Mastitis is a complex and multifactorial disease caused by a combination of environmental factors (e.g. hygiene, milking practices) and biological factors, including various pathogens like Escherichia coli, Streptococcus uberis and Staphylococcus aureus. Mastitis affects milk quality and quantity, thereby reducing animal productivity. Traits like somatic cell score (SCS), which is used as an indicator of udder health, serve as proxies for evaluating mastitis resistance and can be associated with specific genetic variants. In addition to numerous therapeutic, prophylactic and farm management techniques to control and reduce mastitis to a low level, resistance to intramammary infection can be achieved by selective breeding strategies (Brajnik and Ogorevc, Reference Brajnik and Ogorevc2023). As highlighted by Ablondi et al. (Reference Ablondi, Summer, Stocco, Degano, Vicario, Stefanon, Sabbioni and Cipolat-Gotet2023) and Brito et al. (Reference Brito, Bedere, Douhard, Oliveira, Arnal, Peñagaricano, Schinckel, Baes and Miglior2021), nowadays the strategies for selecting dairy cows are increasingly directed towards improving health, resilience, and animal welfare while considering functional and productive traits.
Breeding for disease resistance in cattle requires both a more comprehensive understanding of the immune response as well as the identification of genes underlying variation in immune responsiveness and disease resistance. Despite the polygenic nature of mastitis, the candidate gene approach allows targeting specific genes known to have a functional role in immune regulation and disease resistance (Khan et al., Reference Khan, Wang, Ma, Chen, Ma, Ullah, Khan, Khan, Cao and Liu2023) and enables the simultaneous evaluation of polymorphisms within a specific gene. Furthermore, the candidate gene approach provides insight into how specific genetic variants may impact the immune response to intramammary infection. Therefore, identifying key functional genes for mastitis resistance can enhance genomic strategies to combat mastitis by promoting genetic selection for increased resistance. Numerous genes are known to influence mastitis, each contributing significantly to either resistance or susceptibility; therefore, it is essential to consider the combined effects of these genes,as some have only a minor impact (Khan et al., Reference Khan, Wang, Ma, Chen, Ma, Ullah, Khan, Khan, Cao and Liu2023). There is a limited understanding of how polymorphisms in immune-related genes contribute to mastitis resistance in the Italian Simmental breed. This study addresses this gap by focusing on the Italian Simmental population, a dual-purpose breed known for its adaptability to harsh farming conditions and combined use for dairy and beef production. This breed provides a unique opportunity to examine genetic resistance to mastitis in a population that balances functional traits like disease resistance with productive traits such as milk and meat yield.
This study specifically investigates polymorphisms in candidate genes involved in the immune response, which have established roles in inflammation and pathogen defence: the cluster of differentiation 4 (CD4), the transforming growth factor beta 1 (TGF-β1), the interferon gamma (IFN-γ), and the C-X-C motif chemokine receptor 2 (CXCL8). CD4 plays a crucial role in various inflammation-related conditions in many species (Rasheed et al., Reference Rasheed, Ali, Niaz, Zeb, Khattak, Khan and Usman2020). Evidence suggested that polymorphisms in the CD4 gene are linked to production and health traits in cattle. For instance, Usman et al. (Reference Usman, Yu, Zhai, Liu, Wang and Wang2016) reported associations between specific CD4 gene polymorphisms and milk fat percentage in Chinese Friesian cattle, while Zeb et al. (Reference Zeb, Ali, Niaz, Rasheed, Khattak, Khan, Wang and Usman2020) identified four SNPs located between exon 2 and exon 4 of the CD4 gene that were significantly associated with clinical mastitis incidence and annual milk yield. These findings reinforce the hypothesis that causative mutations in the CD4 gene may have profound effects on immune function and production traits in cattle.
The immune and inflammatory response to bacteria is largely regulated by genes associated with immune and inflammatory pathways (Segal and Hill, Reference Segal and Hill2003). Among the potential candidate genes implicated in the mammary immune response, TGF-β1, IFN-γ and CXCL8 have been identified as particularly significant (Mohammadi et al., Reference Mohammadi, Farahani, Moradi, Mastrangelo, Di Gerlando, Sardina, Scatassa, Portolano and Tolone2022; Wang et al., Reference Wang, Bissonnette, Laterrière, Dudemaine, Gagné, Roy, Sirard and Ibeagha-Awemu2023).
TGF-β1 is a multifunctional cytokine with a wide range of biological effects in epithelial cells. During bacterial infection of the udder, levels of TGF-β1 increase in both the serum and the milk. Studies have shown that S. aureus can enhance the production of TGF-β1 during intramammary infection, with the cytokine subsequently modulating the immune response (Zhang et al., Reference Zhang, Che, Zhao, Xia, Liu, Liu, Wang, Han, Yang, Zhou and Lei2019). Furthermore, Wu et al. (Reference Wu, Ding, Bi, Wang, Zhi, Wang and Wang2016) demonstrated that S. aureus stimulates TGF-β1 secretion by activating NF-κB and AP-1 pathways in bovine mammary epithelial cells (BMECs).
The IFN-γ gene is crucial for orchestrating the immune system's response to bacterial infection. As a key pro-inflammatory cytokine, the IFN-γ, promotes Th1-mediated immunity and enhances macrophage activity, helping to control bacterial spread (Gopi et al., Reference Gopi, Vir Singh, Kumar, Kumar, Chauhan, Sonwane, Kumar, Bharati and Vir Singh2022).
Finally, interleukin 8 (CXCL8) plays an essential role in the recruitment of neutrophils to the site of infection, thereby aiding in the resolution of bacterial infections in dairy cattle (Bernhard et al., Reference Bernhard, Hug, Stratmann, Erber, Vidoni, Knapp, Thomaß, Fauler, Nilsson, Nilsson Ekdahl, Föhr, Braun, Wohlgemuth, Huber-Lang and Messerer2021).
Simmental is a cosmopolitan dual-purpose cattle breed, reared in Italy mainly in small herds located in Northern areas well-known for its adaptability to various farming and breeding conditions (Cesarani et al., Reference Cesarani, Hidalgo, Garcia, Degano, Vicario, Masuda, Misztal and Lourenco2020). Dual-purpose breeds are ideal for low-impact farming systems, especially in marginal areas where livestock provides social and territorial benefits. Studies indicate that these breeds can produce the same amount of milk and meat while requiring fewer animals than specialised breeds, leading to reduced resource use and lower pollution from manure and greenhouse gases (Buonaiuto et al., Reference Buonaiuto, Lopez-Villalobos, Niero, Degano, Dadati, Formigoni and Visentin2022).
This study aimed to explore the potential association between SCS breeding values and polymorphisms in candidate genes encoding cytokines (CXCL8, TGF-β1 and IFN-γ) as well as CD4, focusing on their roles in the immune response to mastitis in Italian Simmental cattle.
Materials and Methods
The study was carried out in the following steps:
1) a pre-trial in 10 simmental sires that have undergone whole genome sequencing to discover single nucleotide polymorphisms in the IFN-γ and TGF-β1 genes;
2) genotyping and linking the SNPs of the selected four genes with genomic estimated breeding values (GEBV) and deregressed conventional breeding values (EDP, equivalent daughter performance) of productive and functional traits in cattle;
3) combination of the relevant SNPs into functional haplotypes and their association with the SCS GEBV and EDP, considering also the effect of the above breeding values;
4) finally, an association among functional haplotype combinations and SCS breeding values.
Animals
This study was carried out on 558 Italian Simmental lactating cows from 52 farms located in 5 different regions in Northern Italy (Friuli Venezia Giulia, Lombardia, Piemonte, Trentino Alto Adige, and Veneto). First, animals with at least 3 calvings were enrolled on their average SCS (log10 transformation of milk somatic cell content). Cows were classified as “TOP" and “BOTTOM", based on the value corresponding to the 85th and the 15th percentile of SCS (average SCS of all test-day records across lactations) within each farm, respectively. Milk recordings were carried out monthly throughout the animals’ productive lives, while on average the SCS index of each cow has been calculated on an average number of recordings equal to 38. Breeding values (GEBV and EDP) of productive traits (milk, fat and protein yield) and the functional trait of somatic cells were provided by the Italian Simmental Breeders Association (ANAPRI).
Sampling and DNA extraction
The management and care of the experimental animals were carried out in compliance with the European Directive 2010/63/UE and the Italian regulation D. Lgs n. 26/2014. Blood samples were collected from the jugular vein using vacutainer tubes containing Li-Heparin (Beckton Dickinson) and stored at 4°C until analysis. Genomic DNA was extracted from 2 mL whole-blood samples using the NucleoSpin Blood L, Midi kit for DNA from blood (Mackerey-Nagel), by following customer protocol conditions. DNA concentration and purity, based on the A260/280 absorbance ratio was calculated directly by NanoPhotometer® Pearl (Implen GmbH). DNA samples with a ratio within the range of 1.7–1.9 were then diluted to 50 ng/μL in Milli-Q water and stored at −80°C.
SNPs investigation
This study examined only polymorphisms identified in the coding and regulatory regions of CXCL8, CD4, IFN-γ and TGF-β1 genes. In the CXCL8 gene, De Matteis et al. (Reference De Matteis, Grandoni, Signorelli, Degano, Vicario, Buttazzoni and Napolitano2022) identified five SNPs highly associated with each other, with a linkage disequilibrium greater than 90%; therefore, it was decided to use only the SNP IL8-697, as it was located within a crucial region for accurate gene transcription. Based on a prior study by Napolitano et al. (Reference Napolitano, Grandoni, De Matteis, Degano, Vicario and Buttazzoni2021) focusing on polymorphisms and haplotypes, only three CD4 SNPs were selected for association analyses with the breeding values: CD4-3720, CD4-7230 and CD4-7191, which exhibited a significant effect on milk indexes. A pre-trial was carried out to identify SNPs in IFN-γ and TGF-β1 genes in 34 genomes of Simmental sires provided by Associazione Nazionale Allevatori Bovini di Razza Pezzata Rossa (ANAPRI), in which SNPs were selected from 10 animals that represented the highest and lowest values of the SCS breeding value (EBV): Five sires’ EBVs ranged between 110 and 124, and five between 55 and 78. Expression of SCS EBV is a relative number, i.e. standardised on the mean value of 100 and 12 as genetic standard deviation units (i.e. 112 means +1 genetic standard deviation and 88 means −1 genetic standard deviation); moreover, the EBV sign is inverse, to have higher values as more desirable. Then, we matched target gene sequences against the complete genome of the selected bulls (Supplementary Table S1). Genome-wide sequence from each of the 10 bulls was uploaded to the Galaxy server at https://usegalaxy.eu (Version 2.3.4.3). Bowtie 2, a tool of the Galaxy software, was used to map reads of the 10 bulls to chromosome 5 for the IFN-γ and chromosome 18 for the TGF-β1 gene, after assessing the quality of the raw reads through Galaxy's FastQC tool. For IFN-γ and TGF-β1 genes, we selected the polymorphisms with a linkage disequilibrium below 85%. The nine selected SNPs in the four target genes (Table 1) were genotyped in our cohort of lactating cattle.
Table 1. Information on genetic variants of the CXCL8, CD4, IFN-γ and TGF-β1 genes in the Italian simmental breed
High-throughput SNP genotyping
The assay set used to genotype the nine SNPs was designed by D3 Assay Design, a service of Standard BioTools Inc., previously known as Fluidigm Corp. (South San Francisco, CA, USA).
Genotyping was performed in Dynamic Array Integrated Fluidic Circuits (IFCs) using Juno and Biomark™ HD systems (StandardBioTools Inc., ex Fluidigm Corp.), as described in the manufacturer's protocol. Briefly, after preamplification, 3 μL of Sample Mix and 3 μL of 10X Assay Mix were separately put into the IFC. Amplification was performed using Real Time program, with the following thermocycling protocol: hot start at 95°C for 5 min; touchdown for four cycles of 95°C for 15 s, 64°C to 61°C (decreasing by 1°C per cycle) for 45 s and 72°C for 15 s; additional PCR cycles of 34 cycles of 95°C for 15 s, 60°C for 45 s and 72°C for 15 s, cool at 25°C for 10 s. Fluorescence data collected from the Biomark™ HD system were analysed using Fluidigm SNP Genotyping Analysis software to classify the samples into three genotypes based on the fluorescence intensities of FAM and HEX.
Statistical analysis
On each SNP site, the χ2 test for deviation from Hardy-Weinberg equilibrium, along with expected and observed heterozygosity, polymorphism information content (PIC) and linkage disequilibrium (LD) were calculated using the algorithms provided by the SAS software 9.4 (ALLELE procedure).
The associations between SNPs and traits were performed using the general linear model (GLM procedure of SAS software 9.4):
\begin{equation*}{{{Y}}_{{{ij}}}} = \mu + {{G_i + }}{{{e}}_{{{ij}}}}\end{equation*}where Yij is the phenotypic value of the tested trait, μ is the overall population mean, Gi is the fixed effect of the genotype at the SNP (i = AA, AB, BB) and eij is the random residual effect.
To select which SNP to use among those with high linkage disequilibrium (LD > 80) a GLM analysis was performed on the Linear Score of milk differential somatic cells with the following model:
\begin{equation*}{{{Y}}_{{{ijkm}}}} = \mu + {{{G}}_{{i}}}{{ + }}{{{A}}_{{j}}}{{ + }}{{{L}}_{{k}}}{{ + }}{{{e}}_{{{ijkm}}}}\end{equation*}where Yijkm is the phenotypic value of the tested trait, μ is the overall population mean, Gi is the fixed effect of the genotype at the SNP (i = AA, AB, BB), Aj is the fixed effect of the farm (j = 1…53), Lk is the fixed effect of the number of controlled lactations (k = 3–6, 3–8, > 8) and eijkm is the random residual effect.
A GLM analysed the association between haplotypes and traits on each haplotype by inserting the number of copies as a fixed term:
\begin{equation*}{{{Y}}_{{{ij}}}} = \mu + {{{H}}_{{i}}}{{ + }}{{{e}}_{{{ij}}}}\end{equation*}where Yij is the phenotype, μ is the overall population mean for the trait, Hi is the fixed effect of the haplotype [i = 0, subject without haplotype; 1, subject with only one copy (heterozygote); 2, homozygous subject for that haplotype] and eij is the random error.
The statistical significance with a P-value < 0.05 of all traits and least-square means were determined by Tukey multiple test available in the GLM procedure.
Results
Whole-genome sequencing analysis of 10 bulls uploaded to the Galaxy server revealed an average of approximately 181 million raw reads per bull for both forward and reverse sequencing. The FastQC report showed that the quality of the sequences was good, as expressed by an average Phred Score of 38, where the minimum value for a good sequence is 20 (Kwong et al., Reference Kwong, McCallum, Sintchenko and Howden2015). Aligning the reads with the Bowtie2 tool, on the 6500 bp genomic sequence of the IFN-γ gene and 20,000 bp of the TGF-β1, showed an average of 500 and one million reads, respectively, for both forward and reverse sequencing. Only genomic variants with a Phred Quality Score > 40 were considered.
Table 1 presents the genomic data of the nine SNPs selected for association analysis with the breeding values in 558 cows from the Italian Simmental population. Only three out of the nine SNPs, CD4-7230, CD4-7191, and IFNG-646, result in an amino acid sequence change. All SNPs are informative markers with a PIC value between 0.13 and 0.37, except IFNG-231, which has a value of 0.09 (Supplementary Table S2).
The association analysis revealed that only five out of the nine selected markers were associated with the breeding values (Supplementary Table S3). Specifically, CD4-3720 showed a strong influence on SCS GEBV (P = 0.0009) and EDP (P = 0.002), as CD4-7230 with P = 0.01 and P = 0.02, respectively, alongside IFNG-231, which had a notable effect only on SCS GEBV (P = 0.04).
There are clear differences between heterozygous individuals and those with the most common homozygous genotype across all three SNPs. However, no differences were observed with the less frequent homozygous, likely due to their low frequency within the examined population.
Table 2 reports the association between these three SNPs and the productive breeding values. The presence of the T allele in both CD4 polymorphisms is associated with a significant improvement in the SCS breeding value. The two SNPs related to the CD4 gene show a linkage disequilibrium of 0.84. Considering the importance of this gene in neutrophil mobilisation, we chose the SNP for inclusion in the haplotype analysis based on its effect on the linear score of somatic cells and differential somatic cell parameters. The results shown in the Supplementary Table S4 highlight that CD4-3720 significantly influences both phenotypic traits.
Table 2. Association between the polymorphisms of Ifn-γ and CD4 genes and breeding values in simmental cattle
^ Within each trait, values with different superscript letters mean a significant difference (a, b = P < 0.05; a, b = P < 0.01 ÷ 0.001).
Four different haplotypes were identified using the two polymorphisms selected in CD4 and IFN-
$\gamma $ genes. The frequencies of these haplotypes are shown in the Supplementary Table S5. The analysis of the associations with breeding values was performed for haplotypes with a frequency > 1%. Cows with Haplotype-1 (A–G), the most frequent haplotype in our population, didn't present any significant differences in both indexes among the three animal groups (0, 1, and 2 copies of Haplotype-1). On the other hand, the presence of the other two, Haplotype-2 (A–A) and Haplotype-3-G) should be changed to 3 (T-G), both with a very low frequency (5 and 7%, respectively) caused a significant change in breeding values: the combination of alleles A-A in Haplotype-2 decreased SCS GEBV (P = 0.04) while Haplotype-3, containing allele T of CD4 markers, was associated with an improvement in SCS breeding values (P = 0.01), as shown in Table 3, without significantly affecting the other milk yield breeding values. Fig. 1 shows the difference between the averages of the cows with 0 (blue), 1 (orange) and 2 (green) copies of each haplotype, compared to the average value of the single index set equal to 100.
Figure 1. (A) Haplotype effect on SCS genomic breeding value (GEBV), and (B) haplotype effect on SCS deregressed conventional breeding value (EDP). Data are presented as the difference between the averages of the cows with 0 (blue), 1 (Orange) and 2 (green) copies of the single haplotype, compared to the average value of the single index set equal to 100 (*P < 0.05; **P < 0.01).
Table 3. Association between the functional haplotypes of CD4 and IFN-γ genes and breeding values in simmental cattle
^ Within each trait, values with different superscript letters mean a significant difference (a, b = P < 0.05; a, b = P < 0.01).
Both the genomic indices SCS GEBV (Fig. 1A) and SCS EDP (Fig. 1B) showed a large, though not significant, difference between the mean values of the homozygous cows for Haplotype-2 and Haplotype-3 (two copies of the single haplotype) compared to that of the cows in which it was not present (no copies of the single haplotype), most likely because the number of homozygous cows was very limited in our sample (two and three, respectively).
Table 4 shows the functional haplotype combinations after removing those with a frequency of less than 2%. This confirms the negative impact of Haplotype-2 and the positive impact of Haplotype-3. In particular, compared to the homozygous haplotypic combination for Haplotype-1, which does not determine significant differences in the deregressed EBVs and genomic breeding values of SCS, the presence of Haplotype-2 does not significantly reduce the GEBV (95.0) or EDP (94.3). In contrast, Haplotype-3 is associated with an improvement in the breeding values examined (GEBV = 103.4, P = 0.001; EDP = 110.5, P = 0.01). It should be emphasised that the reliability of genomic breeding values in most cases exceeds 60%. As to the productive breeding values examined, there was no significant difference in their quantities.
Table 4. Association between the functional haplotype combination and breeding values in Italian simmental cattle
^ Within each trait, values with different superscript letters mean a significant difference (a, b = P < 0.05; a, b = P < 0.01 ÷ 0.001).
Discussion
Mastitis impacts both animal welfare and the profitability and sustainability of dairy farms (Ablondi et al., Reference Ablondi, Summer, Stocco, Degano, Vicario, Stefanon, Sabbioni and Cipolat-Gotet2023). Breeding for animal health, longevity and fertility can indirectly reduce the environmental impact of dairy farming. Proper housing and management practices further enhance animal health by minimising the need for antibiotics, thereby increasing the lifespan and profitability of individual animals and benefiting the entire farm. Similarly, the improved longevity of cows contributes to higher profitability while also reducing greenhouse gas emissions per kilogram of milk produced. Optimising both the longevity of cows and the efficiency of milk production helps mitigate the environmental impact of dairy farming. This research focused on detecting gene polymorphisms related to the immune response in Italian Simmental lactating cows.
Previous studies have already highlighted the connection between deregressed multiple across-country evaluation EBV indexes and genomic variants of CD4 (Napolitano et al., Reference Napolitano, Grandoni, De Matteis, Degano, Vicario and Buttazzoni2021), as well as interleukin 8 and its receptor CXCR2 gene (De Matteis et al., Reference De Matteis, Grandoni, Signorelli, Degano, Vicario, Buttazzoni and Napolitano2022) in Simmental sires. This study examined the most intriguing SNPs located within coding or regulatory regions in a large group of lactating cows. Additionally, two other genes, TGF-β1 and IFN-γ, were included to provide a more comprehensive understanding of their involvement in the immune response to udder infection.
Results showed that the evaluated breeding values for mammary health were significantly influenced by three SNPs, two located in the CD4 and one in the IFN-γ genes. The crucial role of the CD4 gene in the immune response to pathogen-induced mastitis has been extensively reviewed by Rasheed et al. (Reference Rasheed, Ali, Niaz, Zeb, Khattak, Khan and Usman2020), highlighting the importance of CD4+ T cells in combating mastitis in dairy animals. The SNPs identified in the bovine CD4 gene have been associated with milk somatic cell count (SC) in the Holstein breed (Banos et al., Reference Banos, Wall, Coffey, Bagnall, Gillespie, Russell and McNeilly2013). Our findings confirm this association in the Italian Simmental population.
Schoenborn and Wilson (Reference Schoenborn and Wilson2007) analysed the regulation of the cytokine Interferon-γ during innate and adaptative immunity. It can directly inhibit viral replication and has important immunostimulatory and immunomodulatory effects. It also regulates the differentiation of naïve CD4+ T cells into Th1 effectors, responsible for cellular immunity against viral and intracellular bacterial infections. In response to antigen stimulation, T helper cells differentiate into distinct lineages defined by the cytokines they produce in response to secondary antigen challenge.
The IFN-γ gene has not been extensively studied in animal genomics, despite its known ability to induce resistance to viruses and pathogens such as Mycobacterium avium, Neospora caninum, and Brucella. To our knowledge, there have been only three studies on this topic: Pant et al. (Reference Pant, Verschoor, Skelding, Schenkel, You, Biggar, Kelton and Karrow2011), Verschoor et al. (Reference Verschoor, Pant, Biggar, Schenkel, Sharma and Karrow2011), and Gopi et al. (Reference Gopi, Vir Singh, Kumar, Kumar, Chauhan, Sonwane, Kumar, Bharati and Vir Singh2022), that examined the potential impact of the c.-639 T > C (rs111005721) and c.432 G > A (rs110853455) variants on paratuberculosis and somatic cell score. The c.-639 variant was hypothesized to be present in a hypothetical promoter/5'UTR but not found in our samples. The c.432 variant is a synonymous polymorphism located in exon 4 and we detected it in our analysed cows (IFNG-673). In our current study, we have identified two variants (IFNG-231 and 298) in the terminal part of the 3'UTR region of the IFN-
$\gamma $ gene. As reported by Eskandari-Nasab et al. (Reference Eskandari-Nasab, Moghadampour, Hasani, Hadadi-fishani, Mirghanizadeh-Bafghi, Asadi-Saghandi, Zare, Sadeghi-Kalani and Ghazali-bina2013) the 3' UTR can control gene expression for various processes including nuclear export, polyadenylation, translation efficiency and mRNA degradation. Within the context of human brucellosis, Davoudi et al. (Reference Davoudi, Amirzargar, Hajiabdolbaghi, Rasoolinejad, Soodbakhsh, Jafari, Piri, Maleknejad, Bagherian, Madadi and Nikbin2006) have suggested that a polymorphism in the 3'UTR of the IFN-γ gene increases the production of IFN-γ. Therefore, it would be appropriate to conduct further research on the structural aspects of the protein produced by IFN-γ concerning the different types of mRNA and to evaluate its impact both in association with other genes involved in the genetic determinism of immune traits and by verifying the interactions with epigenetic factors.
The simultaneous impact of the IFN-γ and CD4 genes on mammary gland health may not be coincidental. The haplotypic approach allows us to gain a systemic view of the probable role of CD4 and IFN-γ genes in determining livestock traits. By combining the SNP alleles of the two genes into functional haplotypes of bovine chromosome 5, the genetic effect takes into account both the additive effects between the individual genes and the epistatic interactions between them on the analysed livestock traits. Moreover, the haplotypic combinations examined did not significantly influence the production traits in our bovine population.
Selecting the best animals in animal husbandry has always been crucial for farmers. In the past, the primary breeding goal was to increase milk yield and enhance its protein and fat content. However today the focus has shifted. The key aspect of livestock production is now the quality and sustainability of the product, not only in nutritional terms but especially regarding its healthiness. Furthermore, the production and consumption of animal products must adhere to higher standards of animal welfare while also prioritising the protection of animals, environmental sustainability, and public health. Environmental sustainability is now a crucial requirement for all types of companies and production, particularly within the food supply chain for animal products. Farmers are aware of the costs associated with medications and the potential loss of income from the inability to market their products, particularly when their animals are frequently affected by diseases like mastitis. This has become increasingly significant for consumers in recent years, who are demonstrating a growing concern for animal welfare and, to a lesser extent, sustainability. Improving the health and animal welfare of cattle through direct genetic selection offers dairy producers a valuable opportunity to manage disease incidence and enhance the profitability of their herds. (Gonzalez-Peña et al., Reference Gonzalez-Peña, Vukasinovic, Brooker, Przybyla, Baktula and DeNise2020).
Breeding tools like animal welfare indices (GEBV and EDP of SCS) offer early indicators of animals less susceptible to infections. Therefore, they could be used as genetic markers in future breeding programmes. Our research has led to the identification of a combination of polymorphisms (CD4-3720 and IFNG-231) in genes associated with welfare and health parameters, making them promising candidates for genetic selection. For example, it should be possible to increase the frequency of Haplotype-3 in the population if the selection goals included health improvement. Furthermore, several authors (Pritchard et al., Reference Pritchard, Coffey, Mrode and Wall2013; Martin et al., Reference Martin, Barkema, Brito, Narayana and Miglior2018) have reported that selecting mastitis resistance can also improve resistance to other diseases and enhance both fertility and longevity.
In conclusion, within the scope of our sample, this study highlighted how the haplotypic approach provides valuable insights into the role of the CD4 and IFNG genes in the productivity and functionality of the Simmental breed. Additionally, these findings could be applied to both cosmopolitan and local dairy cattle breeds to improve health outcomes through genetic selection.
Supplementary material
The supplementary material for this article can be found at https://doi.org/10.1017/S0022029925101404.
Acknowledgements
We gratefully acknowledge the Associazione Nazionale Allevatori Bovini di Razza Pezzata Rossa (ANAPRI) for providing the breeding values of the animals considered in this study. The authors are grateful to Dr Caterina Marè, dr. Delfina Barabaschi, and dr. Stefano Delbono from the Centre for Genomics and Bioinformatics (CREA-GB) for their scientific support in High-throughput SNP Genotyping. This study was conducted within the framework of the DUAL BREEDING project (grant no. J21J18000010005).
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