ABSTRACT
Background and Aim: Kalmyk cattle represent a resilient indigenous beef breed of Russia, valued for their exceptional adaptation to harsh continental climates and growing importance in sustainable beef production. Despite their economic and ecological relevance, genetic determinants underlying meat productivity and quality in this breed remain fragmented across largely regional studies. This systematic review aimed to synthesize available evidence on polymorphisms in four major candidate genes, growth hormone (
Materials and Methods: The review was conducted in accordance with PRISMA 2020 guidelines. A comprehensive literature search covering January 2004 to December 2024 was performed using international (PubMed, Scopus, Google Scholar) and Russian (eLibrary.ru) databases. Eligible studies included peer-reviewed articles, dissertations, and conference proceedings reporting primary genotyping data for
Results: The synthesis revealed pronounced inter-herd and regional heterogeneity in the frequency of favorable alleles. The
Conclusion: Kalmyk cattle exhibit marked genetic heterogeneity for key meat productivity and quality markers, reflecting founder effects, localized selection, and breeding history. While
Keywords: beef cattle genetics,
INTRODUCTION
The Kalmyk cattle (
In the context of climate change, the inherent heat and drought tolerance of Kalmyk cattle [3, 4] elevates the breed from a regional asset to a genetic resource of global relevance. Strategic utilization of this resilience is critical for the development of sustainable beef production systems in arid and warming regions, thereby contributing to long-term food security. As of January 1, 2024, the breeding stock of beef cattle in the Russian Federation comprised 308.6 thousand head, of which Kalmyk cattle represented the largest proportion (33.8%), underscoring their pivotal role in the national livestock sector [5]. According to the All-Russian Research Institute of Breeding, approximately 50% of the Kalmyk cattle population is concentrated in the Republic of Kalmykia. Additional major populations occur in the Far Eastern Federal District, mainly in the Republic of Buryatia and the Trans-Baikal Territory (29.2%), and in the North Caucasus Federal District (27.2%), including the Republics of Dagestan, Kabardino-Balkaria, North Ossetia–Alania, and the Stavropol region [6].
Kalmyk cattle possess a robust constitution and pronounced homeostatic capacity, enabling stable physiological function under diverse and often extreme environmental conditions. The breed exhibits high endurance and disease resistance, with notable tolerance to thermal and nutritional stressors, traits attributed to factors such as skin and hair coat lability and localized adipose tissue distribution [7]. High resistance to infectious and non-infectious diseases, including tuberculosis, brucellosis, leukemia, and acute respiratory and intestinal disorders, has been documented [8]. Successful acclimatization and reproductive performance in northern regions of Russia, including the Republic of Sakha (Yakutia), further demonstrate their adaptive potential [9]. Genetic determinants likely underlie this innate plasticity. A genomic region on chromosome 16 (4,116,037–4,616,037 bp) containing six immune-related genes (
Beyond adaptability, Kalmyk cattle are valued for meat quality. Analyses of the longissimus dorsi muscle indicate intramuscular fat (marbling) levels ranging from 1.6% to 5.9%, showing a strong correlation with marbling scores (r = 0.97). Although total protein content in Kalmyk bull meat is marginally lower than that of Aberdeen-Angus (by 0.55%–1.95%), the protein quality index is reportedly superior [11]. Conservation and enhancement of genetic diversity are therefore essential for sustainable livestock production. Advances in molecular genetics, particularly genomic selection, have enabled precise identification and selection of animals carrying favorable alleles, resulting in measurable improvements in meat productivity and quality traits [12]. Key candidate genes associated with growth, feed efficiency, and meat quality in beef cattle include leptin (
Despite its importance, Kalmyk cattle have been investigated at the molecular level far less extensively than widely used commercial breeds. Available evidence indicates substantial genetic diversity within the breed, supported by inter-simple sequence repeat analyses [26] and microsatellite studies reporting 7–18 alleles per locus across nine markers [27]. Whole-genome single-nucleotide polymorphism analyses further revealed the presence of unique allelic combinations characteristic of indigenous, well-adapted breeds [28]. Additional studies from Russia have reinforced evidence of strong adaptive capacity and resistance to local diseases and extreme climatic conditions [29, 30].
Notwithstanding extensive zootechnical and population genetic information, a critical gap persists in the systematic synthesis of data on key polymorphisms influencing meat productivity in Kalmyk cattle. Numerous Russian-language studies have genotyped individual herds for genes such as
Despite the recognized importance of Kalmyk cattle as a resilient beef breed, current knowledge on the genetic basis of meat productivity and quality remains fragmented. Existing studies on
The aim of this systematic review was to comprehensively synthesize and critically evaluate published evidence on polymorphisms in
MATERIALS AND METHODS
Ethical approval
Ethical approval was not required because this study was based exclusively on the analysis of previously published literature and did not involve live animals, biological sampling, or experimental interventions.
Study period and location
This review included a comprehensive search and analysis conducted from July 7 through August 8, 2025, in Elista (Russia).
Review design and reporting standards
This study was designed as a systematic review to synthesize available evidence on genetic polymorphisms associated with meat productivity and quality in Kalmyk cattle. The review was conducted and reported in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 guidelines for systematic reviews [35].
Protocol development and registration
A review protocol was developed a priori to define the objectives, eligibility criteria, search strategy, and analytical framework. However, the protocol was not registered in an international prospective registry. This was due to the retrospective consolidation of regionally published and non-indexed literature, particularly Russian-language sources. All methodological steps were predefined and applied consistently throughout the review.
Review question and analytical framework
The review question was formulated using a PECO framework. The population of interest comprised Kalmyk cattle. The exposure was genetic polymorphisms in candidate genes associated with meat productivity and quality, specifically
Literature search strategy
A systematic literature search was conducted to identify all relevant publications addressing genetic polymorphisms in Kalmyk cattle. The search covered the period from January 2004 to December 2024, with the final search completed in December 2024. Electronic searches were performed using international databases (PubMed, Scopus, and Google Scholar) and the Russian scientific database eLibrary.ru.
Search strategies combined Boolean operators (AND, OR) with general keywords and gene-specific terms in both English and Russian. Core search terms included “Kalmyk cattle” AND (“genetic polymorphism” OR SNP OR “molecular marker”). These were further refined using gene-specific combinations, including (“GH” OR “growth hormone”) AND “Kalmyk cattle” AND “Russia”, (“LEP” OR “leptin”) AND “beef cattle” AND (polymorphism OR genotype), and (“CAPN1” OR “calpain”) AND “meat tenderness” AND cattle. Database-specific syntax was adapted as required, and searches were conducted across title, abstract, and keyword fields.
Publications written in English and Russian were eligible. Reference lists of all included articles and relevant reviews were manually screened to identify additional eligible studies and minimize publication bias.
Language and gray literature inclusion
No language restrictions were applied. English- and Russian-language publications were screened and extracted using identical eligibility criteria and data extraction procedures. Gray literature, including dissertations and conference proceedings indexed in eLibrary.ru, was eligible when full texts and primary genotyping data were available. Google Scholar results were screened systematically by relevance.
Eligibility criteria
Studies were eligible for inclusion if they met all of the following criteria: (i) involved purebred or crossbred first filial (F1) or second filial (F2) Kalmyk cattle, (ii) reported polymorphisms, primarily single-nucleotide polymorphisms (SNPs), in
Studies were excluded if they lacked primary genotyping data, focused exclusively on other cattle breeds without direct comparative data for Kalmyk cattle, or were available only as abstracts or summaries with insufficient information for data extraction. Non-genetic studies addressing nutrition, reproduction, or morphology alone were excluded. In cases of duplicate publications, only the most complete and most recent version was retained.
Study selection process
Study selection followed a multistage process consistent with PRISMA 2020 recommendations. All retrieved records were imported into a reference management system, and duplicates were removed. Titles and abstracts were screened for relevance based on eligibility criteria, followed by full-text evaluation of potentially relevant articles. The study selection process is summarized in the PRISMA flow diagram (Figure 1).
Figure 1. Preferred Reporting Items for Systematic Reviews and Meta-Analyses flow diagram illustrating the identification, screening, eligibility assessment, and inclusion of studies reporting genetic analyses of
Reviewer roles and data extraction
Study screening and data extraction were performed independently by two reviewers. Discrepancies during screening or extraction were resolved through discussion and consensus, with re-evaluation of the original publication when necessary.
Data were extracted using a predefined framework, including sample size, year of publication, geographic region and farm location, breed composition, genotyping methods, investigated polymorphisms, allele and genotype frequencies, reported genotype–phenotype associations, and applied statistical analyses (e.g., χ² test, Hardy–Weinberg equilibrium), where available. When genotype counts were reported without allele frequencies, allele frequencies were calculated.
Quality assessment and risk of bias
Methodological quality was assessed using predefined criteria adapted for genetic association studies. Studies were evaluated for clarity in reporting animal characteristics (breed, age, sex, sample size), appropriateness and transparency of genotyping methods, completeness of allele or genotype frequency reporting, and internal consistency of results. Studies lacking critical methodological information were considered to have a higher risk of bias and were interpreted cautiously.
Handling of population structure and heterogeneity
Substantial heterogeneity among studies was anticipated due to differences in herd structure, regional breeding practices, sample sizes, and genetic backgrounds. Population stratification, founder effects, and localized selection pressures were therefore considered during interpretation. No assumptions of population homogeneity were made across studies.
Data synthesis and analytical approach
Due to heterogeneity in study design, genotyping platforms, investigated polymorphisms, and reported outcomes, quantitative meta-analysis was not feasible. A narrative synthesis approach was therefore applied a priori. Allele and genotype frequency distributions were qualitatively compared across studies, herds, and regions to identify trends, patterns, and pronounced differences in the prevalence of economically important alleles. Results were organized by gene and discussed thematically.
Assessment of publication bias and certainty of evidence
Formal quantitative assessment of publication bias was not conducted because of the narrative synthesis and heterogeneous reporting formats. Potential publication bias was considered qualitatively, particularly selective reporting of significant associations in small or region-specific studies. The overall certainty of evidence was evaluated qualitatively based on sample size, consistency of findings, and methodological quality.
Methodological limitations
Several limitations were identified across the included literature. Many studies involved small sample sizes and uneven geographic representation. A strong reliance on PCR-RFLP and other low-throughput methods was observed, with limited use of whole-genome sequencing or high-density SNP platforms. Inclusion of a substantial number of Russian-language publications was essential for regional coverage but may introduce language and publication bias. Finally, heterogeneity among studies necessitated narrative synthesis, which should be considered when interpreting the findings.
RESULTS AND DISCUSSION
Distribution of favorable alleles for economic traits in the population
An investigation into the allele frequencies of genes linked to economically advantageous traits in the Kalmyk cattle breed revealed substantial variations in the distribution of favorable genotypes across diverse populations and farms. Table 1 presents a summary of the genes controlling economically valuable traits in Kalmyk cattle and their frequencies [36–47].
Table 1. Summary of the genes controlling economically valuable traits in Kalmyk cattle.
| Trait | Gene | Chr: bp (alleles) | SNP ID | Polymorphism (favorable allele) | Region | Farm | n | Breed/ Generation | Research method | Frequency of desirable genotype (%) | Ref. |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Live weight gain increase |
| 19:48118256 C>G | rs41923484 | c.2141C>G (G) | Republic of Kalmykia | LLC BR “Agrofirma Aduchi”, Tselinny district | 46 | Crossbred (F2) | PCR-RFLP | 2.2 | 36 |
|
| 19:48118256 C>G | rs41923484 | c.2141C>G (G) | Republic of Kalmykia | LLC BR “Agrofirma Aduchi”, Tselinny district | 112 | Purebred | PCR-RFLP | 19.7 | 37 | |
|
| 19:48118256 C>G | rs41923484 | c.2141C>G (G) | Republic of Kalmykia | JSC BF named after A. Chapchaev, Ketchenerovsky district | 60 | Purebred | PCR-RFLP | 78.3 | 38 | |
|
| 19:48118256 C>G | rs41923484 | c.2141C>G (G) | Republic of Kalmykia | JSC BF Kirovsky, Ketchenerovsky district | 100 | Purebred | PCR-RFLP | 2.0 | 39 | |
|
| 19:48118256 C>G | rs41923484 | c.2141C>G (G) | Stavropol region | APC BF “Sofievsky” | 16 | Purebred | Real-time PCR | 0 | 40 | |
|
| 19:48118256 C>G | rs41923484 | c.2141C>G (G) | Stavropol region | APC BF “Druzhba” | 40 | Purebred | PCR-RFLP | 0 | 41 | |
|
| 19:48118256 C>G | rs41923484 | c.2141C>G (G) | Stavropol region | Breeding farms | 96 | Purebred | PCR-RFLP | 42 | 42 | |
|
| 19:48118256 C>G | rs41923484 | c.2141C>G (G) | Stavropol region | Breeding farms | 46 | Purebred | PCR-RFLP | 2.0 | 43 | |
|
| 19:48118256 C>G | rs41923484 | c.2141C>G (G) | Stavropol region | APC BF “Druzhba” | 156 | Purebred | PCR-RFLP | 25 | 44 | |
| Meat marbling |
| 14:8453776 G>A | rs135751032 | c.-422C>T (T) | Republic of Kalmykia | LLC BR “Agrofirma Aduchi”, Tselinny district | 46 | Crossbred (F2) | PCR-RFLP | 6.5 | 36 |
|
| 14:8453776 G>A | rs135751032 | c.-422C>T (T) | Republic of Kalmykia | LLC BR “Agrofirma Aduchi”, Tselinny district | 112 | Purebred | PCR-RFLP | 13.4 | 37 | |
|
| 14:8453776 G>A | rs135751032 | c.-422C>T (T) | Stavropol region | APC BF “Druzhba” | 40 | Purebred | PCR-RFLP | 12.5 | 40 | |
|
| 14:8453776 G>A | rs135751032 | c.-422C>T (T) | Stavropol region | Breeding farms | 96 | Purebred | PCR-RFLP | 4.0 | 42 | |
| Fat accumulation, body weight, linear growth |
| 4:92451008 C>T | rs29004508 | c.314C>T (T) | Republic of Kalmykia | LLC PR “Agrofirma Aduchi”, Tselinny district | 112 | Purebred | PCR-RFLP | 49.1 | 37 |
|
| 4:92451008 C>T | rs29004508 | c.314C>T (T) | Stavropol region | Breeding farms | 96 | Purebred | PCR-RFLP | 1.0 | 42 | |
|
| 4:92451008 C>T | rs29004508 | c.314C>T (T) | Stavropol region | Breeding farms | 46 | Purebred | PCR-RFLP | 76 | 43 | |
|
| 4:92449032 A>T | rs29004487 | c.95A>T (T) | Stavropol region | Breeding farms | 46 | Purebred | PCR-RFLP | 83 | 43 | |
|
| 20:4543301 A>G | rs474894316 | c.73T>C (C) | Stavropol region | APC BF “Druzhba” | 156 | Purebred | PCR-RFLP | 29 | 45 | |
| Meat marbling and tenderness |
| 29:43405875 C>G | rs17872000 | c.4568G>C (C) | Republic of Kalmykia | LLC BR “Agrofirma Aduchi”, Tselinny district | 46 | Crossbred (F2) | PCR-RFLP | 6.5 | 36 |
|
| 29:43405875 C>G | rs17872000 | c.4568G>C (C) | Republic of Kalmykia | Breeding farms | 100 | Purebred | Real-time PCR | 20 | 39 | |
|
| 29:43405875 C>G | rs17872000 | c.4568G>C (C) | Stavropol region | APC BF “Sofievsky” | 16 | Purebred | Real-time PCR | 6 | 41 | |
|
| 29:43405875 C>G | rs17872000 | c.4568G>C (C) | Stavropol region | Breeding farms | 96 | Purebred | Real-time PCR | 3 | 42 | |
|
| 29:43405875 C>G | rs17872000 | c.4568G>C (C) | Stavropol region | Breeding farms | 46 | Purebred | Real-time PCR | 6 | 43 | |
|
| 29:43405875 C>G | rs17872000 | c.4568G>C (C) | Stavropol region | APC BF “Sofievsky” | 42 | Purebred | Real-time PCR | 5 | 46 | |
|
| 29:43405875 C>G | rs17872000 | c.4568G>C (C) | Orenburg region | Breeding farms | 122 | Purebred | Real-time PCR | 19.7 | 47 |
CAPN1 = Calpain-1 gene, GH = Growth hormone gene, LEP = Leptin gene, TG = Thyroglobulin gene, APC BF = Agricultural production cooperation breeding farm, Chr = Chromosome, F2 = Second filial, JSC BF = Joint-stock company breeding farm, LLC BR = Limited liability breeding reproducer, PCR = Polymerase chain reaction, PCR-RFLP = Polymerase chain reaction–restriction fragment length polymorphism, RFLP = Restriction fragment length polymorphism, SNP = Single-nucleotide polymorphism.
As illustrated in Figure 2, this review synthesizes key genes and their associated polymorphisms reported in Kalmyk cattle. For the
Figure 2. Diagram of the genes and traits discussed in the review, illustrating the relationships between growth hormone, thyroglobulin, leptin, and calpain 1 and their associated production and meat quality traits.
With regard to the influence of genes on meat quality traits, the distribution of favorable alleles exhibited significant heterogeneity. For the rs135751032, c.422C>T in the
The analysis of the
The
This review provides the first integrated genetic landscape of meat quality in Kalmyk cattle, visualized as a comparative heatmap (Figure 3). The analysis qualitatively contrasts genotype frequency distributions across three major breeding regions for Kalmyk cattle: the Republic of Kalmykia, Stavropol, and Orenburg.
Figure 3. Heatmap of desirable genotype frequencies by region and gene, illustrating regional variation in polymorphisms of growth hormone, thyroglobulin, leptin, and calpain
The previously unreported stratification of allele frequencies between herds and different regions of Russia is of significant scientific value. The analysis demonstrates that Kalmyk cattle are genetically heterogeneous, indicating the potential influence of founder effects, genetic drift, and local selection pressure.
GH gene
Biological role and gene structure
Given that the polymorphisms in the four genes described above are considered to be the most associated with economically advantageous traits, each gene and its SNPs should be considered in more detail.
Major polymorphisms and functional variants
Numerous polymorphic variants have been identified within the
Single-nucleotide polymorphisms associated with economic traits
Extensive research has identified numerous other polymorphisms associated with key economic traits within
Distribution of polymorphisms within the GH gene
A total of 63 single-nucleotide polymorphisms (SNPs) have been identified in the bovine
Application in marker-assisted selection
The identification of polymorphisms, such as c.2141C>G, is a valuable tool for marker-assisted selection, allowing for the prediction of productivity potential and meat amino acid composition [62]. Research on Hereford bull calves has demonstrated that the
GH polymorphism variability in Kalmyk cattle populations
The genetic composition of the Kalmyk cattle population with respect to the
Comparative analyses across breeds and regions
A comprehensive comparative analysis of the
Low-frequency alleles and inter-herd heterogeneity
However, other studies have reported a significantly lower V allele prevalence within specific Kalmyk subpopulations. An analysis of animals from two breeding farms in Kalmykia, SPK Plodovitoye and NAO Kirovsky, found a VV genotype frequency of only 2% at both locations [39]. Despite its low frequency, VV homozygous bulls exhibited the highest live weight at 16 months, with a notable difference of 7 kg between farms, confirming a reliable association between genotype and live weight [39]. This finding of the extreme rarity of the V allele is corroborated by several other studies. Research conducted in the Stavropol Territory revealed the complete absence of the V allele, which is considered desirable, in Kalmyk cattle [40]. This finding is consistent with the results of earlier genotyping of breeding bulls, which showed a 94% prevalence of the LL genotype [41]. A subsequent investigation documented a higher frequency of the VV genotype in the Kalmyk breed, such as 42% [42]. However, a recent analysis from Stavropol breeding farms substantiated the low proportion, as only 2% of Kalmyk animals were homozygous for VV. Notably, a considerable proportion of patients were found to be heterozygous LV (43%) [43]. Another study investigating the
Summary and transition to subsequent gene analysis
Thus,
TG gene
Biological role and functional relevance
TG is a high-molecular-weight glycoprotein synthesized in the follicular cells of the thyroid gland. It functions as a pivotal precursor and carrier molecule in the synthesis of the thyroid hormones triiodothyronine and thyroxine. These hormones play a critical role in regulating metabolism and influencing the tendency of tissues to accumulate fat [64]. The bovine
Key polymorphisms associated with meat quality traits
The TG polymorphism X05380.1:g.-422C>T, identified in the 5′-UTR of the
In addition to the c.-422C>T polymorphism, further variations in this region have been identified. A thorough review of extant Chinese studies revealed four novel SNPs (c.275A>G, c.277C>G, c.280A>G, and c.281G>C) in the 5′-flanking region of the
Distribution of favorable genotypes in Kalmyk cattle
An investigation into the genetic composition of Kalmyk cattle in relation to the
Comparative breed and regional variability
However, comparative analyses across beef breeds demonstrated considerable variation in TT frequency, underscoring population-specific differences. An investigation of genotype frequency among cattle in the Stavropol region revealed the highest prevalence of the TT genotype in Kalmyk cattle (12.5%), based on a sample of 40 animals [40]. This frequency exceeded that observed in the Kazakh Whiteheaded, Hereford, Aberdeen-Angus, and Simmental breeds at 6.3%, 0%, 3.03%, and 7.7%, respectively, with sample sizes of 16, 37, 33, and 39 animals, respectively [40].
Conversely, a separate study of herds in the same region reported a substantially lower frequency of this genotype in the Kalmyk breed, with a frequency of 4% from a sample of 96 animals [42]. This result placed it below the frequencies reported for Hereford cattle at 16% and Kazakh Whiteheaded cattle at 6% [42]. This pronounced discrepancy in reported genotype frequencies is likely attributable to factors such as geographical isolation, distinct breeding objectives and histories, and genetic drift within different subpopulations of the same breed.
Interpretation and limitations
In contrast to the
LEP gene
Biological role and gene structure
The
Functionally important polymorphisms
The polymorphism LEP c.466C>T, also known as LEP73, R4C, and R25C, is of particular interest. It is located 73 base pairs from the start of exon 2 and causes an amino acid substitution of arginine for cysteine at position 4 of the mature leptin protein [71]. The T allele of this single-nucleotide polymorphism is associated with increased leptin messenger ribonucleic acid expression. This directly influences feeding behavior, determines the highest feed efficiency, and consequently results in the formation of carcasses with high fat content. In contrast, the C allele has been linked to reduced carcass fat deposition, leading to leaner carcasses in animals carrying this genotype [72].
The LEP c.528T>C polymorphism is located in the 5′ UTR and has been shown to have a positive influence on marbling and meat yield in beef cattle. Additionally, the study describes the LEP c.73 polymorphism T>C, which is located in exon 2, has been shown to have a positive effect on adipose tissue formation and carcass quality [51, 73, 74].
Polymorphism c.95A>T, also known as Y7F, is located in the coding part of the
Significant associations between novel exonic SNPs and enhanced carcass traits in Chinese Simmental cattle have been identified. These SNPs, specifically 169T>C and 299T>A in exon 2 and exon 3, respectively, have been shown to impact characteristics such as slaughter weight, marbling score, and intramuscular fat [78]. A subsequent review of polymorphisms associated with beef traits emphasized the role of LEP SNPs in meat flavor, fat thickness, and meat yield [79].
Evidence from Kalmyk cattle populations
The most comprehensive genotyping study in Kalmyk cattle (n = 156) identified c.73T>C as a key LEP polymorphism [45]. Allele frequencies for c.73T>C were C: 0.52 and T: 0.48. Robust association analyses were conducted: individuals heterozygous CT or homozygous CC for the c.73T>C polymorphism exhibited a 7.3% and 9.5% greater live weight, respectively, than TT homozygous counterparts [45].
The genetic architecture of the LEP locus in Kalmyk cattle demonstrates considerable population-specific variation, reflecting diverse breeding histories and selection pressures. An investigation of a novel high-productivity crossbreed beef type of Kalmyk and Aberdeen-Angus in the Republic of Kalmykia revealed a high prevalence of the TT genotype for the c.314C>T polymorphism; therefore, 49.1% out of 112 animals were TT homozygous, suggesting that this allele was selected for active or inadvertent selection during herd formation [37]. The polymorphism is frequently characterized by an amino acid change at position A80V, resulting in the following genotypes: AA, AV, and VV.
The highest frequency of reliable genotypes was observed in the Kalmyk cattle population from the Stavropol region, with a sample size of 46. In this group, 79% of the animals possessed the AA genotype at the c.314C>T locus, whereas 83% were homozygous for the YY genotype at the c.95A>T position [43]. In stark contrast, a study of 96 Kalmyk animals from the Stavropol region reported a markedly low frequency, with only 1% having a desirable LEP TT haplotype at position 140 of exon 3, which was considerably more common in contemporary Hereford (12%) and Kazakh Whiteheaded (10%) populations from the same region [42]. This discrepancy underscores the presence of significant genetic stratification within the Kalmyk breed, likely attributable to geographical isolation and divergent breeding objectives.
Interpretation and implications
These findings provide compelling evidence of a significant association between specific LEP alleles and growth traits in Kalmyk cattle. However, the contradictory results regarding allele frequencies across studies highlight the lack of population homogeneity and preclude a definitive consensus. Consequently, while the LEP gene represents a promising candidate for marker-assisted selection in Kalmyk cattle, further validation in larger, well-defined cohorts is essential to confirm its utility and to account for population substructure, genotype-by-environment interactions, and potential epistatic effects before its implementation in breeding programs.
CAPN1 gene
Structure and functional relevance
The
Key polymorphisms associated with meat tenderness
Several SNPs within
Functional significance of the c.4568G>C (rs17872000) polymorphism
Within the coding region of
The biological mechanism underlying this association involves the role of μ-calpain in post-mortem degradation of myofibrillar proteins, particularly Z-disks, leading to weakening of sarcomere structures and enhanced tenderization [89–91].
Distribution of CAPN1 polymorphisms in Kalmyk cattle
Investigations into the
Inter-herd variation and regional stratification
Pronounced subpopulation stratification has been identified within the Kalmyk breed. A comparative analysis of two breeding farms in Kalmykia, SPK Plodovitoye and NAO Kirovsky, revealed higher frequencies of the desirable c.4568G>C CC genotype at SPK Plodovitoye compared with NAO Kirovsky (22% vs. 18%). Bulls from SPK Plodovitoye carrying the favorable genotype also exhibited a higher live weight at 16 months (489.3 kg), exceeding those from NAO Kirovsky by 7.1 kg, suggesting a potential pleiotropic effect or linked selection for growth traits [39]. In the Orenburg region, a relatively higher CC genotype frequency (19.7%) was reported among 122 Kalmyk cattle, exceeding that observed in other local breeds [47], highlighting the influence of regional breeding strategies on allele distribution.
Implications for marker-assisted selection
In conclusion, while
CONCLUSION
This systematic review provides an integrated synthesis of available evidence on genetic polymorphisms influencing meat productivity and quality in Kalmyk cattle. Across studies, substantial inter-herd and inter-regional variability was consistently observed for favorable genotypes of
The pronounced variability in allele and genotype frequencies across regions indicates that uniform breeding strategies are unlikely to be effective. Instead, region-specific and herd-level selection programs incorporating
A major strength of this review lies in the systematic consolidation of fragmented literature, including region-specific studies that are often excluded from global syntheses. By integrating genotype distributions, association results, and regional comparisons, this review provides the first coherent genetic landscape of meat productivity traits in Kalmyk cattle. The gene-focused synthesis enables direct comparison of evidence across studies while maintaining biological and breeding relevance.
Several limitations should be considered when interpreting the findings. Many studies were characterized by small sample sizes and uneven geographic representation, which may limit generalizability. The predominance of low-throughput genotyping methods restricted haplotype-level and genome-wide inference. In addition, heterogeneity in study design, phenotypic measurements, and statistical approaches precluded quantitative meta-analysis, necessitating a narrative synthesis. Population stratification and genotype-by-environment interactions were not consistently accounted for across studies.
Future research should prioritize large-scale, well-designed studies incorporating high-throughput genotyping and whole-genome approaches to validate candidate polymorphisms and identify novel loci. Integrating genomic data with detailed phenotypic, environmental, and management information will be essential to disentangle population structure effects and improve predictive accuracy. Longitudinal studies assessing correlated responses to selection and gene–environment interactions would further support the sustainable application of marker-assisted and genomic selection in Kalmyk cattle.
In conclusion,
DATA AVAILABILITY
All data analyzed in this review were derived from previously published studies. No new datasets were generated. Extracted data are presented within the article tables and figures.
AUTHORS’ CONTRIBUTIONS
NC and ZB: Designated and conducted the study and drafted and edited the manuscript. AU and CU: participated in data collection and discussion. All authors have read and approved the final version of the manuscript.
COMPETING INTERESTS
The authors declare that they have no competing interests.
PUBLISHER’S NOTE
Veterinary World remains neutral with regard to jurisdictional claims in the published institutional affiliations.
ACKNOWLEDGMENTS
The authors would like to express their gratitude to the Russian Federation’s Ministry of Science and Higher Education for funding the research through project number 075-03-2025-420/3 and their colleague Danzan Mashtykov for helping with preparing Figures 1 and 2.
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