Single nucleotide polymorphism arrays have created new possibilities for performing genome-wide studies to detect genomic regions harboring sequence variants that affect complex traits. However, the majority of validated SNPs for which allele frequencies have been estimated are limited primarily to European breeds. The objective of this study to perform SNP discovery in three South African indigenous breeds (Afrikaner, Drakensberger, and Nguni) using whole genome sequencing. DNA was extracted from blood and hair samples, quantified and prepared at 50 ng/μl concentration for sequencing at the Agricultural Research Council Biotechnology Platform using an Illumina HiSeq 2500. The fastq files were used to call the variants using the Genome Analysis Tool Kit. A total of 1,678,360 were identified as novel using Run 6 of 1000 Bull Genomes Project. Annotation of the identified variants classified them into functional categories. Within the coding regions, about 30% of the SNPs were non-synonymous substitutions that encode for alternate amino acids. The study of distribution of SNP across the genome identified regions showing notable differences in the densities of SNPs among the breeds and highlighted many regions of functional significance. Gene ontology terms identified genes such as MLANA, SYT10, and CDC42EP5 that have been associated with coat color in mouse, and ADAMS3, DNAJC3, and PAG5 genes have been associated with fertility in cattle. Further analysis of the variants detected 688 candidate selective sweeps (ZH_p Z-scores ≤ -4) across all three breeds, of which 223 regions were assigned as being putative selective sweeps (ZH_p scores ≤-5). We also identified 96 regions with extremely low ZH_p Z-scores (≤-6) in Afrikaner and Nguni. Genes such as KIT and MITF that have been associated with skin pigmentation in cattle and CACNA1C, which has been associated with biopolar disorder in human, were identified in these regions. This study provides the first analysis of sequence data to discover SNPs in indigenous South African cattle breeds. The information will play an important role in our efforts to understand the genetic history of our cattle and in designing appropriate breed improvement programmes.

Goats were amongst the first livestock to be domesticated more than 10,000 years ago for their meat, milk, skin, and fiber. They were introduced to Southern Africa by migrating nations from Central Africa to the south. Due to local adaptation to the different agro-ecological zones and selection, indigenous goats are identified as ecotypes within the indigenous veld goat breed. Their ability to thrive in a resource-limited production system and in challenging environmental conditions makes them valuable animal resources for small-scale and emerging farmers. They play important roles in household agriculture and cultural activities as well as in poverty alleviation. Studies have described the phenotypic and genetic variations in indigenous goats, targeting the major goat-producing regions and the breeds of South Africa. In turn, information is restricted to certain breeds and regions, and the experimental design is often not adequate to inform the conservation status and priorities in changing environments. Advances in genomics technologies have availed more opportunities for the assessment of the biodiversity, demographic histories, and detection regions associated with local adaptation. These technologies are essential for breeding and conservation strategies for sustainable production for food security. This review focuses on the status of indigenous goats in South Africa and the application of genomics technologies for characterization, with emphasis on prioritization for conservation and sustainable utilization.

Access to genotyping assays enables the identification of informative markers that discriminate between cattle breeds. Identification of these markers can assist in breed assignment, improvement and conservation. The objective of this study was to identify breed informative markers to discriminate between three South African indigenous cattle breeds. Data from BovineSNP50 and GeneSeek Genomic Profiler (GGP-80K) assays were generated for Afrikaner, Drakensberger and Nguni, and were analysed for their genetic differentiation. Hereford and Angus were included as outgroups. Breeds were differentiated using principal component analysis (PCA). Single-nucleotide polymorphisms (SNPs) within the breeds were determined when minor allele frequency (MAF) was ≥ 0.05. Breed-specific SNPs were identified using Reynolds Fst and extended Lewontin and Krakauer's (FLK) statistics. These SNPs were validated using three African breeds, namely N’Dama, Kuri and Zebu from Madagascar. PCA discriminated among the breeds. A larger number of polymorphic SNPs was detected in Drakensberger (73%) than in Afrikaner (56%) and Nguni (65%). No substantial numbers of informative SNPs (Fst ≥ 0.6) were identified among indigenous breeds. Eleven SNPs were validated as discriminating the indigenous breeds from other African breeds. This is because the SNPs on BovineSNP50 and GGP-80K assays were ascertained as being common in European taurine breeds. Lower MAF and SNP informativeness observed in this study limits the application of these assays in breed assignment, and could have other implications for genome-wide studies in South African indigenous breeds. Sequencing should therefore be considered to discover new SNPs that are common among indigenous South African breeds and also SNPs that discriminate among these indigenous breeds.

Phenotyping of animals is a routine task in agriculture which can provide large datasets for the functional annotation of genomes. Using the livestock farming sector to study complex traits enables genetics researchers to fully benefit from the digital transformation of society as economies of scale substantially reduces the cost of phenotyping animals on farms. In the agricultural sector genomics has transitioned towards a model of ‘Genomics without the genes’ as a large proportion of the genetic variation in animals can be modelled using the infinitesimal model for genomic breeding valuations. Combined with third generation sequencing creating pan-genomes for livestock the digital infrastructure for trait collection and precision farming provides a unique opportunity for high-throughput phenotyping and the study of complex traits in a controlled environment. The emphasis on cost efficient data collection mean that mobile phones and computers have become ubiquitous for cost-efficient large-scale data collection but that the majority of the recorded traits can still be recorded manually with limited training or tools. This is especially valuable in low- and middle income countries and in settings where indigenous breeds are kept at farms preserving more traditional farming methods. Digitalization is therefore an important enabler for high-throughput phenotyping for smaller livestock herds with limited technology investments as well as large-scale commercial operations. It is demanding and challenging for individual researchers to keep up with the opportunities created by the rapid advances in digitalization for livestock farming and how it can be used by researchers with or without a specialization in livestock. This review provides an overview of the current status of key enabling technologies for precision livestock farming applicable for the functional annotation of genomes.

Adaptation of animals to different environments is typically associated with structural and functional genomic variations. High throughput SNP genotyping and next-generation sequencing (NGS) have made it possible to study positive selection footprints and adaptation traits. Nguni is a small frame-size breed, mostly horned, and well known for being adapted to diverse South African environmental conditions. This study used previously identified selective sweeps to perform functional analysis of genes related to phenotypic characteristics in Nguni. Two hundred and sixty-four candidate selective sweeps were used for gene-set enrichment analysis in molecular functional categories (KEGG pathways) using the database for annotation, visualization, and integrated discovery (DAVID). In total, 107 genes were identified across all the chromosomes with 74 genes associated with eight phenotype queries, including fat content, milk production, walking ability, heat tolerance, meat production, reproduction, and bone and muscle development. Gene CRHR2 was associated with meat quality (juiciness and flavour). The IRAK3 gene was associated with decreased body size, feed intake and fatness in cattle, and CARD15 with disease resistance. Gene annotation using phenotype queries identified four genes (SPI, YWHAZ, RGS4, and RGS5) that were associated with myometrial relaxation in cattle. Genes such as NOD2 and IL21R were associated with inflammatory bowel diseases in cattle, whereas CPLS gene was associated with fat content. These genes are important to the phenotypic and adaptive characteristics present in South African Nguni cattle and hold potential for selection for traits of economic importance.

South African cattle comprise diverse breeds with distinct evolutionary histories, potentially reflecting differences in recombination landscapes. This study assessed genome-wide recombination rates and hotspots in Bonsmara (n = 190) and Nguni (n = 119) cattle using three-generation half-sib pedigrees genotyped with the Illumina Bovine SNP50 BeadChip. Phasing across 29 autosomes was conducted using SHAPEIT v2, and crossover events were inferred using the DuoHMM algorithm. The total number of crossover events detected was higher in Nguni (n = 8982) than in Bonsmara (n = 7462); however, the average recombination rate per 1 Mb window was significantly higher in Bonsmara (0.31) compared to Nguni (0.18) (p < 0.01). This apparent discrepancy reflects differences in genomic distribution and crossover clustering across breeds, rather than overall recombination frequency. A critical limitation of the study is the reliance on half-sib families with small family sizes, which may underestimate recombination rates due to limited meiotic sampling and increased variance in crossover detection. We identified 407 recombination hotspots in Bonsmara and 179 in Nguni, defined as intervals exceeding 2.5 standard deviations above the mean recombination rate. Genes such as PDE1B and FP which are associated with productions traits were located within hotspot-enriched regions. However, functional causality between these genes and local recombination activity remains unverified. Our results provide statistically supported evidence for breed-specific recombination patterns and hotspot distributions, underscoring the importance of incorporating recombination architecture into genetic improvement strategies for South African cattle.