Pemanfaatan informasi genom untuk eksplorasi struktur genetik dan asosiasinya dengan performan ternak di Indonesia

Pita Sudrajad, Slamet Diah Volkandari, Muhammad Cahyadi, Amrih Prasetyo, Komalawati Komalawati, Sujatmiko Wibowo, Subiharta Subiharta


Currently, livestock development strategies in various countries have made extensive use of molecular technology at the genome level. Genome contains information of the entire DNA within the livestock cells, therefore it is believed that this technology is able to map the relationship between genotypes and phenotypes more precisely. Genome technology describes all genes in the body and how they interact and influence the growth and performance of livestock. Genome information can support every effort for livestock development including breeding, optimizing the feed nutrition efficiency through the use of feed ingredients more efficiently, and improving reproduction performance. In terms of efforts to improve livestock performance in Indonesia, genomic technology can be utilized to increase the accuracy and efficiency of livestock selection programs. The application of genomic technology in Indonesia still encounters many constraints, including lack of confidence on the benefits that can be generated, the high cost, as well as the incomplete recording data of livestock performance. Currently, researchers in Indonesia have started utilizing genome information to explore the genetic structure of livestock and its association with the livestock performance. Support from all stakeholders are needed to minimize the limitations of genome technology application on livestock in Indonesia.


Genome; Livestock; Indonesia


  1. Hocquette, J. F., S. Lehnert, W. Barendse, I. Cassar-Malek, and B. Picard. 2007. Recent advances in cattle functional genomics and their application to beef quality. Animal. 1:159–173.
  2. Lestari, P., H. Rijzaani, D. Satyawan, A. Anggraeni, D. W. Utami, I. Rosdianti , M. Lutfi, and I. M. Tasma. 2015. Identification of Single Nucleotide Polymorphisms on Cattle Breeds in Indonesia Using Bovine 50K. Indo. J. Agri. Sci. 16(2):59-70.
  3. Sudrajad, P., S. D. Volkandari, D. Prasetyo, M. Cahyadi, J. Pujianto, dan Subiharta. 2019. Estimasi ukuran populasi efektif sapi Bali berdasarkan data genom. Pros. Sem. Nas. Tek. Pet. Vet. IAARD Press, Jakarta. p. 55-59.
  4. Sudrajad, P., S. Subiharta, Y. Adinata, A. Lathifah, J. H. Lee, J. A. Lenstra, and S. H. Lee. 2020. An insight into the evolutionary history of Indonesian cattle assessed by whole genome data analysis. PLoS One. 15(11):e0241038.
  5. Hartati, H., Y. T. Utsunomiya, T. S. Sonstegard, J. F. Garcia, J. Jakaria, and M. Muladno. 2015. Evidence of Bos javanicus x Bos indicus hybridization and major QTLs for birth weight in Indonesian Peranakan Ongole cattle. BMC Genet. 16:75.
  6. Sudrajad, P., A. Prasetyo, dan Subiharta. 2018. Identifikasi Potensi Genetik Sapi Bali Bernilai Ekonomi Tinggi Melalui Analisis Asosiasi Berbasis Total Genom. Laporan Akhir Kegiatan INSINAS (Tahun Ke-1). Balai Pengkajian Teknologi Pertanian Jawa Tengah, Semarang.
  7. Macrogen. Illumina whole genome SNP. [cited 2018 Feb 15]. Available from:
  8. Raadsma, H. W., K. J. Fullard, N. M. Kingsford, E. T. Margawati, E. Estuningsih, S. Widjayanti, Subandriyo, N. Clairoux, T. W. Spithill, and D. Piedrafita. 2008. Ovine Disease Resistance: Integrating Comparative and Functional Genomics Approaches in a Genome Information-Poor Species. Dalam: J. P. Gustafson et al., editor, Genomics of Disease. Springer, USA. p. 89-113.
  9. Decker, J. E., S. D. McKay, M. M. Rolf, J. Kim, A. Molina Alcala, et al. 2014. Worldwide patterns of ancestry, divergence, and admixture in domesticated cattle. PLoS Genet. 10(3):e1004254.
  10. Wangkumhang, P., A. Wilantho, P. J. Shaw, L. Flori, K. Moazami-Goudarzi, et al. 2015. Genetic analysis of Thai cattle reveals a Southeast Asian indicine ancestry. PeerJ. 3:e1318.
  11. Heard, E., S. Tishkoff, J. A. Todd, M. Vidal, G. P. Wagner, J. Wang, D. Weigel, and R. Young. 2010. Ten years of genetics and genomics: what have we achieved and where are we heading? Nat. Rev. Genet.11(10):723–733.
  12. Djikeng, A., S. Ommeh, S. Sangura, I. Njaci, and M. Ngara. 2012. Genomics and Potential Downstream Applications in the Developing World. Dalam: K.E. Nelson dan B. Jones-Nelson, editor, Genomics Applications for the Developing World - Advances in Microbial Ecology. Springer, USA. p. 335-356.
  13. Nicolazzi, E. L., S. Biffani, F. Biscarini, P. Orozco ter Wengel, A. Caprera, N. Nazzicari, and A. Stella. 2015. Software solutions for the livestock genomics SNP array revolution. Anim. Genet. 46:343–353.
  14. Vignal, A., D. Milan, M. SanCristobal, and A. Eggen. 2002. A review on SNP and other types of molecular markers and their use in animal genetics. Gen. Sel. Evol. 34:275-305.
  15. Anggraeni, A. S. Nutrigenomik, Jembatan Antara Ilmu Nutrisi dan Genetik. 2016 [cited 2018 Feb 20]. Available from: nutrigenomik-jembatan-antara-ilmu.html
  16. Benitez, R., Y. Nunez, and C. Ovilo. 2017. Nutrigenomics in farm animals. J. Investig. Genom. 4(1):00059.
  17. Mujibi, F. D. N., J. D. Nkrumah, O. N. Durunna, P. Stothard, J. Mah, Z. Wang, J. Basarab, G. Plastow, D. H. Crews Jr, S. S. Moore. 2011. Accuracy of genomic breeding values for residual feed intake in crossbred beef cattle. J. Anim. Sci. 89:3353–3361.
  18. Higgins, M. G., C. Fitzsimons, M. C. McClure, C. McKenna, S. Conroy, D. A. Kenny, M. McGee, S. M. Waters, and D. W. Morris. 2018. GWAS and eQTL analysis identifies a SNP associated with both residual feed intake and GFRA2 expression in beef cattle. Sci. Rep. 8:14301.
  19. de Haas, Y., J. J. Windig, M. P. L. Calus, J. Dijkstra, M. de Haan, A. Bannink, R. F. Veerkamp. 2011. Genetic parameters for predicted methane production and potential for reducing enteric emissions through genomic selection. J. Dairy Sci. 94:6122–6134.
  20. Brito, L. F., H. R. Oliveira, K. Houlahan, P. A. S. Fonseca, S. Lam, A. M. Butty, D. J. Seymour, G. Vargas, T. C. S. Chud, F. F. Silva, C. F. Baes, A. Cánovas, F. Miglior, and F. S. Schenkel. 2020. Genetic mechanisms underlying feed utilization and implementation of genomic selection for improved feed efficiency in dairy cattle. Can. J. Anim. Sci. 100:587-604.
  21. Elgendy, R., M. Giantin, F. Castellani, L. Grotta, F. Palazzo, M. Dacasto, and G. Martino. 2016. Transcriptomic signature of high dietary organic selenium supplementation in sheep: A nutrigenomic insight using a custom microarray platform and gene set enrichment analysis. J. Anim. Sci. 94:3169–3184.
  22. Iannaccone, M., R. Elgendy, A. Ianni, C. Martino, F. Palazzo, M. Giantin, L. Grotta, M. Dacasto, and G. Martino. 2020. Whole-transcriptome profiling of sheep fed with a highiodine-supplemented diet. Animal. 14(4):745-752.
  23. Kim, J. H., W. S. Jeong, I. H. Kim, H. J. Kim, S. H. Kim, G. H. Kang, H. G. Lee, H. G. Yoon, H. J. Ham, and Y. J. Kim. 2009. Effect of an oil byproduct from conjugated linoleic acid (CLA) purification on CLA accumulation and lipogenic gene expression in broilers. J. Agric. Food Chem. 57(6):2397–2404.
  24. Romé, H., A. Varenne, F. Hérault, H. Chapuis, C. Alleno, P. Dehais, A. Vignal, T. Burlot, and P. Le Roy. 2015. GWAS analyses reveal QTL in egg layers that differ in response to diet differences. Gen. Sel. Evol. 47:83.
  25. Sudrajad, P., A. Sharma, C. G. Dang, J. J. Kim, K. S. Kim, J. H. Lee, S. Kim, and S. H. Lee. 2016. Validation of Single Nucleotide Polymorphisms Associated with Carcass Traits in a Commercial Hanwoo Population. Asian-Australas. J. Anim. Sci. 29(11):1541-1546.
  26. Sudrajad, P., M. Cahyadi, dan S. D. Volkandari. 2019. Identifikasi Potensi Genetik Sapi Bali Bernilai Ekonomi Tinggi Melalui Analisis Asosiasi Berbasis Total Genom. Laporan Akhir Kegiatan INSINAS (Tahun Ke-2). Balai Pengkajian Teknologi Pertanian Jawa Tengah, Semarang.
  27. Meredith, B. K., F. J. Kearney, E. K. Finlay, D. G. Bradley, A. G. Fahey, D. P. Berry, D. J. Lynn. 2012. Genome-wide associations for milk production and somatic cell score in Holstein-Friesian cattle in Ireland. BMC Genet. 13:21.
  28. Yang, S-H., X-J. Bi, Y. Xie, C. Li, S-L. Zhang, Q. Zhang, and D-X. Sun. 2015. Validation of PDE9A gene identified in GWAS showing strong association with milk production traits in Chinese Holstein. Int. J. Mol. Sci. 16:26530–26542.
  29. Moradi, M. H., A. Nejati-Javaremi, M. Moradi-Shahrbabak, K. G. Dodds, and J. C. McEwan. 2012. Genomic scan of selective sweeps in thin and fat tail sheep breeds for identifying of candidate regions associated with fat deposition. BMC Genet. 13:10.
  30. Zhao, F., T. Deng, L. Shi, W. Wang, Q. Zhang, L. Du, and L. Wang. 2020. Genomic scan for selection signature reveals fat deposition in Chinese indigenous sheep with extreme tail types. Animals. 10:0. doi:10.3390/ani10050000.
  31. Gu, X., C. Feng, L. Ma, C. Song, Y. Wang, Y. Da, H. Li, K. Chen, S. Ye, C. Ge, X. Hu, and N. Li. 2011. Genome-wide association study of body weight in chicken F2 resource population. PLoS One. 6(7):e21872.
  32. Moreira, G. C. M., C. Boschiero, A. S. M. Cesar, J. M. Reecy, T. F. Godoy, F. Pértille, M. C. Ledur, A. S. A. M. T. Moura, D. J. Garrick, and L. L. Coutinho. 2018. Integration of genome wide association studies and whole genome sequencing provides novel insights into fat deposition in chicken. Sci. Rep. 8:16222.
  33. Sugimoto, M., S. Sasaki, Y. Gotoh, Y. Nakamura, Y. Aoyagi, T. Kawahara, and Y. Sugimoto. 2013. Genetic variants related to gap junctions and hormone secretion influence conception rates in cows. Proc. Nat. Ac. Sci. USA. 110(48):19495-19500.
  34. Speidel, S.E., B. A. Buckley, R. J. Boldt, R. M. Enns, J. Lee, M. L. Spangler, and M. G. Thomas. 2018. Genome-wide association study of stayability and heifer pregnancy in Red Angus cattle. J. Anim. Sci. 96(3):846–853.
  35. Demars, J., S. Fabre, J. Sarry, R. Rossetti, H. Gilbert, L. Persani, et al. 2013. Genome-Wide Association Studies Identify Two Novel BMP15 Mutations Responsible for an Atypical Hyperprolificacy Phenotype in Sheep. PLoS Genet. 9(4):e1003482.
  36. Benavides, M. V., C. J. H. Souza, and J. C. F. Moraes. 2018. How efficiently Genome-Wide Association Studies (GWAS) identify prolificity-determining genes in sheep. Genet.Mol.Res. 17(2):gmr16039909.
  37. Liu, W., D. Li, J. Liu, S. Chen, L. Qu, J. Zheng, G. Xu, and N. Yang. 2011. A genome-wide SNP scan reveals novel loci for egg production and quality traits in white leghorn and brown-egg dwarf layers. PLoS One. 6(12):e28600.
  38. Wolc, A., J. Arango, T. Jankowski, I. Dunn, P. Settar, J. E. Fulton, N. P. O'Sullivan, R. Preisinger, R. L. Fernando, D. J. Garrick, and J. C. M. Dekkers. 2014. Genome‐wide association study for egg production and quality in layer chickens. J. Anim. Breed. Genet. 131(3):173-182.
  39. Kim, K. S., N. Larsen, T. Short, G. Plastow, and M. F. Rothschild. 2000. A missense variant of the melanocortin 4 receptor (MC4R) gene is associated with fatness, growth and feed intake traits. Mamm. Genom. 11:131-135.
  40. Lee, B-Y., K-N. Lee, T. Lee, J-H. Park, S-M. Kim, H-S. Lee, D-S. Chung, H-S. Shim, H-K. Lee, and H. Kim. 2015. Bovine genome-wide association study for genetic elements to resist the infection of foot-and-mouth disease in the field. Asian-Australas. J. Anim. Sci. 28(2):166-170.
  41. Freebern, E., D. J. A. Santos, L. Fang, J. Jiang, K. L. P. Gaddis, G. E. Liu, P. M. VanRaden, C. Maltecca, J. B. Cole, and L. Ma. 2020. GWAS and fine-mapping of livability and six disease traits in Holstein cattle. BMC Genom. 21:41.
  42. Benavides, M. V., T. S. Sonstegard, and C. Van Tassell. 2016. Genomic regions associated with sheep resistance to gastrointestinal nematodes. Trends in Parasit. 32(6):470-480.
  43. Atlija, M., J-J. Arranz, M. Martinez-Valladares, and B. Gutiérrez-Gil. 2016. Detection and replication of QTL underlying resistance to gastrointestinal nematodes in adult sheep using the ovine 50K SNP array. Gen. Sel. Evol. 48:4.
  44. Luo, C., H. Qu, J. Ma, J. Wang, C. Li, C. Yang, X. Hu, N. Li, and D. Shu. 2013. Genome-wide association study of antibody response to Newcastle disease virus in chicken. BMC Genet. 14:42.
  45. Saelao, P., Y. Wang, G. Chanthavixay, R. A. Gallardo, A. Wolc, J. C. M. Dekkers, S. J. Lamont, T. Kelly, and H. Zhou. 2019. Genetics and genomic regions affecting response to Newcastle disease virus infection under heat stress in layer chickens. Genes. 10:61.
  46. Lestari, P., dan I. M. Tasma. 2012. Analisis genotipe sapi berdasarkan total genom. Warta Penelitian dan Pengembangan Pertanian. 34(4):17-18.
  47. Felius, M., M. L. Beerling, D. S. Buchanan, B. Theunissen, P. A. Koolmees, and J. A. Lenstra. 2014. On the history of cattle genetic resources. Diversity. 6(4):705-750.
  48. Wiyatna, M. F. 2007. Perbandingan indek perdagingan sapi-sapi Indonesia (sapi Bali, Madura, PO) dengan sapi Australian Commercial Cross (ACC). J. Ilmu Ternak. 7(1):22-25.
  49. Romjali, E. 2018. Program pembibitan sapi potong lokal Indonesia. Wartazoa. 28(4):190-210.
  50. Sudrajad, P., S. D. Volkandari, dan Subiharta. 2016. Strategi peningkatan mutu genetik ternak sapi melalui marker assisted selection. Dalam: Hermawan et al., editor, Teori, Strategi, dan Implementasi Pendampingan Program Peningkatan Produksi Pangan. IAARD Press, Jakarta. p. 367-381.
  51. Soewandi, B. D. P. 2017. Penerapan Bioteknologi Reproduksi dan Genetika Molekuler untuk Meningkatkan Produktivitas Babi Lokal. Wartazoa. 27(4):177-186.
  52. Rikhanah. 2008. Sistem pemuliaan inti terbuka upaya peningkatan mutu genetik sapi potong. Mediagro. 4(1):37-43.
  53. Dekkers, J. C. M., and F. Hospital. 2002. The use of molecular genetics in the improvement of agricultural populations. Nature Rev. Genet. 3:22–32.
  54. Hayes, B. J., H. A. Lewin, and M. E. Goddard. 2013. The future of livestock breeding: genomic selection for efficiency, reduced emissions intensity, and adaptation. Trends in Genet. 29(4):206- 214.
  55. Kurnianto, E. 2017. Sumber Daya Genetik Ternak Lokal. Pros. Sem. Tek. Agri. Pet. V. Fakultas Peternakan Universitas Jenderal Soedirman, Purwakarta.
  56. Georges, M., C. Charlier, and B. Hayes. 2019. Harnessing genomic information for livestock improvement. Nature Rev. Gen. 20:135-156.
  57. Volkandari, S. D., P. Sudrajad, D. Prasetyo, Subiharta, A. Prasetyo, J. Pujianto, dan M. Cahyadi. 2020. Dampak sistem pemeliharaan intensif dan semi intensif terhadap ukuran tubuh sapi Bali jantan di Balai Pembibitan Ternak Unggul (BPTU) Sapi Bali. Pros. Sem. Nas. Tek. Pertanian. Balai Besar Pengkajian dan Pengembangan Teknologi Pertanian, Bogor.
  58. Sudrajad, P., D. W. Seo, T. J. Choi, B. H. Park, S. H. Roh, W. Y. Jung, S. S. Lee, J. H. Lee, S. Kim, and S. H. Lee. 2016. Genome-wide linkage disequilibrium and past effective population size in three Korean cattle breeds. Anim. Genet. 48:85-89.
  59. Khatkar, M. S., F. W. Nicholas, A. R. Collins, K. R. Zenger, J. A. L. Cavanagh, W. Barris, R. D. Schnabel, J. F. Taylor, and H. W. Raadsma. 2008. Extent of genome-wide linkage disequilibrium in Australian Holstein-Friesian cattle based on a high-density SNP panel. BMC Genom. 9:187.
  60. Weller, J. I. 2016. Genomic Selection in Animals. Wiley Blackwell, New Jersey.
  61. Muchadeyi, F. C., E. M. Ibeagha-Awemu, A. N. Javaremi, G. A. Gutierrez Reynoso, J. M. Mwacharo, M. F. Rothschild, and J. Sölkner. 2020. Editorial: Why Livestock Genomics for Developing Countries Offers Opportunities for Success. Front. Genet. 11:626.


  • There are currently no refbacks.