Application of modern genomic breeding technologies in African livestock
Rapid improvement of African livestock productivity can benefit from current modern breeding technologies but many limitations abound (Eveline et al., 2019). Some breeding programs that have been implemented for genetic improvement of livestock in Africa and the challenges faced shall be highlighted below.
Livestock in the African continent are highly adapted to the prevailing environmental conditions characterized by heavy disease burden and marginal feed resources, but with marginal productivity because they are still largely unselected. African countries can benefit from genomic selection because it could be done even without pedigree information which is essential to traditional best linear unbiased prediction (BLUP)-EBV and the selection of candidates does not necessarily have to be based on trait records (Eveline et al., 2019). The potential to generate GEBV using molecular information makes genomic selection a very attractive alternative to improving livestock in developing countries where adequate phenotypes and pedigree records are lacking. Genomic breeding has been reported to be more accurate than traditional BLUP because genomic relationships are more accurate than pedigree relationships (Meuwissen et al., 2016). Moreover, understanding of the fundamental genetic mechanisms influencing traits can be useful for setting up priors for (genetic) variances to increase the accuracy of genomic selection. Several successful approaches have been introduced such as BLUP| GA (BLUP-given genetic architecture; Zhang et al., 2014) or BayesRC (which adapted BayesR methods) incorporating prior biological information in the analysis by defining classes of variants likely to be enriched for causal mutations (MacLeod et al., 2016) or single step GBLUP with prior information (Fragomeni et al., 2017). These methods can be particularly useful for genomic selection in Africa with some prior biological knowledge of traits obtained from studies in the populations and other populations. Using genomic selection, Pitchford et al. (2017) concluded that heterozygosity effects were substantial for reproduction and growth in a tropically adapted composite beef program.
The high enthusiasm about the potential application of genomic selection in African countries is immediately dampened with the reality that animals are held in small populations and in many small holder units. Furthermore, male animals that drive the genetic gain are often sold to generate income for farm families. These caveats can be overcome by the formation and practice of communal management and breeding systems.
Lack of phenotypes recorded in accurately defined contemporary groups is one of the constraints to the implementation of genomic selection in Africa and many developing countries (Burrow et al., 2017). Acquiring the genomic information for genomic selection is limited because genotyping is still expensive in many developing countries because incomes are very low compared to developed countries. The few studies on genomic selection in developing countries are characterized by small population sizes and validations were undertaken with test day data sets (Neves et al., 2014; Brown et al., 2016; Kariuki et al., 2017; Ducrocq et al., 2018; Mrode et al., 2019).
Traditional animal breeding requires the use of pedigree records to support selection decisions but most small holder farms in Africa do not have these types of records and the measure of relationships between animals are merely speculative. Furthermore, the application of genomic selection will require the use of reference populations which are generally lacking in Africa and many developing countries (Burrow et al., 2017). Mrode et al. (2019) reported the presence of small reference populations of between 500 and 3,000 animals (composed of mostly cows) in dairy and beef cattle in developing countries. The use of small reference populations that combined both bull and cow data, as in the case in Africa, has implications for the accuracy of genomic prediction, which is lower when compared to those obtained in Western countries given the limited information of the response variables when using cow records. It is important to state here that the inclusion of cows in the reference population has resulted to up to fivefold increase in the size of the reference population in some cases and increases of up to 12% in accuracy of selection compared to using bulls alone (Boison et al., 2017; Mrode et al., 2019). Mrode et al. (2018) reported some success by modeling and pooling data on the accuracy of genomic prediction in limited dairy data in East Africa. Brown et al. (2016) specifically reported the practice of genomic selection in a crossbred cattle population using data from the dairy genetic project of East Africa.
The cost of genotyping is a major issue limiting the adoption of genomic selection in Africa and to overcome this problem, the use of low density SNP panels have been suggested and this can be followed with imputation to improve the accuracy of genomic predictions (Meuwissen et al., 2016; Boison et al., 2017). Furthermore, low cost genome wide genotyping solution like genotyping-by-sequencing can generate high numbers of population specific SNPs (De Donato et al., 2013; Ibeagha- Awemu et al., 2016; Gurgul et al., 2018) that can support genomic selection in African livestock populations. Illumina7 and Affymetrix8 commercial SNP panels used for genotyping contains SNPs discovered in breeds and population of animals of Western origin and only very few breeds of African origin were included in the discovery of SNPs. This is the reason for ascertainment bias, which may affect accuracies of genomic selection from the use of commercially available SNP panels to genotype African indigenous livestock. Thus, the development of genotyping solutions specific for African breeds is necessary and the genotyping-by-sequencing approach can play a major role.
Some notable developments in the use of genomic tools include the sequencing of some indigenous cattle in Africa (Kim et al., 2017), developments on the genomic selection for disease resistance (Hanotte et al., 2010; Mwai et al., 2015) and for adaptation to hot arid condition (Kim et al., 2016). Other important efforts that may increase the quality of data includes the project of epidemiology of the Infectious Diseases of East African Livestock and a longitudinal calf cohort study in western Kenya (de Clare Bronsvoort et al., 2013) and strategies for bridging the gap between the developed and developing livestock sector (Van Marle-Koster and Visser, 2018). Recently, Canovas et al. (2017) discussed the application of new genomic technologies including transcriptomics, metagenomics, metabolomics, and epigenomics that are pertinent to speed-up genetic improvement of cattle. As a matter of priority, Burrow et al. (2017) suggested that research to improve grazing livestock should include cross-country genetic/genomic evaluations, use of sequence data in genetic evaluations, multi-breed genomic evaluations, selection index and genotype _ environment interactions. Furthermore, numerous studies in Nellore, an indicine beef cattle breed suggests that genomic selection is a realistic alternative to traditional selection strategies (Neves et al., 2014). In small ruminants like sheep and goats, Mrode et al. (2018) observed that innovative genetic selection strategies will be needed to ensure adaptive balance between production and adaptation.
Emerging gene editing technologies like transcription activator-like effector nucleases (TALEN), zinc finger nucleases (ZFN), and clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 which can achieve any change in the genome, including introduction of alleles of interest into a recipient genome and switching on/off genes of interest can also play vital roles in rapid genomic improvement of African livestock traits. These tools offer an opportunity to intensify the frequency of desired alleles in a population through gene edited individuals more rapidly than conventional breeding (Bhat et al., 2017). Genome editing in livestock has been reported for the double muscling gene in cattle, sheep, and pigs (Proudfoot et al., 2015; Qian et al., 2015), the polled allele introduction in dairy cattle (Tan et al., 2013; Carlson et al., 2016); gene edits that confer resistance to African Swine fever virus in pigs (Lillico et al., 2013; Whitworth et al., 2016) and the low-density lipoprotein receptor gene in a pig model of atherosclerosis (Carlson et al., 2012). These examples indicate that attempts at gene editing in livestock have targeted traits controlled by few variants with major effects. However, most livestock traits of economic importance are quantitatively controlled by many genes each contributing small effects, suggesting potential pitfalls in the implementation of these technologies for such traits. However, a recent simulation study indicated that editing for fewer casual variants of polygenic traits can double the rate of both short term and long-term genetic gains when compared to conventional genomic selection (Jenko et al., 2015).