Exploring Genetic Interactions in Colombian Women with Polycystic Ovarian Syndrome: A Study on SNP-SNP Associations
Abstract
:1. Introduction
2. Results
2.1. Characteristics of the Study Sample
2.2. Epistasis Analysis
3. Discussion
4. Materials and Methods
4.1. Study Participants
4.2. SNP Selection and Genotyping
4.3. Statistic and SNP-SNP Interaction Analysis
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Rasquin, L.I.; Anastasopoulou, C.; Mayrin, J.V. Polycystic Ovarian Disease. In StatPearls; StatPearls Publishing: Treasure Island, FL, USA, 2024. [Google Scholar]
- Siddiqui, S.; Mateen, S.; Ahmad, R.; Moin, S. A Brief Insight into the Etiology, Genetics, and Immunology of Polycystic Ovarian Syndrome (PCOS). J. Assist. Reprod. Genet. 2022, 39, 2439–2473. [Google Scholar] [CrossRef] [PubMed]
- Thien Tay, C.; Garrad, R.; Mousa, A.; Bahri, M.; Joham, A.; Teede, H. Polycystic Ovary Syndrome (PCOS): International Collaboration to Translate Evidence and Guide Future Research. J. Endocrinol. 2023, 257, e220232. [Google Scholar] [CrossRef]
- Zhu, T.; Goodarzi, M.O. Causes and Consequences of Polycystic Ovary Syndrome: Insights From Mendelian Randomization. J. Clin. Endocrinol. Metab. 2022, 107, e899–e911. [Google Scholar] [CrossRef] [PubMed]
- Sharma, P.; Senapati, S.; Goyal, L.D.; Kaur, B.; Kamra, P.; Khetarpal, P. Genome-Wide Association Study (GWAS) Identified PCOS Susceptibility Variants and Replicates Reported Risk Variants. Arch. Gynecol. Obstet. 2024, 309, 2009–2019. [Google Scholar] [CrossRef]
- Lee, J.W.; Lee, S. A Comparative Study on the Unified Model Based Multifactor Dimensionality Reduction Methods for Identifying Gene-Gene Interactions Associated with the Survival Phenotype. BioData Min. 2021, 14, 17. [Google Scholar] [CrossRef] [PubMed]
- Gola, D.; Mahachie John, J.M.; Van Steen, K.; König, I.R. A Roadmap to Multifactor Dimensionality Reduction Methods. Brief. Bioinform. 2016, 17, 293–308. [Google Scholar] [CrossRef]
- Moore, J.H.; Andrews, P.C. Epistasis Analysis Using Multifactor Dimensionality Reduction. Methods Mol. Biol. 2015, 1253, 301–314. [Google Scholar] [CrossRef]
- Ritchie, M.D.; Hahn, L.W.; Roodi, N.; Bailey, L.R.; Dupont, W.D.; Parl, F.F.; Moore, J.H. Multifactor-Dimensionality Reduction Reveals High-Order Interactions among Estrogen-Metabolism Genes in Sporadic Breast Cancer. Am. J. Hum. Genet. 2001, 69, 138–147. [Google Scholar] [CrossRef]
- Pan, Q.; Hu, T.; Moore, J.H. Epistasis, Complexity, and Multifactor Dimensionality Reduction. In Genome-Wide Association Studies and Genomic Prediction; Gondro, C., van der Werf, J., Hayes, B., Eds.; Humana Press: Totowa, NJ, USA, 2013; pp. 465–477. ISBN 978-1-62703-447-0. [Google Scholar]
- He, H.; Oetting, W.S.; Brott, M.J.; Basu, S. Pair-Wise Multifactor Dimensionality Reduction Method to Detect Gene-Gene Interactions in a Case-Control Study. Hum. Hered. 2010, 69, 60–70. [Google Scholar] [CrossRef]
- Motsinger, A.A.; Ritchie, M.D. The Effect of Reduction in Cross-Validation Intervals on the Performance of Multifactor Dimensionality Reduction. Genet. Epidemiol. 2006, 30, 546–555. [Google Scholar] [CrossRef]
- Alarcón-Granados, M.C.; Moreno-Ortíz, H.; Esteban-Pérez, C.I.; Ferrebuz-Cardozo, A.; Camargo-Villalba, G.E.; Forero-Castro, M. Assessment of THADA Gene Polymorphisms in a Sample of Colombian Women with Polycystic Ovary Syndrome: A Pilot Study. Heliyon 2022, 8, e09673. [Google Scholar] [CrossRef] [PubMed]
- Alarcón-Granados, M.C.; Moreno-Ortíz, H.; Rondón-Lagos, M.; Camargo-Villalba, G.E.; Forero-Castro, M. Study of LHCGR Gene Variants in a Sample of Colombian Women with Polycystic Ovarian Syndrome: A Pilot Study. J. King Saud Univ. Sci. 2022, 34, 102202. [Google Scholar] [CrossRef]
- Fang, G.; Wang, W.; Paunic, V.; Heydari, H.; Costanzo, M.; Liu, X.; Liu, X.; VanderSluis, B.; Oately, B.; Steinbach, M.; et al. Discovering Genetic Interactions Bridging Pathways in Genome-Wide Association Studies. Nat. Commun. 2019, 10, 4274. [Google Scholar] [CrossRef] [PubMed]
- Watkinson, J.; Anastassiou, D. Synergy Disequilibrium Plots: Graphical Visualization of Pairwise Synergies and Redundancies of SNPs with Respect to a Phenotype. Bioinformatics 2009, 25, 1445–1446. [Google Scholar] [CrossRef] [PubMed]
- Itriyeva, K. The Normal Menstrual Cycle. Curr. Probl. Pediatr. Adolesc. Health Care 2022, 52, 101183. [Google Scholar] [CrossRef] [PubMed]
- Barbieri, R.L. The Endocrinology of the Menstrual Cycle. Methods Mol. Biol. 2014, 1154, 145–169. [Google Scholar] [CrossRef] [PubMed]
- Constantin, S.; Bjelobaba, I.; Stojilkovic, S.S. Pituitary Gonadotroph-Specific Patterns of Gene Expression and Hormone Secretion. Curr. Opin. Pharmacol. 2022, 66, 102274. [Google Scholar] [CrossRef] [PubMed]
- Wide, L.; Eriksson, K. Low-Glycosylated Forms of Both FSH and LH Play Major Roles in the Natural Ovarian Stimulation. Upsala J. Med. Sci. 2018, 123, 100–108. [Google Scholar] [CrossRef] [PubMed]
- Malini, N.A.; Roy George, K. Evaluation of Different Ranges of LH:FSH Ratios in Polycystic Ovarian Syndrome (PCOS)—Clinical Based Case Control Study. Gen. Comp. Endocrinol. 2018, 260, 51–57. [Google Scholar] [CrossRef]
- Pratama, G.; Wiweko, B.; Asmarinah; Widyahening, I.S.; Andraini, T.; Bayuaji, H.; Hestiantoro, A. Mechanism of Elevated LH/FSH Ratio in Lean PCOS Revisited: A Path Analysis. Sci. Rep. 2024, 14, 8229. [Google Scholar] [CrossRef]
- Ruddenklau, A.; Campbell, R.E. Neuroendocrine Impairments of Polycystic Ovary Syndrome. Endocrinology 2019, 160, 2230–2242. [Google Scholar] [CrossRef]
- Walker, K.; Decherney, A.H.; Saunders, R. Menstrual Dysfunction in PCOS. Clin. Obstet. Gynecol. 2021, 64, 119–125. [Google Scholar] [CrossRef]
- Zehravi, M.; Maqbool, M.; Ara, I. Polycystic Ovary Syndrome and Infertility: An Update. Int. J. Adolesc. Med. Health 2021, 34, 1–9. [Google Scholar] [CrossRef]
- Han, C.-C.; Yue, L.-L.; Yang, Y.; Jian, B.-Y.; Ma, L.-W.; Liu, J.-C. TOX3 Protein Expression Is Correlated with Pathological Characteristics in Breast Cancer. Oncol. Lett. 2016, 11, 1762–1768. [Google Scholar] [CrossRef]
- Tian, Y.; Li, J.; Su, S.; Cao, Y.; Wang, Z.; Zhao, S.; Zhao, H. PCOS-GWAS Susceptibility Variants in THADA, INSR, TOX3, and DENND1A Are Associated With Metabolic Syndrome or Insulin Resistance in Women With PCOS. Front. Endocrinol. 2020, 11, 274. [Google Scholar] [CrossRef] [PubMed]
- Zhou, X.; Chan, K.C.C.; Huang, Z.; Wang, J. Determining Dependency and Redundancy for Identifying Gene-Gene Interaction Associated with Complex Disease. J. Bioinform. Comput. Biol. 2020, 18, 2050035. [Google Scholar] [CrossRef]
- Pérez-Pérez, J.M.; Candela, H.; Micol, J.L. Understanding Synergy in Genetic Interactions. Trends Genet. 2009, 25, 368–376. [Google Scholar] [CrossRef] [PubMed]
- Ma, Z.P.; Chen, J. Nonsense mutations and genetic compensation response. Yi Chuan 2019, 41, 359–364. [Google Scholar] [CrossRef] [PubMed]
- Thathapudi, S.; Jayashankar, E.; Putcha, U.K.; Mullapudi, S.; Challa, S. Multifactor Dimensionality Reduction Analysis for Detecting SNP-SNP, SNP-Environment Interactions Associated with Polycystic Ovarian Syndrome among South Indian Women. Int. J. Mol. Biol. Open Access 2019, 4, 59–65. [Google Scholar] [CrossRef]
- Castillo-Higuera, T.; Alarcón-Granados, M.C.; Marin-Suarez, J.; Moreno-Ortiz, H.; Esteban-Pérez, C.I.; Ferrebuz-Cardozo, A.J.; Forero-Castro, M.; Camargo-Vill alba, G. A Comprehensive Overview of Common Polymorphic Variants in Genes Related to Polycystic Ovary Syndrome. Reprod. Sci. 2020, 28, 2399–2412. [Google Scholar] [CrossRef]
- Crespo, R.P.; Bachega, T.A.S.S.; Mendonça, B.B.; Gomes, L.G. An Update of Genetic Basis of PCOS Pathogenesis. Arch. Endocrinol. Metab. 2018, 62, 352–361. [Google Scholar] [CrossRef]
- Oeth, P.; Mistro, G.D.; Marnellos, G.; Shi, T.; Boom, D. Qualitative and Quantitative Genotyping Using Single Base Primer Extension Coupled with Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (MassARRAY®). In Single Nucleotide Polymorphisms; Komar, A.A., Ed.; Methods in Molecular Biology; Humana Press: Totowa, NJ, USA, 2009; Volume 578, pp. 307–343. ISBN 978-1-60327-410-4. [Google Scholar]
- Park, M.; Kim, S.A.; Shin, J.; Joo, E.-J. Investigation of Gene-Gene Interactions of Clock Genes for Chronotype in a Healthy Korean Population. Genom. Inform. 2020, 18, e38. [Google Scholar] [CrossRef]
PCOS Group | Control Group | p-Value | |
---|---|---|---|
Case number | 49 | 49 | |
Age (years) | 28 (24–33) | 27 (24–30) | 0.448 † |
Weight (kg) | 60.8 (55–74) | 60 (52–64) | 0.037 † |
Height (m) | 1.62 (1.59–1.66) | 1.6 (1.56–1.64) | 0.064 † |
BMI (kg/m2) | 23.16 (21.48–25.6) | 22.6 (20–24.98) | 0.22 † |
Menarche (years) | 13 (12–14) | 12 (11.5–14) | 0.185 † |
Menstrual cycle length (days) | 31 (29.5–45) | 28 (28–30) | <0.0001 † |
Period length (days) | 5 (4–8) | 5 (4–5) | 0.129 † |
FSH (mUl/mL) | 5.95 ± 3.47 | 9.5 ± 5 | <0.0001 †† |
AMH (ng/mL) | 8.02 (5.07–12.55) | 4.87 (3.05–6.77) | <0.0001 † |
LH (mUl/mL) | 6.8 (4.55–10.3) | 3.2 (2.12–5.17) | <0.0001 † |
LH/FSH ratio | 1.27 (0.83–1.74) | 0.38 (0.18–0.64) | <0.0001 † |
TSH (mUl/mL) | 1.67 (1.29–2.69) | 1.65 (1.05–2.47) | 0.284 † |
E2 (pg/mL) | 53.3 (32.72–72.87) | 29.7 (15–40.6) | <0.0001 † |
Total ovarian volume (cm3) | 12.25 (9.62–18.75) | 7.61 (6.63–9.47) | <0.0001 † |
Total AFC (number of follicles) | 27 (23–34,75) | 16 (13–20) | <0.0001 † |
Family History | |||
Family history of polycystic ovaries | 22 (44.8%) | 6 (12.24%) | <0.0001 ††† |
Family history of endometriosis | 10 (20.4%) | 4 (8.16%) | 0.013 ††† |
Family history of breast and ovarian cancer | 10 (20.4%) | 6 (12.24%) | 0.196 ††† |
Reproductive traits | |||
Pregnancies | 12 (24.48%) | 33 (67.34%) | <0.0001 ††† |
Early pregnancy loss | 8 (16.32%) | 2 (4.08%) | 0.045 ††† |
Spontaneous abortion | 7 (14.28%) | 2 (4.08%) | 0.091 ††† |
Endocrine–Metabolic Parameters | Value † |
Androstenedione (ng/mL) | 1.49 ± 0.59 |
DHEAS (μg/dL) | 152.8 ± 64.51 |
Free testosterone (pg/mL) | 1.34 (0.91–2.40) |
Fasting insulin (μUl/mL) | 4.68 (2.62–9.16) |
Post-meal insulin (μUl/mL) | 28.3 (13.1–43.6) |
Fasting blood glucose (mg/dL) | 83.91 ± 8.51 |
Post-meal glucose (mg/dL) | 80.5 (72.5–95) |
HOMA-IR | 0.84 (0.48–1.95) |
HOMA-IS | 0.49 (0.02–0.08) |
Glycosylated hemoglobin (%) | 5.24 (5.01–5.74) |
Clinical Parameters | n (%) †† |
Acne | 30 (60%) |
Hair loss | 43 (86%) |
Facial hair | 34 (68%) |
Abdominal hair | 30 (60%) |
Fatty discharge from scalp and face | 33 (66%) |
Acanthosis nigricans | 10 (20%) |
Cystic lesion resection | 2 (4%) |
Menstrual bleeding stopped for more than 3 months | 30 (60%) |
Multiple menstrual bleeds in one month | 25 (50%) |
Postcoital bleeding | 5 (10%) |
Dysmenorrhea | 29(58%) |
Gene | SNP ID | Chr | Position | Consequence | Alleles | MAF | HWE-p | OR (95% CI) | p-Value | |
---|---|---|---|---|---|---|---|---|---|---|
Case | Control | |||||||||
THADA | rs13429458 | 2 | 43411699 | Intron Variant | A > C | 0.12 | 0.1 | 0.34 | 1.23 (0.50–2.99) | 0.65 |
THADA | rs12478601 | 2 | 43494369 | Intron Variant | C > T | 0.43 | 0.39 | 0.53 | 1.18 (0.67–2.09) | 0.56 |
THADA | rs12468394 | 2 | 43334022 | Intron Variant | C > A | 0.32 | 0.32 | 0.49 | 1.01 (0.55–1.86) | 0.97 |
THADA | rs6544661 | 2 | 43484786 | Intron Variant | A > G | 0.44 | 0.4 | 0.84 | 1.18 (0.67–2.09) | 0.56 |
THADA | rs11891936 | 2 | 43305163 | Intron Variant | C > T | 0.12 | 0.13 | 0.65 | 0.91 (0.39–2.11) | 0.83 |
LHCGR | rs13405728 | 2 | 48751020 | Intron Variant | A > G | 0.1 | 0.14 | 0.63 | 0.68 (0.2–1.6) | 0.38 |
LHCGR | rs7371084 | 2 | 48712814 | Intron Variant | T > C | 0.13 | 0.21 | 0.29 | 0.56 (0.26–1.19) | 0.13 |
LHCGR | rs4953616 | 2 | 48714289 | Intron Variant | T > C | 0.32 | 0.28 | 0.63 | 1.21 (0.6–2.25) | 0.53 |
LHCGR | rs2293275 | 2 | 48694236 | Missense Variant | C > T | 0.36 | 0.29 | 0.49 | 1.52 (0.30–7.53) | 0.28 |
LHCGR | rs6732721 | 2 | 48738464 | Intron Variant | T > C | 0.12 | 0.16 | 0.4 | 0.71 (0.32–1.60) | 0.41 |
FSHR | rs2268361 | 2 | 48974473 | Intron Variant | T > C | 0.35 | 0.41 | 0.28 | 0.77 (0.43–1.37) | 0.38 |
FSHR | rs2349415 | 2 | 49020693 | Intron Variant | C > T | 0.4 | 0.31 | 1 | 1.50 (0.83–2.70) | 0.18 |
FSHR | rs11692782 | 2 | 49064754 | Intron Variant | T > A | 0.37 | 0.48 | 0.84 | 0.63 (0.36–1.12) | 0.11 |
DENND1A | rs2479106 | 9 | 123762933 | Intron Variant | A > G | 0.22 | 0.23 | 0.77 | 0.94 (0.49–1.84) | 0.87 |
DENND1A | rs10818854 | 9 | 123684499 | Intron Variant | G > A | 0.09 | 0.04 | 0.35 | 2.38 (0.71–7.99) | 0.15 |
DENND1A | rs10986105 | 9 | 123787676 | Intron Variant | T > G | 0.09 | 0.03 | 0.3 | 3.20 (0.84–12.21) | 0.07 |
DENND1A | rs12337273 | 9 | 123804666 | Intron Variant | A > G | 0.08 | 0.03 | 0.26 | 2.81 (0.72–10.94) | 0.12 |
DENND1A | rs1778890 | 9 | 123769476 | Intron Variant | T > C | 0.15 | 0.14 | 1 | 1.08 (0.49–2.39) | 0.84 |
DENND1A | rs1627536 | 9 | 123780425 | Intron Variant | A > T | 0.23 | 0.24 | 1 | 0.95 (0.49–1.82) | 0.87 |
DENND1A | rs7857605 | 9 | 123745334 | Intron Variant | T > C | 0.09 | 0.04 | 0.35 | 2.38 (0.71–7.99) | 0.15 |
YAP1 | rs1894116 | 11 | 102199908 | Intron Variant | A > G | 0.03 | 0.05 | 1 | 0.59 (0.14–2.53) | 0.47 |
HMGA2 | rs2272046 | 12 | 65830681 | Intron Variant | A > C | 0.03 | 0.01 | 1 | 3.06 (0.31–29.97) | 0.31 |
ERBB3 | rs2292239 | 12 | 56088396 | Intron Variant | G > T | 0.21 | 0.22 | 1 | 0.94 (0.48–1.85) | 0.86 |
AMHR2 | rs2272002 | 12 | 53424132 | Intron Variant | T > A | 0.07 | 0.1 | 1 | 0.68 (0.25–1.86) | 0.45 |
TOX3 | rs4784165 | 16 | 52313907 | Intron Variant | T > G | 0.28 | 0.37 | 0.65 | 0.66 (0.36–1.20) | 0.17 |
INSR | rs2059807 | 19 | 7166098 | Intron Variant | G > A | 0.45 | 0.5 | 1 | 0.82 (0.47–1.43) | 0.47 |
AMH | rs10407022 | 19 | 2249478 | Missense Variant | T > G | 0.16 | 0.19 | 0.5 | 0.81 (0.39–1.69) | 0.58 |
Model | Bal. Acc. CV Training | Bal. Acc. CV Testing | CV Consistency | OR (95% CI) | p-Value |
---|---|---|---|---|---|
rs7371084 | 0.61 | 0.4184 | 5/10 | 2.59 (1.077–6.232) | 0.0312 |
rs11692782, rs4784165 | 0.6667 | 0.4082 | 3/10 | 3.78 (1.6–8.949) | 0.002 |
rs11692782, rs2268361, rs4784165 | 0.7574 | 0.6327 | 7/10 | 11.29 (4.183–30.49) | p < 0.0001 |
SNP_ID | 2nd-PCRP | 1st-PCRP | AMP_LEN | UP_CONF | MP_CONF | Tm (NN) | PcGC | UEP_DIR | UEP_MASS | UEP_SEQ | EXT1_CALL | EXT1_MASS | EXT1_SEQ | EXT2_CALL | EXT2_MASS | EXT2_SEQ |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
rs10407022 | ACGTTGGATGTCTTCCGAGAAGACTTGGAC | ACGTTGGATGAGCTGCTGCCATTGCTGTC | 110 | 95.6 | 72.3 | 53.7 | 66.7 | F | 4538.0 | ACTGGCCTCCAGGCA | G | 4825.2 | ACTGGCCTCCAGGCAG | T | 4865.1 | ACTGGCCTCCAGGCAT |
rs10818854 | ACGTTGGATGGTGCTTAAAGGTGGGAATGC | ACGTTGGATGCACTGCCTTCTGTAAGACAC | 90 | 99.6 | 72.3 | 47.5 | 60.0 | R | 4664.0 | GGGAATGCTTGCTGG | G | 4911.2 | GGGAATGCTTGCTGGC | A | 4991.1 | GGGAATGCTTGCTGGT |
rs2349415 | ACGTTGGATGAAAAACAGGTGTCAGGCTGG | ACGTTGGATGACAACTCCACGATCTAGGAC | 92 | 99.7 | 72.3 | 46.0 | 50.0 | F | 4952.2 | GTCAGGCTGGATTTGA | C | 5199.4 | GTCAGGCTGGATTTGAC | T | 5279.3 | GTCAGGCTGGATTTGAT |
rs2272002 | ACGTTGGATGGTAAGGGTGAAGGATAGAGC | ACGTTGGATGTATGGTAAAGCCACAGGAGG | 111 | 99.5 | 72.3 | 55.1 | 73.3 | F | 5162.4 | ttTCCCCATGGCAGGGC | A | 5433.6 | ttTCCCCATGGCAGGGCA | T | 5489.5 | ttTCCCCATGGCAGGGCT |
rs12468394 | ACGTTGGATGTCTGTGGCTAACTGCAGAAG | ACGTTGGATGAATGCTGTTTTCAGCTGTTG | 88 | 93.8 | 72.3 | 45.9 | 50.0 | R | 5241.4 | gCTGCAGAAGTTCTGGT | C | 5528.6 | gCTGCAGAAGTTCTGGTG | A | 5568.5 | gCTGCAGAAGTTCTGGTT |
rs1627536 | ACGTTGGATGCATGGCAATAGTAAGTGCTC | ACGTTGGATGCATCCAGTGAATGATGGTGC | 116 | 97.6 | 72.3 | 45.9 | 44.4 | R | 5360.5 | CTTCCCTTCTTAATCCGA | T | 5631.7 | CTTCCCTTCTTAATCCGAA | A | 5687.6 | CTTCCCTTCTTAATCCGAT |
rs13405728 | ACGTTGGATGCTTCAATATCCTGGGCTTAC | ACGTTGGATGGATTTAGAAACCTGCTCTGG | 120 | 95.6 | 72.3 | 49.2 | 42.1 | R | 5762.8 | CCATAATGCAGCCATTTGT | G | 6010.0 | CCATAATGCAGCCATTTGTC | A | 6089.9 | CCATAATGCAGCCATTTGTT |
rs6544661 | ACGTTGGATGAACACATATAGGTGCTCCTC | ACGTTGGATGTCCTCTCATTAGAACATCTC | 93 | 92.8 | 72.3 | 45.6 | 52.9 | F | 5770.7 | gcGGTGCTCCTCTTAGTAC | A | 6042.0 | gcGGTGCTCCTCTTAGTACA | G | 6058.0 | gcGGTGCTCCTCTTAGTACG |
rs2293275 | ACGTTGGATGCAATGTGAAAGCACAGTAAG | ACGTTGGATGCACACAGAACAAGATACGAC | 111 | 92.6 | 72.3 | 47.1 | 44.4 | R | 5934.9 | gGCACAGTAAGGAAAGTGA | T | 6206.1 | gGCACAGTAAGGAAAGTGAA | C | 6222.1 | gGCACAGTAAGGAAAGTGAG |
rs12337273 | ACGTTGGATGAGTGGCTGATACATTGGCTC | ACGTTGGATGACATCTCCACTTGACGTCTC | 109 | 99.7 | 72.3 | 47.3 | 35.0 | R | 6140.0 | AAAGATCAGGAGTTCCATTT | G | 6387.2 | AAAGATCAGGAGTTCCATTTC | A | 6467.1 | AAAGATCAGGAGTTCCATTTT |
rs2268361 | ACGTTGGATGTTGATGCTGTGAGACGAAGG | ACGTTGGATGTTCTTACCAAGAGCTCCCTC | 110 | 99.6 | 72.3 | 46.4 | 50.0 | F | 6173.0 | gtgcGACGAAGGCATCTTGT | C | 6420.2 | gtgcGACGAAGGCATCTTGTC | T | 6500.1 | gtgcGACGAAGGCATCTTGTT |
rs2059807 | ACGTTGGATGATGTGAATCAGACCTCTTGC | ACGTTGGATGAGCCAATAACCATATCAAGG | 98 | 93.0 | 72.3 | 48.0 | 33.3 | R | 6355.2 | AATCAGACCTCTTGCTTTTAA | G | 6602.3 | AATCAGACCTCTTGCTTTTAAC | A | 6682.3 | AATCAGACCTCTTGCTTTTAAT |
rs2272046 | ACGTTGGATGGGATTCAGTAATTGGCCTTG | ACGTTGGATGACATTCTGCATGCATTGTCC | 109 | 96.8 | 72.3 | 50.4 | 52.9 | F | 6533.2 | ggagTGGCCTTGGGACATTTG | C | 6780.4 | ggagTGGCCTTGGGACATTTGC | A | 6804.4 | ggagTGGCCTTGGGACATTTGA |
rs11692782 | ACGTTGGATGACAGTTTCTCAGATCCCTTG | ACGTTGGATGTGGTGTTGTACTTCAGTACG | 97 | 97.1 | 72.3 | 50.1 | 40.9 | R | 6642.3 | TTCTCAGATCCCTTGGTTATTC | T | 6913.5 | TTCTCAGATCCCTTGGTTATTCA | A | 6969.4 | TTCTCAGATCCCTTGGTTATTCT |
rs12478601 | ACGTTGGATGAGAGCTGGAAGTAAAGCCCG | ACGTTGGATGTTCTTTCATTCCTGCTGGTC | 93 | 97.0 | 72.3 | 48.4 | 38.1 | R | 6740.4 | gCGGGTCCTAACATTTTATTGA | T | 7011.6 | gCGGGTCCTAACATTTTATTGAA | C | 7027.6 | gCGGGTCCTAACATTTTATTGAG |
rs4953616 | ACGTTGGATGACTTCATCAGCCACTCTATG | ACGTTGGATGCTACATAACCACACTGAGGG | 116 | 97.6 | 72.3 | 47.1 | 34.8 | F | 6868.5 | CCTCATCATCATTTCCATTATAC | C | 7115.7 | CCTCATCATCATTTCCATTATACC | T | 7195.6 | CCTCATCATCATTTCCATTATACT |
rs1778890 | ACGTTGGATGGAATGTTAAGAATGGTATGG | ACGTTGGATGATGTGGACAGGTAGTGTCAG | 116 | 86.9 | 72.3 | 46.1 | 26.1 | F | 7058.6 | ATTTTCTATAGCAGGTTTATTGA | C | 7305.8 | ATTTTCTATAGCAGGTTTATTGAC | T | 7385.7 | ATTTTCTATAGCAGGTTTATTGAT |
rs6732721 | ACGTTGGATGGACATAGCAGGAGTTGTCAG | ACGTTGGATGTTCCTGTCACTCCATCGTTG | 90 | 99.6 | 72.3 | 45.7 | 40.0 | R | 7152.7 | cggTGTCAGGAAGAGTAATCTAG | T | 7423.9 | cggTGTCAGGAAGAGTAATCTAGA | C | 7439.9 | cggTGTCAGGAAGAGTAATCTAGG |
rs11891936 | ACGTTGGATGCACTCTTAACGTCAATGTCC | ACGTTGGATGGTTCCTATGGTTTCCTTTTC | 100 | 93.0 | 72.3 | 45.4 | 36.8 | F | 7234.7 | tcattTCCTGTTATGCAATTTCTC | C | 7481.9 | tcattTCCTGTTATGCAATTTCTCC | T | 7561.8 | tcattTCCTGTTATGCAATTTCTCT |
rs2479106 | ACGTTGGATGGACTCCTGTCCTTTTGGTTC | ACGTTGGATGACAGGGCACTGGGTTGTTTC | 120 | 97.0 | 72.3 | 47.9 | 36.4 | R | 7348.8 | tgTTGGTTCCTTGATCATAACTAG | G | 7596.0 | tgTTGGTTCCTTGATCATAACTAGC | A | 7675.9 | tgTTGGTTCCTTGATCATAACTAGT |
rs7857605 | ACGTTGGATGAAAGCCCATGAGATCTAGGT | ACGTTGGATGTAGCAACACCTCTGCAAACG | 104 | 97.3 | 72.3 | 47.1 | 30.4 | R | 7525.9 | gaCCTTATTTACTTCTCCAAACATT | T | 7797.1 | gaCCTTATTTACTTCTCCAAACATTA | C | 7813.1 | gaCCTTATTTACTTCTCCAAACATTG |
rs7371084 | ACGTTGGATGCAGTCCCACTATTTAACAGC | ACGTTGGATGCAAGCCTATTATTGGATCCAT | 120 | 85.2 | 72.3 | 47.7 | 38.1 | R | 7634.0 | agacGCAAGTTACTTAACCGATCTA | T | 7905.2 | agacGCAAGTTACTTAACCGATCTAA | C | 7921.2 | agacGCAAGTTACTTAACCGATCTAG |
rs13429458 | ACGTTGGATGATGCACAATGGAGACTGCTG | ACGTTGGATGTAATTAGTGGCAGGGTATAG | 99 | 94.4 | 72.3 | 46.9 | 33.3 | F | 7738.1 | gcttTGCAAAGTTAGAAGATGAAAC | C | 7985.3 | gcttTGCAAAGTTAGAAGATGAAACC | A | 8009.3 | gcttTGCAAAGTTAGAAGATGAAACA |
rs2292239 | ACGTTGGATGGCTATCACCCTTACTTCTGC | ACGTTGGATGACCCTAGATCCCTTAAGTGC | 106 | 99.9 | 72.3 | 45.5 | 33.3 | F | 7761.1 | gggcGTGAAGAGACTTTTGAATCTA | G | 8048.3 | gggcGTGAAGAGACTTTTGAATCTAG | T | 8088.2 | gggcGTGAAGAGACTTTTGAATCTAT |
rs1894116 | ACGTTGGATGAAATTTAGTTGCATTGAGG | ACGTTGGATGAAGGATTGACCACTGTCAAG | 113 | 77.8 | 72.3 | 46.7 | 22.2 | R | 8231.4 | TCTACATAATATTGATTCTAGACAATT | G | 8478.6 | TCTACATAATATTGATTCTAGACAATTC | A | 8558.5 | TCTACATAATATTGATTCTAGACAATTT |
rs10986105 | ACGTTGGATGTCCATCACAATTAGCCTGAG | ACGTTGGATGCACTATAGGCAGTTAAACAA | 116 | 84.5 | 72.3 | 50.0 | 36.4 | F | 8363.4 | gggagTTAGCCTGAGTTATGCAACATA | G | 8650.7 | gggagTTAGCCTGAGTTATGCAACATAG | T | 8690.5 | gggagTTAGCCTGAGTTATGCAACATAT |
rs4784165 | ACGTTGGATGGAGCCAGCCGTACATTAATC | ACGTTGGATGGGAATTTAAGTTATTTTCCC | 115 | 78.6 | 72.3 | 49.3 | 28.6 | R | 8612.7 | GTCACATAATAACTTGAAAAACTATGAG | G | 8859.8 | GTCACATAATAACTTGAAAAACTATGAGC | T | 8883.9 | GTCACATAATAACTTGAAAAACTATGAGA |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 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
Alarcón-Granados, M.C.; Camargo-Villalba, G.E.; Forero-Castro, M. Exploring Genetic Interactions in Colombian Women with Polycystic Ovarian Syndrome: A Study on SNP-SNP Associations. Int. J. Mol. Sci. 2024, 25, 9212. https://doi.org/10.3390/ijms25179212
Alarcón-Granados MC, Camargo-Villalba GE, Forero-Castro M. Exploring Genetic Interactions in Colombian Women with Polycystic Ovarian Syndrome: A Study on SNP-SNP Associations. International Journal of Molecular Sciences. 2024; 25(17):9212. https://doi.org/10.3390/ijms25179212
Chicago/Turabian StyleAlarcón-Granados, Maria Camila, Gloria Eugenia Camargo-Villalba, and Maribel Forero-Castro. 2024. "Exploring Genetic Interactions in Colombian Women with Polycystic Ovarian Syndrome: A Study on SNP-SNP Associations" International Journal of Molecular Sciences 25, no. 17: 9212. https://doi.org/10.3390/ijms25179212