Introduction

Increasing the efficiency of beef cattle production systems appears to be a critical aspect of addressing global food security, at least in the short term. In tropical and subtropical areas, beef cattle are mainly influenced by Bos indicus, although management practices are often based on techniques formulated for Bos taurus breeds, which are usually reared in temperate environments. It is important to recognize that B. taurus and B. indicus are separate subspecies that have developed distinct biological functions as a result of selective pressures arising from complex evolutionary and domestication processes (Cooke et al., 2020; Maciel et al., 2019).

The adaptation of zebu cattle to these environments significantly improves their efficiency in coping with challenges related to heat, humidity, nutrition, and disease. In high temperature environments, this genetic group exhibits a more efficient balance between heat generation and heat dissipation compared to taurine breeds (Utsunomiya et al., 2019). However, their potential for meat production remains limited (Scheffler, 2022). In contrast, European breeds have a high capacity for meat production, but are unable to fully exploit their genetic capabilities in tropical environments. Given the limitations of both zebu and European breeds, the most promising strategy for improving meat production in these regions is to use cattle with optimal genetic traits obtained through crossbreeding (Rubio Lozano et al., 2021). This methodology produces several advantageous results, especially in commercial cattle production, such as the integration of two or more favorable traits (complementarity) of the progenitor lines in the commercial offspring.

The importance of crossbreeding lies in the widely recognized phenomenon of hybrid vigor, or heterosis. This term refers to the variation in phenotype observed between the average of crossbred individuals and that of their purebred parents. Increased heterosis is observed when the parents are less genetically related. Consequently, the first cross (F1) exhibits maximum heterozygosity, resulting in the highest level of hybrid vigor (Bunning et al., 2019). The main objective is to produce more efficient animals by combining the exceptional adaptability of zebu cattle with the productive capacity of taurine breeds, thus improving beef quality (Fernandes Júnior et al., 2022).

Crossbreeding plays an important role in beef production systems in tropical climates to improve growth, meat quality and adaptability. However, recent decades have seen remarkable advances in genotyping technologies, which have a wide range of applications, including their valuable use in animal research (Kockum et al., 2023). Advances in this field have been critical to the establishment and application of genomic selection in animal production. This approach involves the use of genomic data from individuals to estimate their genetic value, enabling informed decisions on selection and crossing strategies (Eiríksson et al., 2022; Strandén et al., 2019). In addition, these advances help address potential pedigree errors and allow accurate prediction of the true breed composition of animals (Dodds et al., 2014; Munoz et al., 2014).

Advances in genotyping have revolutionized genetic research. Early methods relied on inferring genotypes from phenotypes, progressing to tissue typing for transplant compatibility. The introduction of RFLP-based genotyping in the 1970s was transformative, and PCR further revolutionized the field in 1985. Microarray technology increased the resolution of genotyping, allowing the simultaneous assessment of many genetic variants. Genomic studies reached a new high watermark with the advent of whole genome sequencing in 2014. Access to genotyping has been democratized in recent years with the development of high-throughput and cost-effective next-generation sequencing (Kockum et al., 2023).

Genomic selection (GS) is a state-of-the-art breeding method that uses genetic markers distributed throughout the genome to predict the genetic merit of candidates for selection based on specific quantitative traits. The concept introduced by Meuwissen et al. (2001), is characterized as a specialized form of marker-assisted breeding (MAB), where each quantitative trait locus (QTL) is tested for linkage disequilibrium (LD) with at least one genetic marker. This allows efficient selection of the desired traits. The feasibility of this strategy has been greatly enhanced by the large number of single nucleotide polymorphisms (SNP) identified by genome sequencing and the emergence of innovative methods that can competently genotype a large number of SNP (Goddard y Hayes, 2007). These molecular markers provide a dense map of genetic variation in the genome, allowing more accurate estimation of the genetic potential of parental lines and more accurate prediction of progeny performance by selecting individuals carrying favorable alleles. GS offers numerous benefits, including minimizing the need for extensive field evaluations and accelerating the transfer of genetic advances through shorter generation intervals and reduced costs. It also streamlines the breeding cycle by facilitating the rapid identification of superior genotypes. A phenotyped and genotyped training population (TP) is used to project genomic estimated breeding values (GEBVs) for specific animals under GS. By eliminating the laborious process of additional phenotyping, a breeding population (BP) can be formed from the selected individuals and propagated through multiple generations (Sinha et al., 2023). This method has fundamentally changed the conduct of breeding programs, allowing more accurate and efficient selection for favorable traits by using genomic data to estimate breeding values (Budhlakoti et al., 2022).

However, its implementation has been mainly in "simple" scenarios such as dairy cattle, where a single breed is used worldwide. Many other situations require evaluation across breeds or in crossbred populations. However, prediction accuracy has not been consistently high. Several factors contribute to this: First, the genotype-environment interaction (GEI) can vary due to different environments as well as non-additive effects (Sinha et al., 2023). Second, even in the absence of a significant GEI, differences in allele frequencies can lead to large differences in the variance explained by each locus between breeds. Therefore, further evaluation of the importance of each factor is required.

Adoption of GS in beef cattle has been slow. The potential benefits for improving genetic progress are substantial, especially considering the critical importance of traits that are difficult and costly to measure routinely, but have a significant impact on profitability. Despite this potential, the precision of reported genetic breeding values for these traits in cattle remains limited, typically falling within a range of low to moderate levels (Esrafili Taze Kand Mohammaddiyeh et al., 2023). Hayes et al. (2013) acknowledge this circumstance in two ways: First, the reference populations established for beef cattle tend to be smaller than those for dairy cattle, resulting in a lack of bulls with extensive accurate progeny testing in the beef industry. Second, in contrast to the dominance of a few breeds in dairy cattle populations worldwide, the beef cattle landscape includes a variety of significant breeds, including two distinct subspecies (B. taurus and B. indicus).

Given the complex nature of breeding initiatives in tropical and subtropical climates and the variety of breeds involved, our research aims to address the obstacles associated with implementing GS in beef cattle. The primary goal of this research is to assess the efficacy of various crossbreeding methodologies and SNP selection approaches in enhancing the precision of genomic predictions. By analyzing distinct SNP selection criteria, including those that consider allele frequency variations among breeds, we endeavor to augment the accuracy of GS within crossbred populations. This inquiry aspires to contribute to the progression of beef cattle breeding programs, especially in areas where crossbred animals are commonly utilized. Through the optimization of genomic prediction frameworks, we can substantially enhance the selection of superior livestock, thereby promoting the sustainability and profitability of beef production systems in tropical and subtropical environments.

Materials and methods

Genotype data

High-density genotyping data (∼777K SNP marker variants) were obtained from the WIDDE public database (http://widde.toulouse.inra.fr). After applying a quality filter, the genotyping density ranged from 708,206 and 541,246 genetic markers for the eight included (details provided in Table 1). Genotypes from Angus (N=42), Brangus (N=12), Brahman (N=46), Hereford (N=35), Nelore (N=31), Red Angus (N=10), Senepol (N=12), and Santa Gertrudis (N=32) were downloaded, as these breeds are the most commonly used in intensive production systems in subtropical climate. It is noteworthy that Angus (ANG), Hereford (HFD), and Red Angus (RGU) breeds belong to the Bos taurus subspecies, while Brahman (BRM) and Nelore (NEL) breeds represent the Bos indicus subspecies. The hybrid or crossbred breeds used in this study were Brangus (BRG), Senepol (SEN) and, Santa Gertrudis (SGT) breeds. Only autosomal SNP with a maximum missing rate of 25% and a minimum allele frequency of 1% were retained. Individuals with more than 5% missing genotypes were also excluded. Missing SNP were imputed using BEAGLE 4.1 (Browning & Browning, 2016). Principal component analysis (PCA) was performed to cluster individuals and remove outliers (Figure 1). Eight outliers from the Hereford samples were excluded during this process for an initial analysis. Subsequently, samples from Brahman and Nelore breeds were clustered into a single generic population of the Indicus subspecies (IND), as were animals from the Red and Standard Angus breeds (ANG). This was done to obtain a “retained” population that combines the two main subspecies, taking into account the homogeneity of the samples within each group. Similarly, samples from Brangus and Senepol were grouped under the abbreviation BSP. Plink v1.9 software (Purcell et al., 2007) was utilized to calculate allele frequencies for each population and genetic differentiation indices (Fst) between them. Following SNP and sample filtering, 721,136 SNP from 212 samples were used for an initial analysis, and 659,644 SNP from 129 samples were used for the “retained” population (Table 1).

Table 1. Summary of samples analyzed. The “retained” population, composed of the Angus and Red Angus (ANG) breeds and the Brahman and Nelore (IND) breeds, is indicated in italics.

Tabla 1. Resumen de muestras analizadas. La población “retenida” está compuesta por las razas Angus y Red Angus (ANG) y las razas Brahman y Nelore (IND), en cursiva.

Breed	Code	No. of samples	New Code	No. of samples after SNP quality control	No. of markers before/after quality filtering
Angus	ANG	42	ANG	52	732,993 / 618,367
Red Angus	RGU	10	ANG	52	732,993 / 574,342
Brangus	BRG	12	BSP	24	732,993 / 670,560
Senepol	SEN	12	BSP	24	732,993 / 626,727
Hereford	HFD	35	HFD	27	732,993 / 644,421
Brahman	BRM	46	IND	77	732,993 / 657,641
Nelore	NEL	31	IND	77	732,993 / 541,246
Santa Gertrudis	SGT	32	SGT	32	732,993 / 708,206

Genetic architectures

In temperate climates, cattle breeding is mainly aimed at growth rate (O’Neill et al., 2010) and meat quality (Schutt et al., 2009). In tropical climates, parasite and heat resistance are also relevant, as these are the main constraints on animal performance. To represent these related traits, we aimed to simulate body weight gain, shear force (meat quality), and general heat and parasite tolerance. To generate “realistic” genetic architectures, we downloaded QTL regions for each of these three sets of phenotypes from the QTL database Supplementary Table 1 (Hu et al., 2022; Ramírez Ayala, 2024). We selected SNP within these QTL regions from the HD bovine array as putative causal SNP for each of the phenotypes. Causal SNP were selected from those with the 10% highest average Fst within regions smaller than 1MB around the QTL positions. From all potential candidate markers, 200 SNP were selected as causal for each phenotype.

Genetic effects were generated from a gamma distribution Γ (shape = 0.2 and scale = 5) (Caballero et al., 2015), multiplied by the sign of the difference between the mean of the allele frequencies of the two purebred taurine breeds (ANG and HFD) and the allele frequency of the zebu population (IND). In agreement with the literature (Boonkum & Duangjinda, 2015; Elzo et al., 2012; Kim et al., 2003; Machado et al., 2010), complete dominance was assumed for tolerance, and additivity in the two remaining phenotypes. Heritability values (h²) were taken from the literature (Alencar et al., 2005; Berry & Crowley, 2013; Burrow, 2001, 2012; Burrow et al., 2001; Cucco et al., 2010; Henshall, 2004; Hewetson, 1972; Su et al., 2010; Watterson, 1975), with shear force h² = 0.3, growth h² = 0.24, and tolerance h² = 0.4.

Evaluated crossing programs and genotyping strategies

We compared three of the most common crosses used in tropical climates: F1, grading up and rotational crosses (Galukande et al., 2013; Leroy et al., 2016). The terminal or F1 system involves crossing pure zebu with taurine animals, maximizing individual heterosis in the cross and combining the desirable effects of the two breeds for direct and maternal genetic effects. The grading up system is a backcross that replaces an initial F1 population by systematically crossing the female progeny with taurine bulls. The objective is to replace animals with low productivity with individuals showing better performance while maintaining resistant animals. The two-breed rotational cross, also known as the “criss-cross” system, is a widely used and straightforward approach to crossbreeding. In this system, two different breeds are crossed, and the resulting female offspring are kept as replacements and then crossed back to one of the original breeds. Subsequently, these female offspring are bred with males from their sire's alternate breed. Implementing this system requires at least two separate breeding pastures (if relying solely on natural service), with each pasture dedicated to a specific breed of sire. Additionally, cows need to be accurately identified based on the breed of their sire. Over multiple generations, this system allows for the realization of approximately 67% of the maximum possible heterosis. Another advantage is the significant number of heifers available for replacement selection.

In each of the three schemes, 400 animals were simulated. One generation was simulated for the terminal cross, while four generations were simulated for the other two schemes, with each generation consisting of 100 progenies. Individual genotypes from the base population (ANG=52 and IND=77) were downloaded and processed as described. We evaluated the performance of genomic prediction (GP) by cross-validation using GBLUP (VanRaden, 2008). In the F1 cross, phenotypes from 200 animals were removed, and the correlation between predicted and actual phenotypes for each of the traits was compared. We will refer to this correlation as “predictive ability” (PA). In either the grading or rotational crosses, PA was calculated for phenotypes from the last two generations (N=200). Three SNP chips were compared:

1. Chip 1: 50 k SNP selected at random, evenly distributed across the genome.

2. Chip 2: 50 k SNP randomly selected from those with a maximum allele difference of 0.09 between Angus and Indicus.

3. Chip 3: 50 k SNP randomly selected from those with a maximum allele difference of 0.09 between Angus and Indicus frequencies and a maximum allele frequency (MAF) of the Angus population of 0.2 < MAF < 0.8.

SNP were selected from those available on the HD chip.

The SeqBreed tool (Pérez-Enciso et al., 2020) was used to implement the simulation and GP described above. SeqBreed allows automatic implementation of standard genomic selection procedures such as GBLUP.

Results

Principal Component Analysis of Populations

The genetic structure between and within breeds was assessed using PCA. We conducted two analyses, first considering all breeds initially selected and then only the "retained" populations. For all breeds analyzed (Figure 1.A), PC1 accounts for 29% of the total variation, separating the zebu population (Brahman and Nelore) from the taurine breeds (Angus, Hereford and Red Angus). We also observed that the hybrid breeds are closer to the taurine breeds than to the Indicus breeds, likely due to their higher percentage of European genetic background. Notably, the Angus (ANG) and Red Angus (RGU) breeds form a single group, as do the Brahman (BRM) and Nelore (Nel) zebus. PC2, which accounts for 5% of the total variation, separates the zebu and Hereford breeds from the others. The second PCA, performed only on the "retained" populations, shows a clear separation between the two subspecies, Bos taurus and Bos indicus (Figure 1B).

Figure 1. (A) Principal component analysis using all samples. Individuals are grouped in to Bos taurus, Bos indicus and Hybrid categories. Black: Angus, red: Brangus, blue: Brahman, cyan: Hereford, green: Nelore, orange: Red Angus, magenta: Senepol, purple: Santa Gertrudis; (B) Principal component analysis for “retained” population. Red: Angus (Angus + Red Angus), blue: Indicus (Brahman + Nelore).

Figura 1. (A) Análisis de componentes principales utilizando todas las muestras. Los individuos se agrupan en Bos taurus, Bos indicus e híbrido. Negro: Angus, rojo: Brangus, azul: Brahman, cian: Hereford, verde: Nelore, naranja: Red Angus, magenta: Senepol, púrpura: Santa Gertrudis; (B) Análisis de componentes principales para la población “retenida”. Rojo: Angus (Angus + Red Angus), azul: Indicus (Brahman + Nelore)

Predictive accuracy

Figure 2 illustrates the predictive ability (PA) across chips and breeding schemes for each phenotype. Overall, Chip 1 (50k random SNP) and rotational crosses were identified as the most effective strategies, although variations were observed between traits. Notably, for tolerance, PA was significantly lower, and the differences between genotyping or breeding strategies were less pronounced.

Gráfico, Gráfico de cajas y bigotes

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Figure 2. Correlations according to the crossbreeding scheme. (A). Shear, (B). Weight, (C). Tolerance vs. all chips. Red boxes indicate F1 or terminal crossing scheme, green boxes indicate grading up and, blue boxes indicate rotational, for all cases.

Figura 2. Correlaciones según el esquema de cruzamiento. (A). Corte, (B). Peso, (C). Tolerancia vs. todos los chips. Los recuadros rojos indican esquema de cruzamiento F1 o terminal, los recuadros verdes indican clasificación absorbente y los recuadros azules indican rotacional, para todos los casos.

The overall effect of the chips on PA is illustrated in Figure 3. Chip 1 (50k random) exhibited the highest PA (0.26), followed by Chip 2 (0.18) and Chip 3 (0.17). The random selection of SNP is the most effective strategy while setting restrictions on Fst or allele frequency does not significantly improve PA. Regarding the breeding scheme, the advantage of the rotational cross is prominently highlighted on average (Figure 4). The rotational scheme demonstrated a higher correlation (0.38) compared to the other two schemes, F1 (0.12) and grading up (0.16).

Figure 3. Global effect of the chips on the phenotypes.

Figura 3. Efecto global de los chips sobre los fenotipos.

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Figure 4. Global effect of the crossing on the phenotypes.

Figure 4. Efecto global del cruce sobre los fenotipos.

Discussion

The Principal Component Analysis (PCA) applied to genotype data, comprising 721,136 SNP from 212 cattle across 8 breeds, reveals distinct clustering based on their Indicus or Taurus lineages, as illustrated in Figure 1. Furthermore, within the "retained” population, which encompasses 659,644 SNP from 129 samples representing 4 breeds grouped by homogeneity in the two subspecies (ANG and IND), similar separations are observed. These observations align with documented cattle history (Bovine Hapmap Consortium, 2009; Kasarapu et al., 2017; Porto-Neto et al., 2014; Ward et al., 2022).

Random selection of markers, such as SNP, could significantly enhance the accuracy of genomic prediction for economically important traits in beef cattle production systems raised in tropical ecosystems. Among the SNP selection criteria compared (see Materials and Methods), Chip 1, corresponding to the random selection of SNP from a high-density (700k) chip, emerged as the optimal choice for all proposed traits (Figure 3). Pérez Enciso et al. (2017), who reported a modest advantage in prediction accuracy between sequences and commercial or random SNP matrices using GBLUP. Moreover, recent research by Rodriguez Neira et al. (2022), employing another genomic prediction method (ssGBLUP), found compelling evidence supporting the feasibility and cost-effectiveness of employing genomic selection in beef cattle using custom panels with low SNP density. They noted that this approach maintained the predictive accuracy of genomic information. These results offer promising prospects for beef cattle breeding programs, suggesting the potential development of in silico SNP marker panels for GEBV prediction. By minimizing the number of SNP requiring imputation, this approach may streamline the process. Additionally, the study implies that stringent criteria for SNP selection in custom panels may not be necessary, as long as the markers are adequately distributed across the genome and possess high informative value.

Contrary to the benefits observed with random marker selection, the strategy of selecting markers based on differences in allele frequencies between breeds may not prove effective. Previous studies (Dadi et al., 2012; Moghaddar et al., 2014), have highlighted that disparities in allele frequencies among breeds can arise from various factors, including recent genetic drift and ancient divergence among cattle breeds. These differences can lead to the selection of markers that inadequately capture relevant genetic variability within each breed. Moreover, incorporating data from animals of different breeds may decrease the accuracy of genomic estimations, as noted in the literature. This suggests that marker selection solely based on allele frequency differences between breeds may not be optimal and could, in fact, detrimentally impact the precision of genomic predictions. Therefore, considering these factors and the potential for allele frequency-based marker selection to yield unexpected outcomes, it is prudent not to recommend this strategy, as it may lead to erroneous decisions in cattle genetic improvement programs.

Crossbreeding strategies such as either terminal or rotational crossing, synthetic breed creation, or breed replacement are frequently advocated for enhancing farmers' incomes by boosting livestock productivity. Rotational crossing involves the utilization of crossbred dams mated alternately to different breeds, varying the genetic composition of the dams from one generation to the next. While similar to terminal crossing in requiring a steady supply of purebred genetic material, rotational crossing requires it only on the male side, reducing costs for the breeders, particularly in situations where semen or low-cost males are readily available. Both terminal and rotational crosses aim to optimize heterozygosity and the resulting heterosis effect, although heterosis tends to be less under rotational crosses. These strategies necessitate managing two or more parental lines, with a market chain providing farmers with either purebred reproducers or semen (Leroy et al., 2016). Upgrading, or top-crossing, involves using the same sire breed in each generation to increase the proportion of a particular purebred within a herd, commonly observed in taurine breeds. However, upgrading beyond 75% temperate blood may pose challenges under severe climatic conditions or if management practices do not keep pace with the genetic potential of the herd (Rendel, 1974). Our findings suggest that the rotational scheme (Figure 4) yields the highest overall accuracy compared to other crossing systems such as F1 and grading up. Rotational crossbreeding has demonstrated substantial enhancements in animal productivity. However, its maintenance requires meticulous planning and record-keeping. Notably, in the initial generations of crossbreeding, significant diversity in size, body condition, and other traits may arise, contingent upon the breed used. The efficacy of this approach hinges on selecting superior sires within purebreds, which, in turn, depends on the cost and consistent supply of material with high genetic potential. Likewise, purebreds rely on within-breed selection for genetic enhancement (Cv, 2015; Galukande et al., 2013, Leroy et al., 2016).

Conclusion

Our genomic selection (GS) simulations offer valuable insights into crossbreeding schemes in tropical climates. Our findings underscore the effectiveness of rotational crossing systems in achieving higher predictive accuracy, highlighting the importance of genetic diversity for improving desired traits in tropical cattle. Furthermore, our results advocate for the random selection of SNP as a practical and effective approach in trait prediction. However, caution is warranted regarding SNP selection based on allele frequency differences between breeds, as our study suggests it may not yield significant benefits and could even be detrimental. While our conclusions provide valuable guidance for cattle breeding programs, it is important to acknowledge the limitations inherent in sample sizes across breeds and the necessity for further research to validate and refine these findings.

Acknowledgements: MPE is funded by MINECO grant AGL2016-78709-R and from the EU through the BFU2016-77236-P (MINECO/AEI/FEDER, EU) and the “Centro de Excelencia Severo Ochoa 2016-2019” award SEV-2015-0533. LCRA is funded by "Don Carlos Antonio López" Graduate program (BECAL) from Paraguay.

Authors' contributions: L.C.R.A., and M.P.E. designed the study, analyzed the data, interpreted the results, and wrote the manuscript. J.L.C., M.L.Z., E.R.G., and Y.R.C. edited and reviewed the manuscript and approved the final version. Conceptualization: L.C.R.A., M.P.E. Experiment desing: L.C.R.A., M.P.E. Experiment execution: L.C.R.A., M.P.E. Experiment verification: L.C.R.A., M.P.E. Data analysis/interpretation: L.C.R.A., M.P.E. Statitical analysis: L.C.R.A., M.P.E. Manuscript preparation: L.C.R.A., M.P.E. Manuscript editing and revision: L.C.R.A., J.L.C., L.M.Z., E.R., Y.R.-C., M.P.E. Approval of the final manuscript version: L.C.R.A., J.L.C., L.M.Z., E.R., Y.R.-C., M.P.E.

Financing: No external financing.

Data availability: Data supporting the findings of this study are available from the corresponding autor, upon reasonable request.

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SUPPLEMENTAL INFORMATION

Supplementary Table 1. QTL regions for each of these three sets of phenotypes from the QTL database.

Tabla suplementaria 1. Regiones QTL para cada uno de los tres conjuntos de fenotipos de la base de datos QTL.

Supplementary data to this article can be found online at Ramírez Ayala, L. C. (2024). Supplementary Table 1. QTL regions for each of these three sets of phenotypes from the QTL database. figshare. Dataset. https://doi.org/10.6084/m9.figshare.27935052