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Recent advances in breeding and genetics for dairy goats

  • Gipson, Terry A. (American Institute for Goat Research, Langston University)
  • Received : 2019.05.06
  • Accepted : 2019.07.03
  • Published : 2019.08.01

Abstract

Goats (Capra hircus) were domesticated during the late Neolithic, approximately 10,500 years ago, and humans exerted minor selection pressure until fairly recently. Probably the largest genetic change occurring over the millennia happened via natural selection and random genetic drift, the latter causing genes to be fixed in small and isolated populations. Recent human-influenced genetic changes have occurred through biometrics and genomics. For the most part, biometrics has concentrated upon the refining of estimates of heritabilities and genetic correlations. Heritabilities are instrumental in the calculation of estimated breeding values and genetic correlations are necessary in the construction of selection indices that account for changes in multiple traits under selection at one time. Early genomic studies focused upon microsatellite markers, which are short tandem repeats of nucleic acids and which are detected using polymerase chain reaction primers flanking the microsatellite. Microsatellite markers have been very important in parentage verification, which can impact genetic progress. Additionally, microsatellite markers have been a useful tool in assessing genetic diversity between and among breeds, which is important in the conservation of minor breeds. Single nucleotide polymorphisms are a new genomic tool that have refined classical BLUP methodology (biometric) to provide more accurate genomic estimated breeding values, provided a large reference population is available.

Keywords

References

  1. Alberto FJ, Boyer F, Orozco-terWengel P, et al. Convergent genomic signatures of domestication in sheep and goats. Nat Commun 2018;9:813. https://doi.org/10.1038/s41467-018-03206-y
  2. MacHugh DE, Bradley DG. Livestock genetic origins: goats buck the trend. Proc Natl Acad Sci USA 2001;98:5382-4. https://doi.org/10.1073/pnas.111163198
  3. Amills M, Capote J, Tosser-Klopp G. Goat domestication and breeding: a jigsaw of historical, biological and molecular data with missing pieces. Anim Genet 2017;48:631-44. https://doi.org/10.1111/age.12598
  4. Lande R. Natural selection and random genetic drift in phenotypic evolution. Evolution (N Y) 1976;30:314-34. https://doi.org/10.2307/2407703
  5. Canon J, Garcia D, Garcia-Atance MA, et al. Geographical partitioning of goat diversity in Europe and the Middle East. Anim Genet 2006;37:327-34. https://doi.org/10.1111/j.1365-2052.2006.01461.x
  6. Colli L, Milanesi M, Talenti A, et al. Genome-wide SNP profiling of worldwide goat populations reveals strong partitioning of diversity and highlights post-domestication migration routes. Genet Sel Evol 2018;50:58. https://doi.org/10.1186/s12711-018-0422-x
  7. Oget C, Servin B, Palhiere I. Genetic diversity analysis of French goat populations reveals selective sweeps involved in their differentiation. Anim Genet 2019;50:54-63. https://doi.org/10.1111/age.12752
  8. Laland KN, Odling-Smee J, Myles S. How culture shaped the human genome: Bringing genetics and the human sciences together. Nat Rev Genet 2010;11:137-48. https://doi.org/10.1038/nrg2734
  9. Sabeti PC, Schaffner SF, Fry B, et al. Positive natural selection in the human lineage. Science 2006;312:1614-20. https://doi.org/10.1126/science.1124309
  10. Fan S, Hansen MEB, Lo Y, Tishkoff SA. Going global by adapting local: A review of recent human adaptation. Science 2016;354:54-9. https://doi.org/10.1126/science.aaf5098
  11. Watson WE. The battle of tours-poitiers revisited. Provid Stud West Civiliz 1993;2:51-68.
  12. Jenot F, Desmaison P. History and strategies of the Charentes-Poitou dairy firms for goat cheese production: between the logic of the goat sector and the local area. Renc Rech Rumin 2009;16:325-8.
  13. Gilbert R. From Bakewell to BLUP modern livestock breeding's short history. The Shepherd; 2008;August:10-5.
  14. Iloeje MU, Van Vleck LD, Wiggans GR. Components of variance for milk and fat yields in dairy goats. J Dairy Sci 1981;64:2290-3. https://doi.org/10.3168/jds.S0022-0302(81)82844-5
  15. Analla M, Jimenez-Gamero I, Munoz-Serrano A, Serradilla JM, Falagan A. Estimation of genetic parameters for milk yield and fat and protein contents of milk from Murciano-Granadina goats. J Dairy Sci 1996;79:1895-8. https://doi.org/10.3168/jds.S0022-0302(96)76558-X
  16. Iloeje MU, Van Vleck LD. Genetics of dairy goats: a review. J Dairy Sci 1978;61:1521-8. https://doi.org/10.3168/jds.S0022-0302(78)83760-6
  17. Wiggans GR, Misztal I, Van Vleck LD. Implementation of an Animal Model for genetic evaluation of dairy cattle in the United States. J Dairy Sci 1988;71(Suppl 2):54-69. https://doi.org/10.1016/S0022-0302(88)79979-8
  18. Wiggans GR, Van Vleck LD, Dickinson FN. Projection factors for goat lactation records. J Dairy Sci 1979;62:797-801. https://doi.org/10.3168/jds.S0022-0302(79)83328-7
  19. Iloeje MU, Rounsaville TR, McDowell RE, Wiggans GR, Van Vleck LD. Age-season adjustment factors for Alphine, LaMancha, Nubian, Saanen, and Toggenburg dairy goats. J Dairy Sci 1980;63:1309-16. https://doi.org/10.3168/jds.S0022-0302(80)83082-7
  20. Wiggans GR. Animal model evaluation of dairy goats for milk, fat, and protein yields with crossbred animals included. J Dairy Sci 1989;72:2411-6. https://doi.org/10.3168/jds.S0022-0302(89)79374-7
  21. Luo MF, Wiggans GR, Hubbard SM. Variance component estimation and multitrait genetic evaluation for type traits of dairy goats. J Dairy Sci 1997;80:594-600. https://doi.org/10.3168/jds.S0022-0302(97)75975-7
  22. Wiggans GRR, Hubbard SMM. Genetic evaluation of yield and type traits of dairy goats in the United States. J Dairy Sci 2001;84:E69-73. https://doi.org/10.3168/jds.S0022-0302(01)70199-3
  23. Manfredi E, Piacere A, Lahaye P, Ducrocq V. Genetic parameters of type appraisal in Saanen and Alpine goats. Livest Prod Sci 2001;70:183-9. https://doi.org/10.1016/S0301-6226(01)00180-4
  24. Andonov S, Odegard J, Boman IA, et al. Validation of test-day models for genetic evaluation of dairy goats in Norway. J Dairy Sci 2007;90:4863-71. https://doi.org/10.3168/jds.2006-626
  25. Castaneda-Bustos VJ, Montaldo HH, Valencia-Posadas M, et al. Linear and nonlinear genetic relationships between type traits and productive life in US dairy goats. J Dairy Sci 2016;100:1232-45. https://doi.org/10.3168/jds.2016-11313
  26. Molina A, Munoz E, Diaz C, et al. Goat genomic selection: Impact of the integration of genomic information in the genetic evaluations of the Spanish Florida goats. Small Rumin Res 2018;163:72-5. https://doi.org/10.1016/j.smallrumres.2017.12.010
  27. Barillet F. Genetic improvement for dairy production in sheep and goats. Small Rumin Res 2007;70:60-75. https://doi.org/10.1016/j.smallrumres.2007.01.004
  28. Barbieri M, Manfredi E, Elsen J, et al. Effects of the ${\alpha}s1-casein$ locus on dairy performances and genetic parameters of Alpine goats. Genet Sel Evol 1995;27:437. https://doi.org/10.1186/1297-9686-27-5-437
  29. Yahyaoui MH, Coll A, Sanchez A, Folch JM. Genetic polymorphism of the caprine kappa casein gene. J Dairy Res 2001;68:209-16. https://doi.org/10.1017/S0022029901004733
  30. Grosclaude F, Mahe M-F, Brignon G, Di Stasio L, Jeunet R. A Mendelian polymorphism underlying quantitative variations of goat ${\alpha}s1$-casein. Genet Sel Evol 1987;19:399. https://doi.org/10.1186/1297-9686-19-4-399
  31. Marletta D, Criscione A, Bordonaro S, et al. Casein polymorphism in goat's milk. Lait 2007;87:491-504. https://doi.org/10.1051/lait:2007034
  32. Manfredi E, Ricordeau G, Barbieri M, Amigues Y, Bibe B. Genotype at the ${\alpha}s1$-casein locus and selection of bucks on progeny test in the Alpine and Saanen breeds. Genet Sel Evol 1995;27:451. https://doi.org/10.1186/1297-9686-27-5-451
  33. Wang K, Yan H, et al. A novel indel within goat casein alpha S1 gene is significantly associated with litter size. Gene 2018;671:161-9. https://doi.org/10.1016/j.gene.2018.05.119
  34. Luikart G, Biju-Duval MP, Ertugrul O, Zagdsuren Y, Maudet C, Taberlet P. Power of 22 microsatellite markers in fluorescent multiplexes for parentage testing in goats (Capra hircus). Anim Genet 1999;30:431-8. https://doi.org/10.1046/j.1365-2052.1999.00545.x
  35. Azhar PM, Chakraborty D, Iqbal Z, Malik AA. Microsatellite markers as a tool for characterization of small ruminants: a review. Int J Curr Microbiol Appl Sci 2018;7:1330-42. https://doi.org/10.20546/ijcmas.2018.701.162
  36. Siwek M, Knol EF. Parental reconstruction in rural goat population with microsatellite markers. Ital J Anim Sci 2010;9:e50. https://doi.org/10.4081/ijas.2010.e50
  37. White S, Genestout L, Penedo C. Applied genetics in sheep and goats. ISAG Standing Comm 2012. pp. 1-4.
  38. Jimenez-Gamero I, Dorado G, Munoz-Serrano A, Analla M, Alonso-Moraga A. DNA microsatellites to ascertain pedigreerecorded information in a selecting nucleus of Murciano-Granadina dairy goats. Small Rumin Res 2006;65:266-73. https://doi.org/10.1016/j.smallrumres.2005.07.019
  39. Pepin L, Amigues Y, Lepingle A, Berthier JL, Bensaid A, Vaiman D. Sequence conservation of microsatellites between Bos taurus (cattle), Capra hircus (goat) and related species. Examples of use in parentage testing and phylogeny analysis. Heredity (Edinb) 1995;74:53-61. https://doi.org/10.1038/hdy.1995.7
  40. Ajmone-Marsan P, Colli L, Han JL, et al. The characterization of goat genetic diversity: Towards a genomic approach. Small Rumin Res 2014;121:58-72. https://doi.org/10.1016/j.smallrumres.2014.06.010
  41. Dodds KG, McEwan JC, Davis GH. Integration of molecular and quantitative information in sheep and goat industry breeding programmes. Small Rumin Res 2007;70:32-41. https://doi.org/10.1016/j.smallrumres.2007.01.010
  42. Martinez AM, Acosta J, Vega-Pla JL, et al. Analysis of the genetic structure of the canary goat populations using microsatellites. Livest Sci 2006;102:140-5. https://doi.org/10.1016/j.livsci.2005.12.002
  43. Purcell S, Neale B, Todd-Brown K, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007;81:559-75. https://doi.org/10.1086/519795
  44. Wang GZ, Chen SS, Chao TL, et al. Analysis of genetic diversity of chinese dairy goats via microsatellite markers. J Anim Sci 2017;95:2304-13. https://doi.org/10.2527/jas.2016.1029
  45. Seilsuth S, Seo JH, Kong HS, Jeon GJ. Microsatellite analysis of the genetic diversity and population structure in dairy goats in Thailand. Asian-Australas J Anim Sci 2016;29:327-32. https://doi.org/10.5713/ajas.15.0270
  46. Machado TMM, Fonseca CG Da, Rodrigues MT, et al. Genetic diversity between herds of Alpine and Saanen dairy goats and the naturalized Brazilian Moxoto breed. Genet Mol Biol 2006; 29:67-74. http://dx.doi.org/10.1590/S1415-47572006000100014
  47. Sardina MT, Tortorici L, Mastrangelo S, Di Gerlando R, Tolone M, Portolano B. Application of microsatellite markers as potential tools for traceability of Girgentana goat breed dairy products. Food Res Int 2015;74:115-22. https://doi.org/10.1016/j.foodres.2015.04.038
  48. Mastrangelo S, Bonanno A. The Girgentana goat breed: A zootechnical overview on genetics, nutrition and dairy production aspects. In: Simoes J, Gutierrez C, editors. Sustainable Goat Production in Adverse Environments: Volume II. Springer International Publishing; 2018. pp. 191-203. https://doi.org/10.1007/978-3-319-71294-9_14
  49. Mastrangelo S, Tolone M, Montalbano M, et al. Population genetic structure and milk production traits in Girgentana goat breed. Anim Prod Sci 2017;57:430-40. https://doi.org/10.1071/AN15431
  50. Tosser-Klopp G, Bardou P, Bouchez O, et al. Design and characterization of a 52K SNP chip for goats. PLoS One 2014;9:e86227. DOI: 10.1371/journal.pone.0086227
  51. Lashmar SF, Visser C, Van Marle-Koster E, van Marle-Koster E. Validation of the 50k Illumina goat SNP chip in the South African Angora goat. South African J Anim Sci 2015;45:56-9. http://dx.doi.org/10.4314/sajas.v45i1.7
  52. Juditsky A, Nazin A, Tsybakov A, Vayatis N. Recursive aggregation of estimators by mirror descent algorithm with averaging. Proc 10th World Congr Genet Appl to Livest Prod; 2005. http://dx.doi.org/10.13140/2.1.1550.7207
  53. Mucha S, Mrode R, Coffey M, Kizilaslan M, Desire S, Conington J. Genome-wide association study of conformation and milk yield in mixed-breed dairy goats. J Dairy Sci 2018;101:2213-25. https://doi.org/10.3168/jds.2017-12919
  54. Meuwissen T, Hayes B, Goddard M. Genomic selection: a paradigm shift in animal breeding. Anim Front 2016;6:6-14. https://doi.org/10.2527/af.2016-0002
  55. Wasike CB, Rolf M, Silva NCD, et al. Genome-wide association analysis of residual feed intake and milk yield in dairy goats. J Anim Sci 2016;94:820. https://doi.org/10.2527/jam2016-1683
  56. 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
  57. Martin P, Palhiere I, Maroteau C, et al. Genome-wide association mapping for type and mammary health traits in French dairy goats identifies a pleiotropic region on chromosome 19 in the Saanen breed. J Dairy Sci 2018;101:5214-26. https://doi.org/10.3168/jds.2017-13625
  58. Ilie DE, Kusza S, Sauer M, Gavojdian D. Genetic characterization of indigenous goat breeds in Romania and Hungary with a special focus on genetic resistance to mastitis and gastrointestinal parasitism based on 40 SNPs. PLoS One 2018;13:e0197051. https://doi.org/10.1371/journal.pone.0197051
  59. Martin P, Palhiere I, Tosser-Klopp G, Rupp R. Heritability and genome-wide association mapping for supernumerary teats in French Alpine and Saanen dairy goats. J Dairy Sci 2016;99:8891-900. https://doi.org/10.3168/jds.2016-11210
  60. Talenti A, Nicolazzi EL, Chessa S, et al. A method for single nucleotide polymorphism selection for parentage assessment in goats. J Dairy Sci 2016;99:3646-53. https://doi.org/10.3168/jds.2015-10077
  61. Talenti A, Palhiere I, Tortereau F, et al. Functional SNP panel for parentage assessment and assignment in worldwide goat breeds. Genet Sel Evol 2018;50:55. https://doi.org/10.1186/s12711-018-0423-9
  62. Martin P, Palhiere I, Maroteau C, et al. A genome scan for milk production traits in dairy goats reveals two new mutations in DGAT1 reducing milk fat content. Sci Rep 2017;7:1872. https://doi.org/10.1038/s41598-017-02052-0
  63. Mucha S, Mrode R, MacLaren-Lee I, Coffey M, Conington J. Estimation of genomic breeding values for milk yield in UK dairy goats. J Dairy Sci 2015;98:8201-8. https://doi.org/10.3168/jds.2015-9682
  64. Ding X, Zhang Z, Li X, et al. Accuracy of genomic prediction for milk production traits in the Chinese Holstein population using a reference population consisting of cows. J Dairy Sci 2013;96:5315-23. https://doi.org/10.3168/jds.2012-6194
  65. Carillier C, Larroque H, Robert-Granie C. Comparison of joint versus purebred genomic evaluation in the French multibreed dairy goat population. Genet Sel Evol 2014;46:67. https://doi.org/10.1186/s12711-014-0067-3
  66. Teissier M, Larroque H, Robert-Granie C. Weighted singlestep genomic BLUP improves accuracy of genomic breeding values for protein content in French dairy goats: A quantitative trait influenced by a major gene. Genet Sel Evol 2018;50:31. https://doi.org/10.1186/s12711-018-0400-3
  67. Carillier-Jacquin C, Larroque H, Robert-Granie C. Including ${\alpha}s1$ casein gene information in genomic evaluations of French dairy goats. Genet Sel Evol 2016;48:54. https://doi.org/10.1186/s12711-016-0233-x
  68. Desire S, Mucha S, Coffey M, Mrode R, Broadbent J, Conington J. Deriving genomic breeding values for feed intake and body weight in dairy goats. Proceedings of the World Congress on Genetics Applied to Livestock Production 2016;11:818.

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