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Population diversity, admixture, and demographic trend of the Sumba Ongole cattle based on genomic data

  • Pita Sudrajad (Faculty of Animal Science, Universitas Gadjah Mada) ;
  • Hartati Hartati (Research Center for Animal Husbandry, Research Organization for Agriculture and Food, National Research and Innovation Agency (BRIN)) ;
  • Bayu Dewantoro Putro Soewandi (Research Center for Animal Husbandry, Research Organization for Agriculture and Food, National Research and Innovation Agency (BRIN)) ;
  • Saiful Anwar (Research Center for Applied Zoology, Research Organization for Life Sciences and Environment, National Research and Innovation Agency (BRIN)) ;
  • Angga Ardhati Rani Hapsari (Indonesian Research Institute for Animal Production, Indonesian Agency for Agricultural Research and Development, Ministry of Agriculture) ;
  • Tri Satya Mastuti Widi (Faculty of Animal Science, Universitas Gadjah Mada) ;
  • Sigit Bintara (Faculty of Animal Science, Universitas Gadjah Mada) ;
  • Dyah Maharani (Faculty of Animal Science, Universitas Gadjah Mada)
  • Received : 2023.08.07
  • Accepted : 2023.10.20
  • Published : 2024.04.01

Abstract

Objective: Sumba Ongole (SO) cattle are valuable breed due to their important role in the development of Indonesian cattle. Despite rapid advances in molecular technology, no genomic studies on SO cattle have been conducted to date. The aim of this study is to provide genomic profile related to the population diversity, admixture, and demographic trends of SO cattle. Methods: Genomic information was gathered from 79 SO cattle using the Illumina Bovine SNP50 v3 Beadchip, and for comparative purposes, additional genotypes from 209 cattle populations worldwide were included. The expected and observed heterozygosity, inbreeding coefficient, pairwise fixation indices between-population, and Nei's genetic distance were examined. Multidimensional scaling, admixture, and treemix analyses were used to investigate the population structure. Based on linkage disequilibrium and effective population size calculations, the demographic trend was observed. Results: The findings indicated that the genetic diversity of SO cattle was similar to that of other indicine breeds. SO cattle were genetically related to indicines but not to taurines or Bali cattle. The study further confirmed the close relationship between SO, Ongole, and Nellore cattle. Additionally, a small portion of the Ongole mixture were identified dominant in the SO population at the moment. The study also discovered that SO and Bali cattle (Bos javanicus) could have been ancestors in the development of Ongole Grade cattle, which corresponds to the documented history of Ongolization. Our finding indicate that SO cattle have maintained stability and possess unique traits separate from their ancestors. Conclusion: In conclusion, the genetic diversity of the SO cattle has been conserved as a result of the growing significance of the present demographic trend. Consistent endeavors are necessary to uphold the fitness of the breed.

Keywords

Acknowledgement

We thank the animal owners and scientists who willingly shared their genetic information with the general public. Sample collection and genotyping of SO cattle were supported by the Indonesian Agency for Agricultural Research and Development through KP4S scheme.

References

  1. Hardjosubroto W. Alternative policies for the sustainable management of local beef cattle genetic resources within the national livestock breeding system. Wartazoa 2004;14:93-7. 
  2. Barwegen M. Golden Horns: The History of Livestock Farming on Java, 1850-2000. Ann Arbor, MI, USA: ProQuest LLC; 2020.
  3. Astuti M. Potential and genetic resources diversity of Ongole Grade cattle. Wartazoa 2004;14:98-106. 
  4. Diwyanto K. Utilization of local resources and technological innovation to support the development of beef cattle in Indonesia. Pengembangan Inovasi Pertanian 2008;1:173-88. 
  5. Sumadi, Siliwolu. Research on the genetic quality of Ongole and Brahman cattle in East Sumba Regency, East Nusa Tenggara. In: Proceedings of National Beef Cattle Workshop. Yogyakarta: Indonesian Center for Animal Research and Development; 2004. pp. 31-41. 
  6. Statistics of Sumba Timur. Sumba Timur in Figures 2023. Waingapu, Indonesia: BPS-Statistics of Sumba Timur; 2023. 
  7. Lenstra JA, Groeneveld LF, Eding H, et al. Molecular tools and analytical approaches for the characterization of farm animal genetic diversity. Anim Genet 2012;43:483-502. https://doi.org/10.1111/j.1365-2052.2011.02309.x 
  8. Agung PP, Anwar S, Wulandari AS, Sudiro A, Said S, Tappa B. The potency of Sumba Ongole (SO) cattle: A study of genetic characterization and carcass productivity. J Indones Trop Anim Agric 2015;40:71-8. https://doi.org/10.14710/jitaa.40.2.71-78 
  9. Jakaria J, Musyaddad T, Rahayu S, Muladno M, Sumantri C. Diversity of D-loop mitochondrial DNA (mtDNA) sequence in Bali and Sumba Ongole cattle breeds. J Indones Trop Anim Agric 2019;44:335-45. https://doi.org/10.14710/jitaa.44.4.335-345 
  10. Vignal A, Milan D, SanCristobal M, Eggen A. A review on SNP and other types of molecular markers and their use in animal genetics. Genet Sel Evol 2002;34:275. https://doi.org/10.1186/1297-9686-34-3-275 
  11. Wilkinson S, Wiener P. Population genomics of animal domestication and breed development. In: Rajora OP, editor. Population genomics: concepts, approaches and applications. Cham, Switzerland: Springer Nature; 2019. pp. 709-53. https://doi.org/10.1007/13836_2017_8 
  12. Sudrajad P, Subiharta S, Adinata Y, et al. An insight into the evolutionary history of Indonesian cattle assessed by whole genome data analysis. PLoS ONE 2020;15:e0241038. https://doi.org/10.1371/journal.pone.0241038 
  13. Adinata Y, Noor RR, Priyanto R, Cyrilla L, Sudrajad P. Comparison of growth curve models for Ongole Grade cattle. Trop Anim Health Prod 2022;54:252. https://doi.org/10.1007/s11250-022-03254-z 
  14. Dixit SP, Singh S, Ganguly I, et al. Genome-wide Runs of Homozygosity revealed selection signatures in Bos indicus. Front Genet 2020;11:92. https://doi.org/10.3389/fgene.2020.00092 
  15. Decker JE, McKay SD, Rolf MM, et al. Worldwide patterns of ancestry, divergence, and admixture in domesticated cattle. PLoS Genet 2014;10:e1004254. https://doi.org/10.1371/journal.pgen.1004254 
  16. Chang CC, Chow CC, Tellier LCAM, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience 2015;4:s13742-015-0047-8. https://doi.org/10.1186/s13742-015-0047-8 
  17. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria; 2021 [cited 2023 Mar 10]. Available from: https://www.R-project.org 
  18. Yang J, Lee SH, Goddard ME, Visscher PM. Genome-wide complex trait analysis (GCTA): methods, data analyses, and interpretations. In: Gondro C, van der Werf J, Hayes B, editors. Genome-wide association studies and genomic prediction. London, UK: Springer Science+Business Media; 2013. pp. 215-36. 
  19. Raj A, Stephens M, Pritchard JK. fastSTRUCTURE: Variational inference of population structure in large SNP data sets. Genetics 2014;197:573-89. https://doi.org/10.1534/genetics.114.164350 
  20. Pickrell JK, Pritchard JK. Inference of population splits and mixtures from genome-wide allele frequency data. PLoS Genet 2012;8:e1002967. https://doi.org/10.1038/npre.2012.6956.1 
  21. Reich D, Thangaraj K, Patterson N, Price AL, Singh L. Reconstructing Indian population history. Nature 2009;461:489-94. https://doi.org/10.1038/nature08365 
  22. Sved JA, Hill WG. One hundred years of linkage disequilibrium. Genet 2018;209:629-36. https://doi.org/10.1534/genetics.118.300642 
  23. Barbato M, Orozco-terWengel P, Tapio M, Bruford MW. SNeP: a tool to estimate trends in recent effective population size trajectories using genome-wide SNP data. Front Genet 2015;6:109. https://doi.org/10.3389/fgene.2015.00109 
  24. Santiago E, Novo I, Pardinas AF, Saura M, Wang J, Caballero A. Recent demographic history inferred by high-resolution analysis of linkage disequilibrium. Mol Biol Evol 2020;37:3642-53. https://doi.org/10.1093/molbev/msaa169 
  25. Utsunomiya YT, Milanesi M, Fortes MRS, et al. Genomic clues of the evolutionary history of Bos indicus cattle. Anim Genet 2019;50:557-68. https://doi.org/10.1111/age.12836 
  26. Hegde NG. Livestock development for sustainable livelihood of small farmers. Asian J Res Anim Vet Sci 2019;3:1-17. 
  27. Movahedin MR, Amirinia C, Noshary A, Mirhadi SA. Detection of genetic variation in sample of Iranian proofed Holstein cattle by using microsatellite marker. Afr J Biotechnol 2010;9:9042-5. 
  28. Watuwaya BK, Syamsu JA, Budiman, Useng D. Analysis of the potential development of beef cattle in East Sumba Regency, East Nusa Tenggara Province, Indonesia. IOP Conf Ser: Earth Environ Sci 2020;492:012153. https://doi.org/10.1088/1755-1315/492/1/012153 
  29. Adepoju D. Estimating the effective population size of Swedish native cattle [thesis]. Uppsala, Sweden: Swedish University of Agricultural Sciences; 2022.