Genetic and Epigenetic Mechanisms Contributing to ADHD Aetiology in Children in South-west and South-south Nigeria

Overview

Scientific Rationale and Importance

ADHD is a leading childhood neurodevelopmental disorder with major academic and mental health impacts.
Genomic (GWAS) and epigenomic (EWAS) studies have identified risk mechanisms in European and North American populations, but African children remain completely unstudied.
Africa’s genetic diversity offers unique opportunities for discovery.
This project will establish the first DNA resource from 500 Nigerian children (with and without ADHD), enabling downstream genome-wide and epigenome-wide studies.
Outcomes will help reduce Africa’s exclusion from precision medicine and may uncover population-specific ADHD risk variants.

Funding, Facilities and Ethics

Funding: Supervisory/institutional support in place; ResearchHub grant will cover sample collection, consumables, DNA extraction & QC.
Facilities: University of Ibadan–affiliated laboratories equipped for centrifugation, electrophoresis, and spectrophotometry.
Ethics: Ethical clearance will be obtained from the UI/UCH Health Research Ethics Committee, College of Medicine, University of Ibadan and the research and ethics committee of Federal Neuropsychiatric Hospital, Yaba, Lagos State. Ethical approval will also be obtained from the State Ministries of Health and Education in Oyo and Akwa Ibom States. Using informed consent procedures, the assent and consent of the children and their parents/guardians will be obtained in writing before participation in the project.

Estimated Timeline

Months 1–2 (September - October, 2025): Finalize ethics approval, government and school permissions, and staff training.
Months 3–6 (December 2025 - February 2026): Recruit participants; collect 2 mL blood samples (n=500), DNA extraction & QC (spectrophotometer + agarose gels).

Data Sharing Commitment

All data will be de-identified and shared per institutional and international standards.
Processed datasets will be deposited in controlled-access repositories (EGA/dbGaP).
Summary statistics and protocols will be openly shared via ResearchHub and peer-reviewed publications to maximize reuse and global impact.

Total Request: 5,000 USD (10 USD/sample for 500 samples)

Background of the Research

Attention deficit hyperactivity disorder (ADHD) is a highly heritable neurodevelopmental disorder affecting mostly children but can persist into adulthood (Willcutt, 2012). ADHD has three subtypes/traits; the inattentive type (ADHD-I), the hyperactive-impulsive type (ADHD-HI) and the combined type (ADHD-C). It is a complex condition influenced by genetic, environmental, and developmental factors, with genetic factors accounting for 80% of cases (Willcutt, 2012; Mick and Faraone, 2008).

Untreated ADHD can lead to academic performance issues, low self-esteem, social skills, and increased physical injury, amongst others. ADHD typically presents in early childhood, but many children continue to experience symptoms into adolescence and adulthood, increasing their risk of other psychiatric disorders. ADHD significantly raises the likelihood of developing secondary mental health issues in adolescence and adulthood, including substance misuse, depression, and psychosis (Faraone et al., 2021). Given that it seems to be a contributing factor in the development of other neuro-disorders, primary and secondary prevention efforts should concentrate on it.

Recent research on ADHD has identified genetic risk loci but has yet to determine the exact causal genes and mechanisms. A large genome-wide association study (GWAS) meta-analysis of ADHD identified 27 significant genetic loci and 76 potential risk genes, many linked to early brain development (Demontis et al., 2023). This study reported that ADHD genetic risk was associated with specific brain cell types, and exome sequencing highlighted rare variants in key genes like SORCS3. The study also found substantial genetic overlap with other psychiatric disorders and associations with cognitive impairments in executive function and verbal reasoning.

Cabana-Domínguez et al., (2022) analyzed gene expression in peripheral blood using a multi-omics approach and found seven ADHD-associated gene modules linked to genetic and epigenetic markers, as well as key psychiatric pathways. Their research also highlighted potential regulatory mechanisms involving microRNAs and genetic variants, emphasizing the importance of multi-omics approaches in understanding ADHD’s biological basis.

Studies carried out in Africa have not considered the role of genetics (genome-wide association studies, GWAS) and epigenetics (epigenome-wide association studies, EWAS), transcriptomics, proteomics, and metabolomics in ADHD; hence, there is limited or a dearth of knowledge and data on such biological determinants in Africa. Given that ADHD is a complex disorder resulting from an interplay between an individual’s genetic makeup and other environmental risk factors, these may impact the transcription of genes that regulate behaviour and may account for the disparities observed in studies on ADHD prevalence in Africa.

Overview of ADHD studies in Africa

The prevalence of ADHD in Africa ranges between 5.4% and 8.7% among populations of school-aged children (Bakare, 2012), with Nigeria showing a similar prevalence (Chinawa et al., 2014). Studies in Africa have focused on the prevalence of ADHD, and their findings suggest an increasing trend in the prevalence of ADHD (Ayano et al., 2020). While the prevalence rates in these studies vary across different African countries, one common thing is that there is variation in the prevalence of the different ADHD subtypes/traits (the inattentive type (ADHD-I), the hyperactive-impulsive type (ADHD-HI), and the combined type (ADHD-C) (Olatunji et al., 2023). Hence, there is a need for further investigation into the biological mechanisms underlying these findings.

Several studies done outside Africa support the role of genetic variants, epigenetic signatures and gene expression in the development of ADHD, although this (causality) still remains largely unknown (Cabana-Domínguez et al., 2022). ADHD is a heterogeneous disorder, and individual differences in genetics, methylation patterns, and gene expression levels can vary significantly. This variability poses a challenge for developing universal biomarkers. Despite promising findings in ADHD genetics, several challenges remain, especially in terms of racial and African representation, as little is known about its genetic architecture in the African population. Most genetic studies have focused on European populations, limiting their relevance for other populations due to genetic diversity and unique environmental influences.

Many omics studies, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics of ADHD in the world population, have been performed either independently or via multi-omics studies, but usually in people of European descent (Cabana-Domínguez et al., 2022; Hubers et al., 2024; Meijer et al., 2020). Exploring multi-omics in ADHD represents a promising avenue for developing biomarkers that could enhance diagnosis and treatment. Most importantly, address the large knowledge gap in African-specific genetic and epigenetic data on ADHD. This may establish a standard for future research in ADHD for determining molecular patterns that can aid in the identification of novel biomarkers and drug targets, improve therapies, and improve early diagnosis, all driving towards personalized treatment, which is the future of multi-omics integration studies.

Statement of Problem

Most genetic studies on ADHD have been conducted in populations of European descent, limiting their generalizability across diverse populations. As a result, African populations remain significantly underrepresented in global ADHD genomic datasets, despite the potential for novel insights into the genetic and epigenetic architecture of the disorder. Additionally, findings highlight the polygenic nature of ADHD and suggest that additional genetic variance may arise from gene-environment interactions. Furthermore, as research advances toward personalized medicine, multi-omics integration offers a powerful approach to uncovering biological markers of ADHD across diverse populations. Therefore, this study will employ an integrative multi-omics (genomics and epigenomics) approach to identify SNPs and differential DNA methylation marks associated with ADHD in Nigerian children. We hypothesize that this study will uncover novel SNPs and DNA methylation marks that may be specific to African populations, thereby enhancing the representation of African genetic data in global ADHD research. Furthermore, this study will contribute GWAS and EWAS data to support the expansion of international ADHD genetic research efforts.

Objectives of the research

Short term

This project seeks to identify SNPs and DNA methylation patterns in Nigerian children with ADHD and its subtypes.

Long term

The project aims to identify biomarkers that may aid in the early diagnosis of ADHD and intervention for children with ADHD in Nigeria. It will also serve as a foundation for future larger cohort omics integration studies for newborns, adolescents, and adults, potentially identifying biomarkers that can be used for targeted treatments in Africa.

Research Questions

Do ADHD SNPs and epigenetic marks share the same loci in children with ADHD that may be unique to Nigeria?
What genes are associated with ADHD in the Nigerian population?
What are the biomarker potentials for ADHD in Nigeria?
Is there an association between these molecular patterns and ADHD subtypes in children for severity grading?

Specific Objectives

To identify the genomic and epigenomic pattern of ADHD in Nigerian children;
To identify biological pathways associated with ADHD that the combined genetic and epigenetic factors may impact
To identify the potential biomarkers that may be suitable for early diagnosis of ADHD in Nigerian children
To use a multi-omics approach to assess known biomarkers and their potential to improve early diagnosis and guide personalized treatment

Literature Review

Attention-deficit/hyperactivity disorder (ADHD) is one of the most common and heritable neurodevelopmental disorders, affecting approximately 5-6% of children and adolescents worldwide, with 40-65% of cases persisting into adulthood (Sayal et al., 2018). ADHD is characterized by inattention, hyperactivity, and impulsivity, and has a complex etiology involving both genetic and environmental risk factors. Despite a heritability estimate of 70-80% (Faraone et al., 2015), the precise genetic architecture of ADHD remains incompletely understood.

Family and twin studies have confirmed the high heritability of ADHD, suggesting that genetic susceptibility plays a crucial role in its etiology. Traditional candidate gene association studies have implicated dopamine-related genes (SLC6A3, DRD5), serotonin-related genes (SLC6A4, HTR1B), and the synaptic vesicle membrane docking gene SNAP-25 (Faraone et al., 2015). However, these associations have generally had small effect sizes and inconsistent replication (Thapar & Cooper, 2016).

GWAS has provided a hypothesis-free method for identifying novel risk variants across the entire genome. Early GWAS studies failed to identify single nucleotide polymorphisms (SNPs) reaching genome-wide significance, but more recent large-scale meta-analyses have identified significant ADHD risk loci. The first major GWAS meta-analysis (Demontis et al., 2019) identified 12 independent ADHD risk loci, with common variants accounting for 22% of total heritability. A subsequent meta-analysis (Demontis et al., 2023) expanded the number of significant loci to 27 but reported a lower SNP heritability estimate of 14%. These findings highlight the polygenic nature of ADHD and suggest that additional genetic variance may arise from gene-environment interactions.

Epigenetic modifications, such as DNA methylation, histone modifications, and microRNAs, mediate gene expression changes in response to environmental exposures and may contribute to ADHD pathophysiology (Kubota et al., 2012). Gene-environment interactions are increasingly recognized as key factors influencing ADHD susceptibility. For instance, environmental stressors, maternal smoking, and early-life adversity have been associated with altered DNA methylation patterns in genes related to neuronal function (Waltes et al., 2016; Hamza et al., 2017).

Several EWAS have examined ADHD-related methylation changes. The first EWAS in peripheral blood mononuclear cells identified four differentially methylated regions linked to autoimmune disorders, cancer, and neuroticism (Wang et al., 2017). Additional studies in saliva and whole blood have reported inconsistent results, underscoring the need for larger, well-powered studies (Levy et al., 2022). Despite these challenges, epigenetic studies offer valuable insights into the interplay between genetic and environmental factors in ADHD.

Multi-Omics Approaches to ADHD Research

Single-omics studies have provided important insights, but integrating multiple omics layers—genomics, epigenomics, transcriptomics, and metabolomics—holds greater potential for understanding ADHD’s heterogeneity. Transcriptomic studies have identified altered gene expression in neuronal and immune-related pathways (McCaffrey et al., 2020; Mortimer et al., 2020), while metabolomic analyses have revealed associations with oxidative stress pathways (Wang et al., 2021). Furthermore, pathway analyses suggest dysregulation of potassium channels and RhoA signaling in ADHD pathophysiology (Demontis et al., 2019).

As large-scale cohorts continue to amass multi-omics data, a systems biology approach will be essential for identifying robust biomarkers and potential therapeutic targets. Understanding the relationships between various omics layers will facilitate the transition from symptom-based to biologically defined ADHD diagnostics, ultimately improving treatment strategies and patient outcomes.

Research Methodology

Study design

A cross-sectional design will be used for this study. A total of 808 individuals aged 6-17 years with and without ADHD phenotypes will be recruited into the this study. Diagnosis of ADHD based on the identification of symptoms will be carried out using the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) (Hogan et al., 2022) in addition to psychiatric evaluation by a licensed paediatric neurologist.

Study Setting

Children will be recruited from public primary and public secondary schools in Ibadan-North Local Government Area, Oyo State and Ikot Ekpene Local Government Area, Akwa Ibom State, the paediatric and psychiatric clinics of the University College Hospital, Ibadan and the Federal Neuropsychiatric Hospital, Yaba, Lagos State.

Inclusion Criteria

Children of Nigerian descent
Children with symptoms of ADHD
Children who have been in the class for two consecutive school terms—giving enough time for their teachers to observe their behaviours
Children presenting with symptoms for ADHD subtypes

Exclusion Criteria

Children with any genetic or neurodevelopmental disorder

Sample Size Determination

The sample size is calculated from the formula (Araoye, 2004), N = Z2 (p)(1-p)d2

where,

N = minimum sample size,

Z = the normal deviation, set at 1.96, corresponding to the 95% confidence level,

p = prevalence of ADHD from a previous study (Oke et al., 2019) is 4.5% (0.045),

d²= tolerable error was set at 1.5% (0.015)

N = 734

The sample size will be increased by 10% (73); the minimum sample size will be 807.

Selection of Schools

A multi-stage sampling method will be used to select the public primary and secondary schools in the Ibadan-North Local Government Area, Oyo State. Ibadan-North has 73 public primary schools and 39 public secondary schools. Thirty schools will be chosen using the purposive sampling method (Hogan et al., 2022). The public primary schools will be alphabetically listed separately from the public secondary schools. The public primary schools will be numbered 01 to 73, and the public secondary schools will be numbered 01 to 39. The number of public primary and secondary schools to be sampled will be determined by proportionate sampling using the relative proportions of public primary and secondary schools in the area.

Relative proportions:

Total schools = 73 + 39 = 112.
Primary: 73/112 = 0.6517857 → 65%
Secondary: 39/112 = 0.3482143 → 35%
For public primary schools ≈ 30 × 0.6517857 = 19.55 ⇒ 20
For public secondary schools ≈ 30 × 0.3482143 = 10.45 ⇒ 10

The schools will be selected afterwards as described by Hogan et al., 2022. Briefly, using the table of random numbers, a page of 20x20 random numbers will be printed out. The starting point on the selected page of the table of random numbers will be determined by the investigator dropping one finger on the page with eyes closed to select the column and the corresponding row. Once the number is picked, the column will be read downward, taking the last two digits of each number. The numbers will be kept within the list’s code range while skipping out-of-range and duplicates. If the bottom is reached before the complete number of schools are selected, the next column will be read until the required number of schools are selected. This procedure will be done separately for both public primary and secondary schools until 20 public primary schools and 10 public secondary schools are selected. The same school selection process will be carried out for schools in Ikot Ekpene Local Government Area, Akwa Ibom State which has 33 public primary schools and 7 public secondary schools.

Ethical Approval

Ethical clearance will be obtained from the UI/UCH Health Research Ethics Committee, College of Medicine, University of Ibadan and the research and ethics committee of Federal Neuropsychiatric Hospital, Yaba, Lagos State. Ethical approval will also be obtained from the State Ministries of Health and Education in Oyo and Akwa Ibom States. Using informed consent procedures, the assent and consent of the children and their parents/guardians will be obtained in writing before participation in the project.

Data Collection

Questionnaires will be administered to obtain demographic information and epidemiological data of ADHD

Sample Collection

A mental health practitioner will assess participants via questionnaires, following standard psychiatry protocols to identify children with and without ADHD. Peripheral blood (2 ml) will be collected by venipuncture after informed consent from parents and participants.

DNA Extraction

Genomic DNA will be isolated from blood samples using Qiagen QIAamp DNA Midi Kits following the manufacturer's protocol.

Genome-wide association studies (GWAS)

This will be used to identify genetic variants (single nucleotide polymorphisms, SNPs) associated with ADHD. Genomic DNA extracted from the peripheral blood samples will be sequenced using the Illumina GSA arrays according to the manufacturer’s standard protocol. Raw data preprocessing (sequence alignment and variant calling) and normalization for SNPs will be carried out before downstream analysis will be done using tools like PLINK and 1000 Genomes as previously described by Hubers et al. (2024). Briefly, data preparation by converting raw genotype data into PLINK-compatible format; quality control (QC), which removes individuals and SNPs with excessive missing data; filter variants based on minor allele frequency (MAF) and Hardy-Weinberg equilibrium (HWE); check for sex discrepancies and heterozygosity outliers; population stratification and principal component analysis (PCA) to correct for population structure and reduce confounding effects; genome-wide association analysis via case-control and quantitative trait analyses using linear regression models; multiple testing correction and visualization using Bonferroni and FDR corrections for multiple comparisons and QQ plots for visualization and post-GWAS analysis to identify genome-wide significant SNPs and annotate them using ANNOVAR and explore genetic associations using linkage disequilibrium (LD) analysis.

GWAS Data Analysis

Association testing will be carried out by logistic regression to identify significant SNPs using the PLINK tool (Loh et al., 2015).
Data visualization will then be carried out via Manhattan and QQ plots using the qqman [R] (Turner, 2014).
SNP Annotation will be carried out by mapping significant SNPs to genes using ANNOVAR tool (Watanabe et al., 2017).

Epigenome-wide association studies (EWAS)

Extracted genomic DNA will be bisulphite-converted using the Zymo EZ DNA MethylationTM kit (Zymo, Irvine, CA, USA) according to the manufacturer's protocol. Following conversion, genome-wide methylation status will be assayed using the Illumina Infinium MethylationEPIC BeadChip according to the standard protocol and methylation data acquired with GenomeStudio (version 2011.1). Pipeline analysis of raw DNA methylation data, including but not limited to preprocessing, quality control, and visualization, will be carried out using the SEnsible Step-wise Analysis of DNA MEthylation (SeSAMe) bioinformatics tool.

EWAS Data Analysis

Exploratory Analysis will first be used for statistical association tests and visualization in order to discover associations of the methylation data with various sample characteristics using the RnBeads tool (Müller et al., 2019). Differential methylation by enrichment analysis of differentially methylated regions will be used to characterize differentially methylated regions by computing enrichments for Gene Ontology (GO) using the LOLA tool as described by Müller et al., 2019.

Multi-Omics Integration

After preprocessing, quality control and association testing of the genomic and epigenomic data, multi-omics integration will be carried out using the MOFA (Multi-Omics Factor Analysis) algorithm to determine key features related to ADHD. Pathway and network analysis will be analyzed using the pathway-level, p-value-based integrative approach tool, PaintOmics3 to map data across layers onto biological pathways in order to find convergent pathways linked to ADHD.

Expected Results

We expect to see unique SNPs and differential DNA methylation patterns associated with ADHD in children in Nigeria, and how their combined impact influences ADHD outcomes.

Budget Breakdown (500 samples, total = 10 USD/sample)

Category	Key Items	Subtotal ($)
Collection consumables	Tubes, needles, swabs, gloves, labels, consent forms, and disposal	375
Logistics & cold chain	Ice packs, courier fuel, inter-site transport	600
Personnel	Phlebotomist & assistant stipends	1250
Lab processing	Centrifuge use, aliquot cryovials	150
DNA extraction	Reagents (salting-out + proteinase K), plastics	900
Quality control (QC)	Agarose gels, buffers, ladders, stains	200
Lab PPE & cleaning	Disinfectants, protective wear	50
Coordination & data	Field coordination, data entry	600
Participant support	Snack/juice tokens	250
Site support	Clinic/school coordination honorarium	300
Contingency	Re-extraction allowance + 3.8% buffer	325
Total		5000

The total budget for this proposed research is $589,000. For this grant award, we want to initiate sample collection, up to DNA extraction; hence, the 5,000 USD grant money request.

References

Akpan MU, Hogan EJ, Okpokowuruk FS, Ikpeme EE. Conduct disorder among primary schoolchildren in Southern Nigeria. Niger Med J 2020;61:334-9.

Araoye MO. Research Methodology with statistics for Health and Social Science. Illorin: Nathadex Publishers; 2004:115-29.

Ayano, G., Yohannes, K., & Abraha, M. (2020). Epidemiology of attention-deficit/hyperactivity disorder (ADHD) in children and adolescents in Africa: a systematic review and meta-analysis. Annals of general psychiatry, 19, 21. https://doi.org/10.1186/s12991-020-00271-w

Bakare MO. Attention deficit hyperactivity symptoms and disorder (ADHD) among African children: a review of epidemiology and co-morbidities. Afr J Psychiatry (Johannesbg). 2012 Sep;15(5):358-61.

Bonvicini, C., Faraone, S. V., & Scassellati, C. (2016). Attention-deficit hyperactivity disorder in adults: A systematic review and meta-analysis of genetic, pharmacogenetic and biochemical studies. Molecular Psychiatry, 21(11), 1643. https://doi.org/10.1038/mp.20

Bonvicini, C., Faraone, S. V., & Scassellati, C. (2018). Common and specific genes and peripheral biomarkers in children and adults with attentiondeficit/hyperactivity disorder. The World Journal of Biological Psychiatry, 19(2), 80–100. https://doi.org/10.1080/15622

Cabana-Domínguez, J., Soler Artigas, M., Arribas, L., Alemany, S., Vilar-Ribó, L., Llonga, N., Fadeuilhe, C., Corrales, M., Richarte, V., Ramos-Quiroga, J. A., & Ribasés, M. (2022). Comprehensive analysis of omics data identifies relevant gene networks for Attention-Deficit/Hyperactivity Disorder (ADHD). Translational psychiatry, 12(1), 409. https://doi.org/10.1038/s41398-022-02182-8.

Caye, A., Swanson, J. M., Coghill, D., & Rohde, L. A. (2019). Treatment strategies for ADHD: An evidence-based guide to select optimal treatment. Molecular Psychiatry, 24(3), 390–408. https://doi.org/10.1038/s41380-018-0116-3

Chaumette, B., Grizenko, N., Fageera, W., Fortier, M., Ter-Stepanian, M., Labbe, A. and Joober, R. (2023). Correlation of the Methylomic Signature of Smoking during Pregnancy with Clinical Traits in ADHD. Journal of Psychiatry & Neuroscience 48: E390–99. https://doi.org/10.1503/jpn.230062

Cheng, S., Wen, Y., Ma, M., Zhang, L., Liu, L., Qi, X., Cheng, B., Liang, C., Li, P., Kafle, O. P., & Zhang, F. (2020). Identifying 5 common psychiatric disorders associated chemicals through integrative analysis of genome-wide association study and chemical-gene interaction datasets. Schizophrenia Bulletin, 46(5), 1182–1190. https://doi.org/10 . 1093/schbul/sbaa053

Chinawa JM, Odetunde OI, Obu HA, Chinawa AT, Bakare MO, Ujunwa FA. Attention deficit hyperactivity disorder: a neglected issue in the developing world. Behav Neurol. 2014;2014:694764.

Choi, YJ., Cho, J., Hong, YC. et al. (2023). DNA methylation is associated with prenatal exposure to sulfur dioxide and childhood attention-deficit hyperactivity disorder symptoms. Sci Rep 13, 3501. https://doi.org/10.1038/s41598-023-29843-y

Demontis D, Walters GB, Athanasiadis G, Walters R, Therrien K et al. (2023) Genome-wide analyses of ADHD identify 27 risk loci, refine the genetic architecture and implicate several cognitive domains. Nat Genet. 2023 Feb;55(2):198-208. doi: 10.1038/s41588-022-01285-8. Epub 2023 Jan 26. Erratum in: Nat Genet. 2023 Apr;55(4):730. doi: 10.1038/s41588-023-01350-w. PMID: 36702997; PMCID: PMC10914347.

Demontis D, Walters RK, Martin J, Mattheisen M, Als TD, Agerbo E, et al. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat Genet. 2019;51:63–75.

E. Mick and S. V. Faraone, “Genetics of attention deficit hyperactivity disorder,” Child and Adolescent Psychiatric Clinics of North America, vol. 17, no. 2, pp. 261–284, 2008.

Faraone, S. V., Banaschewski, T., Coghill, D., Zheng, Y., Biederman, J., Bellgrove, M. A., Newcorn, J. H., Gignac, M., Al Saud, N. M., Manor, I., Rohde, L. A., Yang, L., Cortese, S., Almagor, D., Stein, M. A., Albatti, T. H., Aljoudi, H. F., Alqahtani, M. M. J., Asherson, P., Atwoli, L., … Wang, Y. (2021). The World Federation of ADHD International Consensus Statement: 208 Evidence-based conclusions about the disorder. Neuroscience and biobehavioral reviews, 128, 789–818.

Faraone, S. V., Larsson, H., & Org, S. (2018). Genetics of attention deficit hyperactivity disorder. Molecular Psychiatry, 24(4), 562–575. https:// doi.org/10.1038/s41380-018-0070-0

Faraone SV, Larsson H. Genetics of attention deficit hyperactivity disorder. Mol Psychiatry. 2019;24:562–75.

Gervin, K., Nordeng, H., Ystrom, E. et al. (2017). Long-term prenatal exposure to paracetamol is associated with DNA methylation differences in children diagnosed with ADHD. Clin Epigenet 9, 77. https://doi.org/10.1186/s13148-017-0376-9

Hawi, Z., Yates, H., Pinar, A., Arnatkeviciute, A., Johnson, B., Tong, J., Pugsley, K., Dark, C., Pauper, M., Klein, M., Heussler, H. S., Hiscock, H., Fornito, A., Tiego, J., Finlay, A., Vance, A., Gill, M., Kent, L., & Bellgrove, M. A. (2018). A case-control genome-wide association study of ADHD discovers a novel association with the tenascin R (TNR) gene. Translational psychiatry, 8(1), 284. https://doi.org/10.1038/s41398-018-0329-x

Hinney, A. et al. (2011). Genome-wide association study in German patients with attention deficit/hyperactivity disorder. Am. J. Med. Genet. B Neuropsychiatr. Genet. 156B, 888–897.

Hogan E, Akpan M, Ijezie E, Edem K. Prevalence of attention deficit hyperactivity disorder symptoms among primary school children in Nigeria: a comparison of teachers and parent reports. Int J Res Med Sci 2022;10:1021-7.

Hubers, N., Hagenbeek, F. A., Pool, R., Déjean, S., Harms, A. C., Roetman, P. J., van Beijsterveldt, C. E. M., Fanos, V., Ehli, E. A., Vermeiren, R. R. J. M., Bartels, M., Hottenga, J. J., Hankemeier, T., van Dongen, J., & Boomsma, D. I. (2024). Integrative multi-omics analysis of genomic, epigenomic, and metabolomics data leads to new insights for Attention-Deficit/Hyperactivity Disorder. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 195B:e32955. https://doi.org/10.1002/ajmg.b.32955

Hubers, N., Hagenbeek, F. A., Pool, R., Déjean, S., Harms, A. C., Roetman, P. J. et al. (2024). Integrative multi-omics analysis of genomic, epigenomic, and metabolomics data leads to new insights for Attention-Deficit/Hyperactivity Disorder. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 195B:e32955. https://doi.org/10.1002/ajmg.b. 32955.

Joseph, N., Zhang-James, Y., Perl, A., & Faraone, S. V. (2015). Oxidative stress and ADHD: A meta-analysis. Journal of Attention Disorders, 19(11), 915–924. https://doi.org/10.1177/1087054713510354

Kweon K, Shin E, Park KJ, Lee J, Joo Y, Kim H. (2018). Genome-Wide Analysis Reveals Four Novel Loci for Attention-Deficit Hyperactivity Disorder in Korean Youths. J Korean Acad Child Adolesc Psychiatry; 29:62-72. https://doi.org/10.5765/jkacap.2018.29.2.62

Li, S.-C., Kuo, H.-C., Huang, L.-H., Chou, W.-J., Lee, S.-Y., Chan, W.-C., & Wang, L.-J. (2021). DNA Methylation in LIME1 and SPTBN2 Genes Is Associated with Attention Deficit in Children. Children, 8(2), 92. https://doi.org/10.3390/children8020092

Liu, J., Li, X., & Luo, X. J. (2021). Proteome-wide association study provides insights into the genetic component of protein abundance in psychiatric disorders. Biological Psychiatry, 90(11), 781–789. https://doi.org/ 10.1016/j.biopsych.2021.06.022

Lorenzo G, Braun J, Muñoz G, Casarejos MJ, Bazán E, Jimenez-Escrig A. RNA-Seq blood transcriptome profiling in familial attention deficit and hyperactivity disorder (ADHD). Psychiatry Res. 2018;270:544–6.

Luo, Y., Weibman, D., Halperin, J. M., & Li, X. (2019). A review of heterogeneity in attention deficit/hyperactivity disorder (ADHD). Frontiers in Human Neuroscience, 13, 42. https://doi.org/10.3389/fnhum.2019.00042

Matandika I, Mategula D, Kasenda S, Adeniyi Y, Muula A. Prevalence and correlates of common mental disorders among children and adolescents in Blantyre-Urban, Malawi. Malawi Med J. 2022 Jun;34(2):105-110.

McCaffrey TA, St. Laurent G, Shtokalo D, Antonets D, Vyatkin Y, Jones D, et al. Biomarker discovery in attention deficit hyperactivity disorder: RNA sequencing of whole blood in discordant twin and case-controlled cohorts. BMC Med Genomics. 2020;13. https://doi.org/10.1186/s12920-020-00808-8.

Meijer M, Klein M, Hannon E, van der Meer D, Hartman C, Oosterlaan J, Heslenfeld D, Hoekstra PJ, Buitelaar J, Mill J and Franke B (2020). Genome-Wide DNA Methylation Patterns in Persistent Attention-Deficit/Hyperactivity Disorder and in Association With Impulsive and Callous Traits. Front. Genet. 11:16.

Middeldorp, C. M., Hammerschlag, A. R., Ouwens, K. G., GroenBlokhuis, M. M., St. Pourcain, B., Greven, C. U., Pappa, I., Tiesler, C. M. T., Ang, W., Nolte, I. M., Vilor-Tejedor, N., Bacelis, J., Ebejer, J. L., Zhao, H., Davies, G. E., Ehli, E. A., Evans, D. M., Fedko, I. O., Guxens, M., ... Boomsma, D. I. (2016). A genome-wide association meta-analysis of attention-deficit/hyperactivity disorder symptoms in population-based pediatric cohorts. Journal of the American Academy of Child & Adolescent Psychiatry, 55(10), 896–905.e6. https://doi.org/10.1016/J.JAAC.2016.05.025

Mooney, M.A., Ryabinin, P., Wilmot, B. et al. (2020). Large epigenome-wide association study of childhood ADHD identifies peripheral DNA methylation associated with disease and polygenic risk burden. Transl Psychiatry 10, 8. https://doi.org/10.1038/s41398-020-0710-4

Mortimer N, Sánchez-Mora C, Rovira P, Vilar-Ribó L, Richarte V, Corrales M, et al. Transcriptome profiling in adult attention-deficit hyperactivity disorder. Eur Neuropsychopharmacol. 2020;41:160–6.

Neale, B. M. et al. Case–control genome-wide association study of attentiondeficit/hyperactivity disorder. J. Am. Acad. Child Adolesc. Psychiatry 49, 906–920 (2010)

Neumann, A., Walton, E., Alemany, S. et al. (2020). Association between DNA methylation and ADHD symptoms from birth to school age: a prospective meta-analysis. Transl Psychiatry 10, 398. https://doi.org/10.1038/s41398-020-01058-z

Oke OJ, Oseni SB, Adejuyigbe EA, Mosaku SK. Pattern of attention Deficit hyperactivity disorder among primary school children in Ile-Ife, South-West, Nigeria. Niger J Clin Pract 2019;22:1241-51.

Olatunji G, Faturoti O, Jaiyeoba B, Toluwabori AV, Adefusi T, Olaniyi P, Aderinto N, Abdulbasit MO. Navigating unique challenges and advancing equitable care for children with ADHD in Africa: a review. Ann Med Surg (Lond). 2023 Aug 14;85(10):4939-4946

Reale, L., Bartoli, B., Cartabia, M., Zanetti, M., Costantino, M. A., Canevini, M. P., Termine, C., Bonati, M., & Lombardy, A. G. (2017). Comorbidity prevalence and treatment outcome in children and adolescents with ADHD. European Child & Adolescent Psychiatry, 26(12), 1443–1457. https://doi.org/10.1007/s00787-017-1005-z

Roehr, B. (2013). American Psychiatric Association explains DSM-5. BMJ, 346, f3591. https://doi.org/10.1136/BMJ.F3591

Sayal K, Prasad V, Daley D, Ford T, Coghill D. ADHD in children and young people: prevalence, care pathways, and service provision. Lancet Psychiatry. 2018;5:175–86.

Stergiakouli, E. et al. (2012). Investigating the contribution of common genetic variants to the risk and pathogenesis of ADHD. Am. J. Psychiatry 169, 186–194.

Thapar, A. and Cooper, M. (2016). Attention deficit hyperactivity disorder. The Lancet., 387(10024); 1240–1250.

Walton, E., Pingault, JB., Cecil, C. et al. (2017). Epigenetic profiling of ADHD symptoms trajectories: a prospective, methylome-wide study. Mol Psychiatry 22, 250–256. https://doi.org/10.1038/mp.2016.85

Wang, J., Zhu, Q. W., Mai, J. H., Zhang, S., Wang, Y., Liang, J., & Zhou, J. Y. (2024). A multi-omics study of brain tissue transcription and DNA methylation revealing the genetic pathogenesis of ADHD. Briefings in bioinformatics, 25(6), bbae502. https://doi.org/10.1093/bib/bbae502

Wang, L. J., Chou, W. J., Tsai, C. S., Lee, M. J., Lee, S. Y., Hsu, C. W., Hsueh, P. C., & Wu, C. C. (2021). Novel plasma metabolite markers of attention-deficit/hyperactivity disorder identified using highperformance chemical isotope labelling-based liquid chromatographymass spectrometry. The World Journal of Biological Psychiatry, 22, 139– 148. https://doi.org/10.1080/15622975.2020.1762930

Willcutt EG. The prevalence of DSM-IV attention-deficit/hyperactivity disorder: a meta-analytic review. Neurotherapeutics 2012;9:490–9.

Yang L, Neale BM, Liu L, Lee SH, Wray NR, Ji N, Li H, Qian Q, Wang D, Li J, Faraone SV, Wang Y, Psychiatric GWAS Consortium: ADHD Subgroup. (2013). Polygenic Transmission and Complex Neuro Developmental Network for Attention Deficit Hyperactivity Disorder: Genome-Wide Association Study of Both Common and Rare Variants. Am J Med Genet Part B 162B:419–430.

Genetic and Epigenetic Mechanisms Contributing to ADHD Aetiology in Children in South-west and South-south Nigeria

Genetic and Epigenetic Mechanisms Contributing to ADHD Aetiology in Children in South-west and South-south Nigeria

Funding Progress

Funders

Peer Reviews

Related Funding

Request for Proposals - Bioinformatics & Genomics Research

Nonprofit Recipient

Topics

DOI