Genetic and Epigenetic Mechanisms Contributing to ADHD Aetiology in Children in South-west 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 genetic and epigenetic resource from Nigerian children, 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: ResearchHub RFP Neuroscience grant will cover sample collection, consumables, DNA extraction, genotyping, methylation array and quality control
Facilities: University of Ibadan–affiliated laboratories equipped for DNA extraction
Ethics: Ethical clearance has been obtained from the UI/UCH Health Research Ethics Committee, College of Medicine, University of Ibadan with ethics number: UI/EC/25/0401. This will cover for recruiting participants from the Child and Adolescents Mental Health Clinic of the University College Hospital, Ibadan. Approval has also been obtained from the Oyo State Universal Basic Education Board to recruit participants in selected public primary schools. 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 Sharing Commitment

All data will be de-identified and shared per institutional and international standards
Summary statistics and protocols will be openly shared via ResearchHub, GitHub and peer-reviewed publications to maximize reuse and global impact following necessary guidelines

Total Grant Request: 10,000 USD

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

There is a notable genetic imbalance in the current molecular research on Attention-Deficit/Hyperactivity Disorder (ADHD), with more than 80% of genome-wide association studies (GWAS) being carried out in individuals of European ancestry. In African environments, where the distinctive genomic architecture—characterized by high genetic disparity and shorter linkage disequilibrium—remains largely unmapped, this lack of diversity is especially problematic. Furthermore, there is a dearth of integrative research that connects genetic variants with epigenetic alterations (EWAS) in African paediatric cohorts, despite the fact that the aetiology of ADHD involves a complex interaction between genetic predisposition and environmental variables. In environments with limited resources, the main obstacle is the high expense and practical difficulties of collecting huge amounts of multi-omic data. In order to uncover population-specific molecular markers, it is necessary to develop a fundamental integrative framework that illustrates the use of small-scale, well-phenotyped Nigerian cohort. In the absence of such a proof-of-concept, global genomic models would continue to ignore the genetic diversity of African populations, and the molecular causes behind ADHD in Nigerian children will remain poorly understood.

Primary Research Objective

To develop and evaluate a multi-omic integration pipeline that identifies functional overlaps between genetic variants and DNA methylation patterns in a pilot cohort of Nigerian children with ADHD.

Specific Aims

1. To implement a bioinformatic workflow for integrating GWAS and EWAS data specifically optimized for low-sample-size, high-diversity African cohort

2. To identify differentially methylated regions and risk alleles that distinguish 20 ADHD cases from 20 age- and sex-matched controls in a Nigerian cohort

3. To identify high-effect-size mQTLs that are unique to this cohort, providing a foundation for future large-scale African genomic studies

4. To replicate the identified genetic signatures against public databases like the 1000 Genomes Project (YRI population) to isolate ADHD-specific signals from the baseline Nigerian genomic background

Innovation

First pilot ADHD GWAS–EWAS integration in an African population
Molecular map of ADHD in African children
Multi-omics framework that may be applicable to other neurodevelopmental disorders

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 40 individuals aged 6-17 years (20 cases and 20 age- and sex-matched controls) will be recruited into this study. Standard statistical heuristics suggest that a sample size of 30 to 50 is sufficient to estimate parameters (like standard deviation) needed to power a future, larger study. In addition, for underrepresented populations (Nigeria), a high-quality deep phenotyping of 40 children is likely going to provide more scientifically valuable data insight in terms of GWAS and EWAS integration.

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 psychiatrist.

Study Setting

Children will be recruited from public primary and public secondary schools in Ibadan-North Local Government Area, Oyo State.

Inclusion Criteria

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

Exclusion Criteria

Children with any genetic or neurodevelopmental disorder

Selection of Schools

A multi-stage sampling method will be used to select public primary and secondary schools in the Ibadan-North Local Government Area, Oyo State. Ibadan-North contains 73 public primary schools and 39 public secondary schools (Total = 112). To achieve the pilot sample size of 40 participants (20 cases and 20 controls), ten (10) schools will be selected to ensure a sufficient recruitment pool while maintaining logistical feasibility for high-depth molecular sampling.

The number of schools sampled from each category will be determined by proportionate sampling:

Relative proportions:

Total schools = 73 + 39 = 112.
Primary: 73/112 = 0.6517857 → 65%
Secondary: 39/112 = 0.3482143 → 35%
For public primary schools ≈ 10 × 0.6517857 = 6.52 ⇒ 7 schools
For public secondary schools ≈ 10 × 0.3482143 = 3.48 ⇒ 3 schools

Random Selection Process:

The schools will be selected following the randomization protocol described by Hogan et al. (2022). Primary schools will be numbered 01–73 and secondary schools 01–39. Using a table of random numbers, a starting point will be determined by the "blind-drop" method. The investigator will read down the columns, selecting the last two digits within the code range and skipping duplicates until 7 primary and 3 secondary schools are identified.

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 selection of 10 schools provides a sufficiently large screening base to identify 20 ADHD cases (assuming a 5-8% prevalence) while optimizing the budget for high-resolution epigenetic sequencing. This sampling strategy is not intended to be epidemiologically representative of the wider population, but rather to generate a well-characterized pilot cohort for genomic and epigenomic analysis. The emphasis is on biological feasibility, technical pipeline development and hypothesis generation rather than prevalence estimation or population-level inference.

Ethical Approval

Ethical approval has been obtained from the UI/UCH Health Research Ethics Committee, College of Medicine, University of Ibadan with ethics number: UI/EC/25/0401. While the initial approval covers a cohort of up to 800 participants, this pilot phase will focus on a strategic subset of 40 participants. In order to reflect the shift to a comprehensive, multi-omic pilot investigation, a Protocol Amendment will be presented to the Ethics Committee. This downsizing represents a favorable risk-benefit ratio; it reduces the total number of pediatric participants exposed to study procedures while significantly increasing the scientific yield per participant through the integration of GWAS and EWAS data. Approval has also been obtained from the Oyo State Universal Basic Education Board to recruit participants in selected public primary schools.

Using informed consent procedures (translated into the local Yoruba Language), the assent and consent of the children and their parents/guardians will be obtained in writing before participation in the project. All data will be de-identified and stored on encrypted servers to ensure the privacy of all participants.

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 the DSM-5, 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 Infinium Global diversity (GDA-8) array. 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 (Loh et al., 2015) 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.

GWAS Data Analysis

Association testing will be carried out using the REGENIE software (Mbatchou et al., 2021).
Data visualization will then be carried out via Manhattan and QQ plots using the qqman [R] (Turner, 2014).
Functional annotation, pathway and network analysis will be carried out by mapping significant SNPs to genes using Functional Mapping and Annotation of Genetic Associations (FUMA) (Watanabe et al., 2017).

Epigenome-wide association studies (EWAS)

Extracted genomic DNA will be bisulphite-converted using the EZ DNA Methylation-Lightning Kit (50 rxns). Following conversion, genome-wide methylation status will be assayed using the Infinium Methylation Screening Array (MSA). 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 (Zhou et al., 2018).

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 (Argelaguet et al, 2018) to determine key features related to ADHD.

Methylation Quantitative Trait Locus (mQTL) mapping

mQTL mapping will be performed using the Matrix eQTL package in R (Shabalin, 2012), testing associations between genotype dosage and CpG methylation levels while adjusting for covariates (cell-type proportions, age, sex) and genetic principal components.

Ancestry Baseline Comparison

To isolate ADHD-specific signals from the baseline Nigerian genomic background:

Reference Integration: Study genotypes will be merged with the 1000 Genomes Project (1KGP) African panel (specifically YRI and ESN populations) (1000 Genomes Project Consortium, 2015)

Population Stratification: Principal Component Analysis (PCA) will be executed to confirm that the cohort (N = 40) clusters with the appropriate ancestral reference

Allele Frequency Deviation: A Fisher’s Exact Test will compare the frequency of identified risk alleles in our ADHD cases versus the 1KGP Nigerian baseline. Variants showing significant enrichment (p < 0.05 after FDR correction) in cases but appearing at baseline frequencies in 1KGP will be prioritized as ADHD-specific candidate variants

Statistical Significance and Power

Given the pilot scale (N = 40), a false discovery rate (FDR) threshold of q < 0.05 (Benjamini-Hochberg) will be used to define significant mQTLs. This study is powered to detect large-effect size associations (β > 0.5), which are highly informative for identifying the primary biological drivers of ADHD in underrepresented populations.

Integration Strategy (Multi-Omics Framework)

A systematic analytical framework will guide the integration of multi-omics. First, find genetic variations linked to ADHD using genome-wide association analysis (GWAS). Second, find differentially methylated CpG sites linked to ADHD status using epigenome-wide association analysis (EWAS).

Third, methylation quantitative trait locus (mQTL) mapping will be performed using the Matrix eQTL framework to test whether ADHD-associated genetic variants predict DNA methylation levels. Fourth, causal overlap between GWAS and EWAS signals will be evaluated using colocalization methods (COLOC) (Giambartolomei et al, 2014).

Finally, Multi-Omics Factor Analysis (MOFA) will be used to integrate genetic variation, DNA methylation and phenotype data into latent biological factors. This approach enables testing of biologically meaningful hypotheses regarding whether genetic risk for ADHD is mediated through epigenetic regulatory mechanisms, especially in an underrepresented population (Nigerian children).

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.

Data Sources

Data	Source
ADHD GWAS	Psychiatric Genomics Consortium (PGC) ADHD-Working Group, All of Us
ADHD EWAS	PGC-ADHD Working Group
African samples	Nigerian ADHD cohort (pilot cohort)

Notes:

1. I recently completed a 3-months (November 3, 2025 – February 2, 2026) IBRO Exchange Fellowship at the Precision Healthcare University Research Institute, Queen Mary University of London for GWAS training. I generated a yet to be published summary statistics for ADHD of participants of African ancestry on the All of Us platform, which can be incorporated into this work for further analysis.

2. I am also a member of the PGC-ADHD Working Group, where I can access available GWAS and EWAS data following the standard protocol of the group (https://pgc.unc.edu/for-researchers/working-groups/adhd-working-group/).

Budget ($10,000)

Category	Item Description	Estimated Cost
Blood Collection	Phlebotomy & Lab Supplies: EDTA Vacutainer tubes, needles, and local centrifuge fees for 40 participants	$200
Lab Reagents	Extraction & Bisulfite Conversion: Blood DNA mini-kits plus one Zymo EZ DNA Methylation-Lightning Kit (50 rxns)	$700
Genomic Analysis (GWAS)	Global diversity array (GDA): processing 40 samples using the Illumina Infinium GDA-8	$2,600
Epigenomic Analysis (EWAS)	Methylation Screening Array: To remain within budget, 40 samples will be processed using the Infinium Methylation Screening Array ($150/sample)	$6,000
Data Analysis	Bioinformatics + cloud computing	$500
Total		$10,000

Budget Justification

Total Amount Requested: $10,000

This budget is designed to execute a high-impact, multi-omic "Proof-of-Concept" (PoC) study. By utilizing a deep-phenotyping approach with a focused cohort of 40 participants, this project establishes the first foundational GWAS-EWAS integrative framework for ADHD in a Nigerian pediatric population. To stay within budget, GWAS and EWAS will be performed on all 40 (20 cases and 20 controls) participants.

Clinical phenotyping and ADHD diagnosis will be led by co-investigators specializing in child psychiatry. This professional collaboration provides the project with expert medical oversight at no direct cost to the grant, allowing the full allocation of funds toward the high-cost GWAS and EWAS arrays.

The computational phase of this research will through existing institutional partnerships leverage the High-Performance Computing (HPC) infrastructure at the Genomic Diversity Research Group, Precision Healthcare University Research Institute, Queen Mary University of London, where the principal investigator recently completed a specialized GWAS analysis internship. Hence, the small amount ($500) assigned to cloud computing.

Six-Month Workplan

Month 1: Biological data collection and ethics request for access to GWAS and EWAS data

Month 2: African pilot genotyping and methylation

Month 3: GWAS quality control and association analysis

Month 4: EWAS quality control and association analysis

Month 5: mQTL mapping and GWAS–EWAS integration

Month 6: African data analysis, report and manuscript draft

The proposed work is designed to be fully feasible within a 6-month funding window using existing infrastructure. The African pilot component focuses on a limited number of samples and can be completed within this timeframe.

Impact

This study will produce the first integrated genomic and epigenomic molecular framework for ADHD in an African population. By linking genetic variation, epigenetic regulation, and clinical phenotype, the project will generate biologically grounded hypotheses about the mechanisms underlying ADHD risk in an underrepresented population.

Although exploratory in scale, the integrated risk map will represent a foundational resource for further research and larger grant proposals, future African psychiatric genomics, contribute to more equitable precision medicine, and provide a scalable model for multi-omics research in other neurodevelopmental disorders.

Community Benefit and Feedback

To ensure the research is culturally sensitive and beneficial, this study will provide families with a summary of the clinical ADHD assessments. This will ensure that even in a small-N pilot, there is immediate diagnostic value provided to the participating individuals.

Limitations and Future Directions

This study is designed as a pilot-scale investigation. The Nigerian cohort is not statistically powered for genome-wide discovery or validation, and all findings will therefore be interpreted as exploratory and hypothesis-generating.

The primary scientific contribution lies in establishing analytical pipelines, generating the first integrated genomic and epigenomic dataset for ADHD in an African population and identifying candidate loci and pathways for future investigation.

Further work will focus on expanding recruitment through regional and international collaborations, integrating additional omics layers such as transcriptomics, and developing a fully powered African ADHD genomics. The integrated risk map that will be generated in this project will directly inform sample size estimation, fine-mapping strategies and ancestry-aware polygenic risk score development in subsequent studies.

Personnel:

Author/PI: Akpabio, Cephas Akpabio

PhD Student (Cell Biology and Genetics), Department of Zoology

Faculty of Science, University of Ibadan

Supervisor: Prof. Chiaka I. Anumudu

Department of Zoology, Faculty of Science, University of Ibadan

Collaborators: Dr. Yetunde Adeniyi MBBS, FWACP, MSC, ACCAPsych

Senior Lecturer and Consultant

University of Ibadan and University College Hospital, Ibadan, Nigeria

Dr Phinuella Okunamiri

Senior Registrar in Psychiatry

Federal Neuropsychiatric Hospital, Yaba, Lagos

Irene Namara

Doctoral Student/Researcher

Institute of Translational Genomics, Helmholtz, Munich, Germany

Has experience in MOFA

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

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

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