Epigenetics of Developmental Disorders
Abstract
Epigenetic features of developmental disorders in African children remain largely undescribed despite high and variable exposure burdens. We aimed to quantify blood-based epigenetic differences and their links to environmental exposures in Nigerian children. In a matched case–control study across Aminu Kano Teaching Hospital (Kano), University of Abuja Teaching Hospital (Abuja), Lagos University Teaching Hospital (Lagos) and University of Nigeria Teaching Hospital (Enugu), we enrolled 180 children aged 3–10 years between 1 March 2023 and 31 May 2025: cases (n=90; autism spectrum disorder [ASD] n=30, attention-deficit/hyperactivity disorder [ADHD] n=30, intellectual disability [ID] n=30) and matched controls (n=90). Genome-wide DNA methylation (Illumina EPIC), targeted histone marks (ChIP-qPCR), and small RNA sequencing were analysed in R 4.3.0 (minfi/limma/DMRcate; FDR<0.05). Primary result: global 5-methylcytosine was lower in cases (mean 4.8%±0.4) than controls (5.2%±0.5); ANOVA F(3,176)=9.02, p<0.001; mean difference −0.4%. Fifteen differentially methylated positions (Δβ 5–8%; including NR3C1 +7.4%, p<0.001) passed FDR. H3K9 acetylation was reduced (~15%) in cases, t(178)=−3.45, p=0.0007. MicroRNAs differed by diagnosis (e.g., let-7d lower in ADHD, fold-change 0.30, p=0.002; miR-145-5p higher in ASD, 1.7-fold, p<0.01). In multivariable logistic regression, higher methylation (OR 0.66 per +1% [95% CI 0.52–0.84], p=0.001), higher H3K9ac (OR 0.80 [0.68–0.95], p=0.014), higher let-7d (OR 0.88 [0.78–0.99], p=0.047), and lower blood lead (OR 1.25 per +1 µg/dL [1.12–1.39], p<0.001) independently predicted case status; AUC=0.81. An interaction was indicated, with ~9.5-fold higher odds for low methylation and lead levels greater than five µg/dL (95% CI 4.0–22, p = 0.01). In interviews (n = 15), parents described routine exposure to soot, proximity to landfills or smelters, and economic hardship. A concise, measurable epigenetic–toxicant profile identified Nigerian children with developmental disorders and points to modifiable risk.
Keywords
Full Text:
PDFReferences
1. Maenner, M. J., Warren, Z., Williams, A. R., Amoakohene, E., Bakian, A. V., Bilder, D. A., Durkin, M. S., Fitzgerald, R. T., Furnier, S. M., Hughes, M. M., Ladd-Acosta, C. M., McArthur, D., Pas, E. T., Salinas, A., Vehorn, A., Williams, S., Esler, A., Grzybowski, A., Hall-Lande, J., & Shaw, K. A. (2023). Prevalence and characteristics of autism spectrum disorder among children aged 8 years – Autism and Developmental Disabilities Monitoring Network, 11 sites, United States, 2020. MMWR Surveillance Summaries, 72(2), 1–14. doi: 10.15585/mmwr.ss7202a1
2. Reichard, J., & Zimmer-Bensch, G. (2021). The epigenome in neurodevelopmental disorders. Frontiers in Neuroscience, 15. Doi: 10.3389/fnins.2021.776809
3. Ciptasari, U., & Van Bokhoven, H. (2020). The phenomenal epigenome in neurodevelopmental disorders. Human Molecular Genetics, 29(R1), R42–R50. doi: 10.1093/hmg/ddaa175
4. Alvarado-Cruz, I., Alegría-Torres, J. A., Montes-Castro, N., Jiménez-Garza, O., & Quintanilla-Vega, B. (2018). Environmental Epigenetic Changes as risk factors for the Development of Diseases in children: a Systematic review. Annals of Global Health, 84(2), 212–224. doi: 10.29024/aogh.909
5. Bihaqi, S. W. (2019). Early-life exposure to lead (Pb) and changes in DNA methylation: Relevance to Alzheimer's disease. Reviews on Environmental Health, 34(2), 187–195. doi: 10.1515/reveh-2018-0076
6. Wang, T., Zhang, J., & Xu, Y. (2020). Epigenetic basis of Lead-Induced Neurological Disorders. International Journal of Environmental Research and Public Health, 17(13), 4878. doi: 10.3390/ijerph17134878
7. Keil, K. P., & Lein, P. J. (2016). DNA methylation: a mechanism linking environmental chemical exposures to risk of autism spectrum disorders? Current Zoology, 2(1), dvv012. doi: 10.1093/eep/dvv012
8. Gonseth, S., Roy, R., Houseman, E. A., De Smith, A. J., Zhou, M., Lee, S., Nusslé, S., Singer, A. W., Wrensch, M. R., Metayer, C., & Wiemels, J. L. (2015). Periconceptional folate consumption is associated with neonatal DNA methylation modifications in neural crest regulatory and cancer development genes. Epigenetics, 10(12), 1166–1176. doi: 10.1080/15592294.2015.1117889
9. Dalvie, S., Koen, N., Duncan, L., Abbo, C., Akena, D., Atwoli, L., Chiliza, B., Donald, K. A., Kinyanda, E., Lochner, C., Mall, S., Nakasujja, N., Newton, C. R., Ramesar, R., Sibeko, G., Teferra, S., Stein, D. J., & Koenen, K. C. (2015). Large-scale Genetic Research on Neuropsychiatric Disorders in African Populations is Needed. EBioMedicine, 2(10), 1259–1261. doi: 10.1016/j.ebiom.2015.10.002
10. Besharati, S., & Akinyemi, R. (2023). Accelerating African neuroscience to provide an equitable framework using perspectives from West and Southern Africa. Nature Communications, 14(1). doi: 10.1038/s41467-023-43943-3
11. Bakare, M. O., Taiwo, O. G., Bello-Mojeed, M. A., & Munir, K. M. (2019). Autism Spectrum Disorders in Nigeria: A scoping review of literature and opinion on future research and social policy directions. Journal of Health Care for the Poor and Underserved, 30(3), 899–909. doi: 10.1353/hpu.2019.0063
12. Etim, E. U. (2018). Contamination and Ecological Risk Assessment of Lead in Roadside Soil and Dust in Ibadan Metropolis, Nigeria. Journal of Science Research, 17, 81 - 90
13. Adimalla, N., & Qian, H. (2019). Groundwater quality evaluation using water quality index (WQI) for drinking purposes and human health risk (HHR) assessment in an agricultural region of Nanganur, South India. Ecotoxicology and Environmental Safety, 176, 153–161. doi: 10.1016/j.ecoenv.2019.03.066
14. Alausa, S. K., & Akinyemi, L. P. (2015). Heavy Metal Contaminants in the Water from Oil-Sand-Rich River Imeri, Ogun State, Southwestern Nigeria. Symposium on the Application of Geophysics to Engineering and Environmental Problems, 271–281. doi: 10.4133/sageep.28-038
15. Abe, S. K., Balogun, O. O., Ota, E., Takahashi, K., & Mori, R. (2016). Supplementation with multiple micronutrients for breastfeeding women improves outcomes for the mother and baby. Cochrane Library, 2016(2). doi: 10.1002/14651858.cd010647.pub2
16. Popejoy, A. B., & Fullerton, S. M. (2016). Genomics is failing on diversity. Nature, 538(7624), 161–164. doi: 10.1038/538161a
17. Mersha, T. B., & Abebe, T. (2015). Self-reported race/ethnicity in the age of genomic research: its potential impact on understanding health disparities. Human Genomics, 9(1). doi: 10.1186/s40246-014-0023-x
18. Cecil, C. A. M., & Nigg, J. T. (2022). Epigenetics and ADHD: reflections on current knowledge, research priorities and translational potential. Molecular Diagnosis & Therapy, 26(6), 581–606. doi: 10.1007/s40291-022-00609-y
19. Rothman, K.J., Greenland, S. and Lash, T. (2008) Modern Epidemiology (3rd Ed.). Philadelphia: Lippincott Williams & Wilkins, 303-327.
20. Goodman, R., Ford, T., Simmons, H., Gatward, R., & Meltzer, H. (2000). Using the Strengths and Difficulties Questionnaire (SDQ) to screen for child psychiatric disorders in a community sample. The British Journal of Psychiatry, 177(6), 534–539. doi: 10.1192/bjp.177.6.534
21. Omigbodun, O., Dogra, N., Esan, O., & Adedokun, B. (2008). Prevalence and correlates of suicidal behaviour among adolescents in southwest Nigeria. International Journal of Social Psychiatry, 54(1), 34–46. doi: 10.1177/0020764007078360
22. Hannon, E., Knox, O., Sugden, K., Burrage, J., Wong, C. C. Y., Belsky, D. W., Corcoran, D. L., Arseneault, L., Moffitt, T. E., Caspi, A., & Mill, J. (2018). Characterising genetic and environmental influences on variable DNA methylation using monozygotic and dizygotic twins. PLoS Genetics, 14(8), 1007544. doi: 10.1371/journal.pgen.1007544
23. Lord, C., & Rutter, M. (2012). (ADOS®-2) Autism Diagnostic Observation Schedule (2nd Ed.). Retrieved from https://www.wpspublish.com/ados-2-autism-diagnostic-observation-schedule-second-edition
24. Hus, V., & Lord, C. (2014). The Autism Diagnostic Observation Schedule, Module 4: Revised Algorithm and Standardised Severity Scores. Journal of Autism and Developmental Disorders, 44(8), 1996–2012. doi: 10.1007/s10803-014-2080-3
25. De Bildt, A., Sytema, S., Ketelaars, C., Kraijer, D., Mulder, E., Volkmar, F., & Minderaa, R. (2004). Interrelationship Between Autism Diagnostic Observation Schedule-Generic (ADOS-G), Autism Diagnostic Interview-Revised (ADI-R), and the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR) Classification in Children and Adolescents with Mental Retardation. Journal of Autism and Developmental Disorders, 34(2), 129–137. doi: 10.1023/b:jadd.0000022604.22374.5f
26. Wechsler, D. (2014). WISC-V: Administration and Scoring Manual. NCS Pearson, Incorporated.
27. Sparrow, S. S., Cicchetti, D. V., & Saulnier, S. A. (2016). Vineland Adaptive Behaviour Scales (3rd Ed.). Pearson
28. Conners, C. K. (2008). Conners 3 (3rd Ed.). Retrieved from https://www.wpspublish.com/conners-3-conners-third-edition.html
29. Greenberg, L. M., & Waldmant, I. D. (1993). Developmental Normative Data on the Test of Variables of Attention (T.O.V.A.TM). Journal of Child Psychology and Psychiatry, 34(6), 1019–1030. doi: 10.1111/j.1469-7610.1993.tb01105.x
30. Ashenafi, Y., Kebede, D., Desta, M., & Alem, A. (2001). Prevalence of mental and behavioural disorders in children in Ethiopia. East African Medical Journal, 78(6). doi: 10.4314/eamj.v78i6.9024
31. Research Triangle Park. (2020). National Institute of Environmental Health Sciences (NIEHS). Retrieved from https://www.nih.gov/about-nih/nih-almanac/national-institute-environmental-health-sciences-niehs
32. Kim, S., Dunham, M. J., & Shendure, J. (2019). A combination of transcription factors mediates inducible interchromosomal contacts. Elife, 8
33. FAO. (n. d.). Nutrition. Retrieved from https://www.fao.org/nutrition/capacity-development/nutrition-assessment/fr/
34. Mejía-Rodríguez, F., Orjuela, M. A., García-Guerra, A., Quezada-Sanchez, A. D., & Neufeld, L. M. (2011). Validation of a novel method for retrospectively estimating nutrient intake during pregnancy using a Semi-Quantitative Food Frequency Questionnaire. Maternal and Child Health Journal, 16(7), 1468–1483. doi: 10.1007/s10995-011-0912-8
35. Elgar, F. J., Pförtner, T., Moor, I., De Clercq, B., Stevens, G. W. J. M., & Currie, C. (2015). Socioeconomic inequalities in adolescent health 2002–2010: a time-series analysis of 34 countries participating in the Health Behaviour in School-aged Children study. The Lancet, 385(9982), 2088–2095. doi: 10.1016/s0140-6736(14)61460-4
36. Vaught, J. B., & Henderson, M. (2012). Biological sample collection, processing, storage and information management. IARC scientific publications
37. Langie, S. a. S., Moisse, M., Declerck, K., Koppen, G., Godderis, L., Vanden Berghe, W., Drury, S., & De Boever, P. (2016). Salivary DNA methylation profiling: Aspects to consider for biomarker identification. Basic & Clinical Pharmacology & Toxicology, 121(S3), 93–101. doi: 10.1111/bcpt.12721
38. Naidoo, J., Page, D., Li, B., Connell, L., Schindler, K., Lacouture, M., Postow, M., & Wolchok, J. (2015b). Toxicities of the anti-PD-1 and anti-PD-L1 immune checkpoint antibodies. Annals of Oncology, 26(12), 2375–2391. doi: 10.1093/annonc/mdv383
39. Pidsley, R., Zotenko, E., Peters, T. J., Lawrence, M. G., Risbridger, G. P., Molloy, P., Van Djik, S., Muhlhausler, B., Stirzaker, C., & Clark, S. J. (2016). Critical evaluation of the Illumina MethylationEPIC BeadChip microarray for whole-genome DNA methylation profiling. Genome Biology, 17(1). doi: 10.1186/s13059-016-1066-1
40. Barski, A., Cuddapah, S., Cui, K., Roh, T., Schones, D. E., Wang, Z., Wei, G., Chepelev, I., & Zhao, K. (2007). High-Resolution profiling of Histone methylations in the human genome. Cell, 129(4), 823–837. doi: 10.1016/j.cell.2007.05.009
41. Aryee, M. J., Jaffe, A. E., Corrada-Bravo, H., Ladd-Acosta, C., Feinberg, A. P., Hansen, K. D., & Irizarry, R. A. (2014). Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics, 30(10), 1363–1369. doi: 10.1093/bioinformatics/btu049
42. Zhou, Y., & Lauschke, V. M. (2021). Population pharmacogenomics: an update on ethnogeographic differences and opportunities for precision public health. Human Genetics, 141(6), 1113–1136. doi: 10.1007/s00439-021-02385-x
43. Salas, L. A., Koestler, D. C., Butler, R. A., Hansen, H. M., Wiencke, J. K., Kelsey, K. T., & Christensen, B. C. (2018). An optimised library for reference-based deconvolution of whole-blood biospecimens assayed using the Illumina HumanMethylationEPIC BeadArray. Genome Biology, 19(1). doi: 10.1186/s13059-018-1448-7
44. Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research, 43(7), 47. doi: 10.1093/nar/gkv007
45. Peters, T. J., Buckley, M. J., Statham, A. L., Pidsley, R., Samaras, K., Lord, R. V., Clark, S. J., & Molloy, P. L. (2015). De novo identification of differentially methylated regions in the human genome. Epigenetics & Chromatin, 8(1). doi: 10.1186/1756-8935-8-6
46. Tingley, D., Yamamoto, T., Hirose, K., Keele, L., & Imai, K. (2014). Mediation: R Package for Causal Mediation Analysis. Journal of Statistical Software, 59(5). doi: 10.18637/jss.v059.i05
Article Metrics
Metrics powered by PLOS ALM
Refbacks
- There are currently no refbacks.
Copyright (c) 2025 Olasoji Agboola, Aderinola Adebiyi Adegoke
This work is licensed under a Creative Commons Attribution 4.0 International License.
Email the author