Trends in DNA Methylation over Time Between Parous and Nulliparous Young Women
Abstract
1. Introduction
2. Results
2.1. Population and Participant Characteristics
2.2. Trends in DNA Methylation from Age 18 to 26 Years Between Parous and Nulliparous Women
2.3. Validation and Comparison with Other Studies
2.4. Biological Pathway Analysis
3. Discussion
4. Materials and Methods
4.1. Study Population
4.2. Parous Status
4.3. DNA Methylation Measurements, Processing, and Quality Control
4.4. Cell Estimation
4.5. Confounding Variables
4.6. Statistical Analysis
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
BMI | Body mass index |
CpG | Cytosine-phosphate-Guanine dinucleotide |
DNAm | DNA methylation |
FDR | False discovery rate |
IOW | Isle of Wight |
SES | Socioeconomic status |
References
- Crump, C.; Sundquist, J.; McLaughlin, M.A.; Dolan, S.M.; Govindarajulu, U.; Sieh, W.; Sundquist, K. Adverse pregnancy outcomes and long term risk of ischemic heart disease in mothers: National cohort and co-sibling study. BMJ 2023, 380, e072112. [Google Scholar] [CrossRef]
- Parikh, N.I.; Cnattingius, S.; Dickman, P.W.; Mittleman, M.A.; Ludvigsson, J.F.; Ingelsson, E. Parity and risk of later-life maternal cardiovascular disease. Am. Heart J. 2010, 159, 215–221.e216. [Google Scholar] [CrossRef] [PubMed]
- Li, W.; Ruan, W.; Lu, Z.; Wang, D. Parity and risk of maternal cardiovascular disease: A dose-response meta-analysis of cohort studies. Eur. J. Prev. Cardiol. 2019, 26, 592–602. [Google Scholar] [CrossRef] [PubMed]
- Staff, A.C.; Redman, C.W.; Williams, D.; Leeson, P.; Moe, K.; Thilaganathan, B.; Magnus, P.; Steegers, E.A.; Tsigas, E.Z.; Ness, R.B.; et al. Pregnancy and Long-Term Maternal Cardiovascular Health: Progress Through Harmonization of Research Cohorts and Biobanks. Hypertension 2016, 67, 251–260. [Google Scholar] [CrossRef]
- de Lange, A.G.; Kaufmann, T.; van der Meer, D.; Maglanoc, L.A.; Alnaes, D.; Moberget, T.; Douaud, G.; Andreassen, O.A.; Westlye, L.T. Population-based neuroimaging reveals traces of childbirth in the maternal brain. Proc. Natl. Acad. Sci. USA 2019, 116, 22341–22346. [Google Scholar] [CrossRef]
- Sun, M.H.; Wen, Z.Y.; Wang, R.; Gao, C.; Yin, J.L.; Chang, Y.J.; Wu, Q.J.; Zhao, Y.H. Parity and Metabolic Syndrome Risk: A Systematic Review and Meta-Analysis of 15 Observational Studies With 62,095 Women. Front. Med. (Lausanne) 2022, 9, 926944. [Google Scholar] [CrossRef]
- Song, S.Y.; Kim, Y.; Park, H.; Kim, Y.J.; Kang, W.; Kim, E.Y. Effect of parity on bone mineral density: A systematic review and meta-analysis. Bone 2017, 101, 70–76. [Google Scholar] [CrossRef]
- Chen, S.; Johs, M.; Karmaus, W.; Holloway, J.W.; Rahimabad, P.K.; Goodrich, J.M.; Peterson, K.E.; Dolinoy, D.C.; Arshad, H.S.; Ewart, S. Assessing the effect of childbearing on blood DNA methylation through comparison of parous and nulliparous females. Epigenetics Commun. 2024, 4, 2. [Google Scholar] [CrossRef]
- Lin, M.W.; Tsai, M.H.; Shih, C.Y.; Tai, Y.Y.; Lee, C.N.; Lin, S.Y. Comparison of DNA Methylation Changes Between the Gestation Period and the After-Delivery State: A Pilot Study of 10 Women. Front. Nutr. 2022, 9, 829915. [Google Scholar] [CrossRef]
- Das, J.; Maitra, A. Maternal DNA Methylation During Pregnancy: A Review. Reprod. Sci. 2021, 28, 2758–2769. [Google Scholar] [CrossRef]
- Dias, S.; Willmer, T.; Adam, S.; Pheiffer, C. The role of maternal DNA methylation in pregnancies complicated by gestational diabetes. Front. Clin. Diabetes Healthc. 2022, 3, 982665. [Google Scholar] [CrossRef] [PubMed]
- Feigman, M.J.; Moss, M.A.; Chen, C.; Cyrill, S.L.; Ciccone, M.F.; Trousdell, M.C.; Yang, S.T.; Frey, W.D.; Wilkinson, J.E.; Dos Santos, C.O. Pregnancy reprograms the epigenome of mammary epithelial cells and blocks the development of premalignant lesions. Nat. Commun. 2020, 11, 2649. [Google Scholar] [CrossRef] [PubMed]
- Best, J.D.; Carey, N. The Epigenetics of Normal Pregnancy. Obstet. Med. 2013, 6, 3–7. [Google Scholar] [CrossRef] [PubMed]
- Carroll, J.E.; Ross, K.M.; Horvath, S.; Okun, M.; Hobel, C.; Rentscher, K.E.; Coussons-Read, M.; Schetter, C.D. Postpartum sleep loss and accelerated epigenetic aging. Sleep. Health 2021, 7, 362–367. [Google Scholar] [CrossRef]
- Hartwig, F.P.; Davey Smith, G.; Simpkin, A.J.; Victora, C.G.; Relton, C.L.; Caramaschi, D. Association between Breastfeeding and DNA Methylation over the Life Course: Findings from the Avon Longitudinal Study of Parents and Children (ALSPAC). Nutrients 2020, 12, 3309. [Google Scholar] [CrossRef]
- Lapato, D.M.; Roberson-Nay, R.; Kirkpatrick, R.M.; Webb, B.T.; York, T.P.; Kinser, P.A. DNA methylation associated with postpartum depressive symptoms overlaps findings from a genome-wide association meta-analysis of depression. Clin. Epigenetics 2019, 11, 169. [Google Scholar] [CrossRef]
- Gruzieva, O.; Merid, S.K.; Chen, S.; Mukherjee, N.; Hedman, A.M.; Almqvist, C.; Andolf, E.; Jiang, Y.; Kere, J.; Scheynius, A.; et al. DNA Methylation Trajectories During Pregnancy. Epigenet Insights 2019, 12, 2516865719867090. [Google Scholar] [CrossRef]
- Fradin, D.; Tost, J.; Busato, F.; Mille, C.; Lachaux, F.; Deleuze, J.F.; Apter, G.; Benachi, A. DNA methylation dynamics during pregnancy. Front. Cell Dev. Biol. 2023, 11, 1185311. [Google Scholar] [CrossRef]
- Johansson, A.; Enroth, S.; Gyllensten, U. Continuous Aging of the Human DNA Methylome Throughout the Human Lifespan. PLoS ONE 2013, 8, e67378. [Google Scholar] [CrossRef]
- Wang, K.; Liu, H.; Hu, Q.; Wang, L.; Liu, J.; Zheng, Z.; Zhang, W.; Ren, J.; Zhu, F.; Liu, G.H. Epigenetic regulation of aging: Implications for interventions of aging and diseases. Signal Transduct. Target. Ther. 2022, 7, 374. [Google Scholar] [CrossRef]
- Lv, L.; Xu, J.; Zhao, S.; Chen, C.; Zhao, X.; Gu, S.; Ji, C.; Xie, Y.; Mao, Y. Sequence analysis of a human RhoGAP domain-containing gene and characterization of its expression in human multiple tissues. DNA Seq. 2007, 18, 184–189. [Google Scholar] [CrossRef] [PubMed]
- OMIM. Available online: https://omim.org/entry/602503?search=FRAT1&highlight=frat1 (accessed on 22 April 2025).
- Tan, C.; Wang, J.; Ye, X.; Kasimu, K.; Li, Y.; Luo, F.; Yi, H.; Luo, Y. Genome-wide CRISPR/Cas9 screening identifies key profibrotic regulators of TGF-beta1-induced epithelial-mesenchymal transformation and pulmonary fibrosis. Front. Mol. Biosci. 2025, 12, 1507163. [Google Scholar] [CrossRef] [PubMed]
- Khan, M.I.; Choi, S.; Zahid, M.; Ahmad, H.; Ali, R.; Jelani, M.; Kang, C. Whole-exome sequencing analysis reveals co-segregation of a COL20A1 missense mutation in a Pakistani family with striate palmoplantar keratoderma. Genes Genom. 2018, 40, 789–795. [Google Scholar] [CrossRef] [PubMed]
- Sandholm, N.; Cole, J.B.; Nair, V.; Sheng, X.; Liu, H.; Ahlqvist, E.; van Zuydam, N.; Dahlstrom, E.H.; Fermin, D.; Smyth, L.J.; et al. Genome-wide meta-analysis and omics integration identifies novel genes associated with diabetic kidney disease. Diabetologia 2022, 65, 1495–1509. [Google Scholar] [CrossRef]
- Vitanza, N.A.; Biery, M.C.; Myers, C.; Ferguson, E.; Zheng, Y.; Girard, E.J.; Przystal, J.M.; Park, G.; Noll, A.; Pakiam, F.; et al. Optimal therapeutic targeting by HDAC inhibition in biopsy-derived treatment-naive diffuse midline glioma models. Neuro Oncol. 2021, 23, 376–386. [Google Scholar] [CrossRef]
- Hill, E.B.; Konigsberg, I.R.; Ir, D.; Frank, D.N.; Jambal, P.; Litkowski, E.M.; Lange, E.M.; Lange, L.A.; Ostendorf, D.M.; Scorsone, J.J.; et al. The Microbiome, Epigenome, and Diet in Adults with Obesity during Behavioral Weight Loss. Nutrients 2023, 15, 3588. [Google Scholar] [CrossRef]
- Liu, K.; Jiang, J.; Lin, Y.; Liu, W.; Zhu, X.; Zhang, Y.; Jiang, H.; Yu, K.; Liu, X.; Zhou, M.; et al. Exposure to polycyclic aromatic hydrocarbons, DNA methylation and heart rate variability among non-current smokers. Environ. Pollut. 2021, 288, 117777. [Google Scholar] [CrossRef]
- Marcelis, L.; Folpe, A.L. “Putting the cart before the horse”: An update on promiscuous gene fusions in soft tissue tumors. Virchows Arch. 2025, 486, 905–921. [Google Scholar] [CrossRef]
- Davis, J.L.; Al-Ibraheemi, A.; Rudzinski, E.R.; Surrey, L.F. Mesenchymal neoplasms with NTRK and other kinase gene alterations. Histopathology 2022, 80, 4–18. [Google Scholar] [CrossRef]
- Dashti, N.K.; Schukow, C.P.; Kilpatrick, S.E. Back to the future! Selected bone and soft tissue neoplasms with shared genetic alterations but differing morphological and immunohistochemical phenotypes. Hum. Pathol. 2024, 147, 129–138. [Google Scholar] [CrossRef]
- Knezevich, S.R.; McFadden, D.E.; Tao, W.; Lim, J.F.; Sorensen, P.H. A novel ETV6-NTRK3 gene fusion in congenital fibrosarcoma. Nat. Genet. 1998, 18, 184–187. [Google Scholar] [CrossRef] [PubMed]
- Alassiri, A.H.; Ali, R.H.; Shen, Y.; Lum, A.; Strahlendorf, C.; Deyell, R.; Rassekh, R.; Sorensen, P.H.; Laskin, J.; Marra, M.; et al. ETV6-NTRK3 Is Expressed in a Subset of ALK-Negative Inflammatory Myofibroblastic Tumors. Am. J. Surg. Pathol. 2016, 40, 1051–1061. [Google Scholar] [CrossRef] [PubMed]
- Roberts, K.G.; Janke, L.J.; Zhao, Y.; Seth, A.; Ma, J.; Finkelstein, D.; Smith, S.; Ebata, K.; Tuch, B.B.; Hunger, S.P.; et al. ETV6-NTRK3 induces aggressive acute lymphoblastic leukemia highly sensitive to selective TRK inhibition. Blood 2018, 132, 861–865. [Google Scholar] [CrossRef] [PubMed]
- Tognon, C.; Knezevich, S.R.; Huntsman, D.; Roskelley, C.D.; Melnyk, N.; Mathers, J.A.; Becker, L.; Carneiro, F.; MacPherson, N.; Horsman, D.; et al. Expression of the ETV6-NTRK3 gene fusion as a primary event in human secretory breast carcinoma. Cancer Cell 2002, 2, 367–376. [Google Scholar] [CrossRef]
- Nie, A.; Sun, B.; Fu, Z.; Yu, D. Roles of aminoacyl-tRNA synthetases in immune regulation and immune diseases. Cell Death Dis. 2019, 10, 901. [Google Scholar] [CrossRef]
- Yoon, I.; Kim, U.; Choi, J.; Kim, S. Disease association and therapeutic routes of aminoacyl-tRNA synthetases. Trends Mol. Med. 2024, 30, 89–105. [Google Scholar] [CrossRef]
- Yao, P.; Fox, P.L. Aminoacyl-tRNA synthetases in cell signaling. Enzymes 2020, 48, 243–275. [Google Scholar] [CrossRef]
- Sung, Y.; Yoon, I.; Han, J.M.; Kim, S. Functional and pathologic association of aminoacyl-tRNA synthetases with cancer. Exp. Mol. Med. 2022, 54, 553–566. [Google Scholar] [CrossRef]
- Deng, X.; Gong, X.; Zhou, D.; Hong, Z. Perturbations in gut microbiota composition in patients with autoimmune neurological diseases: A systematic review and meta-analysis. Front. Immunol. 2025, 16, 1513599. [Google Scholar] [CrossRef]
- Arshad, S.H.; Patil, V.; Mitchell, F.; Potter, S.; Zhang, H.; Ewart, S.; Mansfield, L.; Venter, C.; Holloway, J.W.; Karmaus, W.J. Cohort Profile Update: The Isle of Wight Whole Population Birth Cohort (IOWBC). Int. J. Epidemiol. 2020, 49, 1083–1084. [Google Scholar] [CrossRef]
- Miller, S.A.; Dykes, D.D.; Polesky, H.F. A simple salting out procedure for extracting DNA from human nucleated cells. Nucleic Acids Res. 1988, 16, 1215. [Google Scholar] [CrossRef] [PubMed]
- Lehne, B.; Drong, A.W.; Loh, M.; Zhang, W.; Scott, W.R.; Tan, S.T.; Afzal, U.; Schulz, R.; Scott, J.; Jarvelin, M.R.; et al. Erratum to: A coherent approach for analysis of the Illumina HumanMethylation450 BeadChip improves data quality and performance in epigenome-wide association studies. Genome Biol. 2016, 17, 73. [Google Scholar] [CrossRef] [PubMed]
- Aryee, M.J.; Jaffe, A.E.; Corrada-Bravo, H.; Ladd-Acosta, C.; Feinberg, A.P.; Hansen, K.D.; Irizarry, R.A. Minfi: A flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics 2014, 30, 1363–1369. [Google Scholar] [CrossRef] [PubMed]
- Du, P.; Zhang, X.; Huang, C.C.; Jafari, N.; Kibbe, W.A.; Hou, L.; Lin, S.M. Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinform. 2010, 11, 587. [Google Scholar] [CrossRef]
- Johnson, W.E.; Li, C.; Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 2007, 8, 118–127. [Google Scholar] [CrossRef]
- Horvath, S. DNA methylation age of human tissues and cell types. Genome Biol. 2013, 14, R115, Erratum in Genome Biol. 2015, 16, 96. [Google Scholar] [CrossRef]
- Qi, L.; Teschendorff, A.E. Cell-type heterogeneity: Why we should adjust for it in epigenome and biomarker studies. Clin. Epigenetics 2022, 14, 31. [Google Scholar] [CrossRef]
- Liang, L.; Cookson, W.O. Grasping nettles: Cellular heterogeneity and other confounders in epigenome-wide association studies. Hum. Mol. Genet. 2014, 23, R83–R88. [Google Scholar] [CrossRef]
- Jaffe, A.E.; Irizarry, R.A. Accounting for cellular heterogeneity is critical in epigenome-wide association studies. Genome Biol. 2014, 15, R31. [Google Scholar] [CrossRef]
- Houseman, E.A.; Kim, S.; Kelsey, K.T.; Wiencke, J.K. DNA Methylation in Whole Blood: Uses and Challenges. Curr. Environ. Health Rep. 2015, 2, 145–154. [Google Scholar] [CrossRef]
- Benjamini, Y.; Hochberg, Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J. R. Stat. Society. Ser. B (Methodol.) 1995, 57, 289–300. [Google Scholar] [CrossRef]
Characteristic | Measurement Time | Status | Cohort Population (%) * | Analyzed Parous Samples: Age 18→ Gestation (%) ** | Analyzed Parous Samples: Gestation → Age 26 (%) *** | Analyzed Nulliparous Samples: Age 18 → Age 26 (%) **** |
---|---|---|---|---|---|---|
Active Smoking | Age 18 | Y (%) | 159 (29%) | 25 (39%) | - | 10 (19%) |
N (%) | 388 (71%) | 39 (61%) | - | 44 (81%) | ||
Pregnancy | Y (%) | 178 (32%) | 54 (32%) | 40 (41%) | - | |
N (%) | 385 (68%) | 115 (68%) | 58 (59%) | - | ||
Age 26 | Y (%) | 176 (31%) | - | 20 (42%) | 20 (33%) | |
N (%) | 385 (69%) | - | 28 (58%) | 41 (67%) | ||
Passive Smoking | Age 18 | Y (%) | 438 (67%) | 67 (86%) | - | 43 (70%) |
N (%) | 215 (33%) | 11 (14%) | - | 18 (30%) | ||
Pregnancy | Y (%) | 171 (34%) | 40 (24%) | 38 (39%) | - | |
N (%) | 330 (66%) | 125 (76%) | 60 (61%) | - | ||
Age 26 | Y (%) | 148 (26%) | - | 15 (31%) | 18 (30%) | |
N (%) | 412 (74%) | - | 33 (69%) | 43 (70%) | ||
Socioeconomic Status (SES) | Age 18 | 1: low education, low housing, low income (%) | 130 (20%) | 16 (21%) | - | 13 (21%) |
2: low education, low housing, high income (%) | 269 (42%) | 23 (29%) | - | 29 (48%) | ||
3: high education, low housing, medium income (%) | 122 (19%) | 20 (26%) | - | 14 (23%) | ||
4: high education, high housing, high income (%) | 124 (19%) | 19 (24%) | - | 5 (8%) | ||
Pregnancy | 1: low education, low # rooms, low income (%) | 77 (19%) | 23 (19%) | 20 (21%) | - | |
2: low to medium education, low # rooms, low income (%) | 97 (24%) | 36 (29%) | 29 (30%) | - | ||
3: high education, low # room, low to medium income (%) | 114 (29%) | 41 (33%) | 25 (26%) | - | ||
4: low to medium education, high # rooms, medium income (%) | 61 (15%) | 16 (13%) | 15 (16%) | - | ||
5: medium education, high # rooms, high income (%) | 51 (13%) | 8 (6%) | 7 (7%) | - | ||
Age 26 | 1: low education, low housing, low income (%) | 85 (30%) | - | 21 (44%) | 9 (15%) | |
2: low education, low housing, high income (%) | 34 (12%) | - | 7 (15%) | 6 (10%) | ||
3: medium education, high housing, low income (%) | 63 (22%) | - | 6 (13%) | 16 (26%) | ||
4: high education, low housing, medium income (%) | 65 (23%) | - | 13 (27%) | 18 (30%) | ||
5: medium education, high housing, high income (%) | 38 (13%) | - | 1 (2%) | 12 (20%) | ||
Body Mass Index (BMI) | Age 18 | Mean (SD) | 24 (11.6) | - | - | 23 (4.0) |
Age 26 | Mean (SD) | 27(6.8) | - | - | 26 (5.5) | |
Birth Order | Pregnancy | 1 (%) | 233 (57%) | 61 (52%) | 56 (58%) | - |
2 (%) | 126 (31%) | 41 (35%) | 28 (29%) | - | ||
3 (%) | 38 (9%) | 10 (8%) | 9 (9%) | - | ||
4+ (%) | 12 (3%) | 6 (5%) | 3 (3%) | - |
Study Population (%) * | Analyzed Samples (Age 18→ Gestation) (%) ** | Analyzed Samples (Gestation → Age 26) (%) *** | ||
---|---|---|---|---|
Number of pregnancies with DNAm per mother | 1 | 143 (70%) | 42 (54%) | 34 (70%) |
2 | 53 (26%) | 30 (38%) | 13 (27%) | |
3 | 8 (4%) | 5 (6%) | 1 (2%) | |
4 | 1 (1%) | 1 (1%) | 0 (0%) | |
Number of DNAm measurements per pregnancy | 1 | 176 (64%) | 73 (60%) | 28 (44%) |
2 | 101 (36%) | 48 (40%) | 35 (56%) | |
Gestational age (weeks) | Mean (SD) | 21.5 (8.3) | 21.3 (8.3) | 21.4 (8.5) |
Range | 8~40 | 9~39 | 9~39 | |
Mother’s age at pregnancy (years) | Mean (SD) | 24.4 (3.4) | 25 (3.6) | 22.8 (1.6) |
Range | 18~40 | 17~38 | 18~25 |
Panel | CpG | Chr | Gene Name | Chen et al., 2024 [8] (Parous vs. Nulliparous) | Parous (Pre-Pregnancy to Gestation) | Parous (Gestation to Post- Pregnancy) | Nulliparous (Age 18 to Age 26) | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Coef (FDRp) ** | Coef | p-Value | FDR-p | Coef | p-Value | FDR-p | Coef | p-Value | FDR-p | ||||
A | cg13600489 | 14 | NKX2-1 | 0.51(5 ) | −0.025 | 0.882 | 0.912 | 0.423 | 2.2 | 3.1 | 0.307 | 1.8 | 2.1 |
cg24693287 | 1 | SERINC2 | 0.50(6 ) | 0.435 | 0.097 | 0.206 | 0.799 | 7.2 | 1.9 | 0.741 | 1.4 | 2.1 | |
cg27549834 | 3 | MYRIP * | 0.28(7 ) | 0.236 | 0.336 | 0.495 | 0.716 | 1.3 | 3.1 | 0.808 | 2.3 | 4.5 | |
cg20824761 | 15 | PAQR5 * | 0.53(9 ) | 0.431 | 0.569 | 0.692 | 1.746 | 4.4 | 8.7 | 1.865 | 1.2 | 2.1 | |
cg24737639 | 12 | NUP37 *; C12orf48 * | 0.37(1 ) | 0.187 | 0.691 | 0.776 | 1.206 | 7.3 | 1.4 | 1.417 | 5.5 | 9.3 | |
cg25074185 | 11 | PHOX2A * | 0.45(1 ) | 0.895 | 0.086 | 0.194 | 0.843 | 1.5 | 2.3 | 1.886 | 2.4 | 4.6 | |
cg09754845 | 7 | MICALL2; UNCX | 0.29(2 ) | 0.123 | 0.453 | 0.586 | 0.309 | 2.9 | 4.1 | 0.316 | 3.1 | 3.8 | |
cg27395066 | 17 | ACBD4 | 0.22(4 ) | 0.254 | 0.186 | 0.309 | 0.685 | 2.6 | 5.3 | 0.508 | 5.6 | 7.3 | |
B | cg11236850 | 4 | ACOX3 * | −0.19(9 | 0.362 | 0.101 | 0.208 | 0.846 | 4.2 | 1.2 | 1.135 | 1.5 | 5.9 |
cg25418406 | 17 | RANGRF; SLC25A35 | −0.30(1 ) | 0.787 | 0.103 | 0.208 | 0.712 | 1.2 | 1.9 | 2.480 | 2.6 | 7.4 | |
cg25734490 | 12 | ASCL1 | −0.28(3 | 0.456 | 0.063 | 0.157 | 0.376 | 2.8 | 3.9 | 1.221 | 3.0 | 7.5 | |
cg26316702 | 2 | TEKT4 | −0.09(6 ) | 0.102 | 0.039 | 0.128 | 0.140 | 4.0 | 6.3 | 0.138 | 1.1 | 1.4 | |
C | cg10773016 | 4 | BLOC1S4; KIAA0232 * | −0.21(3 ) | 0.561 | 9.0 | 6.8 | 0.149 | 0.223 | 0.257 | 0.580 | 2.5 | 4.0 |
Panel | CpG | Chr | Gene Name | Chen et al., 2024 [8] (Parous vs. Nulliparous) | Parous (Age 18 to Gestation) | Parous (Gestation to Age 26) | Nulliparous (Age 18 to Age 26) | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Coef (FDRp) * | Coef | p-Value | FDR-p | Coef | p-Value | FDR-p | Coef | p-Value | FDR-p | ||||
A | cg04413148 | 16 | CTRL | 0.16(5 ) | −0.186 | 0.145 | 0.266 | −0.683 | 4.4 | 3.7 | −0.900 | 1.4 | 3.5 |
cg25364469 | 3 | ZBTB20 | 0.18(9 ) | −0.159 | 0.095 | 0.205 | −0.310 | 9.5 | 2.0 | −0.788 | 2.5 | 3.0 | |
cg17675386 | 10 | RGS10 | 0.17(9 ) | 0.022 | 0.820 | 0.869 | −0.544 | 9.9 | 6.8 | −0.685 | 1.7 | 6.7 | |
cg14312661 | 11 | CARS | 0.14(9 ) | −0.139 | 0.039 | 0.128 | −0.707 | 1.2 | 6.9 | −0.951 | 3.4 | 1.3 | |
cg00050271 | 16 | CMIP | 0.29(9 ) | −0.207 | 0.084 | 0.192 | −0.515 | 1.5 | 3.6 | −0.944 | 1.0 | 8.8 | |
cg20009923 | 12 | ATP2B1 | 0.29(1 ) | −0.042 | 0.792 | 0.844 | −0.535 | 3.3 | 7.4 | −0.768 | 4.9 | 1.0 | |
cg20368567 | 17 | NF1; EVI2A | 0.30(1 ) | −0.079 | 0.613 | 0.720 | −0.617 | 5.2 | 1.9 | −0.867 | 9.8 | 3.1 | |
cg00243040 | 7 | SND1; MIR129-1; LEP | 0.19(1 ) | −0.066 | 0.425 | 0.571 | −0.312 | 5.8 | 1.3 | −0.500 | 8.0 | 2.1 | |
cg10705060 | 3 | BFSP2 | 0.33(1 ) | −0.158 | 0.374 | 0.521 | −0.755 | 1.1 | 3.6 | −1.273 | 4.0 | 1.3 | |
cg08557624 | 6 | FARS2 | 0.28(2 ) | −0.172 | 0.257 | 0.406 | −0.298 | 3.5 | 4.9 | −0.679 | 2.5 | 4.3 | |
cg03972656 | 18 | SETBP1 | 0.22(2 ) | −0.227 | 0.034 | 0.120 | −0.700 | 1.4 | 1.3 | −0.886 | 1.5 | 5.9 | |
cg03626857 | 19 | ZNF227 | 0.24(2 ) | −0.069 | 0.655 | 0.751 | −0.525 | 3.8 | 8.4 | −0.553 | 7.4 | 1.4 | |
cg08285768 | 15 | AKAP13 | 0.27(2 ) | −0.413 | 0.062 | 0.157 | −0.829 | 3.8 | 1.1 | −0.941 | 2.4 | 5.3 | |
cg06944982 | 8 | PTK2 | 0.33(2 ) | −0.103 | 0.616 | 0.720 | −0.761 | 4.1 | 1.2 | −0.899 | 2.2 | 4.5 | |
cg23033749 | 7 | ST7 | 0.10(2 ) | −0.160 | 0.045 | 0.136 | −0.159 | 1.1 | 1.6 | −0.205 | 3.9 | 5.7 | |
cg21879513 | 20 | COL20A1 | 0.15(2 ) | 0.081 | 0.567 | 0.692 | −0.313 | 2.0 | 2.9 | −0.396 | 2.3 | 2.9 | |
cg26436731 | 1 | SPMIP3; ZBTB18 | 0.10(2 ) | −0.174 | 0.038 | 0.128 | −0.606 | 7.0 | 7.9 | −0.722 | 7.3 | 3.8 | |
B | cg02133624 | 3 | DLG1; DLG1-AS1 | 0.16(1 ) | −0.310 | 1.3 | 4.0 | −0.294 | 1.3 | 4.1 | −0.705 | 5.9 | 5.3 |
cg19035181 | 20 | NINL; NANP; GINS1 | 0.14(6 ) | −0.307 | 1.4 | 1.8 | −0.496 | 5.5 | 4.0 | −0.885 | 2.1 | 3.7 | |
cg11003536 | 11 | PRDM10; LINC00167 | 0.18(9 ) | −0.341 | 2.0 | 2.3 | −0.750 | 1.0 | 2.3 | −0.983 | 1.7 | 2.5 | |
cg13632630 | 15 | LINC00052; NTRK3 | 0.16 (9 ) | −0.174 | 4.2 | 2.32 | −0.251 | 1.1 | 2.7 | −0.497 | 5.5 | 2.8 | |
cg18777774 | 17 | ABR; BHLHA9 | 0.14(9 ) | −0.524 | 3.7 | 1.7 | −0.783 | 4.1 | 1.1 | −1.209 | 3.9 | 2.3 | |
cg08166720 | 17 | ZZEF1 | 0.25(2 ) | −0.410 | 1.1 | 4.62 | −0.788 | 1.5 | 9.9 | −1.192 | 1.1 | 5.2 | |
cg18909525 | 9 | ASB6 | 0.30(2 ) | −0.412 | 3.3 | 5.3 | −0.330 | 2.5 | 4.1 | −0.883 | 2.4 | 7.1 | |
cg00697880 | 3 | OSBPL10 | 0.19(2 ) | −0.366 | 1.0 | 7.2 | −0.735 | 4.5 | 6.7 | −1.033 | 5.4 | 3.0 | |
cg00335252 | 2 | RBMS1 | 0.17(3 ) | −0.422 | 2.6 | 1.5 | −0.433 | 5.9 | 3.4 | −0.891 | 7.0 | 9.0 | |
C | cg08653258 | 3 | BHLHE40; ARL8B | −0.19(9 ) | −0.253 | 0.013 | 0.051 | −0.402 | 2.9 | 1.5 | −0.737 | 3.1 | 1.9 |
cg17672798 | 10 | ADARB2 | −0.17(9 ) | −0.178 | 0.148 | 0.266 | −0.485 | 5.8 | 2.6 | −0.745 | 1.3 | 8.4 | |
cg21533331 | 19 | AC002116.7; THAP8; WDR62 | −0.19(9 ) | −0.066 | 0.588 | 0.708 | −0.220 | 3.2 | 4.5 | −0.208 | 1.6 | 1.9 | |
cg00647046 | 10 | INPP5A; CFAP46 | −0.34(1 ) | 0.055 | 0.772 | 0.829 | −0.531 | 1.5 | 2.6 | −0.661 | 1.4 | 2.4 | |
cg26328510 | 10 | CUGBP2 | −0.56(1 ) | −0.061 | 0.715 | 0.794 | −1.054 | 3.4 | 1.0 | −0.857 | 3.9 | 5.7 | |
cg14575222 | 9 | NAIF1; SLC25A25 | −0.18(2 ) | −0.144 | 0.184 | 0.309 | −4.79 | 3.3 | 1.3 | −0.770 | 1.7 | 7.8 | |
cg22279507 | 2 | FARSB | −0.30(2 ) | −0.246 | 0.058 | 0.156 | −0.739 | 5.2 | 4.0 | −0.747 | 6.9 | 1.5 | |
cg14594063 | 10 | ADAM12 | −0.27(2 ) | −0.297 | 0.063 | 0.157 | −0.412 | 1.9 | 3.3 | −0.880 | 6.8 | 1.8 | |
cg03964554 | 17 | RAD51C; PPM1E | −0.17(3 ) | 0.085 | 0.395 | 0.535 | −0.532 | 3.3 | 2.0 | −0.281 | 5.8 | 7.4 | |
cg26319015 | 7 | ACTB; FSCN1 | −0.28(3 ) | −0.181 | 0.385 | 0.526 | −0.788 | 3.0 | 6.9 | −0.588 | 6.3 | 9.1 | |
cg01832012 | 7 | TPK1 | −0.14(2 ) | −0.153 | 0.083 | 0.192 | −0.348 | 7.9 | 2.0 | −0.354 | 5.9 | 1.0 | |
cg03029734 | 6 | GRIK2 | −0.12(5 ) | 0.008 | 0.928 | 0.938 | −0.282 | 7.7 | 1.4 | −0.252 | 1.4 | 2.0 | |
D | cg08870757 | 17 | ALOX12 | −0.320(4 ) | −0.451 | 2.5 | 4.5 | −0.845 | 3.3 | 6.0 | −0.820 | 1.7 | 5.2 |
cg22789605 | 12 | SLC11A2 | −0.207(6 ) | −0.433 | 3.4 | 8.7 | −0.294 | 1.1 | 2.0 | −0.573 | 9.3 | 2.6 | |
cg15210276 | 19 | HAPLN4 | −0.183(7 ) | −0.306 | 4.3 | 3.4 | −0.470 | 4.9 | 2.2 | −0.715 | 1.3 | 5.9 | |
cg19681610 | 1 | NOS1AP | −0.155(9 ) | −0.290 | 1.3 | 8.6 | −0.568 | 1.7 | 2.2 | −0.768 | 3.4 | 3.4 | |
cg08288130 | 8 | DOK2 | −0.255(9 ) | −0.402 | 5.4 | 8.1 | −0.506 | 5.5 | 2.5 | −0.710 | 2.0 | 6.2 | |
cg01788221 | 16 | ANKRD11 | −0.106(9 ) | −0.392 | 1.3 | 4.0 | −0.830 | 6.1 | 1.1 | −1.039 | 4.1 | 3.7 | |
cg09043104 | 8 | LINC00536; EIF3H | −0.118(1 ) | −0.338 | 8.1 | 1.8 | −0.372 | 9.6 | 3.2 | −0.650 | 1.9 | 1.6 | |
cg00519039 | 10 | ARHGAP19; FRAT1 | −0.201(2 ) | −0.293 | 7.0 | 3.1 | −0.476 | 5.2 | 1.9 | −0.619 | 5.5 | 1.3 | |
cg13676583 | 5 | DDX41; DOK3 | −0.131(3 ) | −0.200 | 1.3 | 8.6 | −0.368 | 1.1 | 7.3 | −0.476 | 9.0 | 2.9 | |
cg16419756 | 5 | SLC12A8 | −0.094(1 ) | −0.224 | 2.6 | 2.4 | −0.537 | 1.3 | 2.6 | −0.736 | 1.1 | 2.2 |
KEGG Biological Pathway | Source | p-Value | q-Value FDR B&H | Hit Count in Query (Hit Count in Genome) | Hits in the Query List |
---|---|---|---|---|---|
Shigella IpaB/C/D to ITGA/B-TALIN/VINCULIN signaling pathway | KEGG Medicus Pathways | 6.29 | 0.03 | 2 (9) | ACTB, PTK2 |
Aminoacyl-tRNA Biosynthesis | KEGG Legacy Pathways | 6.97 | 0.03 | 3 (41) | CARS1, FARSB, FARS2 |
Disease Name | p-Value | q-Value FDR B&H | Hit Count in Query (Hit Count in Genome) | Hits in the Query List |
---|---|---|---|---|
Schizophrenia | 3.23 | 0.001 | 13 (883) | CELF2, INPP5A, NTRK3, ACTB, NFASC, DLG1, GRIK2, CTRL, ADAM12, BHLHE40, ALOX12, LEP, NOS1AP |
Attention deficit hyperactivity disorder, substance abuse, antisocial behaviour measurement | 5.55 | 0.004 | 11 (801) | PHOX2A, ZBTB20, CELF2, SND1, UNC5B, NTRK3, SETBP1, NINL, NFASC, PARPBP, PTK2 |
Fibrosarcoma | 1.91 | 0.010 | 2 (3) | NF1, NTRK3 |
Glioblastoma | 5.01 | 0.016 | 4 (79) | NF1, GRIK2, BHLHE40, PTK2 |
Susceptibility to shingles measurement | 5.52 | 0.016 | 4 (81) | TPK1, ZKSCAN3, MYRIP, NFASC |
Giant Cell Glioblastoma | 6.37 | 0.016 | 4 (84) | NF1, GRIK2, BHLHE40, PTK2 |
Colorectal Carcinoma | 7.19 | 0.016 | 9 (702) | ACAP1, TCF3, SLC11A2, NF1, SETBP1, PPM1E, ZKSCAN3, EIF3H, ADARB2 |
Risk-taking Behaviour | 1.36 | 0.020 | 9 (764) | ZBTB20, NF1, SND1, NUP37, NTRK3, ZKSCAN3, NFASC, PARPBP, ARHGAP19 |
APOE carrier status, cerebral amyloid angiopathy | 1.69 | 0.020 | 3 (42) | FZR1, SETBP1, GRIK2 |
Glioblastoma Multiforme | 1.88 | 0.020 | 4 (111) | NF1, GRIK2, BHLHE40, PTK2 |
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. |
© 2025 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
Chen, S.; Holloway, J.W.; Karmaus, W.; Zhang, H.; Arshad, S.H.; Ewart, S. Trends in DNA Methylation over Time Between Parous and Nulliparous Young Women. Epigenomes 2025, 9, 24. https://doi.org/10.3390/epigenomes9030024
Chen S, Holloway JW, Karmaus W, Zhang H, Arshad SH, Ewart S. Trends in DNA Methylation over Time Between Parous and Nulliparous Young Women. Epigenomes. 2025; 9(3):24. https://doi.org/10.3390/epigenomes9030024
Chicago/Turabian StyleChen, Su, John W. Holloway, Wilfried Karmaus, Hongmei Zhang, S. Hasan Arshad, and Susan Ewart. 2025. "Trends in DNA Methylation over Time Between Parous and Nulliparous Young Women" Epigenomes 9, no. 3: 24. https://doi.org/10.3390/epigenomes9030024
APA StyleChen, S., Holloway, J. W., Karmaus, W., Zhang, H., Arshad, S. H., & Ewart, S. (2025). Trends in DNA Methylation over Time Between Parous and Nulliparous Young Women. Epigenomes, 9(3), 24. https://doi.org/10.3390/epigenomes9030024