Genetic Diversity and Population Structure of Nile Tilapia <i>Oreochromis niloticus</i> (Linnaeus, 1758) From Selected Lakes and Hatcheries in Kenya

Nile tilapia ( Oreochromis niloticus ) supports both capture and aquaculture fisheries in Kenya, contributing 80% of the total annual aquaculture production. Poor management practices in fish hatcheries, resulting in inbreeding and a lack of genetic improvement and breeding strategies, have hampered the sustainable growth of farmed O. niloticus in Kenya. The native populations of O. niloticus suitable for use as the foundation stock for selective breeding are often threatened by hybridization and introgression, through uncontrolled transfer of genetic material across basins, especially with the introduction of cage aquaculture of O. niloticus in Lake Victoria. A study was initiated to assess the genetic diversity and population structure of O. niloticus from major hatcheries and natural stock from Lakes Victoria and Turkana. Eight microsatellite DNA markers designed for O. niloticus were used to genotype 89 natural and cultured individuals from 11 different sites in Kenya. Genetic diversity was moderate, with an overall mean of 5.46 alleles and 3.88 effective alleles per locus. Kamuthanga farm showed the highest allelic richness (7.63), followed by Turkana natural (6.75), while both Busia caged and Busia natural populations had the lowest (4.00). Analysis of molecular variance (AMOVA) results indicated that 95% of genetic variation occurs within the population, while only 2% is attributed to differentiation among populations, indicating strong within‐population structuring. STRUCTURE outputs were summarized using STRUCTURE HARVESTER, which identified K = 3 as the optimal number of genetic clusters, indicating the presence of three genetically distinct subpopulations among the sampled tilapia. Usenge caged and Turkana formed Cluster 1, Victory, and Kenya Marine and Fisheries Research Institute (KMFRI) farmed populations formed Cluster 2, while Homa Bay and Dunga natural populations comprised Cluster 3, showing close genetic similarity. These results indicate a well‐defined hierarchical structure at K = 3, representing the best fit for the dataset across all populations. High genetic diversity observed in farmed populations with a history of selective breeding, like KMFRI, demonstrates the need to operationalize such programs within the policy framework. The within‐population variability demonstrated in this study could be leveraged to design breeding programs based on marker‐assisted selection framework for increased aquaculture productivity.