employee from 01.01.2020 to 01.01.2025
employee from 01.01.1985 to 01.01.2025
employee from 01.01.1980 to 01.01.2025
employee from 01.01.2018 to 01.01.2025
graduate student from 01.01.2025 to 01.01.2025
graduate student from 01.01.2025 to 01.01.2025
VAK Russia 4.1.1
VAK Russia 4.1.2
VAK Russia 4.1.3
VAK Russia 4.1.4
VAK Russia 4.1.5
VAK Russia 4.2.1
VAK Russia 4.2.2
VAK Russia 4.2.3
VAK Russia 4.2.4
VAK Russia 4.3.3
VAK Russia 4.3.5
UDC 636.234.1
UDC 636.082
The aim of the study is to identify genetic markers associated with dairy productivity parameters (milk yield, milk fat and protein content) in Holstein cattle using the genome-wide association analysis (GWAS) method on a sample of animals from breeding farms in the Sverdlovsk Region for 2018–2024. Objectives: to genotype a sample of cows using DNA bioarrays and carry out data quality control; to perform GWAS analysis of dairy productivity on the full population and on extreme phenotypic groups; to identify statistically significant single nucleotide polymorphisms (SNPs) associated with milk yield, fat and protein content; to analyze genes located near significant SNPs to understand their role in lipid metabolism, immune response and mammary tissue development; to evaluate the coincidence of genetic associations between the full sample and extreme groups to confirm the reliability of markers. GWAS analysis of milk productivity was conducted on 539 Holstein cows from 3 breeding farms in the Sverdlovsk Region (2018–2024). Milk yield, fat, and protein were analyzed on the full sample and at the extreme quartiles Q1/Q4 (milk yield: (10,028 ± 680) and (6,775 ± 653) kg; fat: (4.07 ± 0.068) and (3.79 ± 0.092) %; protein: (3.38 ± 0.066) and (3.12 ± 0.045) %). A total of 20 significant SNPs associated with cattle productivity parameters were identified. Genes responsible for fatty acid and lipid metabolism (SLC27A6), as well as those associated with milk productivity and the immune response (GPX8, CDC20B, and GZMA) are located near some polymorphisms. The identified SNPs and loci can serve as candidate markers for genomic selection of Holstein cattle. The agreement between the GWAS results for the full sample and the extreme quartiles Q1/Q4 confirms the reliability of the identified associations and the effectiveness of the extreme sampling method.
GWAS, milk productivity, SNPs, genomic selection, cattle, Holstein breed
1. Brito LF, Bédère N, Douhard F, et al. Genetic selection of high-yielding dairy cattle toward sustainable farming systems in a rapidly changing world. Animal. 2021;15:100292. DOI:https://doi.org/10.1017/S175173112 100056X.
2. García-Ruiz A, Cole JB, VanRaden PM, et al. Changes in genetic selection differentials and generation intervals in US Holstein dairy cattle as a result of genomic selection. Proc Natl Acad Sci USA. 2016;113(28). DOI:https://doi.org/10.1073/pnas.1603031113.
3. Petrov AF, Bogdanova OV, Narozhnykh KN, et al. Clustering of countries based on dairy productivity characteristics of Holstein cattle for breeding material selection. Vet World. 2024:1108-1118.
4. Dotsev AV, Sermyagin AA. PSXIV-4 Breed purity of Holstein bulls born in Russia and imported from different countries. J Anim Sci. 2018;96(3):142-143.
5. Firsova EV, Kartashova AP. Holstein breed of the cattle in the Russian Federation, the current state and the prospects of development. Genetics and breeding of animals. 2019;1:62-69. (In Russ.).
6. Wang J, Daetwyler HD, Zhang Q, et al. Genome-wide association study and fine-mapping using imputed sequences to prioritize candidate genes for 30 complex traits in 50,309 Holstein bulls. J Dairy Sci. 2025.
7. Ahmed RH, Steyn Y, Banga CB, et al. Genomic-based genetic parameters and genome-wide association studies for productive and reproductive traits in Beef-on-Dairy crossbreds. Front Genet. 2025;16.
8. Lopdell TJ. Using QTL to Identify Genes and Pathways Underlying the Regulation and Production of Milk Components in Cattle. Animals. 2023;13(5):911. DOI:https://doi.org/10.3390/ani13050911.
9. Tarsani E, Freidin S, Mason W, et al. Genome-wide association studies of dairy cattle resistance to digital dermatitis recorded at four distinct lactation stages. Sci Rep. 2025;15(1):8922. DOI: 10.1038/ s41598-025-13443-1.
10. Wiggans GR, Cole JB, Null DJ, et al. Genomic Selection in Dairy Cattle: The USDA Experience. Annu Rev Anim Biosci. 2017;5(1):309-327. DOI:https://doi.org/10.1146/annurev-animal-022516-022747.
11. Li D, Lewinger JP, Gauderman WJ, et al. Using extreme phenotype sampling to identify the rare causal variants of quantitative traits in association studies. Genet Epidemiol. 2011;35(8):790-799. DOI:https://doi.org/10.1002/gepi.20652.
12. Gage JL, de Leon N, Clayton MK. Comparing Genome-Wide Association Study Results from Different Measurements of an Underlying Phenotype. G3 (Bethesda). 2018;8(11):3715-3722. DOI: 10.1534/ g3.118.200596.
13. Yang J, Loos RJF. Extreme-phenotype genome-wide association study (XP-GWAS): a method for identifying trait-associated variants by sequencing pools of individuals selected from a diversity panel. Plant J. 2015;84(3):587-596. DOI:https://doi.org/10.1111/tpj.13031.
14. Lu D, Wang Z, Sargolzaei M, et al. Genome-wide association analyses for growth and feed efficiency traits in beef cattle. J Anim Sci. 2013;91(8):3612-3633. DOI:https://doi.org/10.2527/jas.2012-5972.
15. Mancin E, Tagliapietra F, Shtylla B, et al. Genome Wide Association Study of Beef Traits in Local Alpine Breed Reveals the Diversity of the Pathways Involved and the Role of Time Stratification. Front Genet. 2022;12. DOI:https://doi.org/10.3389/fgene.2021.701301.
16. Zhuang Z, Luo Y, Cai Y, et al. Weighted Single-Step Genome-Wide Association Study for Growth Traits in Chinese Simmental Beef Cattle. Genes (Basel). 2020;11(2):189. DOI:https://doi.org/10.3390/genes11020189.
17. Suzuki T, Terasaki M, Takemoto Y, et al. Structural Compensation for the Deficit of rRNA with Proteins in the Mammalian Mitochondrial Ribosome. J Biol Chem. 2001;276(24):21724-21736. DOI:https://doi.org/10.1074/jbc.M101787200.
18. Merck KGaA. COLEC10. Available at: https://sigmaaldrich.com/NL/en/genes/colec10. Accessed 25.12.2025.
19. Sharma A, Prowse-Wilkins C, Reverter A, et al. Stories and Challenges of Genome Wide Association Studies in Livestock – A Review. Asian-Australas J Anim Sci. 2015;28(10):1371-1379. DOI:https://doi.org/10.5713/ajas.15.0388.
20. Bang NN, Haile-Mariam M, Pryce JE, et al. Genomic Prediction and Genome‐Wide Association Studies for Productivity, Conformation and Heat Tolerance Traits in Tropical Smallholder Dairy Cows. J Anim Breed Genet. 2025;142(3):322-341. DOI:https://doi.org/10.1111/jbg.12532.
21. Meredith BK, Berry DP, Pryce JE, et al. Genome-wide associations for milk production and somatic cell score in Holstein-Friesian cattle in Ireland. BMC Genet. 2012;13:21. DOI:https://doi.org/10.1186/1471-2156-13-21.
22. Azumah R, Lutsay V, Merriman JA, et al. Candidate genes for polycystic ovary syndrome are regulated by TGFβ in the bovine foetal ovary. Hum Reprod. 2022;37(6):1244-1254. DOI:https://doi.org/10.1093/humrep/deac062.
23. Bastian NA, Humpherson PG, Lane EB, et al. Regulation of fibrillins and modulators of TGFβ in fetal bovine and human ovaries. Reproduction. 2016;152(2):127-137. DOI:https://doi.org/10.1530/REP-15-0396.
24. Gao J, Cheng Y, Niu C, et al. Evidence of early genomic selection in Holstein Friesian across African and European ecosystems. BMC Genomics. 2025;26(1):615. DOI:https://doi.org/10.1186/s12864-025-19095-5.
25. Wang X, Huang Y, Zhai Y, et al. Identification of the hub genes related to adipose tissue metabolism of bovine. Front Vet Sci. 2022;9. DOI:https://doi.org/10.3389/fvets.2022.832197.
26. Ren E, Wang Y, Zhou B, et al. Transcriptomic and metabolomic responses induced in the livers of growing pigs by a short-term intravenous infusion of sodium butyrate. Animal. 2018;12(11):2318-2326. DOI:https://doi.org/10.1017/S1751731118000283.
27. Peterson TR, Laplante M, Thoreen CC, et al. DEPTOR Is an mTOR Inhibitor Frequently Overexpressed in Multiple Myeloma Cells and Required for Their Survival. Cell. 2009;137(5):873-886. DOI:https://doi.org/10.1016/j.cell.2009.02.024.
28. Agarwal N, Santhoshkumar T, Shukla AK, et al. MTBP plays a crucial role in mitotic progression and chromosome segregation. Cell Death Differ. 2011;18(7):1208-1219. DOI:https://doi.org/10.1038/cdd.2011.22.
29. Metzger J, Chen S, Willis S, et al. Whole genome sequencing identifies missense mutation in MTBP in Shar-Pei affected with Autoinflammatory Disease (SPAID). BMC Genomics. 2017;18(1):348. DOI:https://doi.org/10.1186/s12864-017-3746-2.
30. Zhang Y, Zhao S, Wei Q, et al. Acetyl‐coenzyme A acyltransferase 2 promotes the differentiation of sheep precursor adipocytes into adipocytes. J Cell Biochem. 2019;120(5):8021-8031. DOI: 10.1002/ jcb.27908.
31. Durán Aguilar M, Gutiérrez-Gil B, Arranz JJ, et al. Genome‐wide association study for milk somatic cell score in holstein cattle using copy number variation as markers. J Anim Breed Genet. 2017;134(1):49-59. DOI:https://doi.org/10.1111/jbg.12217.
32. Wang G, Pan T, Jiang H, et al. Characterization of PRDM9 Multifunctionality in Yak Testes Through Protein Interaction Mapping. Int J Mol Sci. 2025;26(4):1420. DOI:https://doi.org/10.3390/ijms26041420.
33. Gai Z, Hu X, Chen Y, et al. L-arginine alleviates heat stress-induced mammary gland injury through modulating CASTOR1-mTORC1 axis mediated mitochondrial homeostasis. Sci Total Environ. 2024;926:172017. DOI:https://doi.org/10.1016/j.scitotenv.2023.172017.
34. Otto PI, Botha A, de Kock HL, et al. Genome-wide association studies for heat stress response in Bos taurus × Bos indicus crossbred cattle. J Dairy Sci. 2019;102(9):8148-8158. DOI:https://doi.org/10.3168/jds.2018-15676.
35. Casarotto LT, Garcia-Ruiz AL, Lima FS, et al. Late-gestation heat stress alters placental structure and function in multiparous dairy cows. J Dairy Sci. 2025;108(1):1125-1137. DOI:https://doi.org/10.3168/jds.2024-23707.
36. Nayak SS, Kumar S, Verma K, et al. Deciphering climate resilience in Indian cattle breeds by selection signature analyses. Trop Anim Health Prod. 2024;56(2):46. DOI:https://doi.org/10.1007/s11250-023-03624-6.
37. Guo Y, Wang L, Wang Y, et al. Comparative Transcriptome Analysis of Bovine, Porcine, and Sheep Muscle Using Interpretable Machine Learning Models. Animals (Basel). 2024;14(20):2947. DOI:https://doi.org/10.3390/ani14202947.
38. Sun R, Zhang Q, Wang X, et al. Molecular Characterization, Expression Profiles of SMAD4, SMAD5 and SMAD7 Genes and Lack of Association with Litter Size in Tibetan Sheep. Animals (Basel). 2022;12(17):2232. DOI:https://doi.org/10.3390/ani12172232.
39. Wei D, Yang Y, Wang S, et al. Roles of MEF2A and HOXA5 in the transcriptional regulation of the bovine Fox O1 gene. Anim Biotechnol. 2023;34(9):4367-4379. DOI:https://doi.org/10.1080/10495398.2022.2106683.
40. Li P, Wang H, Li Q, et al. Multi-omics analysis of lipids and aroma compounds in beef under grain-fed and grass-fed production methods. Curr Res Food Sci. 2025;11:101216. DOI:https://doi.org/10.1016/j.crfs.2024. 101216.
41. Yu H, Chen L, Zhang K, et al. Integrated multi-omics analysis reveals variation in intramuscular fat among muscle locations of Qinchuan cattle. BMC Genomics. 2023;24(1):367. DOI:https://doi.org/10.1186/s12864-023-09487-9.
42. Hosseinzadeh S, Rafat SA, Fang L. Integrated TWAS, GWAS, and RNAseq results identify candidate genes associated with reproductive traits in cows. Sci Rep. 2025;15(1):1932. DOI:https://doi.org/10.1038/s41598-025-16487-4.
43. Jiang W, Mooney MH, Shirali M. Unveiling the Genetic Landscape of Feed Efficiency in Holstein Dairy Cows: Insights into Heritability, Genetic Markers, and Pathways via Meta-Analysis. J Anim Sci. 2024;102. DOI:https://doi.org/10.1093/jas/skad026.
44. Zheng W, Jiang J, Li X, et al. A pilot multi-omics study reveals genetic mechanisms regulating milk component traits in dairy cattle. Commun Biol. 2025;8(1):1150. DOI:https://doi.org/10.1038/s42003-025-02110-1.
45. Gebreyesus G, Zhang H, Jiang L, et al. Vitamin B12 and transcobalamin in bovine milk: genetic variation and genome-wide association with loci along the genome. JDS Commun. 2021;2(3):127-131. DOI:https://doi.org/10.3168/jdsc.2021-0108.



