Developing a Computational Framework for Integrating Traditional Genetic Knowledge with Genomic Data in Nigerian Indigenous Communities

Indigenous and local communities in Nigeria possess rich traditional genetic knowledge (TGK) manifested in oral genealogy traditions, plant and animal selective breeding practices, and ethnomedical understanding of inherited conditions. This knowledge has been largely disregarded in genomic research frameworks that prioritise molecular data. Integrating TGK with genomic data offers the possibility of richer, context-sensitive genomic discoveries while respecting indigenous intellectual property rights. This dissertation develops an original computational framework for integrating TGK with genomic data from Nigerian indigenous communities, using the Tiv of Benue State and the Nupe of Niger State as case study communities. Community participatory methods including narrative genealogy mapping, ethnomedical plant knowledge surveys, and key informant interviews were conducted with 80 community knowledge holders. Genealogical data were formalised as probabilistic kinship networks and tested for congruence with genome-wide IBD (identity-by-descent) estimates using KING and PRIMUS. Oral plant breeding knowledge was encoded as a structured ontology and cross-referenced with chloroplast genome diversity of cultivated varieties. A novel community knowledge-weighted genomic association model (CKWGAM) incorporating TGK priors into Bayesian GWAS is proposed and evaluated by simulation. This framework advances epistemic justice in genomics and proposes a replicable model for TGK-genomics integration globally. Keywords: traditional genetic knowledge, computational framework, indigenous communities, Tiv, Nupe