Genome-wide association study for milk production and somatic cell score traits in ‎Iranian Holstein cattle

Document Type : Research Paper


1 Ph.D. Candidate in Animal Breeding and Genetics , Department of Animal Science, College of Agriculture and ‎Natural Resources, University of Tehran, Karaj, Iran

2 Professor, Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran

3 Researcher, Department of Animal Science, University of Florida, USA


The main objective of dairy farmers is to maximize their profit. Increased incidence of mastitis in farms is one of the health problems, causing in serious economic losses as a consequence of treatment costs and reduction of production and longevity. The objective of this study was to evaluate the genetic architecture and associated genomic regions with milk production and somatic cell score (SCS) as an indirect measure of mastitis and the quality of raw milk. Thus, an SNP data set from 1938 Holstein bulls were used in a single-step genome-wide association study. The proportion of additive genetic variance (agv) for each of 1.5-Mb genomic window (adjacent SNPs) was used to identify informative genomic regions, accounting for more than 1% of the agv. A total of 11 significant windows over 9 bovine autosomes were found for the SCS. A peak on BTA14 explained the largest proportion of variance (3.85%). These regions together, explained 20% of agv and harbored 94 candidate genes. For milk yield, we identified 6 informative windows across 6 chromosomes, and a peak on BTA10 explained 2.08% of agv. These regions, explained 8.8% of the agv and sheltered 89 candidate genes. For the fat yield, 9 significant windows were identified on 7 chromosomes and explained 15.6% of agv, and 9 windows contained 87 candidate genes on 8 bovine autosomes were associated with milk protein yield (10.6% of agv). Four genomic regions had a pleiotropic effect. These findings can be an important source of information in genomic evaluation of dairy cattle.


  1. Alam, M., Cho, C. I., Choi, T. J., Park, B., Choi, J. G., Choy, Y. H., Lee, S. S. & Cho, K. H. (2015). Estimation of genetic parameters for somatic cell scores of holsteins using multi-trait lactation models in Korea. Asian-Australasian Journal of Animal Sciences, 28(3), 303-310.
  2. Benedet, A., Ho, P., Xiang, R., Bolormaa, S., De Marchi, M., Goddard, M. & Pryce, J. (2019). The use of mid-infrared spectra to map genes affecting milk composition. Journal of Dairy Science, 102(8), 7189-7203.
  3. Carlén, E., Strandberg, E. & Roth, A. (2004). Genetic parameters for clinical mastitis, somatic cell score, and production in the first three lactations of Swedish Holstein cows. Journal of Dairy Science, 87(9), 3062-3070.
  4. Chen, X., Cheng, Z., Zhang, S., Werling, D. & Wathes, D. C. (2015). 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 Journal of Animal Sciences, 5(4), 358-393.
  5. Cole, J. B., Wiggans, G. R., Ma, L., Sonstegard, T. S., Lawlor, T. J., Crooker, B. A., Van Tassell, C. P., Yang, J., Wang, S., Matukumalli, L. K. & Da, Y. (2011). Genome-wide association analysis of thirty one production, health, reproduction and body conformation traits in contemporary U.S. Holstein cows. BMC Genomics, 12(1), 408.
  6. Daetwyler, H. D., Schenkel, F. S., Sargolzaei, M. & Robinson, J. A. B. (2008). A genome scan to detect quantitative trait loci for economically important traits in Holstein cattle using two methods and a dense single nucleotide polymorphism map. Journal of Dairy Science, 91(8), 3225-3236.
  7. Do, D., Bissonnette, N., Lacasse, P., Miglior, F., Sargolzaei, M., Zhao, X. & Ibeagha-Awemu, E. (2017). Genome-wide association analysis and pathways enrichment for lactation persistency in Canadian Holstein cattle. Journal of Dairy Science, 100(3), 1955-1970.
  8. Han, Y. & Peñagaricano, F. (2016). Unravelling the genomic architecture of bull fertility in Holstein cattle. BMC genetics, 17(1), 143.
  9. Heringstad, B., Sehested, E. & Steine, T. (2008). Correlated selection responses in somatic cell count from selection against clinical mastitis. Journal of Dairy Science, 91(11), 4437-4439.
  10. Iung, L., Petrini, J., Ramírez-Díaz, J., Salvian, M., Rovadoscki, G., Pilonetto, F., Dauria, B., Machado, P., Coutinho, L. & Wiggans, G. (2019). Genome-wide association study for milk production traits in a Brazilian Holstein population. Journal of Dairy Science, 102(6), 5305-5314.
  11. Jiang, J., Ma, L., Prakapenka, D., VanRaden, P. M., Cole, J. B. & Da, Y. (2019). A large-scale genome-wide association study in US Holstein cattle. Frontiers in Genetics, 10, 412.
  12. Kadri, N. K., Guldbrandtsen, B., Lund, M. S. & Sahana, G. (2015). Genetic dissection of milk yield traits and mastitis resistance quantitative trait loci on chromosome 20 in dairy cattle. Journal of Dairy Science, 98(12), 9015-9025.
  13. Meredith, B. K., Kearney, F. J., Finlay, E. K., Bradley, D. G., Fahey, A. G., Berry, D. P. & Lynn, D. (2012). Genome-wide associations for milk production and somatic cell score in Holstein-Friesian cattle in Ireland. BMC Genetics, 13(1), 21.
  14. Misztal, I., Wang, H., Aguilar, I., Legarra, A., Tsuruta, S., Lourenço, D., Fragomeni, B., Zhang, X., Muir, W. & Cheng, H. (2014). GWAS using ssGBLUB. In: Proceedings of 10th World Congress. Appl. Livest. Prod., Vancouver, British Columbia, Canada.
  15. Nayeri, S., Sargolzaei, M., Abo-Ismail, M. K., May, N., Miller, S. P., Schenkel, F., Moore, S. S. & Stothard, P. (2016). Genome-wide association for milk production and female fertility traits in Canadian dairy Holstein cattle. BMC Genetics, 17(1), 75. doi:10.1186/s12863-016-0386-1
  16. Oliveira, H. R., Cant, J., Brito, L., Feitosa, F., Chud, T., Fonseca, P., Jamrozik, J., Silva, F., Lourenco, D. & Schenkel, F. (2019a). Genome-wide association for milk production traits and somatic cell score in different lactation stages of Ayrshire, Holstein, and Jersey dairy cattle. Journal of Dairy Science, 102(9), 8159-8174.
  17. Oliveira, H. R., Lourenco, D. A. L., Masuda, Y., Misztal, I., Tsuruta, S., Jamrozik, J., Brito, L. F., Silva, F. F. & Schenkel, F. S. (2019b). Application of single-step genomic evaluation using multiple-trait random regression test-day models in dairy cattle. Journal of Dairy Science, 102(3), 2365-2377.
  18. Peñagaricano, F., Weigel, K. A. & Khatib, H. (2012). Genome-wide association study identifies candidate markers for bull fertility in Holstein dairy cattle. Animal Genetics, 43, 65-71.
  19. Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A., Bender, D., Maller, J., Sklar, P., De Bakker, P. I. & Daly, M. J. (2007). PLINK: a tool set for whole-genome association and population-based linkage analyses. The American Journal of Human Genetics, 81(3), 559-575.
  20. Sahana, G., Guldbrandtsen, B., Thomsen, B., Holm, L. E., Panitz, F., Brøndum, R. F., Bendixen, C. & Lund, M. S. (2014). Genome-wide association study using high-density single nucleotide polymorphism arrays and whole-genome sequences for clinical mastitis traits in dairy cattle. Journal of Dairy Science, 97(11), 7258-7275.
  21. Sanchez, M.-P., Ramayo-Caldas, Y., Wolf, V., Laithier, C., El Jabri, M., Michenet, A., Boussaha, M., Taussat, S., Fritz, S. & Delacroix-Buchet, A. (2019). Sequence-based GWAS, network and pathway analyses reveal genes co-associated with milk cheese-making properties and milk composition in Montbéliarde cows. Genetics Selection Evolution, 51(1), 34.
  22. Sargolzaei, M., Chesnais, J. & Schenkel, F. (2011). FImpute-An efficient imputation algorithm for dairy cattle populations. Journal of Dairy Science, 94(1), 421.
  23. Sigdel, A., Abdollahi-Arpanahi, R., Aguilar, I. & Peñagaricano, F. (2019). Whole Genome Mapping Reveals Novel Genes and Pathways Involved in Milk Production Under Heat Stress in US Holstein Cows. Frontiers in Genetics, 10, 928.
  24. Strucken, E. M., Bortfeldt, R. H., de Koning, D. J. & Brockmann, G. A. (2012). Genome-wide associations for investigating time-dependent genetic effects for milk production traits in dairy cattle. Animal Genetic, 43(4), 375-382.
  25. Sun, C., VanRaden, P. M., Cole, J. B. & O'Connell, J. R. (2014). Improvement of prediction ability for genomic selection of dairy cattle by including dominance effects. PLoS One, 9(8), e103934.
  26. VanRaden, P. M. (2008). Efficient methods to compute genomic predictions. Journal of Dairy Science, 91(11), 4414-4423.
  27. Wang, H., Misztal, I., Aguilar, I., Legarra, A. & Muir, W. (2012). Genome-wide association mapping including phenotypes from relatives without genotypes. Genetics Research, 94(2), 73-83.
  28. Wang, S., Dvorkin, D. & Da, Y. (2012). SNPEVG: a graphical tool for GWAS graphing with mouse clicks. BMC Bioinformatics, 13(1), 319.
  29. Wang, X., Ma, P., Liu, J., Zhang, Q., Zhang, Y., Ding, X., Jiang, L., Wang, Y., Zhang, Y., Sun, D., Zhang, S., Su, G. & Yu, Y. (2015). Genome-wide association study in Chinese Holstein cows reveal two candidate genes for somatic cell score as an indicator for mastitis susceptibility. BMC genetics, 16(1), 111.
  30. Wathes, D. C., Clempson, A. M. & Pollott, G. E. (2012). Associations between lipid metabolism and fertility in the dairy cow. Reproduction, Fertility and Development, 25(1), 48-61.
  31. Zhang, X., Lourenco, D., Aguilar, I., Legarra, A. & Misztal, I. (2016). Weighting strategies for single-step genomic BLUP: an iterative approach for accurate calculation of GEBV and GWAS. Frontiers in genetics, 7, 151.
  32. Zhou, C., Li, C., Cai, W., Liu, S., Yin, H., Shi, S., Zhang, Q. & Zhang, S. (2019). Genome-wide association study for milk protein composition traits in a Chinese Holstein population using a single-step approach. Frontiers in genetics, 10(72).