Proteomic profiling: A tool to unlock the potential of Nigerian indigenous goats
Nigeria possesses a rich diversity of indigenous goats (predominantly the West African Dwarf, Sokoto Red and Sahel goats), that are superbly adapted to local environment but often underperform compared to improved breeds. Proteomic profiling emerges as a powerful avenue to bridge this gap. This approach examines the protein content of an organism, providing insights into its genetic potential. In the case of Nigerian indigenous goats, this method can reveal valuable information about their unique traits, disease resistance, and potential for improvement through selective breeding. This review underscores how integrating proteomic profiling into breeding programs can unlock genetic potential of Nigerian indigenous goats. It is however concluded that with the use of technologies involved in proteomic profiling, strategies can be formulated for Nigerian indigenous goat breeds to improve their productivity, health, and product quality. With strategic application of these technologies, supported by ongoing research and investment, proteomics can empower Nigerian goat farmers to tap into the vast potential of their indigenous breeds thus offering a powerful approach to unlocking the genetic diversity and adaptive potential of Nigerian indigenous goats. Ultimately, proteomic profiling can boost productivity, animal health and product quality, empowering Nigerian goat producers to leverage the full spectrum of their breeds’ adaptive diversity.
Authors
- Adeyinka Oye Akintunde (ORCID: https://orcid.org/0000-0002-6013-0902)
- Rufus Olusegun Animashaun
- Oluwafunmike Omowunmi Oyekale
- Adenike Abosede Adebisi
- Samson Oluwole Oyewumi (ORCID: https://orcid.org/0009-0007-2830-8938)
- Lois Chidinma Ndubuisi-Ogbonna (ORCID: https://orcid.org/0000-0003-3020-4680)
Institutions
- Babcock University (NG)
Publication Details
- Journal
- Acta Scientiarum Biological Sciences
- Published
- 2025-11-25
- DOI
- https://doi.org/10.4025/actascibiolsci.v47i1.72013
- Primary Topic
- Identification and Quantification in Food
- Type
- article
- Field-Weighted Citation Impact
- 0.00