Genetic characterisation of non-descript cattle populations in communal areas of South Africa
M. D. Mamogobo
A B D , N. O. Mapholi C , K. A. Nephawe A , T. L. Nedambale A , T. J. Mpofu A , Y. P. Sanarana B and B. J. Mtileni A
+ Author Affiliations
- Author Affiliations
A Tshwane University of Technology, Department of Animal Sciences, Private Bag X680, Pretoria, 0001, South Africa.
B Agricultural Research Council, Private Bag X2, Irene, 0062, South Africa.
C University of South Africa, Department of Life and Consumer Sciences, Private Bag X6, Florida, Roodepoort, 1709, South Africa.
D Corresponding author. Email: mamogobomd@hotmail.com
Animal Production Science 61(1) 84-91 https://doi.org/10.1071/AN20030
Submitted: 23 January 2020 Accepted: 24 July 2020 Published: 31 August 2020
Journal Compilation © CSIRO 2021 Open Access CC BY-NC-ND
Abstract
Context: Indigenous cattle breeds represent an important genetic resource for livelihood of communal-area inhabitants. Indigenous breeds have the ability to withstand harsh climatic conditions, can adapt genetically to poor-quality forages and are resistant to parasites and diseases. These unique traits possessed by indigenous breeds are under threat because of unrestrained crossing with exotic commercial breeds, and this can lead to total loss of a breed.
Aims: The study was conducted to assess the genetic diversity and population structure of South African non-descript communal beef cattle populations by using 25 microsatellite markers.
Methods: Unrelated and non-descript animals (n = 150) were sampled from communal areas from five (5) provinces of South Africa, namely, Eastern Cape, KwaZulu–Natal, Limpopo, Mpumalanga and the North West, with 30 samples per breed taken. Six (6) known cattle breeds (n = 180) were used as a reference population. This included Angus, Afrikaner, Bonsmara, Brahman, Drakensberger and the Nguni, with 30 samples per breed.
Key results: High level of genetic diversity was found across the five non-descript populations, with an average heterozygosity of 75%. The Limpopo population was found to be the most diverse population, with the highest average number of alleles (8.5) and heterozygosity (ranging between observed heterozygosity of 70% and expected heterozygosity of 79%). STRUCTURE software assigned populations (2 ≤ K ≤ 20), with the most probable cluster being at K = 7. The Eastern Cape, KwaZulu–Natal and Limpopo populations had genetic material similar to those possessed by the Nguni and Bonsmara reference populations.
Conclusions: Results from the study showed that most genetic differentiation occurred within populations rather than among populations, and this might be due to the fact that there is no selection for or against any specific production trait expressed in the populations.
Implications: The obtained information will serve as a baseline for the development and implementation of sound breeding programs that will assist in controlling the gene flow, so as to lower the possible genetic dilution of the currently available genetic material.
Additional keywords: genetic diversity, heterozygosity, microsatellite markers.
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