Identification of markers associated with estimated breeding value and horn colour in Hungarian Grey cattle

  • Zsolnai, Attila (NAIK-Research Institute for Animal Breeding, Nutrition and Meat Science) ;
  • Kovacs, Andras (NAIK-Research Institute for Animal Breeding, Nutrition and Meat Science) ;
  • Kaltenecker, Endre (Association of Hungarian Grey Cattle Breeders) ;
  • Anton, Istvan (NAIK-Research Institute for Animal Breeding, Nutrition and Meat Science)
  • Received : 2019.11.20
  • Accepted : 2020.04.29
  • Published : 2021.04.01


Objective: This study was conducted to estimate effect of single nucleotide polymorphisms (SNP) on the estimated breeding value of Hungarian Grey (HG) bulls and to find markers associated with horn colour. Methods: Genotypes 136 HG animals were determined on Geneseek high-density Bovine SNP 150K BeadChip. A multi-locus mixed-model was applied for statistical analyses. Results: Six SNPs were identified to be associated (-log10P>10) with green and white horn. These loci are located on chromosome 1, 3, 9, 18, and 25. Seven loci (on chromosome 1, 3, 6, 9, 10, 28) showed considerable association (-log10P>10) with the estimated breeding value. Conclusion: Analysis provides markers for further research of horn colour and supplies markers to achieve more effective selection work regarding estimated breeding value of HG.


  1. Tormay B. A szarvasmarha es tenyesztese I-II (the cattle and cattle breeding). Budapest, Hungary: Athenaeum Irodalmi es Nyomdai RT; 1901.
  2. Bodo I, Gera I, Koppany G. The Hungarian Grey cattle breed: a technical publication. Budapest, Hungary: Association of the Hungarian Grey Cattle Breeders; 1996.
  3. Bartosiewicz L. The Hungarian Grey cattle: a traditional European breed. Anim Genet Resour Inf 1997;21:49-60.
  4. Zsolnai A, Kovacs A, Anton I, et al. Comparison of different Hungarian Grey herds as based on microsatellite analysis. Anim Sci Pap Rep 2014;32:121-30.
  5. Radacsi A, Beri B, Bodo I. Szarvszin-valtozatok a magyar szurke szarvasmarha fajtaban (evaluation of horn colour varieties in the Hungarian Grey cattle). Allattenyesztes es Takarmanyozas 2008;57:291-303.
  6. Meissner K. A magyarfajta szarvasmarha standardja (standard of Hungarian cattle). Koztelek 1929;39:150-1.
  7. Sharma A, Lee JS, Dang CG, et al. Stories and challenges of genome wide association studies in livestock - a review. Asian-Australas J Anim Sci 2015;28:1371-9.
  8. Bodo, I. The maintenance of Hungarian breeds of farm animals threatened by extinction. In: Alderson L, editor. Genetic conservation of domestic livestock. Wallingford, UK: CAB International; 1990. pp. 73-84.
  9. Anton I, Kovacs K, Fesus L, et al. Effect of DGAT1 and TG gene polymorphisms on intramuscular fat and on milk production traits in different cattle breeds in Hungary. Acta Vet Hung 2008;56:181-6.
  10. Anton I, Zsolnai A, Hollo I, Repa I, Hollo G. Effect of thyroglobulin gene polymorphism on the intramuscular fat content in cattle examined by x-ray computed tomography and Soxhlet methods. Arch Tierz 2013;56:593-6.
  11. Ardicli S, Samli H, Alpay F, Dincel D, Soyudal B, Balci F. Association of single nucleotide polymorphisms in the FABP4 gene with carcass characteristics and meat quality in Holstein bulls. Ann Anim Sci 2017;17:117-30.
  12. Anton I, Huth B, Fuller I, Gabor G, Hollo G, Zsolnai A. Effect of single-nucleotide polymorphisms on the breeding value of fertility and breeding value of beef in Hungarian Simmental cattle. Acta Vet Hung 2018;66:215-25.
  13. Weller JI, Glick G, Shirak A, et al. Predictive ability of selected subsets of single nucleotide polymorphisms (SNPs) in a moderately sized dairy cattle population. Animal 2014;8:208-16.
  14. Wongpom B, Koonawootrittriron S, Elzo MA, Suwanasopee T, Jattawa D. Accuracy of genomic-polygenic estimated breeding value for milk yield and fat yield in the Thai multibreed dairy population with five single nucleotide polymorphism sets. Asian-Australas J Anim Sci 2019;32:1340-8.
  15. Bene S, Giczi A, Radli A, Polgar JP, Szabo F. Multibreed breeding value estimation based on weaning results in a beef herd in Hungary. Allattenyesztes es Takarmanyozas 2013;62:218-33.
  16. Balteanu VA, Figueiredo-Cardoso T, Amills M, et al. The footprint of recent and strong demographic decline in the genomes of Mangalitza pigs. Animal 2019;13:2440-6.
  17. Segura V, Vilhjalmsson BJ, Platt A, et al. An efficient multilocus mixed-model approach for genome-wide association studies in structured populations. Nat Genet 2012;44:825-30.
  18. Katoh M, Katoh M. Comparative integromics on Eph family. Int J Oncol 2006;28:1243-7.
  19. Kadler KE, Hill A, Canty-Laird EG. Collagen fibrillogenesis: fibronectin, integrins, and minor collagens as organizers and nucleators. Curr Opin Cell Biol 2008;20:495-501.
  20. Xu D, Olson J, Cole JN, et al. Heparan sulfate modulates neutrophil and endothelial function in antibacterial innate immunity. Infect Immun 2015;83:3648-56.
  21. Morlino S, Carbone A, Ritelli M, et al. TAB2 c.1398dup variant leads to haploinsufficiency and impairs extracellular matrix homeostasis. Hum Mutat 2019;40:1886-98.
  22. Gomyo H, Arai Y, Tanigami A, et al. A 2-Mb sequence-ready contig map and a novel immunoglobulin superfamily gene IGSF4 in the LOH region of chromosome 11q23.2. Genomics 1999;62:139-46.
  23. Alshawi A, Essa A, Sahar Al-Bayatti S, Hanotte O. Genome analysis reveals genetic admixture and signature of selection for productivity and environmental traits in Iraqi cattle. Front Genet 2019;10:609.
  24. Weng L, Hubner R, Claessens A, et al. Isolation and characterization of chondrolectin (Chodl), a novel C-type lectin predominantly expressed in muscle cells. Gene 2003;308:21-9.
  25. Weng L, van Bockstaele DR, Wauters J, et al. A novel alternative spliced chondrolectin isoform lacking the transmembrane domain is expressed during T cell maturation. J Biol Chem 2003;278:19164-70.
  26. Han H. Identification of several key genes by microarray data analysis of bovine mammary gland epithelial cells challenged with Escherichia coli and Staphylococcus aureus. Gene 2019;683:123-32.
  27. Dai WT, Wang QJ, ZouYX, White RR, Liu JX, Liu HY. Short communication: comparative proteomic analysis of the lactating and nonlactating bovine mammary gland. J Dairy Sci 2017;100:5928-35.
  28. Standing ASI, Malinova D, Hong Y, et al. Autoinflammatory periodic fever, immunodeficiency, and thrombocytopenia (PFIT) caused by mutation in actin-regulatory gene WDR1. J Exp Med 2017;214:59-71.
  29. Kim Y, Ryu J, Woo J, Kim JB, Kim CY, Lee C. Genome-wide association study reveals five nucleotide sequence variants for carcass traits in beef cattle. Anim Genet 2011;42:361-5.
  30. Cairns DM, Liu R, Sen M, et al. Interplay of Nkx3.2, Sox9 and Pax3 regulates chondrogenic differentiation of muscle progenitor cells. PLoS One 2012;7:e39642.
  31. Hartmann C. Transcriptional networks controlling skeletal development. Curr Opin Genet Dev 2009;19:437-43.
  32. Nemcova L, Jansova D, Vodickova-Kepkova K, et al. Detection of genes associated with developmental competence of bovine oocytes. Anim Reprod Sci 2016;166:58-71.
  33. Baik M, Vu TTT, Piao MY, Kang HJ. Association of DNA methylation levels with tissue-specific expression of adipogenic and lipogenic genes in longissimus dorsi muscle of Korean cattle. Asian-Australas J Anim Sci 2014;27:1493-8.
  34. Neupane M, Geary TW, Kiser JN, et al. Loci and pathways associated with uterine capacity for pregnancy and fertility in beef cattle. PLoS One 2017;12:e0188997.
  35. Kommadath A, te Pas MFW, Smits MA. Gene coexpression network analysis identifies genes and biological processes shared among anterior pituitary and brain areas that affect estrous behavior in dairy cows. J Dairy Sci 2013;96:2583-95.
  36. Liu R, Sun Y, Zhao G, et al. Genome-wide association study identifies loci and candidate genes for body composition and meat quality traits in Beijing-You chickens. PLoS One 2013;8:e61172.
  37. Seong J, Yoon H, Kong HS. Identification of microRNA and target gene associated with marbling score in Korean cattle (Hanwoo). Genes Genomics 2016;38:529-38.
  38. Malchiodi F, Brito LF, Schenkel FS, Christen AM, Kelton DF, Miglior F. Genome-wide association study and functional analysis of infectious and horn type hoof lesions in Canadian Holstein cattle. In: Proceedings of the World Congress on Genetics Applied to Livestock Production 2018: Auckland, New Zealand.
  39. Chen X, Cheng ZR, Zhang S, Werling D, Wathes DC. Combining genome wide association studies and differential gene expression data analyses identifies candidate genes affecting mastitis caused by two different pathogens in the dairy cow. Open J Anim Sci 2015;5:358-93.
  40. Morris DG, Waters SM, McCarthy SD, et al. Pleiotropic effects of negative energy balance in the postpartum dairy cow on splenic gene expression: repercussions for innate and adaptive immunity. Physiol Genomics 2009;39:28-37.
  41. Howard JT. Kachman SD, Snelling WM, et al. Beef cattle body temperature during climatic stress: a genome-wide association study. Int J Biometeorol 2014;58:1665-72.
  42. Li RW, Li C, Gasbarre LC. The vitamin D receptor and inducible nitric oxide synthase associated pathways in acquired resistance to Cooperia oncophora infection in cattle. Vet Res 2011;42:48.
  43. Canovas A, Reverter A, DeAtley KL, et al. Multi-tissue omics analyses reveal molecular regulatory networks for puberty in composite beef cattle. PLoS One 2014;9:e102551.
  44. Dias MM, Canovas A, Mantilla-Rojas C, et al. SNP detection using RNA-sequences of candidate genes associated with puberty in cattle. Genet Mol Res 2017;16:16019522.
  45. Card CJ, Krieger KE, Kaproth M, Sartini BL. Oligo-dT selected spermatozoal transcript profiles differ among higher and lower fertility dairy sires. Anim Reprod Sci 2017;177:105-23.
  46. Yuan Z, Liu E, Liu Z, et al. Selection signature analysis reveals genes associated with tail type in Chinese indigenous sheep. Anim Genet 2017;48:55-66.
  47. Moshaii BA, Rahimi-Mianji G, Nejati-Javaremi A, Moradi MH, Konig S. Genomic scan for selection signatures associated with mastitis in German Holstein cattle. Iran J Anim Sci 2017;48:453-61.