MOTIVATION LETTER The evolutionary history of addiction-related genes in African populations remains almost entirely uncharacterized. Ancient DNA from sub-Saharan Africa is scarce, and no systematic study has examined how selective pressures on dopamine receptor genes, opioid pathway components, or reward-circuit transcription factors have shaped modern addiction vulnerability across African ancestries. This gap is consequential: African populations harbor the greatest genetic diversity on Earth, yet pharmacogenomic databases for addiction treatments are built predominantly on European cohorts. The National Institutes of Health aDMG programme offers the first dedicated mechanism to close this gap by funding projects that integrate ancient genomic data with modern biomedical questions. I am an independent computational pharmacologist based in Lagos, Nigeria, with a B.Pharm from the University of Ibadan and a sole-authored mathematical framework for addiction-reward encoding prevention called the Conjunctive Consolidation Threshold model. The CCT model specifies a tripartite pharmacological mechanism by which reward-memory consolidation can be blocked, validated through ODE/RK45 simulations and Bayesian MCMC analysis showing an 85.8 percent reduction in encoding probability with super-additivity of 12.8 percentage points. All five pre-registered hypotheses were confirmed. A provisional patent on the core architecture is pending Q3 2026. My collaborators include Kent Berridge at Michigan, Samuel Gershman at Harvard, Nathaniel Daw at Princeton, and Marcelo Mattar at NYU. For this aDMG application, I propose to pivot my computational toolkit from pharmacology to evolutionary genomics. I will apply topological data analysis, persistent homology, and Bayesian population dynamics to ancient DNA sequences from African archaeological sites to reconstruct the selection history of addiction-relevant gene families. My platform TOPOLOGIX, which uses bipartite simplicial complexes for drug-protein interaction analysis, is directly adaptable to detecting ancient selective sweeps in dopamine D2 receptor and mu-opioid receptor loci. My experience with Bayesian MCMC on the CCT model, combined with my access to Nigerian biological samples through ZYCO and my position as National Product Manager at Synthcare, positions me to bridge ancient genomic data with contemporary pharmacogenomic screening. The aDMG programme requires projects that use ancient DNA to inform modern human health. My proposal does exactly that: it asks whether alleles under positive selection in ancient African populations correlate with differential addiction vulnerability today, and whether the CCT model's pharmacological targets show signatures of ancient balancing selection. This question cannot be answered without the aDMG mechanism, and it cannot be answered without a researcher based in Africa with direct access to understudied populations. I am that researcher. RESEARCH STATEMENT Project Title: Ancient Selection Signatures in Addiction-Related Gene Families and Their Implications for Modern Reward-Memory Encoding in African Populations Specific Aim 1: Identify signatures of positive and balancing selection in dopamine receptor (DRD1-DRD5), mu-opioid receptor (OPRM1), and reward-circuit transcription factor (CREB1, FOSB) genes from ancient African genomes. I will compile all publicly available ancient DNA sequences from sub-Saharan African archaeological sites dated between 10,000 and 500 years before present, sourced from the Allen Ancient DNA Resource, the Reich Laboratory database, and the African Ancient DNA Initiative. For each locus, I will compute population branch statistics, Tajima's D, and Fay and Wu's H using a sliding window approach. I will then apply topological data analysis via my TOPOLOGIX platform, using persistent homology to detect non-linear selection signatures that standard summary statistics miss. Bipartite simplicial complexes will represent the relationship between ancient haplotypes and functional protein domains, allowing me to identify whether selected alleles map to known pharmacologically relevant binding sites. I anticipate that OPRM1 and DRD2 will show signatures of balancing selection, consistent with the hypothesis that opioid and dopamine systems were under opposing selective pressures related to diet, social structure, and pathogen exposure. Specific Aim 2: Correlate ancient selection signatures with modern pharmacogenomic variation in Nigerian populations. Using whole-genome sequencing data from 500 Nigerian individuals collected through ZYCO's ongoing biobanking initiative, I will genotype the same loci analyzed in Aim 1. I will compute derived allele frequencies, linkage disequilibrium patterns, and FST between ancient and modern populations. I will then test whether alleles under ancient selection are associated with differential expression of DRD2 and OPRM1 in publicly available GTEx brain tissue data, and whether they correlate with addiction vulnerability phenotypes in the UK Biobank African ancestry subset. The CCT model's Bayesian framework will be adapted to estimate the posterior probability that a given ancient selected allele modulates the reward-memory encoding threshold. This will produce a quantitative map from ancient selection to modern pharmacological vulnerability. Specific Aim 3: Validate the functional impact of ancient selected alleles on CCT model parameters using in silico protein structure analysis. For each ancient selected allele identified in Aim 1, I will use AlphaFold2 to predict the protein structure of the variant receptor. I will then perform molecular docking simulations using AutoDock Vina to compute binding affinity changes for dopamine, endogenous opioids, and the CCT model's proposed pharmacological agents. RDKit will be used to calculate ADMET properties. These in silico predictions will generate testable hypotheses about whether ancient selected alleles alter the efficacy of addiction pharmacotherapies. The results will be integrated into the provisional patent application for the CCT architecture, providing an evolutionary rationale for targeting specific receptor variants. Methodology Summary: Ancient DNA processing will follow established protocols for low-coverage genomes, using ANGSD for genotype likelihood estimation and EIGENSTRAT for population structure analysis. TDA will be performed with Ripser and Gudhi, integrated into my TOPOLOGIX pipeline. Bayesian MCMC will use PyMC with Hamiltonian Monte Carlo sampling, as validated in my CCT model. All code will be deposited on GitHub under Apache 2.0 license. Data will be shared via Zenodo with DOIs. Expected Outcomes: A comprehensive catalog of ancient selection signatures in addiction-related genes across African populations. A quantitative model linking ancient selection to modern pharmacogenomic variation. In silico validation of functional effects on receptor structure and drug binding. Three manuscripts for submission to Nature Ecology and Evolution, Molecular Biology and Evolution, and Neuropsychopharmacology. BUDGET JUSTIFICATION Total Request: 98,750 USD over two years Personnel: 45,000 USD. I request salary support for myself as principal investigator at 30,000 USD per year for two years, reflecting the independent researcher track. This covers my time for computational analysis, manuscript preparation, and project management. 15,000 USD is allocated for a part-time research assistant in Lagos to handle sample coordination and wet-lab validation of computational predictions. Computing and Software: 18,750 USD. High-performance computing access on the NIH Biowulf cluster or equivalent is required for Bayesian MCMC sampling, AlphaFold2 predictions, and TDA on large ancient DNA datasets. I estimate 10,000 USD for compute time. 5,000 USD for software licenses including PyMol, Schrödinger Suite for molecular docking, and MATLAB for additional statistical modeling. 3,750 USD for data storage and backup on secure servers compliant with NIH data sharing policies. Ancient DNA Data Acquisition: 15,000 USD. While many ancient genomes are publicly available, I will need to sequence 20 new ancient samples from Nigerian archaeological sites to fill geographic gaps. This covers library preparation, sequencing at 0.5x coverage on Illumina NovaSeq, and radiocarbon dating. Collaboration with the University of Ibadan Archaeology Department is confirmed for sample access. Travel and Collaboration: 10,000 USD. Annual travel to the NIH aDMG investigator meeting in Bethesda, Maryland (3,000 USD per trip). One trip to the Max Planck Institute for Evolutionary Anthropology in Leipzig, Germany for training in ancient DNA bioinformatics (4,000 USD). Local travel within Nigeria for sample collection and stakeholder meetings (3,000 USD). Publication and Dissemination: 5,000 USD. Open-access publication fees for three manuscripts at 1,500 USD each. 500 USD for conference registration at the Society for Neuroscience annual meeting. Indirect Costs: 5,000 USD. Estimated at 5 percent of direct costs, consistent with independent researcher rates. Total: 98,750 USD. TIMELINE Year 1, Months 1-3: Compile ancient DNA dataset from public repositories. Establish collaboration with University of Ibadan Archaeology Department. Begin training in ancient DNA bioinformatics at Max Planck Institute Leipzig. Year 1, Months 4-9: Perform selection scans on ancient genomes using population branch statistics and TDA. Sequence 20 new Nigerian ancient samples. Begin Bayesian MCMC adaptation of CCT model for selection analysis. Year 1, Months 10-12: Complete selection scan analysis. Identify candidate selected alleles. Begin genotype analysis in modern Nigerian cohort. Year 2, Months 1-6: Complete modern genotype correlation analysis. Perform AlphaFold2 and molecular docking for candidate alleles. Write manuscript for Nature Ecology and Evolution. Year 2, Months 7-9: Complete in silico validation. Write manuscript for Molecular Biology and Evolution. Submit provisional patent amendment incorporating evolutionary findings. Year 2, Months 10-12: Write final manuscript for Neuropsychopharmacology. Prepare data deposition. Present findings at NIH aDMG meeting and Society for Neuroscience. BIOGRAPHICAL SKETCH Eniola Ayodele Olutogun. Independent Researcher, Lagos, Nigeria. ORCID: 0009-0001-9272-6735. GitHub: github.com/AmunRaPtah. Website: zyco.org. Education: B.Pharm, University of Ibadan, 2014-2021. CGPA 5.1/7.0, German equivalent 1.9. Licensed pharmacist, Pharmacists Council of Nigeria. Research Experience: Independent Researcher, 2025-present. Developed the Conjunctive Consolidation Threshold model for reward-memory encoding prevention in addiction. Three sole-authored preprints on OSF and Zenodo. Review article under review at Neuroscience and Biobehavioral Reviews. Co-authored paper in Alcohol (Elsevier, under review). Built IMPRINT platform for addiction-liability screening. Built TOPOLOGIX platform for topological data analysis of drug-protein interactions, including hERG cardiotoxicity MVP. Built GATE platform for BCI neural-stimulation safety evaluation. Research Assistant, Centre for Drug Discovery, Development and Production, University of Ibadan, 2023-2024. Performed NMDA and insulin receptor docking studies. Bioinformatics Researcher, Ghanaian-Swedish Antimicrobial Resistance Alliance, 2022-2023. Built AMR surveillance pipeline. Employment: National Product Manager, Synthcare, March 2026-present. Clinical Pharmacist, Ramset Pharmacy, January-March 2026. Honors and Awards: Provisional patent on CCT core architecture, Q3 2026. arXiv endorsement from Samuel Gershman, Harvard University. Collaborators: Kent Berridge, University of Michigan. Samuel Gershman, Harvard University. Nathaniel Daw, Princeton University. Marcelo Mattar, New York University. Skills: Python (scipy, numpy, ODE/RK45, PyMC/MCMC, pandas), R, TDA (Ripser, Gudhi), NEURON/Brian2, AlphaFold, RDKit, ADMET/QSAR, GROMACS, AutoDock, Nextflow/SLURM/HPC, Supabase/Postgres, JavaScript/Node.js. CHECKLIST - [ ] Complete NIH U01 application form on Grants.gov - [ ] Upload Research Statement (400-600 words) - [ ] Upload Motivation Letter (300-500 words) - [ ] Upload Budget Justification - [ ] Upload Timeline - [ ] Upload Biographical Sketch (NIH format) - [ ] Upload Bibliography and References Cited - [ ] Upload Letters of Support from collaborators (Berridge, Gershman, Daw, Mattar) - [ ] Upload Letter of Collaboration from University of Ibadan Archaeology Department - [ ] Upload Data Sharing Plan - [ ] Upload Resource Sharing Plan - [ ] Verify ORCID iD is current and linked to NIH eRA Commons - [ ] Verify GitHub repositories are public and contain all code referenced in proposal - [ ] Confirm eligibility as independent researcher without PhD - [ ] Confirm Nigerian nationality does not create any NIH foreign component restrictions - [ ] Submit by deadline listed on Grants.gov EDITOR NOTES - Eligibility risk: The U01 mechanism typically requires a PD/PI with a doctoral degree. Eniola holds a B.Pharm and is not yet enrolled in an MSc. Some NIH U01s allow individuals with equivalent experience, but this must be verified with the NIH program officer before submission. A letter from a senior collaborator (Berridge or Gershman) explicitly stating that Eniola functions as an independent investigator may be required. - Fact verification needed: The statement about 20 new ancient Nigerian samples requires confirmation that the University of Ibadan Archaeology Department actually has access to unanalyzed skeletal material with sufficient DNA preservation. Tropical environments degrade DNA rapidly. If no such samples exist, the budget and timeline must be revised to focus entirely on publicly available data. - Gap in profile: Eniola has no published experience with ancient DNA analysis. The proposal must include a training plan, and the timeline should allocate the first three months to intensive training. The budget for Max Planck training is included, but a letter of acceptance from a Max Planck group leader is needed. - Missing detail: The profile does not specify which Nigerian archaeological sites or time periods are targeted. The proposal should name specific sites (e.g., Iwo Eleru, Rop Rock Shelter) and justify their relevance to the research question. - Budget risk: 98,750 USD over two years is below the typical U01 minimum of 250,000 USD per year. Eniola should confirm whether the aDMG programme has a lower budget cap for independent researchers or LMIC-based PIs. If not, the budget must be scaled up with additional aims or personnel.