Genetic diversity is of great importance and a prerequisite for genetic improvement and conservation programs in pigs and other livestock populations. The present study provides a genome wide analysis of the genetic variability and population structure of pig populations from different production systems in South Africa relative to global populations. A total of 234 pigs sampled in South Africa and consisting of village (n = 91), commercial (n = 60), indigenous (n = 40), Asian (n = 5) and wild (n = 38) populations were genotyped using Porcine SNP60K BeadChip. In addition, 389 genotypes representing village and commercial pigs from America, Europe, and Asia were accessed from a previous study and used to compare population clustering and relationships of South African pigs with global populations. Moderate heterozygosity levels, ranging from 0.204 for Warthogs to 0.371 for village pigs sampled from Capricorn municipality in Eastern Cape province of South Africa were observed. Principal Component Analysis of the South African pigs resulted in four distinct clusters of (i) Duroc; (ii) Vietnamese; (iii) Bush pig and Warthog and (iv) a cluster with the rest of the commercial (SA Large White and Landrace), village, Wild Boar and indigenous breeds of Koelbroek and Windsnyer. The clustering demonstrated alignment with genetic similarities, geographic location and production systems. The PCA with the global populations also resulted in four clusters that where populated with (i) all the village populations, wild boars, SA indigenous and the large white and landraces; (ii) Durocs (iii) Chinese and Vietnamese pigs and (iv) Warthog and Bush pig. K = 10 (The number of population units) was the most probable ADMIXTURE based clustering, which grouped animals according to their populations with the exception of the village pigs that showed presence of admixture. AMOVA reported 19.92%-98.62% of the genetic variation to be within populations. Sub structuring was observed between South African commercial populations as well as between Indigenous and commercial breeds. Population pairwise F ST analysis showed genetic differentiation (P ≤ 0.05) between the village, commercial and wild populations. A per marker per population pairwise F ST analysis revealed SNPs associated with QTLs for traits such as meat quality, cytoskeletal and muscle development, glucose

In this study, we evaluated an admixed South African Simbra crossbred population, as well as the Brahman (Indicine) and Simmental (Taurine) ancestor populations to understand their genetic architecture and detect genomic regions showing signatures of selection. Animals were genotyped using the Illumina BovineLD v2 BeadChip (7K). Genomic structure analysis confirmed that the South African Simbra cattle have an admixed genome, composed of 5/8 Taurine and 3/8 Indicine, ensuring that the Simbra genome maintains favorable traits from both breeds. Genomic regions that have been targeted by selection were detected using the linkage disequilibrium-based methods iHS and Rsb. These analyses identified 10 candidate regions that are potentially under strong positive selection, containing genes implicated in cattle health and production (e.g., TRIM63, KCNA10, NCAM1, SMIM5, MIER3, and SLC24A4). These adaptive alleles likely contribute to the biological and cellular functions determining phenotype in the Simbra hybrid cattle breed. Our data suggested that these alleles were introgressed from the breed's original indicine and taurine ancestors. The Simbra breed thus possesses derived parental alleles that combine the superior traits of the founder Brahman and Simmental breeds. These regions and genes might represent good targets for ad-hoc physiological studies, selection of breeding material and eventually even gene editing, for improved traits in modern cattle breeds. This study represents an important step toward developing and improving strategies for selection and population breeding to ultimately contribute meaningfully to the beef production industry.

In this study, runs of homozygosity (ROH) and quantitative trait locus/association (QTL) for semen parameters in selected Chinese and South African beef cattle breed were estimated. The computed results showed 7516 ROH were observed between classes 0–5 Mb with no ROH observed in classes >40 Mb. Distribution of ROH showed high level of genomic coverage for ANG, NGU, CSI, and BEL breeds. Approximately 13 genomic regions with QTL were controlling sperm motility, sperm concentration, semen volume, sperm count, sperm head abnormalities, sperm tail abnormalities, sperm integrity, and percentage of abnormal sperm traits. Nine candidate genes, CDF9, MARCH1, WDR19, SLOICI, ST7, DOP1B, CFAF9, INHBA, and ADAMTS1, were suggested to be associated with above mentioned QTL traits. The results for inbreeding coefficient showed moderate correlation between FROH vs FHOM at 0.603 and high correlation between FROH 0–5 Mb 0.929, and lowest correlation for 0–>40 Mb 0.400. This study suggested recent inbreeding in CSI, BEL, ANG, BON, SIM, and NGU breeds. Furthermore, it highlighted varied inbreeding levels and identified QTL for semen traits and genes of association. These results can assist in implementation of genetic improvement strategies for bulls and provide awareness and proper guidelines in developing breeding programs.

The Tswana chicken is native to Botswana and comprises strains such as the naked neck, normal, dwarf, frizzled, and rumples. The origins of the different strains of Tswana chicken remain unknown and it is not yet clear if the different strains represent distinct breeds within the large Tswana chicken population. Genetic characterization of different strains of Tswana chickens using SNP arrays can elucidate their genetic relationships and ascertain if the strains represent distinct breeds of Tswana chicken population. The aim of this study was therefore to investigate population structure and diversity and to estimate genetic distances/identity between the naked neck, normal and dwarf strains of Tswana chickens. A total of 96 chickens (normal strain (n = 39), naked neck strain (n = 32), dwarf strain (n = 13) and commercial broiler (n = 12)) were used in the study. SNP genotyping was carried out using the Illumina chicken iSelect SNP 60 Bead chip using the Infinium assay compatible with the Illumina HiScan SQ genotyping platform. The observed heterozygosity (H o ) values were 0.610 ± 0.012, 0.611 ± 0.014, 0.613 ± 0.0006 for normal, naked neck and dwarf strains of Tswana chickens respectively and averaged 0.611 ± 0.016 across the three strains of Tswana chickens compared to Ho of 0.347 ± 0.023 in commercial broiler chicken. The expected heterozygosity (H e ) values were 0.613 ± 0.00012, 0.614 ± 0.00013, 0.608 ± 0.00021 for normal, naked neck and dwarf strains of Tswana chickens respectively and averaged 0.612 ± 0.00015 across the three strains of Tswana chickens compared to H e of 0.577 ± 0.00022 in commercial broiler chicken. Principal component analysis (PCA) was used to get an insight into the population