Selection signatures associated with adaptation in South African Drakensberger, Nguni, and Tuli beef breeds

doi:10.21203/rs.3.rs-4307890/v1

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Selection signatures associated with adaptation in South African Drakensberger, Nguni, and Tuli beef breeds

https://doi.org/10.21203/rs.3.rs-4307890/v1

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Climate change is an important consideration for sustainable beef production systems. Sanga cattle (Bos taurus africanus) are a unique genetic resource known for their adaptability to diverse climates. Genomic technologies have allowed opportunities to investigate indigenous cattle at the deoxyribonucleic acid (DNA) and genome level for insight into variation associated with adaptive traits. 1709 cattle, including 1118 Drakensberger (DRB), 377 Nguni (NGI), and 214 Tuli (TUL), were genotyped using the GeneSeek® Genomic Profiler™ 150K bovine SNP panel. 122632 quality-filtered SNPs was used to assess runs of homozygosity (ROH) and runs of heterozygosity (ROHet) within the three cattle populations using the detectRUNS R package (Biscarini et al., 2018) and PLINK v1.90 (Purcell et al., 2007). The study identified selection signatures associated with adaptation within-and-between three breeds based on ROH, ROHet, and F_ST-based differentiation of SNPs. The mean number of ROH per animal varied across breeds ranging from 36.09 ± 12.82 (NGI) to 51.82 ± 21.01 (DRB), and the mean ROH length per breed ranged between 2.31Mb (NGI) and 3.90Mb (DRB). The smallest length categories i.e., ROH < 4Mb and ROHet < 0.25Mb were most frequent, indicating predominantly historic inbreeding effects for all breeds. The ROH based inbreeding coefficients (F_ROH) ranged between 0.033 ± 0.024 (NGI) and 0.081 ± 0.046 (DRB). Genes mapped to candidate regions were associated with immunity (ADAMTS12, CYSTM1, WDPCP) and adaptation (LMAN2, TUBB3) in cattle as well as genes previously only reported for immunity in mice and human (EXOC3L1, MYO1G). This study contributes to the understanding of the mechanisms of adaptation, providing information for functional genomic studies and application in genetic evaluation and selection programs.

Adaptation

Candidate genes

Homozygosity

Indigenous breeds

Selection

Selection signatures

In Africa, livestock is integral to food security, as well as social, cultural, and economic development (Mapiye et al., 2019). However, the looming threat of climate change has the potential to adversely affect future food production (Godde et al., 2021). Climate change effects on livestock farming include water scarcity, altered precipitation patterns, and extreme weather events that have the potential to disrupt agricultural production (Gomez-Zavaglia et al., 2020). The demand for animal protein is escalating, particularly in developing countries of sub-Saharan Africa that will, according to projections, contribute to more than 50% of the global population growth by 2050 (United Nations, 2022).

The South African (SA) beef sector comprises a variety of cattle breeds classified as either exotic (indicine, and taurine), indigenous (Sanga), or composite (Strydom, 2008). With their genetic heritage established through DNA analysis, indigenous Sanga cattle are a combination of African taurine, African indicine and European taurine (Makina et al., 2016). In SA, approximately 53% of beef cattle are raised in commercial systems, with the remaining 47% being reared under informal conditions (DALRRD, 2021). Sanga cattle play an important role in both the well-developed (commercial) and developing (non-commercial, and informal) livestock production systems across the country (van Marle-Köster & Visser, 2018), and may harbour unique traits from years of artificial and natural selection. They are known for their unique adaptive traits that include tolerance to endemic diseases and parasites (both internal and external) (Mapholi, 2015; Mapholi et al., 2022), extreme temperatures (Nyamushamba et al., 2017), changes in feed availability, and low management inputs (Scholtz, 2010). According to the SA Department of Agriculture, Land Reform and Rural Development (DALRRD, 2021), 29% of the total meat produced under intensive feedlot conditions originated from Sanga cattle types, signifying their contribution to the country’s beef production.

Initiated in 2015, the Beef Genomics Program (BGP) contributed to the establishment of reference populations for SA Sanga beef breeds (Afrikaner, Drakensberger, Nguni, and Tuli,) and locally developed composite breeds (e.g., the SA Bonsmara), and this provided an influx of genomic data for these breeds, which allowed a multitude of genomic applications that was not possible before (SA Stud Book, 2022). The availability of higher density data for larger numbers of cattle per breed (that are also more representative of the national herd), allows for a more precise detection of selection signatures through better capturing of changes in linkage disequilibrium (LD) and allele frequencies (Saravanan et al., 2021). Long-term selection (natural and/or artificial) changes specific regions of the genome resulting in selection signatures that can assist in identifying genes associated with certain traits (Moravčíková et al., 2018).

Runs of homozygosity (ROH) refer to stretches of homozygous genotypes that are frequently observed in individuals and populations who share a common ancestor (Gibson et al., 2006). Longer ROH can suggest recent inbreeding or a small effective population size (Marras et al., 2015; Doekes et al., 2019), while shorter ROH may indicate ancient inbreeding or a genetically diverse population (Forutan et al., 2018). Shared or consensus ROH within a population can be used to identify conserved genomic regions potentially under selection, which may be involved in defining breed-specific traits or the adaptation to the environment (Purfield et al., 2017). Several studies have used ROH to detect within-breed selection signatures in cattle (Mastrangelo et al., 2016; Yurchenko et al., 2018; Biscarini et al., 2020; Singh et al., 2020). In contrast, heterozygosity-rich regions (or runs of heterozygosity; ROHet) refers to stretches of the genome where an individual inherits different alleles from each parent, and may serve as an indicator of genetic diversity within the population (Kenny et al., 2022). This concept is relatively new and has received limited attention in animal-based genomics studies, with only a few studies conducted on cattle (Biscarini et al., 2020; Kenny et al., 2022; Lashmar et al., 2022; van Marle-Köster et al., 2022). Despite benchmark studies on the distribution and characteristics of ROH, ROHet and selection signatures within and across SA indigenous cattle (Makina et al., 2016; Lashmar et al., 2018; King et al., 2022; Kooverjee et al., 2022), the genetic architecture of adaptive traits that are unique to these populations remains only partially understood.

The aim of this study was to assess the distribution and characteristics of ROH and ROHet and to identify selection signatures associated with adaptation within and between the Drakensberger (DRB), Nguni (NGI), and Tuli (TUL) populations.

Single nucleotide polymorphism (SNP) genotypes were available for 1 722 individuals representing the SA Tuli (216), Nguni (381) and Drakensberger (1 125) breeds. These animals were genotyped with the GeneSeek® Genomic Profiler™ 150K bovine SNP panel (140 113 SNPs) as part of the Beef Genomic Program (BGP) at the Agricultural Research Council’s Biotechnology Platform (ARC-BTP). Genotype quality control (QC) procedures were performed using PLINK version 1.9 software package (Purcell et al., 2007). Only unique (i.e. non-duplicated), autosomal SNP markers were considered for further analysis. For ROH analyses, sample call rate (< 90%), SNP call rate (≤ 95%) and deviations from Hardy-Weinberg equilibrium (P < 0.001) were excluded from the analysis. No MAF pruning was performed to avoid the removal of SNPs that are either fixed or highly homozygous (Meyermans et al., 2020). The same QC measures were applied for ROHet and F_ST analyses, except that MAF pruning (MAF < 5%) was performed to prevent underestimation of the length of segments (Meyermans et al. 2020). No LD pruning was performed for any analyses. Following these QC steps, 1 709 animals (DRB: 1 118; NGI: 377; TUL: 214) and 122 632 common SNPs remained for downstream analyses.

The consecutive-SNP-based detection method of the detectRUNS R-package version 0.96 (Biscarini et al., 2018; Saravanan et al., 2021), was used to identify ROH and ROHet. To minimize the risk of spurious ROH due to low SNP densities, the following criteria were applied: a minimal ROH length set to 1 Mb; a maximum distance between SNPs equal to 1 Mb; no SNPs with a heterozygous genotype were allowed, and a maximum of 2 SNPs with a missing genotypes were allowed (Biscarini et al., 2020). The minimum number of SNPs constituting a ROH segment (l = 52 DRB; l = 54 NGI; l = 50 TUL) was estimated using Purfield et al. (2012):

$$l=\frac{{log}_{e} \frac{\alpha }{{n}_{s}{n}_{i}}}{{log}_{e} \left(1-\stackrel{-}{het}\right)}$$

where n_s and n_i were the numbers of SNPs and individuals, respectively, α (set to 0.05) represented the proportion of false positive identifications, and het was the mean SNP-wide heterozygosity.

The mean number and length of ROH were calculated per animal and the ROH were grouped into four classes: ROH < 4Mb, 4 ≤ ROH < 8Mb, 8 ≤ ROH < 16Mb, and greater than 16Mb. The genomic inbreeding coefficient (F_ROH) was calculated based on McQuillan et al. (2008):

F_ROH = $\frac{{\varvec{S}}_{\varvec{R}\varvec{O}\varvec{H}}}{{\varvec{L}}_{\varvec{G}\varvec{E}\varvec{N}}}$ ;

where ${\varvec{L}}_{\varvec{G}\varvec{E}\varvec{N}}$ and ${\varvec{S}}_{\varvec{R}\varvec{O}\varvec{H}}$ represented the base pair length of the genome covered by SNPs and the summed length of ROH per animal, respectively.

The total sum of ROH lengths in the selected ROH length category for each individual was divided by the sum of lengths of autosomal chromosomes covered by SNPs. The average F_ROH was estimated for various ROH length categories: ROH < 4Mb (F_ROH<4Mb), 4Mb ≤ ROH < 8Mb (F_ROH=4−8Mb), 8Mb ≤ ROH < 16Mb (F_ROH=8−16Mb) and ROH ≥ 16Mb (F_ROH≥16Mb).

Given the absence of a consensus standard criteria for ROHet analysis and the scarcity of published studies, the criteria proposed by Biscarini et al. (2020), Santos et al. (2021) and Li et al. (2022) were applied. These criteria include: the default minimum of 15 SNPs constituting an ROHet; a minimal ROHet length set to 250 Kb; a maximum distance between SNPs equal to 1 Mb; a maximum of 3 SNPs with homozygous genotypes; and a maximum of 2 SNPs with missing genotypes. The ROHet were grouped into the following length categories: ROHet < 0.25Mb, 0.25 ≤ ROHet < 0.5, 0.5 ≤ ROHet < 1, 1 ≤ ROHet ≤ 2, and ROHet > 2 Mb (Biscarini et al., 2020). The number, proportion, and mean length of ROHet were calculated within each length category per breed.

To identify within-breed selection signatures, the detectRUNS R-package version 0.96 (Biscarini et al., 2018) was used to identify genomic regions most commonly associated with ROH and ROHet (ROH and ROHet islands). The percentage occurrence of a SNP in ROH and ROHet was calculated by counting the number of times the SNP appeared in those ROH and ROHet across animals within each population. To identify ROH and ROHet islands, SNPs falling within 99th percentile of the locus homozygosity and heterozygosity distribution were selected. This translated to an in-ROH and in-ROHet frequency threshold of 0.2 and 0.3, respectively (Biscarini et al., 2020). The function “topRuns” from the detectRUNS package was used to retrieve the most common runs using these threshold values. This means that a ROH and ROHet had to be present in at least 20% and 30%, of each population to be included in an ROH or ROHet island, respectively.

The F_ST was used to estimate the genetic differentiation between populations using the formula proposed by Nei (1986):

$${F}_{ST}=\frac{{H}_{T}-{H}_{S}}{{H}_{T}}$$

Where,

F_ST = the reduction in heterozygosity due to the structure of the population, H_S = Average heterozygosity in the subpopulation, H_T = Average heterozygosity in the metapopulation

The three beef cattle populations were compared pairwise (i.e. DRB-versus-NGI, DRB-versus-TUL, and NGI-versus-TUL) using PLINK’s --fst command (Caivio-Nasner et al., 2021) to determine genomic regions that exhibit differentiation. Negative F_ST values were set to 0 since they do not have any biological meaning (Akey et al., 2002). The top 0.1% F_ST values indicated selection signatures as suggested by Kijas et al. (2012), Zhao et al. (2015), and Saravanan et al. (2021). Prior to annotation, windows of 250kb downstream and upstream of the significant SNPs were investigated to verify overlapping gene segments (Maiorano et al., 2018).

After identifying significant regions under selection, annotation was done using the National Centre for Biotechnology Information (NCBI) database (https://www.ncbi.nlm.nih.gov/) based on ARS-UCD2.0 bovine genome assembly (https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_002263795.3/). Functional annotations were then assigned to genes through ShinyGO 0.77 (http://bioinformatics.sdstate.edu/go/) and the PANTHER version 16 software (http://www.pantherdb.org) (Mi et al., 2021). Genes with known functions were enriched for further analysis of molecular functions (MF) biological processes (BP), and cellular components (CC).

The total number of ROH identified varied among the three populations, ranging between 10 260 (TUL) and 57 885 (DRB) (Table 1). The DRB population had the highest mean number of ROH per animal (51.82 ± 21.01), as well the longest ROH (71.9 Mb). The average genome-wide F_ROH values were highest in the DRB, followed by TUL, and NGI populations. The DRB had the most ROHet with 97 162, followed by NGI with 21 103, and the TUL with 16 368. A higher occurrence of ROHet < 0.25Mb was observed compared to other size categories in all populations, and the number of ROHet decreased as the length of ROHet increased.

Table 1

Summary statistics of ROH and ROHet analyses in the three cattle populations.
	Drakensberger	Nguni	Tuli
Mean nROH ± SD per individual	51.82 ± 21.01	36.09 ± 12.84	47.94 ± 15.36
Mean F_ROH±SD	0.081 ± 0.046	0.033 ± 0.024	0.074 ± 0.031
Mean ROH length (Mb)	3.61	2.31	3.76
Longest ROH length (Mb)	71.9	65.5	53.8
ROH < 4Mb	41 358	12 360	7 557
4Mb ≤ ROH < 8Mb	10 229	657	1 570
8Mb ≤ ROH < 16Mb	4 856	379	877
> 16Mb	1 442	137	256
Total	57 885	13 533	10 260
Mean nROHet per individual	86.81 ± 11.73	55.98 ± 10.00	76.49 ± 12.33
Longest ROHet length (Mb)	2.84	1.60	1.48
Mean ROHet length (Mb)	0.3674	0.3673	0.3673
ROHet < 0.25Mb	76 802	16 915	13 138
0.25Mb ≤ ROHet < 0.5Mb	19 655	4 053	3 132
0.5Mb ≤ ROHet < 1Mb	632	97	79
1Mb ≤ ROHet < 2Mb	72	38	19
ROHet > 2Mb	1	-	-
Total	97 162	21 103	16 368
SD = Standard deviation; nROH = number of ROH: F_ROH = ROH-based Inbreeding coefficient, nROHet = Number of Runs of heterozygosity

The highest proportion of ROH across all populations, ranging from 71.45% (DRB) to 91.33% (NGI), was observed within the shortest length category (ROH < 4Mb) (Fig. 1).

In the present study, three different approaches (ROH, ROHet and F_ST) were used to detect candidate regions associated with adaptation and immunity traits. The distribution of ROH and ROHet islands across the chromosomes in three cattle breeds is depicted in Manhattan plots (Supplementary Fig. 1A, B & C and 2 A, B, & C), respectively. The ROH islands harboured genes such as LYZ, LYZ1, LYZ2, and LYZ3 related to immunity, and ROHet islands revealed candidate genes related to immunity (ADAMTS12, CYSTM1, DTX1, WDPCP, ELMO3, and CBFA2T3) and adaptation (FKBP4 and SRA1). To visualize the genome-wide distribution of selection signatures for three breed pairs, the F_ST values for each SNP were plotted against their genomic position (Supplementary Fig. 3A, B & C). Functional analyses of candidate regions within the top 0.1% of F_ST identified genes associated with immunity (CDK10, LY96 and CBFA2T3) and adaptation traits (LMAN2, TUBB3, NMU and MC1R). Only genes associated with adaptation and immunity were discussed further (Table 2).

Table 2

List of candidate genes identified within genomic regions considered to be selection signatures using the ROH, ROHet, and F_ST approach in all three beef breeds.
Gene ID	Population	Method	Ensembl Gene ID	BTA	Position (Mb)	Description
LYZ1	TUL, DRBvsNGI	ROH, F_ST	ENSBTAG00000046511	5	44.39	lysozyme (renal amyloidosis)
LYZ2		ROH, F_ST	ENSBTAG00000026088	5	44.37	Lysozyme C-2
LYZ3		ROH, F_ST	ENSBTAG00000046628	5	44.42	Lysozyme 3
LYZ		ROH, F_ST	ENSBTAG00000026779	5	44.51	Lysozyme
FKBP4	DRB	ROHet	ENSBTAG00000007605	5	106.95	FKBP prolyl isomerase 4
WDPCP		ROHet	ENSBTAG00000005151	11	61.72	WD repeat containing planar cell polarity effector
ELMO3		ROHet	ENSBTAG00000001286	18	34.83	engulfment and cell motility 3
CYSTM1	NGI, TUL	ROHet	ENSBTAG00000016596	7	51.36	cysteine rich transmembrane module containing 1
SRA1		ROHet	ENSBTAG00000001449	7	51.72	steroid receptor RNA activator 1
DTX1		ROHet	ENSBTAG00000016738	17	61.09	deltex E3 ubiquitin ligase 1
ADAMTS12		ROHet	ENSBTAG00000012558	20	39.87	ADAM metallopeptidase with thrombospondin type 1 motif 12
NELL2		ROHet	ENSBTAG00000032183	5	35.48	neural EGFL like 2
CBFA2T3	DRBvsNGI	F_ST	ENSBTAG00000010927	18	14.05	CBFA2/RUNX1 partner transcriptional co-repressor 3
CDK10	DRBvsTUL	F_ST	ENSBTAG00000033333	18	14.57	cyclin dependent kinase 10
MC1R		F_ST	ENSBTAG00000023731	18	14.71	melanocortin 1 receptor
TUBB3		F_ST	ENSBTAG00000023730	18	14.71	tubulin beta 3 class III
MTERF2		F_ST	ENSBTAG00000010144	18	70.25	mitochondrial transcription termination factor 2
LY96		F_ST	ENSBTAG00000008864	14	37.24	lymphocyte antigen 96
KCNE1	NGIvsTUL	F_ST	ENSBTAG00000001150	1	1.04	potassium voltage-gated channel subfamily E regulatory subunit 1
NMU		F_ST	ENSBTAG00000044161	6	71.03	neuromedin U
LMAN2		F_ST	ENSBTAG00000008034	7	38.83	lectin, mannose binding 2
CDH7		F_ST	ENSBTAG00000014488	24	10.78	cadherin 7
Bold = Genes observed in more than one population; Mb = Megabase pairs; DRB = Drakensberger; NGI = Nguni; TUL = Tuli

The study also identified shared candidate genes within genomic regions identified by all three methods. The FAM184B gene, present in all three breeds, was identified through ROH analysis on BTA 6. The present study also identified a genomic segment on BTA 14 harbouring genes such as LYN, SDCBP, CHD7, and CLVS1 common to all three methods. These genes were not discussed further due to their prior links with feed efficiency and carcass traits.

In the present study, SNP genotypes were used to assess the distribution and characteristics of ROH and ROHet and identify selection signatures associated with adaptation within and between three SA indigenous cattle breeds. The distribution and number of ROH observed was consistent with expectations considering the genotypes available for each breed with the DRB (n = 1 118: nROH = 57 885) exhibiting the most ROH, followed by NGI (n = 377: nROH = 13 533) and TUL (n = 214: nROH = 10 260). A high frequency of ROH < 4Mb suggests that these runs likely originated from ancient generations (Peripolli et al., 2018) or recent admixture leading to the breakdown of longer ROH (Liu et al., 2021). However, Ferenčaković et al. (2013), indicated that medium density SNP arrays may overestimate shorter segments due to their limited sensitivity to detection. The high proportion of ROH > 16Mb in the TUL breed suggests that recent inbreeding events may have occurred in this population, given that the TUL breed has the smallest population compared to other indigenous breeds in SA (SA Stud Book, 2022). Despite the low proportions of ROH (ROH > 16 Mb), in NGI (0.101) and TUL (0.249) breeds, routine monitoring is necessary especially in cases where high-impact bulls are used for mating.

The F_ROH is a reliable metric for assessing autozygosity, providing insights into both recent and ancient inbreeding patterns (Ferenčaković et al., 2013). In the ROH < 4Mb category, higher F_ROH values, such as DRB (F_ROH<4Mb = 0.055) and TUL (F_ROH<4Mb = 0.049), suggest ancient inbreeding (Hulsegge et al., 2022). The F_ROH values align with the distribution of ROH in the DRB and TUL populations, showing decreasing F_ROH values with increasing ROH length.

Although ROHet have been linked to specific regions, they remain relatively unexplored compared to ROH (Tsartsianidou et al., 2021). These regions have been associated with loci that prevent the detrimental effects of continuous homozygosity, and the benefits thereof were reported in immune-related genes, as well as in traits related to productivity and reproduction (Chen et al., 2022; Ruan et al., 2022). Typically, ROHet are concentrated in genomic regions associated with disease resistance, where increased diversity can aid populations in addressing potential health challenges, especially when facing novel threats (Sanglard et al., 2021). This is clear in the current study, where ROHet islands were mainly associated with genes related to adaptation and immunity, while ROH islands primarily contained genes associated with reproduction, body size, stature, and production traits.

The high prevalence of ROHet in the DRB aligns with the expectations, as the breed has previously been characterized as an admixed breed with a genetic composition comprising 46% European, 38% African taurine, and 15% indicine ancestry (Makina et al., 2016). Furthermore, the substantial disparity in ROHet prevalence between the DRB population and the other two breeds (NGI and TUL) may be attributed to differences in sample sizes. Unlike several previous studies, such as those conducted by Biscarini et al. (2020) and Lashmar et al. (2022), which reported higher proportions for regions within the ROHet category 0.5–1 Mb, the current study showed that ROHet ≤ 0.25 Mb exhibited the highest mean proportions across all populations. This observation indicates selection favoring heterozygotes, possibly due to admixture within Sanga cattle populations.

Using ROHet approach, the FKBP4 gene was identified in the DRB breed. This gene was previously associated with thermotolerance in Gir cattle, where it plays a role in heat shock protein binding (Saravanan et al., 2021). Heat shock protein (HSP) is a cellular and tissue defense mechanism and is expressed in high volumes during heat shock (Archana et al., 2017). If overexpressed, it protects the animal against hyperthermia during heat stroke which signifies the pivotal role of HSP in cytoprotection (Dangi et al., 2017). The LMAN2 gene identified on BTA 7 using the F_ST approach in the NGI-versus-TUL pairwise comparison, was also previously associated with functions of heat shock protein binding (GO:0031072) in beef cattle (Ghebrewold, 2018). This gene responds to heat shock through intracellular and extracellular signals, which activate the expression of heat shock proteins such as HSP27 (Shibata, 2014), HSP70 (Bhat et al., 2016) and HSP90 in cattle (Archana et al., 2017). Using the same approach, NMU gene was identified in the NGI-versus-TUL pairwise comparison, previously associated with regulating stress responses and thermoregulation in cattle (Yayou et al., 2009).

Mammalian skin and hair play important roles in providing physical protection and regulating body temperature (Jian et al., 2014). Cattle adapted to tropical environments often display light-colored, sleek, and shiny coats, which effectively reflect a higher proportion of incident solar radiation, helping in reducing heat load (Hansen, 2004). In the current study, the TUBB3 gene was identified in the DRB-versus-NGI pair using the F_ST approach. This gene, previously associated with coat colour (Taye et al., 2018), is involved in regulating melanogenesis, important for pigmentation and inflammation processes (Taye et al., 2018; Goud et al., 2020). SA Drakensberger cattle are distinguished by their solid black coat colour, although they can also be red (https://drakensbergers.co.za/English/, 2023), while Nguni cattle exhibit a variety of coat colors, including black, brown, and red as primary hues (Olson, 1999). These diverse coat colours carry significant cultural and breed-specific importance, especially among the Nguni ethnic community, influencing their selection preferences (Oosthuizen, 1996; Kunene et al., 2022). The TUBB3 gene has also been associated with high-altitude adaptation in Ethiopian sheep (Edea et al., 2019).

The present study identified candidate genes associated with immunity in three populations using three different approaches. The CYSTM1 gene, observed in both NGI and TUL breeds, has previously been linked to quantitative trait loci (QTLs) responsible for immune response (Fang et al., 2019). The CD14 gene, identified in NGI cattle is important for innate immunity and offers defense against a wide range of pathogens (Pal et al., 2011). Its roles in immune responses to diseases such as glomerulonephritis (Yoon et al., 2003), mastitis (Lee et al., 2003), and treponemiasis (Schröder et al., 2000) have been documented.

In the DRB breed, the WDPCP gene was also identified, associated with inflammatory response mechanisms to infections (de Las Heras-Saldana et al., 2019). Its involvement in collective cell movement and cilia formation has been linked to disease resistance mechanisms in Nelore cattle (Afonso et al., 2020). The DTX1 gene, identified in the NGI population, has been previously linked to the negative regulation of lymphocyte activation, suggesting its role in immune systems (Silva et al., 2022). The ELMO3 gene, located on BTA 18 in DRB, has been associated with phagocytosis and cell migration in postpartum dairy cows (Cheng et al., 2015). The ADAMTS12 gene, known for its participation in inflammatory responses and the regulation of the hepatocyte growth factor (HGF) receptor signaling pathway (Nakamura & Mizuno, 2010), has been associated with adaptation to high altitudes in pigs, suggesting its potential as an adaptive trait in NGI and TUL cattle (Ai et al., 2013).

Using ROH and F_ST approaches, three lysozyme genes, LYZ1, LYZ2, and LYZ3, were identified on BTA 5 in TUL and DRB-versus-NGI pair, respectively. These genes possess bacteriolytic properties and play diverse roles ranging from digestion to immune response (Wu et al., 2012). Overexpression of these genes have been reported to help sustain mucosal inflammation in the intestines, contributing to host resistance to intestinal worms (Li et al., 2015). CBFA2T3 gene, identified on BTA 18 in the current study, was previously under positive selection in Iraqi cattle (Alshawi et al., 2019). It plays a role in the innate immune response, particularly in response to mammary gland inflammation (Li et al., 2020). The Lymphocyte Antigen 96 gene (LY96), observed on BTA 14 in the DRB-versus-TUL pair, plays an important role in detecting lipopolysaccharide, serving as a pattern recognition receptor and positive regulation of phagocytosis (Dou et al., 2013). This gene is vital in the early identification of pathogens and the activation of immune signaling pathways engaging the adaptive immune response (Dixon et al., 2013). The identification of these genes implies that the three breeds under investigation likely possess a more effective mechanism for recognizing and responding to pathogens.

Several other genes observed were previously associated with immunity in species other than cattle, for example, the ROHet approach identified SLC4A9 gene on BTA 7 in TUL breed, previously linked to saliva secretion (Peña-Münzenmayer et al., 2015). Saliva serves to lubricate the oral mucosa and provide starch degrading enzymes, and the presence of this gene could be important in dry conditions where there is scarcity of water to sufficiently lubricate food and dilute salivary digestive enzymes (Peña-Münzenmayer et al., 2015). Huelsmann et al. (2019), reported the absence, loss, or inactivation of this gene in aquatic mammals, indicating adaptation to aquatic environments. Similarly, the EXOC3L1 gene, associated with several types of immune cells in humans (Zhang, 2023), was detected on BTA 18 in the DRB breed using same approach. Through the F_ST approach, the DEF8 gene was observed in proximity to the MC1R gene known for its role in influencing human pigmentation traits (Siiskonen et al., 2016). Using ROH approach, MYO1G gene was detected, primarily expressed in lymphocytes and associated with phagocytosis and exocytosis processes in mice (Maravillas-Montero et al., 2014). Furthermore, the current study observed DDX56 gene on BTA 4 in TUL using ROH approach, previously linked to an antiviral role against viruses such as Sindbis virus in humans (Taschuk et al., 2020). Similarly, MYO1F gene identified on BTA 7, known for its significance in immune cell motility and innate host defense against infection with Listeria monocytogenes (Kim et al., 2006; Sun et al., 2021) was observed in the TUL breed. Further research is warranted to validate these genes in cattle and other livestock species, given that many of them were initially identified in humans and mice. This cross-species validation could elucidate their functional significance and potential applications in beef production, and it may provide insights into adaptive mechanisms and breed-specific traits.

ROH analysis indicated varying numbers of ROH and lengths, providing insights into population history and recent admixture. The prevalence of shorter ROH was associated with potential selection signatures and ancient inbreeding. The presence of ROHet was also explored, revealing associations with traits related to thermo-tolerance and disease resistance. Selection signatures were identified based on F_ST, ROH, and ROHet, indicating candidate genes associated with traits of economic importance. Overall, this study provides valuable insights into the distribution of ROH and ROHet, as well as selection signatures within South African indigenous cattle breeds. It also gives insights into the domestication and evolutionary mechanisms that have fostered the diversification of numerous livestock breeds, adaptable to diverse environments and production systems, while also holding promise for enhancing livestock health, productivity, and resilience in the face of climate change.

Data availability

The datasets generated and/or analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request.

Data availability

The datasets generated and/or analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request.

Acknowledgements

The authors wish to acknowledge the permission provided by the respective breed societies to use the genotypes for this study.

Funding

MG received research support from the University of Pretoria Postgraduate Masters Research Bursary for 2023 and Professor JC Bonsma Bursary award 2023. The genotyping of the three populations used in the current study was funded by Technology Innovation Agency (TIA) through the SA Beef genomic project (BGP).

Contributions

Conceptualization: E. van Marle-Köster and C. Visser; methodology: E. van Marle-Köster, C. Visser and M. Gomo: formal analysis and investigation: M. Gomo and S.F. Lashmar; writing – original draft preparation: M. Gomo: writing – review and editing: E. van Marle-Köster, S.F. Lashmar, and C. Visser. All authors read and approved the final manuscript.

Ethics approval

The genotypic data used in the current study was provided by SA Stud Book with consent from the Drakensberger, Nguni, and Tuli Cattle Breeders' Society of South Africa. The study was approved by the Ethics Committee in the Faculty of Natural and Agricultural Sciences at the University of Pretoria (NAS235/2022).

Conflict of interest

The authors declare no competing interests.

Supplementary Information

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Selection signatures associated with adaptation in South African Drakensberger, Nguni, and Tuli beef breeds

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