Sex Hormone Candidate Gene Polymorphisms Are Associated with Endometriosis
Abstract
:1. Introduction
2. Results
In Silico SNP Analysis
3. Discussion
4. Materials and Methods
4.1. Study Subjects
4.2. Laboratory Examination of SNPs
4.3. Genetic Data Statistical Analysis
4.4. In Silico SNP Analysis
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Zondervan, K.T.; Becker, C.M.; Koga, K.; Missmer, S.A.; Taylor, R.N.; Viganò, P. Endometriosis. Nat. Rev. Dis. Primers 2018, 4, 9. [Google Scholar] [CrossRef] [PubMed]
- Bulun, S.E.; Yilmaz, B.D.; Sison, C.; Miyazaki, K.; Bernardi, L.; Liu, S.; Kohlmeier, A.; Yin, P.; Milad, M.; Wei, J. Endometriosis. Endocr. Rev. 2019, 40, 1048–1079. [Google Scholar] [CrossRef] [PubMed]
- Foster, W.G.; Leonardi, M. Endometriosis—Novel approaches and controversies debated. Reprod. Fertil. 2021, 2, C39–C41. [Google Scholar] [CrossRef]
- Dinsdale, N.; Nepomnaschy, P.; Crespi, B. The evolutionary biology of endometriosis. Evol. Med. Public Health 2021, 9, 74–191. [Google Scholar] [CrossRef] [PubMed]
- Adewuyi, E.O.; Sapkota, Y.; International Endogene Consortium Iec; Me Research Team; International Headache Genetics Consortium Ihgc; Auta, A.; Yoshihara, K.; Nyegaard, M.; Griffiths, L.R.; Montgomery, G.W.; et al. Shared molecular genetic mechanisms underlie endometriosis and migraine comorbidity. Genes 2020, 11, 268. [Google Scholar] [CrossRef] [Green Version]
- Sapkota, Y.; Steinthorsdottir, V.; Morris, A.P.; Fassbender, A.; Rahmioglu, N.; De Vivo, I.; Buring, J.E.; Zhang, F.; Edwards, T.L.; Jones, S.O.D.; et al. Meta-analysis identifies five novel loci associated with endometriosis highlighting key genes involved in hormone metabolism. Nat. Commun. 2017, 8, 15539. [Google Scholar] [CrossRef] [Green Version]
- Dinsdale, N.L.; Crespi, B.J. Endometriosis and polycystic ovary syndrome are diametric disorders. Evol. Appl. 2021, 14, 1693–1715. [Google Scholar] [CrossRef]
- Saha, R.; Pettersson, H.J.; Svedberg, P.; Olovsson, M.; Bergqvist, A.; Marions, L.; Tornvall, P.; Kuja-Halkola, R. Heritability of endometriosis. Fertil. Steril. 2015, 104, 947–952. [Google Scholar] [CrossRef] [Green Version]
- Lee, S.H.; Harold, D.; Nyholt, D.R.; ANZGene Consortium; International Endogene Consortium; Genetic and Environmental Risk for Alzheimer’s Disease Consortium; Goddard, M.E.; Zondervan, K.T.; Williams, J.; Montgomery, G.W.; et al. Estimation and partitioning of polygenic variation captured by common SNPs for Alzheimer’s disease, multiple sclerosis and endometriosis. Hum. Mol. Genet. 2012, 4, 832–841. [Google Scholar] [CrossRef]
- Ponomarenko, I.; Reshetnikov, E.; Polonikov, A.; Sorokina, I.; Yermachenko, A.; Dvornyk, V.; Churnosov, M. Candidate genes for age at menarche are associated with endometriosis. Reprod. Biomed. Online 2020, 41, 943–956. [Google Scholar] [CrossRef]
- McGrath, I.M.; Mortlock, S.; Montgomery, G.W. Genetic regulation of physiological reproductive lifespan and female fertility. Int. J. Mol. Sci. 2021, 22, 2556. [Google Scholar] [CrossRef]
- Ruth, K.S.; Beaumont, R.N.; Tyrrell, J.; Jones, S.E.; Tuke, M.A.; Yaghootkar, H.; Wood, A.R.; Freathy, R.M.; Weedon, M.N.; Frayling, T.M.; et al. Genetic evidence that lower circulating FSH levels lengthen menstrual cycle, increase age at menopause and impact female reproductive health. Hum. Reprod. 2016, 31, 473–481. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Bianco, B.; Loureiro, F.A.; Trevisan, C.M.; Peluso, C.; Christofolini, D.M.; Montagna, E.; Laganà, A.S.; Barbosa, C.P. Effects of FSHR and FSHB Variants on hormonal profile and reproductive outcomes of infertile women with endometriosis. Front. Endocrinol. 2021, 12, 616. [Google Scholar] [CrossRef] [PubMed]
- Prescott, J.; Thompson, D.J.; Kraft, P.; Chanock, S.J.; Audley, T.; Brown, J.; Leyland, J.; Folkerd, E.; Doody, D.; Hankinson, S.E.; et al. Genome-wide association study of circulating estradiol, testosterone, and sex hormone-binding globulin in postmenopausal women. PLoS ONE 2012, 7, e37815. [Google Scholar] [CrossRef]
- Wood, A.R.; Perry, J.R.; Tanaka, T.; Hernandez, D.G.; Zheng, H.F.; Melzer, D.; Gibbs, J.R.; Nalls, M.A.; Weedon, M.N.; Spector, T.D.; et al. Imputation of variants from the 1000 Genomes Project modestly improves known associations and can identify low-frequency variant-phenotype associations undetected by HapMap based imputation. PLoS ONE 2013, 8, e64343. [Google Scholar] [CrossRef] [PubMed]
- Ruth, K.S.; Campbell, P.J.; Chew, S.; Lim, E.M.; Hadlow, N.; Stuckey, B.G.; Brown, S.J.; Feenstra, B.; Joseph, J.; Surdulescu, G.L.; et al. Genome-wide association study with 1000 genomes imputation identifies signals for nine sex hormone-related phenotypes. Eur. J. Hum. Genet. 2016, 24, 284–290. [Google Scholar] [CrossRef] [Green Version]
- Ruth, K.S.; Day, F.R.; Tyrrell, J.; Thompson, D.J.; Wood, A.R.; Mahajan, A.; Beaumont, R.N.; Wittemans, L.; Martin, S.; Busch, A.S.; et al. Using human genetics to understand the disease impacts of testosterone in men and women. Nat Med. 2020, 26, 252–258. [Google Scholar] [CrossRef]
- Garitazelaia, A.; Rueda-Martínez, A.; Arauzo, R.; de Miguel, J.; Cilleros-Portet, A.; Marí, S.; Bilbao, J.R.; Fernandez-Jimenez, N.; García-Santisteban, I. A Systematic two-sample Mendelian randomization analysis identifies shared genetic origin of endometriosis and associated phenotypes. Life 2021, 11, 24. [Google Scholar] [CrossRef]
- Gudjonsson, A.; Gudmundsdottir, V.; Axelsson, G.T.; Gudmundsson, E.F.; Jonsson, B.G.; Launer, L.J.; Lamb, J.R.; Jennings, L.L.; Aspelund, T.; Emilsson, V.; et al. A genome-wide association study of serum proteins reveals shared loci with common diseases. Nat. Commun. 2022, 13, 480. [Google Scholar] [CrossRef]
- Tyrmi, J.S.; Arffman, R.K.; Pujol-Gualdo, N.; Kurra, V.; Morin-Papunen, L.; Sliz, E.; FinnGen Consortium; Estonian Biobank Research Team; Piltonen, T.T.; Laisk, T.; et al. Leveraging Northern European population history: Novel low-frequency variants for polycystic ovary syndrome. Hum. Reprod. 2022, 37, 352–365. [Google Scholar] [CrossRef]
- Kim, S.K. Identification of 613 new loci associated with heel bone mineral density and a polygenic risk score for bone mineral density, osteoporosis and fracture. PLoS ONE 2018, 13, e0200785, Erratum in 2019, 14, e0213962. [Google Scholar] [CrossRef] [PubMed]
- Pickrell, J.K.; Berisa, T.; Liu, J.Z.; Ségurel, L.; Tung, J.Y.; Hinds, D.A. Detection and interpretation of shared genetic influences on 42 human traits. Nat. Genet. 2016, 48, 709–717, Erratum in Nat. Genet. 2016, 48, 1296. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kichaev, G.; Bhatia, G.; Loh, P.R.; Gazal, S.; Burch, K.; Freund, M.K.; Schoech, A.; Pasaniuc, B.; Price, A.L. Leveraging polygenic functional enrichment to improve GWAS power. Am. J. Hum. Genet. 2019, 104, 65–75. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Day, F.; Karaderi, T.; Jones, M.R.; Meun, C.; He, C.; Drong, A.; Kraft, P.; Lin, N.; Huang, H.; Broer, L.; et al. Large-scale genome-wide meta-analysis of polycystic ovary syndrome suggests shared genetic architecture for different diagnosis criteria. PLoS Genet. 2018, 14, e1007813, Erratum in PLoS Genet. 2019, 15, e1008517. [Google Scholar] [CrossRef] [Green Version]
- Sakaue, S.; Kanai, M.; Tanigawa, Y.; Karjalainen, J.; Kurki, M.; Koshiba, S.; Narita, A.; Konuma, T.; Yamamoto, K.; Akiyama, M.; et al. A cross-population atlas of genetic associations for 220 human phenotypes. Nat. Genet. 2021, 53, 1415–1424. [Google Scholar] [CrossRef]
- Das, N.; Kumar, T.R. Molecular regulation of follicle-stimulating hormone synthesis, secretion and action. J. Mol. Endocrinol. 2018, 60, R131–R155. [Google Scholar] [CrossRef] [Green Version]
- Andreev, A.E.; Kleimenova, T.S.; Drobintseva, A.O.; Polyakova, V.O.; Kvetnoi, I.M. Signal molecules involved in the formation of new nerve endings in endometriosis (review). Res. Results Biomed. 2019, 5, 94–107. [Google Scholar] [CrossRef]
- Radzinskii, V.E.; Altukhova, O.B. Molecular-genetic determinants of infertility in genital endometriosis. Res. Results Biomed. 2018, 4, 1–30. (In Russian) [Google Scholar] [CrossRef]
- Crespi, B. Variation among human populations in endometriosis and PCOS A test of the inverse comorbidity model. Evol. Med. Public Health. 2021, 9, 95–310. [Google Scholar] [CrossRef]
- Sinnott-Armstrong, N.; Naqvi, S.; Rivas, M.; Pritchard, J.K. GWAS of three molecular traits highlights core genes and pathways alongside a highly polygenic background. Elife 2021, 10, e58615. [Google Scholar] [CrossRef]
- Day, F.R.; Thompson, D.J.; Helgason, H.; Chasman, D.I.; Finucane, H.; Sulem, P.; Ruth, K.S.; Whalen, S.; Sarkar, A.K.; Albrecht, E.; et al. Genomic analyses identify hundreds of variants associated with age at menarche and support a role for puberty timing in cancer risk. Nat. Genet. 2017, 49, 834–841. [Google Scholar] [CrossRef] [PubMed]
- Laisk, T.; Kukuškina, V.; Palmer, D.; Laber, S.; Chen, C.Y.; Ferreira, T.; Rahmioglu, N.; Zondervan, K.; Becker, C.; Smoller, J.W.; et al. Large-scale meta-analysis highlights the hypothalamic-pituitary-gonadal axis in the genetic regulation of menstrual cycle length. Hum. Mol. Genet. 2018, 27, 4323–4332. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Day, F.R.; Ruth, K.S.; Thompson, D.J.; Lunetta, K.L.; Pervjakova, N.; Chasman, D.I.; Stolk, L.; Finucane, H.K.; Sulem, P.; Bulik-Sullivan, B.; et al. Large-scale genomic analyses link reproductive aging to hypothalamic signaling, breast cancer susceptibility and BRCA1-mediated DNA repair. Nat. Genet. 2015, 47, 1294–1303. [Google Scholar] [CrossRef] [PubMed]
- Mbarek, H.; Steinberg, S.; Nyholt, D.R.; Gordon, S.D.; Miller, M.B.; McRae, A.F.; Hottenga, J.J.; Day, F.R.; Willemsen, G.; de Geus, E.J.; et al. Identification of Common Genetic Variants Influencing Spontaneous Dizygotic Twinning and Female Fertility. Am. J. Hum. Genet. 2016, 98, 898–908. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Gallagher, C.S.; Mäkinen, N.; Harris, H.R.; Rahmioglu, N.; Uimari, O.; Cook, J.P.; Shigesi, N.; Ferreira, T.; Velez-Edwards, D.R.; Edwards, T.L.; et al. Genome-wide association and epidemiological analyses reveal common genetic origins between uterine leiomyomata and endometriosis. Nat. Commun. 2019, 10, 4857. [Google Scholar] [CrossRef] [Green Version]
- Day, F.R.; Hinds, D.A.; Tung, J.Y.; Stolk, L.; Styrkarsdottir, U.; Saxena, R.; Bjonnes, A.; Broer, L.; Dunger, D.B.; Halldorsson, B.V.; et al. Causal mechanisms and balancing selection inferred from genetic associations with polycystic ovary syndrome. Nat. Commun. 2015, 6, 8464. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Hayes, M.G.; Urbanek, M.; Ehrmann, D.A.; Armstrong, L.L.; Lee, J.Y.; Sisk, R.; Karaderi, T.; Barber, T.M.; McCarthy, M.I.; Franks, S.; et al. Genome-wide association of polycystic ovary syndrome implicates alterations in gonadotropin secretion in European ancestry populations. Nat. Commun. 2015, 6, 7502, Erratum in Nat. Commun. 2016, 7, 10762Erratum in Nat. Commun. 2020, 11, 2158. [Google Scholar] [CrossRef] [Green Version]
- Rull, K.; Grigorova, M.; Ehrenberg, A.; Vaas, P.; Sekavin, A.; Nõmmemees, D.; Adler, M.; Hanson, E.; Juhanson, P.; Laan, M. FSHB-211 G>T is a major genetic modulator of reproductive physiology and health in childbearing age women. Hum. Reprod. 2018, 33, 954–966. [Google Scholar] [CrossRef]
- Trevisan, C.M.; de Oliveira, R.; Christofolini, D.M.; Barbosa, C.P.; Bianco, B. Effects of a polymorphism in the promoter region of the follicle-stimulating hormone subunit beta (FSHB) gene on female reproductive outcomes. Genet. Test. Mol. Biomarkers 2019, 23, 39–44. [Google Scholar] [CrossRef]
- Dapas, M.; Lin, F.; Nadkarni, G.N.; Sisk, R.; Legro, R.S.; Urbanek, M.; Hayes, M.G.; Dunaif, A. Distinct subtypes of polycystic ovary syndrome with novel genetic associations: An unsupervised, phenotypic clustering analysis. PLoS Med. 2020, 17, e1003132. [Google Scholar] [CrossRef]
- He, C.; Kraft, P.; Chasman, D.I.; Buring, J.E.; Chen, C.; Hankinson, S.E.; Paré, G.; Chanock, S.; Ridker, P.M.; Hunter, D.J. A large-scale candidate-gene association study of age at menarche and age at natural menopause. Hum. Genet. 2010, 5, 515–527. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Tian, Y.; Zhao, H.; Chen, H.; Peng, Y.; Cui, L.; Du, Y.; Wang, Z.; Xu, J.; Chen, Z.J. Variants in FSHB are associated with polycystic ovary syndrome and luteinizing hormone level in han chinese women. J. Clin. Endocrinol. Metab. 2016, 5, 2178–2184. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Stolk, L.; Perry, J.R.; Chasman, D.I.; He, C.; Mangino, M.; Sulem, P.; Barbalic, M.; Broer, L.; Byrne, E.M.; Ernst, F.; et al. Meta-analyses identify 13 novel loci associated with age at menopause and highlights DNA repair and immune pathways. Nat. Genet. 2012, 3, 260–268. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Ponomarenko, I.; Reshetnikov, E.; Polonikov, A.; Verzilina, I.; Sorokina, I.; Yermachenko, A.; Dvornyk, V.; Churnosov, M. Candidate genes for age at menarche are associated with uterine leiomyoma. Front. Genet. 2021, 11, 512940. [Google Scholar] [CrossRef] [PubMed]
- Ponomarenko, I.; Reshetnikov, E.; Polonikov, A.; Sorokina, I.; Yermachenko, A.; Dvornyk, V.; Churnosov, M. Candidate genes for age at menarche are associated with endometrial hyperplasia. Gene 2020, 757, 4933. [Google Scholar] [CrossRef]
- Ponomarenko, I.; Reshetnikov, E.; Altuchova, O.; Polonikov, A.; Sorokina, I.; Yermachenko, A.; Dvornyk, V.; Golovchenko, O.; Churnosov, M. Association of genetic polymorphisms with age at menarche in Russian women. Gene 2019, 686, 228–236. [Google Scholar] [CrossRef] [Green Version]
- Gajbhiye, R.; Fung, J.N.; Montgomery, G.W. Complex genetics of female fertility. NPJ Genom. Med. 2018, 3, 29. [Google Scholar] [CrossRef] [Green Version]
- Bernstein, K.; Vink, J.Y.; Fu, X.W.; Fu, X.W.; Wakita, H.; Danielsson, J.; Wapner, R.; Gallos, G. Calcium-activated chloride channels anoctamin 1 and 2 promote murine uterine smooth muscle contractility. Am. J. Obstet. Gynecol. 2014, 211, 688.e1–688.e10. [Google Scholar] [CrossRef] [Green Version]
- Schlosser, P.; Li, Y.; Sekula, P.; Raffler, J.; Grundner-Culemann, F.; Pietzner, M.; Cheng, Y.; Wuttke, M.; Steinbrenner, I.; Schultheiss, U.T.; et al. Genetic studies of urinary metabolites illuminate mechanisms of detoxification and excretion in humans. Nat. Genet. 2020, 52, 167–176. [Google Scholar] [CrossRef]
- Pott, J.; Bae, Y.J.; Horn, K.; Teren, A.; Kühnapfel, A.; Kirsten, H.; Ceglarek, U.; Loeffler, M.; Thiery, J.; Kratzsch, J.; et al. Genetic Association Study of Eight Steroid Hormones and Implications for Sexual Dimorphism of Coronary Artery Disease. J. Clin. Endocrinol. Metab. 2019, 104, 5008–5023. [Google Scholar] [CrossRef]
- Zhai, G.; Teumer, A.; Stolk, L.; Perry, J.R.; Vandenput, L.; Coviello, A.D.; Koster, A.; Bell, J.T.; Bhasin, S.; Eriksson, J.; et al. Eight common genetic variants associated with serum DHEAS levels suggest a key role in ageing mechanisms. PLoS Genet. 2011, 7, e1002025. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Shin, S.Y.; Fauman, E.B.; Petersen, A.K.; Krumsiek, J.; Santos, R.; Huang, J.; Arnold, M.; Erte, I.; Forgetta, V.; Yang, T.P.; et al. An atlas of genetic influences on human blood metabolites. Nat. Genet. 2014, 46, 543–550. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Al-Khelaifi, F.; Diboun, I.; Donati, F.; Botrè, F.; Abraham, D.; Hingorani, A.; Albagha, O.; Georgakopoulos, C.; Suhre, K.; Yousri, N.A.; et al. Metabolic GWAS of elite athletes reveals novel genetically-influenced metabolites associated with athletic performance. Sci. Rep. 2019, 9, 19889, Erratum in Sci. Rep. 2020, 10, 10473. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Sinnott-Armstrong, N.; Tanigawa, Y.; Amar, D.; Mars, N.; Benner, C.; Aguirre, M.; Venkataraman, G.R.; Wainberg, M.; Ollila, H.M.; Kiiskinen, T.; et al. Genetics of 35 blood and urine biomarkers in the UK Biobank. Nat. Genet. 2021, 53, 185–194, Erratum in Nat. Genet. 2021, 53, 1622. [Google Scholar] [CrossRef]
- Pulit, S.L.; Stoneman, C.; Morris, A.P.; Wood, A.R.; Glastonbury, C.A.; Tyrrell, J.; Yengo, L.; Ferreira, T.; Marouli, E.; Ji, Y.; et al. Meta-analysis of genome-wide association studies for body fat distribution in 694 649 individuals of European ancestry. Hum. Mol. Genet. 2019, 28, 166–174. [Google Scholar] [CrossRef] [Green Version]
- Johnson, N.; Maguire, S.; Morra, A.; Kapoor, P.M.; Tomczyk, K.; Jones, M.E.; Schoemaker, M.J.; Gilham, C.; Bolla, M.K.; Wang, Q.; et al. CYP3A7*1C allele: Linking premenopausal oestrone and progesterone levels with risk of hormone receptor-positive breast cancers. Br. J. Cancer 2021, 124, 842–854. [Google Scholar] [CrossRef]
- Haas, C.B.; Hsu, L.; Lampe, J.W.; Wernli, K.J.; Lindström, S. Cross-ancestry Genome-wide Association Studies of Sex Hormone Concentrations in Pre- and Postmenopausal Women. Endocrinology 2022, 163, bqac020. [Google Scholar] [CrossRef]
- Yee, S.W.; Stecula, A.; Chien, H.C.; Zou, L.; Feofanova, E.V.; van Borselen, M.; Cheung, K.; Yousri, N.A.; Suhre, K.; Kinchen, J.M.; et al. Unraveling the functional role of the orphan solute carrier, SLC22A24 in the transport of steroid conjugates through metabolomic and genome-wide association studies. PLoS Genet. 2019, 15, e1008208. [Google Scholar] [CrossRef] [Green Version]
- Montasser, M.E.; Aslibekyan, S.; Srinivasasainagendra, V.; Tiwari, H.K.; Patki, A.; Bagheri, M.; Kind, T.; Barupal, D.K.; Fan, S.; Perry, J.; et al. An Amish founder population reveals rare-population genetic determinants of the human lipidome. Commun. Biol. 2022, 5, 334. [Google Scholar] [CrossRef]
- Litovkina, O.; Nekipelova, E.; Dvornyk, V.; Polonikov, A.; Efremova, O.; Zhernakova, N.; Reshetnikov, E.; Churnosov, M. Genes involved in the regulation of vascular homeostasis determine renal survival rate in patients with chronic glomerulonephritis. Gene 2014, 546, 112–116. [Google Scholar] [CrossRef]
- Reshetnikov, E.A.; Akulova, L.Y.; Dobrodomova, I.S.; Dvornyk, V.Y.; Polonikov, A.V.; Churnosov, M.I. The insertion-deletion polymorphism of the ACE gene is associated with increased blood pressure in women at the end of pregnancy. J. Renin Angiotensin Aldosterone Syst. 2015, 16, 623–632. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- American Society for Reproductive Medicine. Revised American Society for Reproductive Medicine classification of endometriosis: 1996. Fertil. Steril. 1997, 5, 817–821. [Google Scholar]
- Ward, L.D.; Kellis, M. HaploReg v4: Systematic mining of putative causal variants, cell types, regulators and target genes for human complex traits and disease. Nucleic. Acids Res. 2016, 44, D877–D881. [Google Scholar] [CrossRef]
- Polonikov, A.V.; Bushueva, O.Y.; Bulgakova, I.V.; Freidin, M.B.; Churnosov, M.I.; Solodilova, M.A.; Shvetsov, Y.D.; Ivanov, V.P. A comprehensive contribution of genes for aryl hydrocarbon receptor signaling pathway to hypertension susceptibility. Pharmacogenet. Genomics. 2017, 27, 57–69. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Polonikov, A.; Kharchenko, A.; Bykanova, M.; Sirotina, S.; Ponomarenko, I.; Bocharova, A.; Vagaytseva, K.; Stepanov, V.; Bushueva, O.; Churnosov, M.; et al. Polymorphisms of CYP2C8, CYP2C9 and CYP2C19 and risk of coronary heart disease in Russian population. Gene 2017, 627, 451–459. [Google Scholar] [CrossRef] [Green Version]
- Moskalenko, M.I.; Milanova, S.N.; Ponomarenko, I.V.; Polonikov, A.V.; Churnosov, M.I. Иccлeдoвaниe accoциaций пoлимopфизмa гeнoв мaтpикcныx мeтaллoпpoтeинaз c paзвитиeм apтepиaльнoй гипepтeнзии y мyжчин. Kardiologiia 2019, 59, 31–39. [Google Scholar] [CrossRef] [Green Version]
- Starikova, D.; Ponomarenko, I.; Reshetnikov, E.; Dvornyk, V.; Churnosov, M. Novel data about association of the functionally significant polymorphisms of the MMP9 gene with exfoliation glaucoma in the caucasian population of Central Russia. Ophthalmic. Res. 2021, 64, 458–464. [Google Scholar] [CrossRef]
- Reshetnikov, E.; Ponomarenko, I.; Golovchenko, O.; Sorokina, I.; Batlutskaya, I.; Yakunchenko, T.; Dvornyk, V.; Polonikov, A.; Churnosov, M. The VNTR polymorphism of the endothelial nitric oxide synthase gene and blood pressure in women at the end of pregnancy. Taiwan J. Obstet. Gynecol. 2019, 58, 390–395. [Google Scholar] [CrossRef]
- Bushueva, O.; Solodilova, M.; Churnosov, M.; Ivanov, V.; Polonikov, A. The Flavin-Containing Monooxygenase 3 Gene and Essential Hypertension: The Joint Effect of Polymorphism E158K and Cigarette Smoking on Disease Susceptibility. Int. J. Hypertens. 2014, 2014, 712169. [Google Scholar] [CrossRef] [Green Version]
- Tikunova, E.; Ovtcharova, V.; Reshetnikov, E.; Dvornyk, V.; Polonikov, A.; Bushueva, O.; Churnosov, M. Genes of tumor necrosis factors and their receptors and the primary open angle glaucoma in the population of Central Russia. Int. J. Ophthalmol. 2017, 10, 1490–1494. [Google Scholar] [CrossRef]
- Reshetnikov, E.; Zarudskaya, O.; Polonikov, A.; Bushueva, O.; Orlova, V.; Krikun, E.; Dvornyk, V.; Churnosov, M. Genetic markers for inherited thrombophilia are associated with fetal growth retardation in the population of Central Russia. J. Obstet. Gynaecol. Res. 2017, 43, 1139–1144. [Google Scholar] [CrossRef] [PubMed]
- Che, R.; Jack, J.R.; Motsinger-Reif, A.A.; Brown, C.C. An adaptive permutation approach for genome-wide association study: Evaluation and recommendations for use. BioData Min. 2014, 7, 9. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Polonikov, A.; Rymarova, L.; Klyosova, E.; Volkova, A.; Azarova, I.; Bushueva, O.; Bykanova, M.; Bocharova, I.; Zhabin, S.; Churnosov, M.; et al. Matrix metalloproteinases as target genes for gene regulatory networks driving molecular and cellular pathways related to a multistep pathogenesis of cerebrovascular disease. J. Cell Biochem. 2019, 120, 16467–16482. [Google Scholar] [CrossRef] [Green Version]
- Eliseeva, N.; Ponomarenko, I.; Reshetnikov, E.; Dvornyk, V.; Churnosov, M. LOXL1 gene polymorphism candidates for exfoliation glaucoma are also associated with a risk for primary open-angle glaucoma in a Caucasian population from central Russia. Mol. Vis. 2021, 27, 262–269. [Google Scholar]
- Purcell, S.; Neale, B.; Todd-Brown, K.; Thomas, L.; Ferreira, M.A.; Bender, D.; Maller, J.; Sklar, P.; de Bakker, P.I.; Daly, M.J.; et al. PLINK: A tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 2007, 81, 559–575. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Calle, M.L.; Urrea, V.; Malats, N.; Van Steen, K. mbmdr: An R package for exploring gene-gene interactions associated with binary or quantitative traits. Bioinformatics 2010, 26, 2198–2199. [Google Scholar] [CrossRef] [Green Version]
- Moskalenko, M.; Ponomarenko, I.; Reshetnikov, E.; Dvornyk, V.; Churnosov, M. Polymorphisms of the matrix metalloproteinase genes are associated with essential hypertension in a Caucasian population of Central Russia. Sci. Rep. 2021, 11, 5224. [Google Scholar] [CrossRef]
- Minyaylo, O.; Ponomarenko, I.; Reshetnikov, E.; Dvornyk, V.; Churnosov, M. Functionally significant polymorphisms of the MMP-9 gene are associated with peptic ulcer disease in the Caucasian population of Central Russia. Sci. Rep. 2021, 11, 13515. [Google Scholar] [CrossRef]
- Polonikov, A.V.; Klyosova, E.Y.; Azarova, I.E. Bioinformatic tools and internet resources for functional annotation of polymorphic loci detected by genome wide association studies of multifactorial diseases (review). Res. Results Biomed. 2021, 7, 5–31, In Russian. [Google Scholar] [CrossRef]
- Sirotina, S.; Ponomarenko, I.; Kharchenko, A.; Bykanova, M.; Bocharova, A.; Vagaytseva, K.; Stepanov, V.; Churnosov, M.; Solodilova, M.; Polonikov, A. A Novel Polymorphism in the Promoter of the CYP4A11 Gene Is Associated with Susceptibility to Coronary Artery Disease. Dis. Markers 2018, 2018, 5812802. [Google Scholar] [CrossRef] [Green Version]
- GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 2020, 36, 1318–1330. [Google Scholar] [CrossRef]
- Adzhubei, I.; Jordan, D.M.; Sunyaev, S.R. Predicting functional effect of human missense mutations using PolyPhen-2. Curr. Protoc. Hum. Genet. 2013, 76, 7–20. [Google Scholar] [CrossRef] [PubMed]
- Gene Ontology Consortium. The Gene Ontology resource: Enriching a GOld mine. Nucleic Acids Res. 2021, 49, D325–D334. [Google Scholar] [CrossRef]
- Szklarczyk, D.; Gable, A.L.; Nastou, K.C.; Lyon, D.; Kirsch, R.; Pyysalo, S.; Doncheva, N.T.; Legeay, M.; Fang, T.; Bork, P.; et al. The STRING database in 2021: Customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2021, 49, D605–D612. [Google Scholar] [CrossRef]
- Franz, M.; Rodriguez, H.; Lopes, C.; Zuberi, K.; Montojo, J.; Bader, G.D.; Morris, Q. GeneMANIA update 2018. Nucleic Acids Res. 2018, 46, W60–W64. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Golovchenko, O.; Abramova, M.; Ponomarenko, I.; Reshetnikov, E.; Aristova, I.; Polonikov, A.; Dvornyk, V.; Churnosov, M. Functionally significant polymorphisms of ESR1 and PGR and risk of intrauterine growth restriction in population of Central Russia. Eur. J. Obstet. Gynecol. Reprod. Biol. 2020, 253, 52–57. [Google Scholar] [CrossRef]
- Churnosov, M.; Abramova, M.; Reshetnikov, E.; Lyashenko, I.; Efremova, O.; Churnosova, M.; Ponomarenko, I. Polymorphisms of hypertension susceptibility genes as a risk factors of preeclampsia in the Caucasian population of central Russia. Placenta 2022, 129, 51–61. [Google Scholar] [CrossRef] [PubMed]
Chr | SNP | Minor allele | Gene | n | Allelic model | Additive model | Dominant model | Recessive model | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
OR | 95% CI | p | OR | 95% CI | p | OR | 95% CI | p | OR | 95% CI | p | |||||||||
L95 | U95 | L95 | U95 | L95 | U95 | L95 | U95 | |||||||||||||
7 | rs148982377 | C | ZNF789 | 1329 | 1.21 | 0.84 | 1.75 | 0.311 | 1.25 | 0.84 | 1.85 | 0.262 | 1.25 | 0.83 | 1.87 | 0.282 | 2.16 | 0.13 | 34.74 | 0.587 |
7 | rs34670419 | T | ZKSCAN5 | 1329 | 1.14 | 0.75 | 1.72 | 0.545 | 1.18 | 0.77 | 1.82 | 0.437 | 1.20 | 0.77 | 1.89 | 0.421 | 1.08 | 0.10 | 2.04 | 0.952 |
11 | rs11031002 | A | FSHB | 1309 | 0.68 | 0.52 | 0.90 | 0.006 | 0.64 | 0.47 | 0.85 | 0.003 | 0.60 | 0.43 | 0.82 | 0.002 | 0.73 | 0.24 | 2.21 | 0.580 |
11 | rs11031005 | C | FSHB | 1330 | 0.66 | 0.50 | 0.87 | 0.003 | 0.65 | 0.49 | 0.87 | 0.004 | 0.65 | 0.49 | 0.92 | 0.009 | 0.17 | 0.03 | 0.78 | 0.023 |
11 | rs112295236 | G | SLC22A10 | 1321 | 1.29 | 0.90 | 1.84 | 0.159 | 1.40 | 0.96 | 2.04 | 0.078 | 1.04 | 0.95 | 2.07 | 0.093 | 2.52 | 0.31 | 20.17 | 0.384 |
12 | rs117585797 | A | ANO2 | 1312 | 0.87 | 0.50 | 1.52 | 0.622 | 0.98 | 0.55 | 1.75 | 0.947 | 0.997 | 0.55 | 1.80 | 0.991 | 0.01 | 0 | inf | 0.999 |
16 | rs117145500 | C | CHD9 | 1311 | 1.18 | 0.90 | 1.55 | 0.220 | 1.13 | 0.85 | 1.51 | 0.398 | 1.11 | 0.81 | 1.52 | 0.503 | 1.81 | 0.54 | 6.06 | 0.332 |
17 | rs727428 | T | SHBG | 1321 | 0.95 | 0.80 | 1.13 | 0.555 | 0.96 | 0.80 | 1.15 | 0.652 | 0.89 | 0.69 | 1.15 | 0.367 | 1.07 | 0.75 | 1.54 | 0.701 |
17 | rs1641549 | T | TP53 | 1312 | 0.89 | 0.73 | 1.09 | 0.250 | 0.89 | 0.72 | 1.09 | 0.266 | 0.83 | 0.64 | 1.08 | 0.164 | 0.99 | 0.60 | 1.65 | 0.979 |
Haplotypes | Frequency | OR | P | Pperm | |
---|---|---|---|---|---|
Endometriosis Patients (n = 395) | Controls (n = 981) | ||||
rs148982377 ZNF789–rs34670419 ZKSCAN5 | |||||
CT | 0.040 | 0.035 | 1.22 | 0.402 | - |
CG | 0.017 | 0.013 | 1.26 | 0.536 | - |
TG | 0.943 | 0.952 | 0.80 | 0.238 | - |
rs11031002–rs11031005 FSHB | |||||
AC | 0.087 | 0.113 | 0.87 | 0.360 | - |
TC | 0.007 | 0.023 | 0.16 | 0.0002 | 0.001 |
AT | 0.008 | 0.022 | 0.09 | 0.000001 | 0.001 |
TT | 0.898 | 0.842 | 2.03 | 0.000002 | 0.001 |
N | SNP × SNP Interaction Models | NH | Beta H | WH | NL | Beta L | WL | pperm |
---|---|---|---|---|---|---|---|---|
Two-order interaction models (p < 6.52 × 10−4) | ||||||||
1 | rs11031002 FSHB × rs11031005 FSHB | 1 | 0.741 | 22.24 | 2 | −2.002 | 32.68 | <0.001 |
2 | rs11031002 FSHB × rs112295236 SLC22A10 | 1 | 0.288 | 4.23 | 1 | −0.629 | 12.95 | 0.001 |
3 | rs117145500 CHD9 × rs11031002 FSHB | 1 | 0.281 | 4.43 | 1 | −0.660 | 11.68 | 0.003 |
4 | rs11031002 FSHB × rs34670419 ZKSCAN5 | 1 | 0.386 | 7.04 | 1 | −0.594 | 11.62 | 0.004 |
5 | rs11031002 FSHB × rs727428 SHBG | 0 | - | - | 1 | −0.850 | 12.15 | 0.009 |
Three-order interaction models (p < 5.01 × 10−8) | ||||||||
1 | rs11031002 FSHB − rs117585797 ANO2 × rs11031005 FSHB | 1 | 0.672 | 20.33 | 2 | −2.377 | 36.04 | <0.001 |
2 | rs11031002 FSHB − rs112295236 SLC22A10 − rs11031005 FSHB | 1 | 0.489 | 12.49 | 2 | −2.502 | 35.58 | <0.001 |
3 | rs11031002 FSHB − rs1641549 TP53 × rs11031005 FSHB | 1 | 0.410 | 10.51 | 5 | −1.975 | 31.58 | <0.001 |
4 | rs11031002 FSHB − rs11031005 FSHB − rs34670419 ZKSCAN5 | 1 | 0.582 | 16.53 | 2 | −2.003 | 29.91 | <0.001 |
5 | rs117145500 CHD9 − rs11031002 FSHB − rs11031005 FSHB | 1 | 0.408 | 9.50 | 3 | −2.001 | 29.71 | <0.001 |
Four-order interaction models (p < 8.20 × 10−9) | ||||||||
1 | rs11031002 FSHB − rs117585797 ANO2 − rs112295236 SLC22A10 − rs11031005 FSHB | 1 | 0.464 | 11.86 | 2 | −2.884 | 35.70 | <0.001 |
2 | rs11031002 FSHB − rs117585797 ANO2 − rs11031005 FSHB − rs34670419 ZKSCAN5 | 1 | 0.560 | 16.26 | 3 | −2.425 | 33.22 | < 0.001 |
Parameters | Cases (n = 395) ± SD/% (n) | Controls (n = 981) ± SD/% (n) | p |
---|---|---|---|
Age, years | 39.75 ± 9.01 | 40.73 ± 8.60 | >0.05 |
Height, m | 1.65 ± 0.06 | 1.65 ± 0.06 | >0.05 |
Weight, kg | 72.65 ± 14.38 | 72.49 ± 13.37 | >0.05 |
BMI, kg/m2 | 26.63 ± 5.31 | 26.66 ± 4.61 | >0.05 |
Proportion of the participants by relative BMI, % (n): | |||
underweight (<18.50) | 4.30 (17) | 1.12 (11) | |
normal weight (18.50–24.99) | 37.72 (149) | 42.41 (416) | |
overweight (25.00–29.99) | 31.65 (125) | 30.49 (299) | >0.05 |
obese (>30.00) | 26.33 (104) | 25.99 (255) | |
Family history of endometriosis (yes) | 6.07 (24) | 1.94 (19) | <0.001 |
Married | 82.53 (326) | 85.93 (843) | >0.05 |
Smoking (yes) | 18.22 (72) | 17.33 (170) | >0.05 |
Drinking alcohol (≥7 drinks per week) | 4.05 (16) | 3.06 (30) | >0.05 |
History of pelvic surgery (laparoscopy and/or laparotomy) | 15.19 (60) | 9.99 (98) | <0.01 |
Oral contraceptive use | 8.10 (32) | 10.09 (99) | >0.05 |
Age at menarche and menstrual cycle | |||
Age at menarche, years | 13.29 ± 1.27 | 13.27 ± 1.25 | >0.05 |
Proportion of the participants by relative age at menarche,% (n) | >0.05 | ||
early (<12 years) | 6.36 (25) | 6.42 (63) | |
average (12–14 years) | 81.17 (319) | 79.51 (780) | |
late (>14 years) | 12.47 (49) | 14.07 (138) | |
Duration of menstrual bleeding (mean, days) | 5.13 ± 1.56 | 4.94 ± 0.94 | >0.05 |
Menstrual cycle length (mean, days) | 27.66 ± 2.28 | 28.15 ± 2.24 | <0.001 |
Reproductive characteristic | |||
Age at first birth (mean, years) | 21.25 ± 3.04 | 21.71 ± 3.49 | >0.05 |
No. of gravidity (mean) | 2.60 ± 2.31 | 2.45 ± 1.55 | >0.05 |
No. of births (mean) | 1.07 ± 0.97 | 1.51 ± 0.67 | <0.001 |
No. of spontaneous abortions (mean) | 0.21 ± 0.61 | 0.24 ± 0.51 | >0.05 |
No. of induced abortions (mean) | 1.25 ± 1.61 | 0.67 ± 0.99 | <0.001 |
No. of induced abortions: | <0.001 | ||
0 | 46.58 (184) | 58.92 (578) | |
1 | 17.22 (68) | 23.75 (233) | |
2 | 19.24 (76) | 10.40 (102) | |
3 | 8.61 (34) | 5.40 (53) | |
≥4 | 8.35 (33) | 1.53 (15) | |
History of infertility | 32.42 (132) | 5.20 (51) | <0.001 |
Gynecological pathologies | |||
Uterine leiomyoma | 52.40 (207) | - | - |
Endometrial hyperplasia | 46.33 (183) | - | - |
Adenomyosis | 43.04 (170) | - | - |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Golovchenko, I.; Aizikovich, B.; Golovchenko, O.; Reshetnikov, E.; Churnosova, M.; Aristova, I.; Ponomarenko, I.; Churnosov, M. Sex Hormone Candidate Gene Polymorphisms Are Associated with Endometriosis. Int. J. Mol. Sci. 2022, 23, 13691. https://doi.org/10.3390/ijms232213691
Golovchenko I, Aizikovich B, Golovchenko O, Reshetnikov E, Churnosova M, Aristova I, Ponomarenko I, Churnosov M. Sex Hormone Candidate Gene Polymorphisms Are Associated with Endometriosis. International Journal of Molecular Sciences. 2022; 23(22):13691. https://doi.org/10.3390/ijms232213691
Chicago/Turabian StyleGolovchenko, Ilya, Boris Aizikovich, Oleg Golovchenko, Evgeny Reshetnikov, Maria Churnosova, Inna Aristova, Irina Ponomarenko, and Mikhail Churnosov. 2022. "Sex Hormone Candidate Gene Polymorphisms Are Associated with Endometriosis" International Journal of Molecular Sciences 23, no. 22: 13691. https://doi.org/10.3390/ijms232213691
APA StyleGolovchenko, I., Aizikovich, B., Golovchenko, O., Reshetnikov, E., Churnosova, M., Aristova, I., Ponomarenko, I., & Churnosov, M. (2022). Sex Hormone Candidate Gene Polymorphisms Are Associated with Endometriosis. International Journal of Molecular Sciences, 23(22), 13691. https://doi.org/10.3390/ijms232213691