This study describes a comprehensive genetic analysis conducted on four South African beef cattle breeds (Bonsmara, Simmental, Nguni, and Angus) using various methods such as iHS, XP-EHH, Rsb, and Fst to identify signatures of selection and candidate genes associated with unique traits within each breed. These methods were reported in several studies, however, not all methods were incorporated in one study for South African beef cattle breeds. An example is a study by [13], using iHS and XP-EHH, to identify candidate regions that show preferential selection in the genome of South African Nguni and Bonsmara cattle. A study by [17] reported on iHS and Rsb analysis to detect selected regions in the local North African cattle. [18] identified meat quality-related gene regions that are positively selected in Ankole (Sanga) and indicus (Boran, Ogaden, and Kenana) breeds using cross-population XP-EHH and XP-CLR methods. [11] research was based on the Fst and iHS methods in six South African Bonsmara, Nguni, Afrikaner, and Sanga breeds.
The filtered data was used, and the population structure was confirmed using a triangle plot, in which 73 individuals were confirmed. The study identified four specific clusters for Angus, Simmental, Nguni, and Bonsmara which highlighted distinct genetic characteristics, reflecting possible breed origins or specific genetic traits. Furthermore, crosses identified between the Nguni and Bonsmara indicated interbreeding, which could have been implications for the introduction of specific traits into the population. Significant genomic regions were identified above a threshold, with specific regions highlighted for each breed. The results obtained from the analysis using iHS identified regions of the genome that have undergone positive selection. Suggestive regions of the genome for Bonsmara on BTA 2 and 12, for Nguni significant regions on BTA 6, 7, 12, and 16, and a shared genomic region on BTA12 between Nguni and Bonsmara: This suggests that there might be common selective pressures associated with this specific genomic region for both the Nguni and Bonsmara. Similar results were also reported by [13] who highlighted shared regions on BTA 12 revealing the highest iHS score of 6.047 for Nguni and Bonsmara.
In the XP-EHH method, significant regions were identified on specific chromosomes for Angus vs Simmental (BTA 3, 6, and 13) and Nguni vs Bonsmara (BTA 1, 2, 11, 14, 17, and 24). This method is useful for detecting selective sweeps and regions of the genome that have undergone recent positive selection in one population compared to another. The Rsb method, which also measures haplotype patterns, revealed genomic regions under selection in Angus vs Simmental (BTA 3, 5, 6, and 14) and Nguni vs Bonsmara (BTA 1, 4, 5, 8, 11, 14, 17, and 24). The Rsb method is particularly sensitive to recent selection events and could provide insights into the evolutionary forces shaping genetic variation. The Fst method, which calculates genetic differentiation between populations, identified significant genomic regions for Simmental vs Angus (BTA 1–29, excluding BTA 2, 12, and 27) and Nguni vs Bonsmara (BTA 1–29, excluding BTA 2, 6 and 29). The Fst method helps to identify regions of the genome that have diverged between populations, suggesting potential targets of natural selection or adaptation.
Gene annotation analyses using the iHS method highlighted the association of the FAM110B gene with QTL for carcass weight and body conformation score in Simmental cattle. [19] reported the same gene on BTA 14 for carcass weight in Korean Hawoo cattle. The gene FAM110B has been previously identified to affect several traits, such as growth, birth weight, average daily gain, feed intake, meat tenderness, height, stature, and carcass weight in different beef cattle breeds ,[20],[21]. The KCNQ3 gene was identified for QTLs that are associated with important traits such as reproduction and disease traits for Bonsmara vs Nguni. The gene was also reported to be associated with milk, reproduction, and production traits in Nellore Cattle[21]. In this study, the PAPPA gene was associated with sperm count and insemination per conception in Bonsmara vs Nguni. The PAPPA gene was also reported on BTA8 for Holstein-Friesin bulls which was significant for several spermatozoa [22]. [23] reported the PAPPA gene to have a strong association with female fertility. Another gene GRIK2 was reported to be related to the nervous system in Bashan cattle [24], for the development of the nervous system in swamp buffalo and has been reported to be related to reproduction functions by its effect on gonadotropin-releasing (GnRH) secretion control [24]. In this study, the CHD7 gene was found for the body conformation score in Bonsmara vs Nguni. [25] reported the CHD7 gene detected at a 5% chromosome-wise level for carcass traits in Korean native cattle. [26] identified PRKDC to be associated with carcass traits in Hanwoo cattle, the gene was also reported to be strongly associated with subcutaneous fat deposits. Genes like RAB2A were associated with scrotal growth and male fertility in cattle [27]. RAB2A gene harbors the most significant genomic region for body confirmation, the study further reported the genes to have the highest proportion of the total additive genetic variance (3.89%) of body confirmation [28]. [29] reported SLIT2 to be associated with growth and carcass traits Simmental vs Angus, or with a function that may be related to meat production, which was different to our fundings. Tetraspanin 9 (TSPAN9) revealed an association with lactation persistency for Simmental vs Angus, which was consistent with a study by [30] who reported the gene to have an association with lactation persistency in Canadian Holstein. R-spondin 2 (RSPO2) was found for calving at ease on BTA 14 for Bonsmara vs Nguni, while [31] reported the RSPO2 gene on the same BTA14 has an association with bovine tetradysmelia in Holstein Friesian. The study also explored gene functions, revealing associations with meat and carcass traits, reproduction, health, diseases, fertility, and conformation. Gene interaction analysis through the STRING database identified a network of 63 candidate genes, highlighting the complexity of genetic interactions. Some genes exhibited multiple functions, emphasizing their multifaceted roles in various biological processes.