAS); (HLK)

    Affiliation Department of Biochemistry, Ewha Womans University School of Medicine, Seoul, Korea



Polycystic ovary syndrome (PCOS) is one of the most common endocrine disorders in women of reproductive age, and it is affected by both environmental and genetic factors. Although the genetic component of PCOS is evident, studies aiming to identify susceptibility genes have shown controversial results. This study conducted a pathway-based analysis using a dataset obtained through a genome-wide association study (GWAS) to elucidate the biological pathways that contribute to PCOS susceptibility and the associated genes.


We used GWAS data on 636,797 autosomal single nucleotide polymorphisms (SNPs) from 1,221 individuals (432 PCOS patients and 789 controls) for analysis. A pathway analysis was conducted using meta-analysis gene-set enrichment of variant associations (MAGENTA). Top-ranking pathways or gene sets associated with PCOS were identified, and significant genes within the pathways were analyzed.


The pathway analysis of the GWAS dataset identified significant pathways related to oocyte meiosis and the regulation of insulin secretion by acetylcholine and free fatty acids (all nominal gene-set enrichment analysis (GSEA) P-values < 0.05). In addition, INS, GNAQ, STXBP1, PLCB3, PLCB2, SMC3 and PLCZ1 were significant genes observed within the biological pathways (all gene P-values < 0.05).


By applying MAGENTA pathway analysis to PCOS GWAS data, we identified significant pathways and candidate genes involved in PCOS. Our findings may provide new leads for understanding the mechanisms underlying the development of PCOS.


Polycystic ovary syndrome (PCOS) is one of the most common endocrine disorders in women of reproductive age, and it is characterized by chronic oligo-anovulation, clinical and/or biochemical hyperandrogenism and polycystic ovaries [1]. PCOS is a heterogeneous disorder with reproductive and metabolic phenotypes [2]. Studies have revealed family clustering in PCOS, suggesting genetic factors for the condition. Among first-degree relatives of women with PCOS, there is an increased prevalence of type 2 diabetes and androgen excess [3, 4]. In addition, the heritability of PCOS has been identified in a twin study [5].

Previous genetic studies on PCOS have been based mainly on candidate gene identification, which has revealed many of the genes involved in insulin expression and steroidogenesis [6, 7]. However, results for only a few of these genes have been confirmed through association studies, and the adopted approaches currently focus on the identification of susceptibility loci through genome-wide association studies (GWAS).

GWAS is a powerful, unbiased method for screening susceptible genes associated with complex diseases [8, 9]. The first GWAS of PCOS was performed in Han Chinese women and identified important susceptibility single nucleotide polymorphisms (SNPs) on chromosomes 2p16.3, 2p21 and 9p33.3; these SNPs were located in genes that included the thyroid adenoma-associated gene (THADA), DENN/MADD domain-containing 1A (DENND1A) and luteinizing hormone/choriogonadotropin receptor (LHCGR) [10]. In a study with a larger Han Chinese cohort, eight new loci were discovered [11]. In Korea, a GWAS of PCOS identified one novel locus with genome-wide significance on chromosome 8q24.2, located upstream of KHDRBS3 (KH domain containing, RNA binding, signal transduction associated 3) associated with telomerase activity [12, 13].

One strength of GWAS is the ability to discover significant SNPs and novel genes associated with a disease. However, GWAS studies primarily focus on individual SNPs that meet a stringent significance criterion, neglecting the interplay of genes. Additionally, most identified SNPs lack functional relevance, explaining only a small portion of genetic heritability [14, 15]. The method also ignores the genetic interactions of complex diseases, and biological function cannot be determined. In GWAS, significant SNPs related to a certain disease may not be identified in other studies of the same disease because of their small effect size. To overcome this limitation, pathway-based approaches have been introduced and applied to GWAS datasets to further elucidate the pathogenesis of diseases [16].

The pathway-based approach integrates GWAS results with genes in biological pathways or gene sets from predefined human databases, ranking all genes according to their statistical significance [15, 17]. This method generates larger effect sizes, showing increased power to detect genes that may have been missed through GWAS, which improves the interpretability of genetic studies [17, 18]. In addition, because this approach can utilize genomic data to the maximal extent, unexpected or undetermined interactions of genes within a disease can also be identified. By applying pathway analysis to GWAS datasets, biological pathways associated with Crohn’s disease and common inflammatory pathways related to type 1 diabetes and rheumatoid arthritis can be discovered [19, 20].

Identifying the genetic pathways involved in PCOS may provide a more contextualized understanding of the mechanism underlying PCOS. The aim of this study was to use a pathway-based analysis of a GWAS dataset to elucidate the biological pathways involved in PCOS and the associated genes.

Study Methods


The pathway analysis was conducted using a PCOS GWAS dataset that we generated previously. The dataset included data from 1,000 patients with PCOS and 1,000 controls. This study was performed in the Endocrinology and Gynecology Clinics of Ewha Womans University Hospital from December 2008 through November 2010. PCOS was diagnosed using the National Institutes of Health (NIH) criteria, which define the disorder as the presence of chronic oligo-anovulation and clinical and/or biochemical hyperandrogenism; the NIH criteria exclude other disorders, such as Cushing syndrome, adult-onset congenital adrenal hyperplasia and androgen secreting neoplasm [21]. Oligo-anovulation was defined as fewer than eight menstrual cycles per year. Biochemical hyperandrogenemia was defined as a total or free testosterone level above the 95th percentile (total testosterone ≥ 67 ng/dL or free testosterone ≥ 0.84 ng/dL) based on the testosterone levels recorded in 1,120 healthy, regularly cycling women [22]. Clinical hyperandrogenism was evaluated based on the presence of hirsutism, defined as a modified Ferriman-Gallwey (mFG) score of 3 or above, which is the cutoff value for East Asian women recommended by the Androgen Excess and Polycystic Ovary Syndrome Society [23, 24].

Anthropometric, biochemical and hormonal measurements

Weight and height were measured in all subjects, and body mass index (BMI) was calculated (kg/m2). Waist circumference was measured to the nearest 0.1 cm on bare skin during mid-respiration at the narrowest indentation between the tenth rib and the iliac crest. Systolic and diastolic blood pressures were also measured. Hirsutism was assessed by a single trained nurse using the mFG scoring method.

After an overnight fast of at least 8 hours, a venous blood sample was obtained from each subject on the third day of the follicular phase of the menstrual cycle. Standardized enzymatic methods were used to analyze lipid profiles, including serum total cholesterol, high-density lipoprotein (HDL) cholesterol and triglyceride levels. For evaluation of glucose tolerance, a standard 75 g oral glucose tolerance test (OGTT) was performed in all subjects after an overnight fast to determine fasting plasma glucose and 2-hour post-load glucose. Total testosterone levels were measured using the chemiluminescent immunoassay method (commercial kit, Siemens, New York, NY, USA), and sex hormone-binding globulin (SHBG) levels were measured using immunoradiometric assays (commercial kit, Diagnostic Products Corporation, Los Angeles, CA, USA). Using the formula from the International Society for the Study of the Aging Male (, free testosterone levels were calculated using total testosterone, SHBG and albumin levels [22].

The institutional review board of Ewha Womans University Mokdong Hospital approved the study. Written informed consent was obtained from all participants.

GWAS dataset analyses

Genomic DNA was extracted from individual peripheral blood samples and genotyped in 2,000 samples using the Illumina HumanOmni1-Quad v1 BeadChip (Illumina Inc., San Diego, CA, USA). Quality control (QC) procedures were applied using PLINK version 1.07 [25], excluding the samples through the following properties: genotyping calls < 95%, heterozygosity > 30%, markers with high missing call rate > 1%, minor allele frequency < 0.05 and significant deviation from Hardy-Weinberg equilibrium < 1 x 10−6. A total of 636,797 autosomal SNPs representing 1,922 individuals were obtained after the QC procedures. After excluding individuals with PCOS who did not satisfy the NIH diagnostic criteria, the data from 1,221 individuals (432 women with PCOS and 789 controls) were available. Additive models were used for analysis.

Pathway-based analysis

A pathway analysis was conducted using meta-analysis gene-set enrichment of variant associations (MAGENTA) ( to identify biological pathways or gene sets associated with PCOS [26]. MAGENTA implements gene-set enrichment analysis (GSEA) associated with GWAS data through pathway annotations from the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Protein Analysis Through Evolutionary Relationships (PANTHER), BioCarta and Reactome databases, which are web-based databases included in the Pathguide ( online resource.

The analytical steps of MAGENTA are as follows [26]. SNP association P-values and chromosome positions obtained from GWAS are mapped with genes that are located at a predetermined boundary. Gene scoring based on regional SNP P-values is completed, and SNPs with the most significant P-value within the predefined boundary (called the “best SNP P-value”) are selected. Gene scores are then corrected for confounders, including gene size, SNP numbers and linkage disequilibrium-related properties. Gene sets enriched with highly ranked gene scores are analyzed with the selected biological pathway or gene sets, and gene-set enrichment P-values are calculated. Additional information, including the 95th and 75th percentile cutoffs, the names of genes within each pathway or gene set, and the nominal GSEA P-value and false discovery rate (FDR), is analyzed through multiple test correction. Because the 75th percentile cutoff demonstrates greater power in interpreting complex diseases with high polygenesis, we used this cutoff value for interpretation [26, 27]. After identifying the top-ranking biological pathways or gene sets associated with PCOS, significant genes that were observed within the identified pathways were further analyzed. Genes showing P-values of less than 0.05 were considered to be significant genes involved in the selected pathways or gene sets.


The clinical and biochemical characteristics of the women with PCOS and controls included in this study are shown in Table 1. The women with PCOS were younger than the controls, and their metabolic profiles, including BMI, waist circumference, systolic and diastolic blood pressure, total cholesterol, triglycerides, and fasting and post-load 2-hour glucose levels, were higher compared with the controls.

Table 1. Clinical and biochemical characteristics of the women with polycystic ovary syndrome and controls included in this study.

The top ten significant biological pathways or gene sets associated with PCOS are displayed in Table 2. Pathways related to ovulation and insulin secretion, including oocyte meiosis (KEGG), the regulation of insulin secretion by acetylcholine (ACh) (Reactome) and the regulation of insulin secretion by free fatty acids (FFAs) (Reactome), were the top-ranking pathways associated with PCOS. Other pathways were also identified (all nominal GSEA P-values < 0.05), including neural tube closure (GO term), other kinases (PANTHER), the calcium signaling pathway (KEGG), acyltransferase (PANTHER), the negative regulation of osteoclast differentiation (GO term), cytoskeletal protein binding (GO term) and developmental processes (PANTHER). The FDR values for oocyte meiosis, the regulation of insulin secretion by ACh and FFAs and calcium signaling pathways were 0.078, 0.152, 0.110 and 0.222, respectively.

Table 2. Top ten significant biological pathways or gene sets associated with polycystic ovary syndrome.

The genes involved in the biological pathways were further evaluated. The significant genes involved in the pathway of oocyte meiosis were SMC3 (structural maintenance of chromosome 3), CCNE2 (cyclin E2), PPP2R5D (protein phosphatase 2, regulatory subunit B, delta), INS (insulin), PPP2R5C (protein phosphatase 2, regulatory subunit B, gamma), PLCZ1 (phospholipase C, zeta 1), PPP2R5A (protein phosphatase 2, regulatory subunit B, alpha), PPP1CB (protein phosphatase 1, catalytic subunit, beta isozyme) and SPDYA (speedy/RINGO cell cycle regulator family member A) (all gene P-values < 0.05). The genes INS, STXBP1 (syntaxin binding protein 1), PLCB3 (phospholipase C, beta 3), GNAQ (guanine nucleotide binding protein, q polypeptide) and PLCB2 (phospholipase C, beta 2) were identified in the pathways related to the regulation of insulin secretion by ACh and the regulation of insulin secretion by FFAs (all gene P-values < 0.05) (Table 3). In the calcium signaling pathway, genes such as LHCGR, PLCB3, PLCZ1, GNAQ, EGFR (epidermal growth factor receptor) and PLCB2 were significant. Detailed information on the genes identified in other biological pathways is shown in S1 Table. All pathway information was downloaded from the Pathguide online resource.

Table 3. Significant genes within the biological pathways or gene sets associated with polycystic ovary syndrome.


In this study, a pathway-based approach was applied to a GWAS dataset of patients with PCOS. The study identified significant pathways involved in ovulation and insulin secretion, including oocyte meiosis and the regulation of insulin secretion by ACh and FFAs.

Pathway analysis is a post-GWAS analysis method that can be applied to further interpret GWAS results. Early pathway-based approaches employed raw genotype data for GSEA, which are not provided in all GWAS, and required intensive computational permutations [16]. To simplify the application of GSEA to GWAS data, pathway approaches using SNP P-values such as MAGENTA have been introduced; these approaches analyze the statistical power of GWAS by integrating the P-values for variant associations into gene scores [26]. Through MAGENTA pathway analysis, important pathways associated with triglycerides, low-density lipoprotein (LDL) cholesterol, BMI and type 2 diabetes can be identified [26, 27].

In the present study, oocyte meiosis was identified as the top-ranking biological pathway associated with PCOS. Oocyte quality, maturation and fertilization are affected by factors such as hyperandrogenemia and insulin resistance, which are important phenotypes of PCOS and can lead to premature follicular luteinization and anovulation [28, 29]. The regulation of insulin secretion by ACh was another top-ranking biological pathway associated with PCOS. Pancreatic ß-cells are regulated by various hormones and neurotransmitters; ACh is an important neurotransmitter that is released by intrapancreatic nerve endings and promotes glucose-stimulated insulin secretion through muscarinic ACh receptors [30]. Variation in this biological pathway could result in abnormal insulin regulation and glucose intolerance, which are important phenotypes of PCOS. The biological pathway related to the regulation of insulin secretion by FFAs was also associated with PCOS. Chronic FFA exposure can have a detrimental effect on insulin secretion and ß-cell function, with elevated FFA levels enhancing hepatic gluconeogenesis and insulin resistance in the liver and peripheral tissues [31]. In addition, obesity can increase fat deposition in islet cells, leading to insulin resistance and hyperinsulinemia, which are important metabolic features of PCOS [32].

INS was observed in all three top ranking pathways associated with PCOS. Previous studies showed an association of this gene with insulin resistance, obesity and type 2 diabetes through variation of the VNTR (variable number of tandem repeats) locus at class III allele [3335]. INS was also associated with anovulation in PCOS, although there are conflicting studies [3638]. These inconsistencies could be due to different diagnostic criteria used for PCOS, as well as different study groups or ethnicities. In our study, we used the NIH criteria for PCOS, which is a strict diagnostic method compared to Rotterdam or Androgen Excess Society criteria [21]. Severe metabolic abnormalities are seen in this group; studies show worse phenotypes for metabolic profiles and higher insulin resistance compared to non-NIH groups [2, 39, 40]. More studies on NIH-PCOS groups will be needed to further elucidate the association between INS and PCOS.

From the genes identified in the pathway of regulation of insulin secretion by acetylcholine and FFA, GNAQ, a Gq protein encoding gene, is a known candidate gene of PCOS that mediates the insulin induced translocation of GLUT4 in adipocytes and is associated with insulin resistance and obesity in PCOS [41]. Other genes such as STXBP1, PLCB2 and PLCB3 have not been identified in PCOS yet. However, published studies have demonstrated abnormal expression in these genes, leading to abnormal insulin secretion and disordered glucose homeostasis [42, 43].

Calcium signaling pathway might have an association with androgen excess. Calcium is crucial in gonadotropin secretion, and studies have shown that calcium signaling is affected by androgen levels [44, 45]. LHCGR was identified in this pathway, which is a known susceptibility loci of PCOS discovered through GWAS, having an association with hyperandrogenism [10, 46, 47]. Other genes such as EGFR and PLCZ1 were also observed in this pathway. Abnormal expression of EGFR was related with oocyte incompetence in PCOS women [48]. PLCZ1 is expressed in sperm, and variations of this gene lead to low fertilization and male infertility [49, 50]. Variation in these genes could be the cause leading to androgen excess in PCOS, although extensive studies proving this association are necessary.

Although there is a lack of studies on the genes identified in the biological pathways of oocyte meiosis and PCOS, many genes have been studied in other human diseases. Mutation of SMC3 is related with Cornelia de Lange syndrome, characterized by features such as growth and mental retardation with abnormal limb formation, and associated with the development of atopic asthma and myeloid neoplasms [5155]. Inactivation of PPP1CB caused chronic lymphocytic leukemia, whereas CCNE2 was related to the development of non-small cell lung cancer and breast cancer [5658]. Mutations in the PP2A regulatory subunit B family of genes resulted in features associated with overgrowth, and because it is an important gene in the phosphorylation of tau protein, which is crucial in neurofibrillary tangle formation, it could lead to Alzheimer’s disease [5961].

We used a pathway-based approach to identify multiple biological pathways or gene sets that are involved in the pathogenesis of PCOS. To our knowledge, this was the first GWAS dataset-based pathway analysis study to be conducted for PCOS. One of the strengths of this study is that the subjects were accurately selected, and homogenous PCOS groups were recruited using well-defined diagnostic criteria. Although the identified pathways did not show an FDR of less than 0.05, significant pathways associated with ovulation and insulin secretion were discovered at an FDR of less than 0.2. Because ovulatory dysfunction and abnormal insulin secretion are major features of PCOS, the biological pathways identified in this study may be important. However, validation of these pathways using other pathway approaches will be necessary.

There are some limitations of this study. First, the number of women with PCOS included in the GWAS dataset is relatively small. Second, the pathway analysis tools applied in the study are biased toward detecting well-defined pathways. However, the majority of the genes in the genome are relatively unknown, and their biological function still needs to be established. Third, our study is confined to Korean women only. Because different phenotypes of PCOS are seen in women with different ethnic backgrounds, our results may not be generalizable to other ethnic groups. However, genes such as LHCGR have been identified as susceptibility loci in Han Chinese, Hui Chinese and Egyptian populations [10, 57, 62]. Therefore, similar biological pathways and genes may be found in these ethnicities, although pathway analysis will be required.

In conclusion, by applying pathway analysis to a GWAS dataset for PCOS, significant biological pathways and genes associated with ovulation and insulin secretion were identified. Our results may contribute to understanding the mechanisms underlying PCOS.

Supporting Information

S1 Table. Significant genes within other biological pathways or gene sets associated with polycystic ovary syndrome.


Author Contributions

Conceived and designed the experiments: UJS HNK HJL JYO YAS HLK. Performed the experiments: UJS HNK HJL JYO YAS HLK. Analyzed the data: UJS HNK HJL JYO YAS HLK. Contributed reagents/materials/analysis tools: UJS HNK HJL JYO YAS HLK. Wrote the paper: UJS.


  1. 1. Azziz R, Woods KS, Reyna R, Key TJ, Knochenhauer ES, Yildiz BO. The prevalence and features of the polycystic ovary syndrome in an unselected population. J Clin Endocrinol Metab. 2004;89(6):2745–9. pmid:15181052
  2. 2. Moran L, Teede H. Metabolic features of the reproductive phenotypes of polycystic ovary syndrome. Hum Reprod Update. 2009;15(4):477–88. pmid:19279045
  3. 3. Ehrmann DA, Kasza K, Azziz R, Legro RS, Ghazzi MN, Group PCTS. Effects of race and family history of type 2 diabetes on metabolic status of women with polycystic ovary syndrome. J Clin Endocrinol Metab. 2005;90(1):66–71. pmid:15507516
  4. 4. Legro RS, Driscoll D, Strauss JF III, 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. pmid:9843997
  5. 5. Vink JM, Sadrzadeh S, Lambalk CB, Boomsma DI. Heritability of polycystic ovary syndrome in a Dutch twin-family study. J Clin Endocrinol Metab. 2006;91(6):2100–4. pmid:16219714
  6. 6. Franks S, Gharani N, McCarthy M. Candidate genes in polycystic ovary syndrome. Hum Reprod Update. 2001;7(4):405–10. pmid:11476353
  7. 7. Simoni M, Tempfer CB, Destenaves B, Fauser BC. Functional genetic polymorphisms and female reproductive disorders: Part I: Polycystic ovary syndrome and ovarian response. Hum Reprod Update. 2008;14(5):459–84. pmid:18603647
  8. 8. Hirschhorn JN, Daly MJ. Genome-wide association studies for common diseases and complex traits. Nat Rev Genet. 2005;6(2):95–108. pmid:15716906
  9. 9. de Bakker PI, Yelensky R, Pe'er I, Gabriel SB, Daly MJ, Altshuler D. Efficiency and power in genetic association studies. Nat Genet. 2005;37(11):1217–23. pmid:16244653
  10. 10. 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. pmid:21151128
  11. 11. 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. pmid:22885925
  12. 12. Zhang L, Guo L, Peng Y, Chen B. Expression of T-STAR gene is associated with regulation of telomerase activity in human colon cancer cell line HCT-116. World J Gastroenterol. 2006;12(25):4056–60. pmid:16810759
  13. 13. Lee H, Oh JY, Sung YA, Chung H, Kim HL, Kim GS, et al. Genome-wide association study identified new susceptibility loci for polycystic ovary syndrome. Hum Reprod. 2015;30(3):723–31. pmid:25574032
  14. 14. Stringer S, Wray NR, Kahn RS, Derks EM. Underestimated effect sizes in GWAS: fundamental limitations of single SNP analysis for dichotomous phenotypes. PLoS One. 2011;6(11):e27964. pmid:22140493
  15. 15. Cantor RM, Lange K, Sinsheimer JS. Prioritizing GWAS results: A review of statistical methods and recommendations for their application. Am J Hum Genet. 2010;86(1):6–22. pmid:20074509
  16. 16. Wang K, Li M, Hakonarson H. Analysing biological pathways in genome-wide association studies. Nat Rev Genet. 2010;11(12):843–54. pmid:21085203
  17. 17. Ramanan VK, Shen L, Moore JH, Saykin AJ. Pathway analysis of genomic data: concepts, methods, and prospects for future development. Trends Genet. 2012;28(7):323–32. pmid:22480918
  18. 18. Shahbaba B, Shachaf CM, Yu Z. A pathway analysis method for genome-wide association studies. Stat Med. 2012;31(10):988–1000. pmid:22302470
  19. 19. Wang K, Zhang H, Kugathasan S, Annese V, Bradfield JP, Russell RK, et al. Diverse genome-wide association studies associate the IL12/IL23 pathway with Crohn Disease. Am J Hum Genet. 2009;84(3):399–405. pmid:19249008
  20. 20. Eleftherohorinou H, Wright V, Hoggart C, Hartikainen AL, Jarvelin MR, Balding D, et al. Pathway analysis of GWAS provides new insights into genetic susceptibility to 3 inflammatory diseases. PLoS One. 2009;4(11):e8068. pmid:19956648
  21. 21. Zawadzki J, Dunaif A. Diagnostic criteria for polycystic ovary syndrome: towards a rational approach. Boston: Blackwell Scientific; 1992.
  22. 22. Vermeulen A, Verdonck L, Kaufman JM. A critical evaluation of simple methods for the estimation of free testosterone in serum. J Clin Endocrinol Metab. 1999;84(10):3666–72. pmid:10523012
  23. 23. Hatch R, Rosenfield RL, Kim MH, Tredway D. Hirsutism: implications, etiology, and management. Am J Obstet Gynecol. 1981;140(7):815–30. pmid:7258262
  24. 24. Escobar-Morreale HF, Carmina E, Dewailly D, Gambineri A, Kelestimur F, Moghetti P, et al. Epidemiology, diagnosis and management of hirsutism: a consensus statement by the Androgen Excess and Polycystic Ovary Syndrome Society. Hum Reprod Update. 2012;18(2):146–70. pmid:22064667
  25. 25. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81(3):559–75. pmid:17701901
  26. 26. Segre AV, Consortium D, investigators M, Groop L, Mootha VK, Daly MJ, et al. Common inherited variation in mitochondrial genes is not enriched for associations with type 2 diabetes or related glycemic traits. PLoS Genet. 2010;6(8).
  27. 27. Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G, Jackson AU, et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet. 2010;42(11):937–48. pmid:20935630
  28. 28. Dumesic DA, Abbott DH. Implications of polycystic ovary syndrome on oocyte development. Semin Reprod Med. 2008;26(1):53–61. pmid:18181083
  29. 29. Willis DS, Watson H, Mason HD, Galea R, Brincat M, Franks S. Premature response to luteinizing hormone of granulosa cells from anovulatory women with polycystic ovary syndrome: relevance to mechanism of anovulation. J Clin Endocrinol Metab. 1998;83(11):3984–91. pmid:9814480
  30. 30. Selway JL, Moore CE, Mistry R, John Challiss RA, Herbert TP. Molecular mechanisms of muscarinic acetylcholine receptor-stimulated increase in cytosolic free Ca(2+) concentration and ERK1/2 activation in the MIN6 pancreatic beta-cell line. Acta Diabetol. 2012;49(4):277–89. pmid:21833779
  31. 31. Lam TK, van de Werve G, Giacca A. Free fatty acids increase basal hepatic glucose production and induce hepatic insulin resistance at different sites. Am J Physiol Endocrinol Metab. 2003;284(2):E281–90. pmid:12531742
  32. 32. Mohlig M, Weickert MO, Ghadamgadai E, Machlitt A, Pfuller B, Arafat AM, et al. Adipocyte fatty acid-binding protein is associated with markers of obesity, but is an unlikely link between obesity, insulin resistance, and hyperandrogenism in polycystic ovary syndrome women. Eur J Endocrinol. 2007;157(2):195–200. pmid:17656598
  33. 33. San Millan JL, Corton M, Villuendas G, Sancho J, Peral B, Escobar-Morreale HF. Association of the polycystic ovary syndrome with genomic variants related to insulin resistance, type 2 diabetes mellitus, and obesity. J Clin Endocrinol Metab. 2004;89(6):2640–6. pmid:15181035
  34. 34. Liu M, Sun J, Cui J, Chen W, Guo H, Barbetti F, et al. INS-gene mutations: from genetics and beta cell biology to clinical disease. Mol Aspects Med. 2015;42:3–18. pmid:25542748
  35. 35. Carmody D, Park SY, Ye H, Perrone ME, Alkorta-Aranburu G, Highland HM, et al. Continued lessons from the INS gene: an intronic mutation causing diabetes through a novel mechanism. J Med Genet. 2015.
  36. 36. Ferk P, Perme MP, Gersak K. Insulin gene polymorphism in women with polycystic ovary syndrome. J Int Med Res. 2008;36(6):1180–7. pmid:19094425
  37. 37. Skrgatic L, Baldani DP, Gersak K, Cerne JZ, Ferk P, Coric M. Genetic polymorphisms of INS, INSR and IRS-1 genes are not associated with polycystic ovary syndrome in Croatian women. Coll Antropol. 2013;37(1):141–6. pmid:23697264
  38. 38. Yun JH, Gu BH, Kang YB, Choi BC, Song S, Baek KH. Association between INS-VNTR polymorphism and polycystic ovary syndrome in a Korean population. Gynecol Endocrinol. 2012;28(7):525–8. pmid:22468791
  39. 39. Wiltgen D, Spritzer PM. Variation in metabolic and cardiovascular risk in women with different polycystic ovary syndrome phenotypes. Fertil Steril. 2010;94(6):2493–6. pmid:20338557
  40. 40. Zhao Y, Qiao J. Ethnic differences in the phenotypic expression of polycystic ovary syndrome. Steroids. 2013;78(8):755–60. pmid:23624030
  41. 41. Klenke S, Tan S, Hahn S, Mann K, Hauner H, Manthey I, et al. A functional GNAQ promoter haplotype is associated with altered Gq expression and with insulin resistance and obesity in women with polycystic ovary syndrome. Pharmacogenet Genomics. 2010;20(8):476–84. pmid:20562673
  42. 42. Spurlin BA, Park SY, Nevins AK, Kim JK, Thurmond DC. Syntaxin 4 transgenic mice exhibit enhanced insulin-mediated glucose uptake in skeletal muscle. Diabetes. 2004;53(9):2223–31. pmid:15331531
  43. 43. Zawalich WS, Bonnet-Eymard M, Zawalich KC. Signal transduction in pancreatic beta-cells: regulation of insulin secretion by information flow in the phospholipase C/protein kinase C pathway. Front Biosci. 1997;2:d160–72. pmid:9159224
  44. 44. Ortmann O, Tomic M, Weiss JM, Diedrich K, Stojilkovic SS. Dual action of androgen on calcium signaling and luteinizing hormone secretion in pituitary gonadotrophs. Cell Calcium. 1998;24(3):223–31. pmid:9883276
  45. 45. Tse FW, Tse A, Hille B, Horstmann H, Almers W. Local Ca2+ release from internal stores controls exocytosis in pituitary gonadotrophs. Neuron. 1997;18(1):121–32. pmid:9010210
  46. 46. Ha L, Shi Y, Zhao J, Li T, Chen ZJ. Association Study between Polycystic Ovarian Syndrome and the Susceptibility Genes Polymorphisms in Hui Chinese Women. PLoS One. 2015;10(5):e0126505. pmid:25978310
  47. 47. Chen RM, Zhang Y, Yang XH, Lin XQ. Analysis of a family affected with familial male-limited precocious puberty due to a Ala568Val mutation in LHCGR gene. Zhonghua Yi Xue Yi Chuan Xue Za Zhi. 2012;29(6):631–4. pmid:23225038
  48. 48. Haouzi D, Assou S, Monzo C, Vincens C, Dechaud H, Hamamah S. Altered gene expression profile in cumulus cells of mature MII oocytes from patients with polycystic ovary syndrome. Hum Reprod. 2012;27(12):3523–30. pmid:22951915
  49. 49. Nomikos M, Swann K, Lai FA. Starting a new life: sperm PLC-zeta mobilizes the Ca2+ signal that induces egg activation and embryo development: an essential phospholipase C with implications for male infertility. Bioessays. 2012;34(2):126–34. pmid:22086556
  50. 50. Amdani SN, Jones C, Coward K. Phospholipase C zeta (PLCzeta): oocyte activation and clinical links to male factor infertility. Adv Biol Regul. 2013;53(3):292–308. pmid:23916605
  51. 51. Ansari M, Poke G, Ferry Q, Williamson K, Aldridge R, Meynert AM, et al. Genetic heterogeneity in Cornelia de Lange syndrome (CdLS) and CdLS-like phenotypes with observed and predicted levels of mosaicism. J Med Genet. 2014;51(10):659–68. pmid:25125236
  52. 52. Gil-Rodriguez MC, Deardorff MA, Ansari M, Tan CA, Parenti I, Baquero-Montoya C, et al. De novo heterozygous mutations in SMC3 cause a range of Cornelia de Lange syndrome-overlapping phenotypes. Hum Mutat. 2015;36(4):454–62. pmid:25655089
  53. 53. Cheng Q, Huang W, Chen N, Shang Y, Zhang H. SMC3 may play an important role in atopic asthma development. Clin Respir J. 2014.
  54. 54. Kon A, Shih LY, Minamino M, Sanada M, Shiraishi Y, Nagata Y, et al. Recurrent mutations in multiple components of the cohesin complex in myeloid neoplasms. Nat Genet. 2013;45(10):1232–7. pmid:23955599
  55. 55. Solomon DA, Kim JS, Waldman T. Cohesin gene mutations in tumorigenesis: from discovery to clinical significance. BMB Rep. 2014;47(6):299–310. pmid:24856830
  56. 56. Velusamy T, Palanisamy N, Kalyana-Sundaram S, Sahasrabuddhe AA, Maher CA, Robinson DR, et al. Recurrent reciprocal RNA chimera involving YPEL5 and PPP1CB in chronic lymphocytic leukemia. Proc Natl Acad Sci U S A. 2013;110(8):3035–40. pmid:23382248
  57. 57. Chen D, Guo W, Qiu Z, Wang Q, Li Y, Liang L, et al. MicroRNA-30d-5p inhibits tumour cell proliferation and motility by directly targeting CCNE2 in non-small cell lung cancer. Cancer Lett. 2015;362(2):208–17. pmid:25843294
  58. 58. Li Z, Meng Q, Yu Q, Zhou Z, Li L. Evaluation of c-myc and CCNE2 amplification in breast cancer with quantitative multi-gene fluorescence in-situ hybridization. Zhonghua Bing Li Xue Za Zhi. 2014;43(7):455–8. pmid:25327794
  59. 59. Loveday C, Tatton-Brown K, Clarke M, Westwood I, Renwick A, Ramsay E, et al. Mutations in the PP2A regulatory subunit B family genes PPP2R5B, PPP2R5C and PPP2R5D cause human overgrowth. Hum Mol Genet. 2015.
  60. 60. Sontag JM, Sontag E. Protein phosphatase 2A dysfunction in Alzheimer's disease. Front Mol Neurosci. 2014;7:16. pmid:24653673
  61. 61. Yu UY, Yoo BC, Ahn JH. Regulatory B Subunits of Protein Phosphatase 2A Are Involved in Site-specific Regulation of Tau Protein Phosphorylation. Korean J Physiol Pharmacol. 2014;18(2):155–61. pmid:24757378
  62. 62. Bassiouny YA, Rabie WA, Hassan AA, Darwish RK. Association of the luteinizing hormone/choriogonadotropin receptor gene polymorphism with polycystic ovary syndrome. Gynecol Endocrinol. 2014;30(6):428–30. pmid:24592983