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).