Genetic diversity was analysed and a high level of allele polymorphism was identified. This is evident through the total number of alleles identified per marker. It indicates an unlimited gene pool and high gene flow. The current findings are in line with the value estimated in six populations in Central Tanzania as reported by Moto and Rubanza (2019) and were in a similar range as reported by Nxumalo et al. (2020). Moreover, high alleles per marker were identified in Bangladesh and China (Rashid et al., 2020), (Azimu et al., 2018).
In contrast to the work published by Yacouba et al. (2022), the mean number of distinct alleles (Na) discovered in this investigation was greater on the other hand was lower compared to the report posed by Luis-Chincoya et al. (2021). Additionally, the number of effective alleles (Ne) looks to be higher compared to the approximate amount disclosed by Yacouba et al. (2022). The current findings show the allelic diversity and genetic variants for a given gene locus in the Tanzania chicken population which might be influenced by migration of individuals between populations and aspects related to natural selection.
The existing variation in heterozygosity might be influenced by nonrandom mating. The expected heterozygosity concurs with the study conducted by Habimana et al. (2020) but, it persists persisting in a higher range compared to what was mentioned in traditional Dutch chicken by Teinlek et al. (2018) and 22 population of Chinese gamecock chicken. Interestingly, the expected heterozygosity in this study seems low compared to Central Tanzania as mentioned by Moto and Rubanza, (2019).
On the other hand, the private alleles identified correspond with the study done by Habimana et al., (2020a), and also were higher than that observed in Norfa chickens and Sinai chickens as reported by Soltan et al. (2018). These findings show genetic variant within a population that describes population relationship and structures.
Based on this study, Wright’s F-statistics (FIS) shows a greater range than what was estimated in several reports including one from Egypt under the study conducted by Abdelaziz et al. (2019), Alsoufi and Changrong, (2022), and Hata et al., (2020). However, the genetic differentiation among the populations, was estimated lower amount than the value estimated in Egypt by Mekky et al. (2021) and China by Alsoufi and Changrong (2022). Additionally, the lower FST value was reported by Yacouba et al. (2022), Okumu et al. (2017) and Soltan et al., (2018). The current findings indicate inbreeding which might be caused by migration. A great genetic variation within a population and only a small difference among the groups were identified by (AMOVA) which might be influenced by several factors including; random mating and the absence of mating restrictions within the population. Moreover, low variation between the groups might be caused by the presence of a close genetic relationship between the two populations. However, these findings were consistent with the findings from Sri Lanka (Samaraweera et al., 2021). In addition, Mwambene et al. (2019) identified a greater variation than the current findings within the population and few variations among the population of Southern highland Tanzania.
The Nei’s genetic distance between the two populations observed in this study concurs with the findings estimated between populations of Tabora and Kondoa Tanzania, as reported by Moto and Rubanza, (2019). Asia reported the same value between the populations of Korean greyish brown and Mongolian Muthiinbor (Roh et al., 2020). Also, a greater value than the current finding was reported in Nigeria between populations of Shika brown and the Noiler chicken (Bakare et al., 2021). These results point to the genetic variation among the populations but not extremely distinct from each other, the value indicates a moderate level of genetic differentiation between the populations.
Population distribution and the genetic relationship among the two populations through a Principal of Coordinate Analysis (PCoA) showed the existence of two clusters with some intermixes. The existence of two clusters might be influenced by geographical distances that create physical barriers and local adaptation due to different pressures from the environment. A similar study was conducted by Lyimo et al. (2013) on five ecotypes of chicken native to Tanzanian from eight regions and found three clusters.
The deviation of all loci from HWE in the populations was significant. It indicates that the indigenous chicken populations were not in HWE. After all, the current findings might be caused by inbreeding which increases the likelihood of homozygosity, population substructures that might have different allele frequencies, random fluctuations in allele frequencies (genetic drift), a natural and artificial selection that selects and favours certain alleles based on their adaptation and fitness over the others, a mutation which changes the DNA sequence through the addition or deletion of a nucleotide hence allele’s change. Furthermore, the movement between populations (migration) with different allele frequencies might have disrupted the allele frequencies through the introduction or change of the existing frequencies (Haunshi et al., 2020). Similar results were reported in Southern highland Tanzania whereby 6 ecotypes out of 10 ecotypes deviated from HWE (Mwambene et al., 2019), The study conducted in India revealed that 22 microsatellite markers out of 24 were found to deviate from HWE in Mewari chicken (Parmar et al., 2022), Nigeria reported the significant microsatellite deviation from HWE in Nigerian indigenous chicken (Ajibike et al., 2022).
The study concluded with the identification of genetic variants and relatedness which is supported by Nei genetic distance which suggests the genetic differences between the populations but not extremely distinct from each other, analysis of molecular variance (AMOVA) suggests the high variations within the population contrasted to among the populations. The research observed deviation of all loci from HWE in the populations which might be caused by natural and artificial selection, genetic drift, mutation and migration. These traits could then be integrated into breeding programs to enhance and select specific chicken characteristics for conservation and sustainable use. Moreover, two clusters identified in the study might be kept apart to preserve their genetic diversity although, more markers, sample size and regions from different zones can be employed in diversity studies.