Animal Bioscience

Asian Australasian Association of Animal Production Societies (아세아태평양축산학회)
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Comparative assessment of the effective population size and linkage disequilibrium of Karan Fries cattle revealed viable population dynamics

  • Shivam Bhardwaj (Animal Genetics and Breeding Division, ICAR- National Dairy Research Institute (NDRI)) ;
  • Oshin Togla (Animal Genetics and Breeding Division, ICAR- National Dairy Research Institute (NDRI)) ;
  • Shabahat Mumtaz (Animal Genetics and Breeding Division, ICAR- National Dairy Research Institute (NDRI)) ;
  • Nistha Yadav (Animal Genetics and Breeding Division, ICAR- National Dairy Research Institute (NDRI)) ;
  • Jigyasha Tiwari (Animal Genetics and Breeding Division, ICAR- National Dairy Research Institute (NDRI)) ;
  • Lal Muansangi (Animal Genetics and Breeding Division, ICAR- National Dairy Research Institute (NDRI)) ;
  • Satish Kumar Illa (Animal Genetics and Breeding Division, ICAR- National Dairy Research Institute (NDRI)) ;
  • Yaser Mushtaq Wani (Animal Genetics and Breeding Division, ICAR- National Dairy Research Institute (NDRI)) ;
  • Sabyasachi Mukherjee (Animal Genetics and Breeding Division, ICAR- National Dairy Research Institute (NDRI)) ;
  • Anupama Mukherjee (Animal Genetics and Breeding Division, ICAR- National Dairy Research Institute (NDRI))
  • Received : 2023.07.15
  • Accepted : 2023.10.20
  • Published : 2024.05.01
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Abstract

Objective: Karan Fries (KF), a high-producing composite cattle was developed through crossing indicine Tharparkar cows with taurine bulls (Holstein Friesian, Brown Swiss, and Jersey), to increase the milk yield across India. This composite cattle population must maintain sufficient genetic diversity for long-term development and breed improvement in the coming years. The level of linkage disequilibrium (LD) measures the influence of population genetic forces on the genomic structure and provides insights into the evolutionary history of populations, while the decay of LD is important in understanding the limits of genome-wide association studies for a population. Effective population size (Ne) which is genomically based on LD accumulated over the course of previous generations, is a valuable tool for e valuation of the genetic diversity and level of inbreeding. The present study was undertaken to understand KF population dynamics through the estimation of Ne and LD for the long-term sustainability of these breeds. Methods: The present study included 96 KF samples genotyped using Illumina HDBovine array to estimate the effective population and examine the LD pattern. The genotype data were also obtained for other crossbreds (Santa Gertrudis, Brangus, and Beefmaster) and Holstein Friesian cattle for comparison purposes. Results: The average LD between single nucleotide polymorphisms (SNPs) was r2 = 0.13 in the present study. LD decay (r2 = 0.2) was observed at 40 kb inter-marker distance, indicating a panel with 62,765 SNPs was sufficient for genomic breeding value estimation in KF cattle. The pedigree-based Ne of KF was determined to be 78, while the Ne estimates obtained using LD-based methods were 52 (SNeP) and 219 (genetic optimization for Ne estimation), respectively. Conclusion: KF cattle have an Ne exceeding the FAO's minimum recommended level of 50, which was desirable. The study also revealed significant population dynamics of KF cattle and increased our understanding of devising suitable breeding strategies for long-term sustainable development.

Keywords

Acknowledgement

Facilities provided by the Director, Indian Council of Agricultural Research-National Dairy Research Institute, Karnal, India to conduct this research is duly acknowledged. Any specific funding was not received.

References

  1. Joshi BK, Singh A, Mukherjee S. Genetic improvement of indigenous cattle for milk and draught: a review. Indian J Anim Sci 2005;75:3. 
  2. Sharma R, Kishore A, Mukesh M, et al. Genetic diversity and relationship of Indian cattle inferred from microsatellite and mitochondrial DNA markers. BMC Genet 2015;16:73. https://doi.org/10.1186/s12863-015-0221-0 
  3. Mason IL. A world dictionary of livestock breeds, types and varieties. 4 ed. CAB International; 1996. 
  4. Mumtaz S. Study on population dynamics in cattle and buffalo using genealogical information in organized herd [dissertation]. Karnal, India: ICAR-National Dairy Research Institute; 2019. 
  5. Felsenstein J. The effect of linkage on directional selection. Genetics 1965;52:349-63. https://doi.org/10.1093/genetics/52.2.349 
  6. Frankham R, Ballou JD, Ralls K, et al. A practical guide for genetic management of fragmented animal and plant populations. Oxford, UK: Oxford University Press; 2019. 
  7. Lacy RC. Clarification of genetic terms and their use in the management of captive populations. Zoo Biol 1995;14:565-77. https://doi.org/10.1002/zoo.1430140609 
  8. Wright S. Evolution in mendelian populations. Genetics 1931;16:97-159. https://doi.org/10.1093/genetics/16.2.97 
  9. Wang J. Estimation of effective population sizes from data on genetic markers. Philos Trans R Soc Lond B Biol Sci 2005;360:1395-409. https://doi.org/10.1098/rstb.2005.1682 
  10. Jimenez-Mena B, Hospital F, Bataillon T. Heterogeneity in effective population size and its implications in conservation genetics and animal breeding. Conserv Genet Resour 2016;8:35-41. https://doi.org/10.1007/s12686-015-0508-5 
  11. Paim TDP, Hay EHA, Wilson C, Thomas MG, Kuehn LA, Paiva SR, McManus C, Blackburn HD. Dynamics of genomic architecture during composite breed development in cattle. Anim Genet 2020; 51(2):224-234. https://doi.org/10.1111/age.12907 
  12. Santiago E, Caballero A. Effective size of populations under selection. Genetics 1995;139:1013-30. https://doi.org/10.1093/genetics/139.2.1013 
  13. 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 
  14. 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 
  15. Slatkin M. Linkage disequilibrium--understanding the evolutionary past and mapping the medical future. Nat Rev Genet 2008;9:477-85. https://doi.org/10.1038/nrg2361 
  16. HapMap Consortium. A haplotype map of the human genome. Nature 2005;437:1299-320. https://doi.org/10.1038/nature04226 
  17. Visscher PM, Brown MA, McCarthy MI, Yang J. Five years of GWAS discovery. Am J Hum Genet 2012;90:7-24.  https://doi.org/10.1016/j.ajhg.2011.11.029
  18. Gurgul A, Semik E, Pawlina K, Szmatola T, Jasielczuk I, Bugno-Poniewierska M. The application of genome-wide SNP genotyping methods in studies on livestock genomes. J Appl Genet 2014;55:197-208. https://doi.org/10.1007/s13353-014-0202-4 
  19. Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaSci 2015;4:s13742-015-0047-8. https://doi.org/10.1186/s13742-015-0047-8 
  20. Lewontin RC. The interaction of selection and linkage. I. General considerations; Heterotic models. Genet 1964;49:49-67. https://doi.org/10.1093/genetics/49.1.49 
  21. Hill WG. Estimation of linkage disequilibrium in randomly mating populations. J Hered 1974;33:229-39. https://doi.org/10.1038/hdy.1974.89 
  22. Veroneze R, Lopes PS, Guimaraes SEF, et al. Linkage disequilibrium and haplotype block structure in six commercial pig lines. J Anim Sci 2013;91:3493-501. https://doi.org/10.2527/jas.2012-6052 
  23. Zwane AA, Maiwashe A, Makgahlela ML, Choudhury A, Taylor JF, van Marle-Koster E. Genome-wide identification of breed-informative single-nucleotide polymorphisms in three South African indigenous cattle breeds. S Afr J Anim Sci 2016;46:302-12. https://doi.org/10.4314/sajas.v46i3.10 
  24. Wall JD, Pritchard JK. Haplotype blocks and linkage disequilibrium in the human genome. Nat Rev Genet 2003;4:587-97. https://doi.org/10.1038/nrg1123 
  25. Bohmanova J, Misztal I, Cole JB. Temperature-humidity indices as indicators of milk production losses due to heat stress. J Dairy Sci 2007;90:1947-56. https://doi.org/10.3168/jds.2006-513 
  26. Meuwissen THE, Hayes BJ, Goddard ME. Prediction of total genetic value using genome-wide dense marker maps. Genet 2001;157:1819-29. https://doi.org/10.1093/genetics/157.4.1819 
  27. Corbin LJ, Liu AYH, Bishop SC, Woolliams JA. Estimation of historical effective population size using linkage disequilibria with marker data. J Anim Breed Genet 2012;129:257-70. https://doi.org/10.1111/j.1439-0388.2012.01003.x 
  28. Sved JA, Feldman MW. Correlation and probability methods for one and two loci. Theor Popul Biol 1973;4:129-32. https://doi.org/10.1016/0040-5809(73)90008-7 
  29. Alexander DH, Lange K. Enhancements to the ADMIXTURE algorithm for individual ancestry estimation. BMC Bioinformatics 2011;12:246. https://doi.org/10.1186/1471-2105-12-246 
  30. Francis RM. Pophelper: an R package and web app to analyse and visualize population structure. Mol Ecol Resour 2017;17:27-32. https://doi.org/10.1111/1755-0998.12509 
  31. Mitchell M. An introduction to genetic algorithms. Cambridge, MA, USA: MIT press; 1998. 
  32. Cervantes I, Goyache F, Molina A, Valera M, Gutierrez JP. Estimation of effective population size from the rate of coancestry in pedigreed populations. J Anim Breed Genet 2011;128:56-63. https://doi.org/10.1111/j.1439-0388.2010.00881.x 
  33. Gutierrez JP, Cervantes I, Goyache F. Improving the estimation of realized effective population sizes in farm animals. J Anim Breed Genet 2009;126:327-32. https://doi.org/10.1111/j.1439-0388.2009.00810.x 
  34. Hayes BJ, Visscher PM, McPartlan HC, Goddard ME. Novel multilocus measure of linkage disequilibrium to estimate past effective population size. Genome Res 2003;13:635-43. https://doi.org/10.1101/gr.387103 
  35. Santana ML, Pereira RJ, Bignardi AB, El Faro L, Tonhati H, Albuquerque LG. History, structure, and genetic diversity of Brazilian Gir cattle. Livest Sci 2014;163:26-33. https://doi.org/10.1016/j.livsci.2014.02.007 
  36. Braude S, Templeton AR . Understanding the multiple meanings of 'inbreeding' and 'effective size' for genetic management of African rhinoceros populations. Afr J Ecol 2009;47:546-55. https://doi.org/10.1111/j.1365-2028.2008.00981.x 
  37. Hill WG. Estimation of effective population size from data on linkage disequilibrium. Genet Res 1981;38:209-16. https://doi.org/10.1017/S0016672300020553 
  38. Jin H, Zhao S, Jia Y, Xu L. Estimation of linkage disequilibrium, effective population size, and genetic parameters of phenotypic traits in dabieshan cattle. Genes 2023;14:107. https://doi.org/10.3390/genes14010107 
  39. Novo I, Santiago E, Caballero A. The estimates of effective population size based on linkage disequilibrium are virtually unaffected by natural selection. PLoS Genet 2022;18:e1009764. https://doi.org/10.1371/journal.pgen.1009764 
  40. Saura M, Caballero A, Santiago E et al. Estimates of recent and historical effective population size in turbot, seabream, seabass and carp selective breeding programmes. Genet Sel Evol 2021;53:85. https://doi.org/10.1186/s12711-021-00680-9 
  41. Adepoju D. Estimating effective population size of Swedish native cattle-understanding the demographic trajectories of indigenous Swedish cattle breeds [master's thesis]. Uppsala, Sweden: Swedish University of Agricultural Sciences; 2022. 
  42. Martinez V, Dettleff PJ, Galarce N, et al. Estimates of Effective Population Size in Commercial and Hatchery Strains of Coho Salmon (Oncorhynchus kisutch (Walbaum, 1792). Animal 2022;12:647. https://doi.org/10.3390/ani12050647 
  43. Frankham R. Conservation genetics. Annu Rev Genet 1995; 29:305-27. https://doi.org/10.1146/annurev.ge.29.120195.001513 
  44. Li Y, Kim JJ. Effective population size and signatures of selection using bovine 50K SNP Chips in Korean Native Cattle (Hanwoo). Evol Bioinform Online 2015;11:143-53. https://doi.org/10.4137/EBO.S24359 
  45. Porter V. Mason's world dictionary of livestock breeds, types and varieties. Wallingford UK: CABI; 2020. 
  46. Biegelmeyer P, Gulias-Gomes CC, Caetano AR, Steibel JP, Cardoso FF. Linkage disequilibrium, persistence of phase and effective population size estimates in Hereford and Braford cattle. BMC Genet 2016;17:32. https://doi.org/10.1186/s12863-016-0339-8 
  47. Singh A, Kumar A, Mehrotra A, et al. Estimation of linkage disequilibrium levels and allele frequency distribution in crossbred Vrindavani cattle using 50K SNP data. Plos one 2021;16:e0259572. https://doi.org/10.1371/journal.pone.0259572 
  48. Chhotaray S, Panigrahi M, Pal D et al. Genome-wide estimation of inbreeding coefficient, effective population size and haplotype blocks in Vrindavani crossbred cattle strain of India. Biol Rhythm Res 2021;52:666-79. https://doi.org/10.1080/09291016.2019.1600266 
  49. Porto-Neto LR, Reverter A, Prayaga KC et al. The genetic architecture of climatic adaptation of tropical cattle. Plos one 2014;9:e113284. https://doi.org/10.1371/journal.pone.0113284 
  50. Espigolan R, Baldi F, Boligon AA, et al. Study of whole genome linkage disequilibrium in Nellore cattle. BMC Genomics 2013;14:305. https://doi.org/10.1186/1471-2164-14-305