Farai C. Muchadeyi1, Khanyisile Hadebe1, Edgar F. Dzomba2
1Agricultural Research Council, Biotechnology Platform, Onderstepoort, South Africa; 2Discipline of Genetics, School of Life Sciences, University of KwaZulu-Natal, Scottsville, South Africa
*Correspondence: MuchadeyiF@arc.agric.za
Local indigenous animal genetic resources play an important role in sustaining the livelihoods of rural and resource-limited farmers. These local breeds thrive on adaptation to unfavourable environment stressors such as extreme temperatures, worsening droughts and disease challenges that characterise most low input production systems. Livestock improvement programs strive to develop genotypes that are adapted to local conditions and are able to produce optimally and sustainably under constrained environments. Genetic mechanisms underlying biological traits for environmental adaptation are unclear but unravelling them would be essential when designing methods to improve and sustain these breeds. It is important to study the interaction between production environments and genetics of animal populations so as to establish selection priorities and develop suitable improvement strategies. Efforts to initiate improvement programs have historically been hindered by absence of performance data and pedigree records compounded by factors such as animals randomly mating within and between flocks on communal pastures. Landscape genomics merges the competing effects of the production system, geographical, and environment landscapes with adaptive genetic variation. Advances in livestock genomics have enabled production of large amounts of genetic data of genome-wide coverage which, when combined with environmental data, enables in-depth studies on the patterns of genetic diversity, identification of genes and elucidating processes underlying genetic adaptation in various indigenous animal populations. In this study, case studies are presented on the use of landscape genomics as a tool to understand the genetics of adaption in indigenous chickens and goat populations of Southern Africa.
Keywords: Livestock, rural farming communities, genetic adaptation; landscape genomics