Advertisement

Whole-Genome Selection in Livestock

  • Birbal SinghEmail author
  • Gorakh Mal
  • Sanjeev K. Gautam
  • Manishi Mukesh
Chapter

Abstract

Selective breeding is a traditional method of improving livestock, but molecular genetic revolution in the last decade of the twentieth century has initiated the modern era of genomics. Molecular genetics has influenced the breeding strategies in a big way by providing genetic maps, individual genes, and quantitative trait loci (QTL) related to performance traits in livestock species. QTL detection in animals led the shift from conventional selective breeding to marker-assisted selection (MAS) and SNPs related to performance traits. Advancements in genomics have motivated animal breeder to formulate high-density SNP chips comprising of lakhs of SNPs covering the whole genome of a species. Selection on the basis of whole-genome markers could make selection of genetically superior animals at very early age and at the same time with the accuracy of 0.8 in predicting their breeding value. The SNP chip analysis has been very popular in livestock in predicting breeding potential at early age, but some traits, i.e., traits involving nonadditive effects and epigenetic effects, are still out of the reach of genomic selection.

Highlights

  • Continuously increasing demand for livestock-based products puts pressure to increase livestock productivity

  • Livestock can be better characterized by genomics approaches and selected for production.

Keywords

Quantitative trait loci Marker-assisted selection Marker-assisted tests SNP selection 

References

  1. Abdel-Azim G, Freeman AE (2002) Superiority of QTL-assisted selection in dairy cattle breeding schemes. J Dairy Sci 85(7):1869–1880CrossRefGoogle Scholar
  2. An B, Xia J, Chang T, Wang X, Miao J, Xu L, Zhang L, Gao X, Chen Y, Li J, Gao H (2018) Genome-wide association study identifies loci and candidate genes for internal organ weights in Simmental beef cattle. Physiol Genomics 50(7):523–531.  https://doi.org/10.1152/physiolgenomics.00022.2018CrossRefPubMedGoogle Scholar
  3. Ashwell MS, Heyen DW, Sonstegard TS, Van Tassell CP, Da Y, VanRaden PM, Ron M, Weller JI, Lewin HA (2004) Detection of quantitative trait loci affecting milk production, health, and reproductive traits in Holstein cattle. J Dairy Sci 87(2):468–475CrossRefGoogle Scholar
  4. Becker D, Otto M, Ammann P, Keller I, Drögemüller C, Leeb T (2015) The brown coat colour of Coppernecked goats is associated with a non-synonymous variant at the TYRP1 locus on chromosome 8. Anim Genet 46(1):50–54.  https://doi.org/10.1111/age.12240 (Epub 2014 Nov 13). Erratum in: Anim Genet. 2015 Aug;46(4):470CrossRefGoogle Scholar
  5. Bolormaa S, Hayes BJ, Savin K, Hawken R, Barendse W, Arthur PF, Herd RM, Goddard ME (2011) Genome-wide association studies for feedlot and growth traits in cattle. J Anim Sci 89(6):1684–1697.  https://doi.org/10.2527/jas.2010-3079CrossRefPubMedGoogle Scholar
  6. Chen HY, Zhang Q, Yin CC, Wang CK, Gong WJ, Mei G (2006) Detection of quantitative trait loci affecting milk production traits on bovine chromosome 6 in a Chinese Holstein population by the daughter design. J Dairy Sci 89(2):782–790CrossRefGoogle Scholar
  7. de Roos AP, Hayes BJ, Spelman RJ, Goddard ME (2008) Linkage disequilibrium and persistence of phase in Holstein-Friesian, Jersey and Angus cattle. Genetics 179(3):1503–1512.  https://doi.org/10.1534/genetics.107.084301 (Epub 2008 Jul 13)CrossRefPubMedPubMedCentralGoogle Scholar
  8. Dekkers JC (2004) Commercial application of marker- and gene-assisted selection in livestock: strategies and lessons. J Anim Sci 82 E-Suppl:E313–328Google Scholar
  9. Dikmen S, Cole JB, Null DJ, Hansen PJ (2013) Genome-wide association mapping for identification of quantitative trait loci for rectal temperature during heat stress in Holstein cattle. PLoS ONE 8(7):e69202.  https://doi.org/10.1371/journal.pone.0069202 Print 2013CrossRefPubMedPubMedCentralGoogle Scholar
  10. Dong Y, Xie M, Jiang Y, Xiao N, Du X, Zhang W, Tosser-Klopp G, Wang J, Yang S, Liang J, Chen W, Chen J, Zeng P, Hou Y, Bian C, Pan S, Li Y, Liu X, Wang W, Servin B, Sayre B, Zhu B, Sweeney D, Moore R, Nie W, Shen Y, Zhao R, Zhang G, Li J, Faraut T, Womack J, Zhang Y, Kijas J, Cockett N, Xu X, Zhao S, Wang J, Wang W (2013) Sequencing and automated whole-genome optical mapping of the genome of a domestic goat (Capra hircus). Nat Biotechnol 31:135–141.  https://doi.org/10.1038/nbt.2478CrossRefPubMedGoogle Scholar
  11. Freyer G, Sørensen P, Kühn C, Weikard R, Hoeschele I (2003) Search for pleiotropic QTL on chromosome BTA6 affecting yield traits of milk production. J Dairy Sci 86:999–1008CrossRefGoogle Scholar
  12. Gholizadeh M, Rahimi-Mianji G, Nejati-Javaremi A (2015) Genomewide association study of body weight traits in Baluchi sheep. J Genet 94:143–6CrossRefGoogle Scholar
  13. Harris BL, Johnson DL, Spelman RJ (2008) Genomic selection in New Zealand and the implications for national genetic evaluation. Proc, Interbull Meeting, Niagara Falls, CanadaGoogle Scholar
  14. Hayes BJ, Bowman PJ, Chamberlain AJ, Goddard ME (2009) Invited review: genomic selection in dairy cattle: progress and challenges. J Dairy Sci 92(2):433–443.  https://doi.org/10.3168/jds.2008-1646 Review. Erratum. In: J Dairy Sci. 2009 Mar;92(3):1313CrossRefPubMedGoogle Scholar
  15. Hayes BJ, Goddard ME (2001) The distribution of the effects of genes affecting quantitative traits in livestock. Genet Sel Evol 33:209–229CrossRefGoogle Scholar
  16. Khatkar MS, Thomson PC, Tammen I, Raadsma HW (2004) Quantitative trait loci mapping in dairy cattle: review and meta-analysis. Genet Sel Evol 36:163–190CrossRefGoogle Scholar
  17. Kucerová J, Lund MS, Sørensen P, Sahana G, Guldbrandtsen B, Nielsen VH, Thomsen B, Bendixen C (2006) Multitrait quantitative trait Loci mapping for milk production traits in danish Holstein cattle. J Dairy Sci 89(6):2245–2256CrossRefGoogle Scholar
  18. Lan R, Lan Zhu L, Yao X, Wang P, Shao Q, Hong Q (2015) Yunnan institute of animal science and veterinary, sheep breeding promotion center in Yunnan province. Chin J Anim Vet Sci 4:549–554Google Scholar
  19. Lee SH, Choi BH, Lim D, Gondro C, Cho YM, Dang CG, Sharma A, Jang GW, Lee KT, Yoon D, Lee HK, Yeon SH, Yang BS, Kang HS, Hong SK (2013) Genome-wide association study identifies major loci for carcass weight on BTA14 in Hanwoo (Korean cattle). PLoS ONE 8(10):e74677.  https://doi.org/10.1371/journal.pone.0074677 (eCollection 2013)CrossRefPubMedPubMedCentralGoogle Scholar
  20. Sharma A, Lee, JS, Dang, CG, Sudrajad P, Kim HC, Yeon SH, Kang HS, Lee SH (2015) Stories and challenges of genome wide association studies in livestock-A review. Asian-Australas J Anim Sci 28:1371–1379.  https://doi.org/10.5713/ajas.14.0715CrossRefGoogle Scholar
  21. Lee B-Y, Lee K-N, Lee T, Park J-H, Kim S-M, Lee H-S, Chung D-S, Shim H-S, Lee H-K, Kim H (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.  https://doi.org/10.5713/ajas.14.0394
  22. Li X, Su R, Wan W, Zhang W, Jiang H, Qiao X, Fan Y, Zhang Y, Wang R, Liu Z, Wang Z, Liu B, Ma Y, Zhang H, Zhao Q, Zhong T, Di R, Jiang Y, Chen W, Wang W, Dong Y, Li J (2017) Identification of selection signals by large-scale whole-genome resequencing of cashmere goats. Sci Rep 7(1):15142.  https://doi.org/10.1038/s41598-017-15516-0CrossRefPubMedPubMedCentralGoogle Scholar
  23. Martin P, Palhière I, Tosser-Klopp G, Rupp R (2016) Heritability and genome-wide association mapping for supernumerary teats in French Alpine and Saanen dairy goats. J Dairy Sci 99(11):8891–8900.  https://doi.org/10.3168/jds.2016-11210 (Epub 2016 Aug 17)CrossRefGoogle Scholar
  24. Matukumalli LK, Lawley CT, Schnabel RD, Taylor JF, Allan MF, Heaton MP, O’Connell J, Moore SS, Smith TP, Sonstegard TS, Van Tassell CP (2009) Development and characterization of a high density SNP genotyping assay for cattle. PLoS One 4(4):e5350.  https://doi.org/10.1371/journal.pone.0005350 (Epub 2009 Apr 24)CrossRefGoogle Scholar
  25. Menzi F, Keller I, Reber I, Beck J, Brenig B, Schütz E, Leeb T, Drögemüller C (2016) Genomic amplification of the caprine EDNRA locus might lead to a dose dependent loss of pigmentation. Sci Rep 22(6):28438.  https://doi.org/10.1038/srep28438CrossRefGoogle Scholar
  26. Meredith BK, Kearney FJ, Finlay EK, Bradley DG, Fahey AG, Berry DP, Lynn DJ (2012) Genome-wide associations for milk production and somatic cell score in Holstein-Friesian cattle in Ireland. BMC Genet 26(13):21.  https://doi.org/10.1186/1471-2156-13-21CrossRefGoogle Scholar
  27. Meuwissen TH, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157(4):1819–1829PubMedPubMedCentralGoogle Scholar
  28. Mosig MO, Lipkin E, Khutoreskaya G, Tchourzyna E, Soller M, Friedmann A (2001) A whole genome scan for quantitative trait loci affecting milk protein percentage in Israeli-Holstein cattle, by means of selective milk DNA pooling in a daughter design, using an adjusted false discovery rate criterion. Genetics 157(4):1683–1698PubMedPubMedCentralGoogle Scholar
  29. Mrode R, Ojango JMK, Okeyo AM, Mwacharo JM (2019) Genomic selection and use of molecular tools in breeding programs for indigenous and crossbred cattle in developing countries: current status and future prospects. Front Genet 9(9):694.  https://doi.org/10.3389/fgene.2018.00694 (eCollection 2018). ReviewCrossRefPubMedPubMedCentralGoogle Scholar
  30. Nazari-Ghadikolaei A, Mehrabani-Yeganeh H, Miarei-Aashtiani SR, Staiger EA, Rashidi A, Huson HJ (2018) Genome-wide association studies identify candidate genes for coat color and mohair traits in the Iranian Markhoz Goat. Front Genet 9:105.  https://doi.org/10.3389/fgene.2018.00105. (eCollection 2018)
  31. Olsen HG, Lien S, Svendsen M, Nilsen H, Roseth A, Aasland Opsal M, Meuwissen TH (2004) Fine mapping of milk production QTL on BTA6 by combined linkage and linkage disequilibrium analysis. J Dairy Sci 87(3):690–698CrossRefGoogle Scholar
  32. Pasandideh M, Rahimi-Mianji G, Gholizadeh M (2018) A genome scan for quantitative trait loci affecting average daily gain and Kleiber ratio in Baluchi Sheep. J Genet 97(2):493–503CrossRefGoogle Scholar
  33. Pryce JE, Bolormaa S, Chamberlain AJ, Bowman PJ, Savin K, Goddard ME, Hayes BJ (2010) A validated genome-wide association study in 2 dairy cattle breeds for milk production and fertility traits using variable length haplotypes. J Dairy Sci 93(7):3331–3345.  https://doi.org/10.3168/jds.2009-2893CrossRefPubMedGoogle Scholar
  34. Pryce JE, Daetwyler HD (2012) Designing dairy cattle breeding schemes under genomic selection: a review of international research. Anim Prod Sci 52:107–114.  https://doi.org/10.1071/AN11098CrossRefGoogle Scholar
  35. Reber I, Keller I, Becker D, Flury C, Welle M, Drögemüller C (2015) Wattles in goats are associated with the FMN1/GREM1 region on chromosome 10. Anim Genet 46:316–320.  https://doi.org/10.1111/age.12279CrossRefPubMedPubMedCentralGoogle Scholar
  36. Rincon G, Weber KL, Eenennaam AL, Golden BL, Medrano JF (2011) Hot topic: performance of bovine high-density genotyping platforms in Holsteins and Jerseys. J Dairy Sci 94(12):6116–6121.  https://doi.org/10.3168/jds.2011-4764CrossRefPubMedGoogle Scholar
  37. Ron M, Feldmesser E, Golik M, Tager-Cohen I, Kliger D, Reiss V, Domochovsky R, Alus O, Seroussi E, Ezra E, Weller JI (2004) A complete genome scan of the Israeli Holstein population for quantitative trait loci by a daughter design. J Dairy Sci 87(2):476–490CrossRefGoogle Scholar
  38. Rupp R, Foucras G (2010) ©CAB International 2010. In: Bishop SC et al (eds) Breeding for disease resistance in farm animals, 3rd ednGoogle Scholar
  39. Sahana G, Iso-Touru T, Wu X, Nielsen US, de Koning DJ, Lund MS, Vilkki J, Guldbrandtsen B (2016) A 0.5-Mbp deletion on bovine chromosome 23 is a strong candidate for stillbirth in Nordic Red cattle. Genet Sel Evol 48:35.  https://doi.org/10.1186/s12711-016-0215-z
  40. Sanna S, Jackson AU, Nagaraja R, Willer CJ, Chen WM, Bonnycastle LL, Shen H, Timpson N, Lettre G, Usala G, Chines PS, Stringham HM, Scott LJ, Dei M, Lai S, Albai G, Crisponi L, Naitza S, Doheny KF, Pugh EW, Ben-Shlomo Y, Ebrahim S, Lawlor DA, Bergman RN, Watanabe RM, Uda M, Tuomilehto J, Coresh J, Hirschhorn JN, Shuldiner AR, Schlessinger D, Collins FS, Davey Smith G, Boerwinkle E, Cao A, Boehnke M, Abecasis GR, Mohlke KL (2008) Common variants in the GDF5-UQCC region are associated with variation in human height. Nat Genet 40(2):198–203.  https://doi.org/10.1038/ng.74 (Epub 2008 Jan 13)CrossRefPubMedPubMedCentralGoogle Scholar
  41. Schaeffer LR (2006) Strategy for applying genome-wide selection in dairy cattle. J Anim Breed Genet 2006(123):218–223CrossRefGoogle Scholar
  42. Schrooten C, Bink MC, Bovenhuis H (2004) Whole genome scan to detect chromosomal regions affecting multiple traits in dairy cattle. J Dairy Sci 87(10):3550–3560CrossRefGoogle Scholar
  43. Snelling WM, Allan MF, Keele JW, Kuehn LA, McDaneld T, Smith TP, Sonstegard TS, Thallman RM, Bennett GL (2010) Genome-wide association study of growth in crossbred beef cattle. J Anim Sci 88(3):837–848.  https://doi.org/10.2527/jas.2009-2257 (Epub 2009 Dec 4)CrossRefPubMedGoogle Scholar
  44. Streit M, Reinhardt F, Thaller G, Bennewitz J (2013) Genome-wide association analysis to identify genotype × environment interaction for milk protein yield and level of somatic cell score as environmental descriptors in German Holsteins. J Dairy Sci 96(11):7318–7324.  https://doi.org/10.3168/jds.2013-7133 (Epub 2013 Sep 18)CrossRefPubMedGoogle Scholar
  45. Szyda J, Liu Z, Reinhardt F, Reents R (2005) Estimation of quantitative trait loci parameters for milk production traits in German Holstein dairy cattle population. J Dairy Sci 88(1):356–367CrossRefGoogle Scholar
  46. Tan ME (2013) Genome-wide association study for stature in New Zealand Dairy Cattle. M. Sc. Thesis. Massey University; Palmerston North, New ZealandGoogle Scholar
  47. Utsunomiya YT, do Carmo AS, Carvalheiro R, Neves HH, Matos MC, Zavarez LB, Pérez O’Brien AM, Sölkner J, McEwan JC, Cole JB, Van Tassell CP, Schenkel FS, da Silva MV, Porto Neto LR, Sonstegard TS, Garcia JF (2013) Genome-wide association study for birth weight in Nellore cattle points to previously described orthologous genes affecting human and bovine height. BMC Genet 14:52.  https://doi.org/10.1186/1471-2156-14-52CrossRefGoogle Scholar
  48. Van Laere AS, Nguyen M, Braunschweig M, Nezer C, Collette C, Moreau L, Archibald AL, Haley CS, Buys N, Tally M, Andersson G, Georges M, Andersson L (2003) A regulatory mutation in IGF2 causes a major QTL effect on muscle growth in the pig. Nature 425(6960):832–836CrossRefGoogle Scholar
  49. VanRaden PM, Van Tassell CP, Wiggans GR, Sonstegard TS, Schnabel RD, Taylor JF, Schenkel FS (2009) Invited review: reliability of genomic predictions for North American Holstein bulls. J Dairy Sci 92(1):16–24.  https://doi.org/10.3168/jds.2008-1514CrossRefPubMedGoogle Scholar
  50. Weller JI, Golik M, Reikhav S, Domochovsky R, Seroussi E, Ron M (2008) Detection and analysis of quantitative trait loci affecting production and secondary traits on chromosome 7 in Israeli Holsteins. J Dairy Sci 91(2):802–813.  https://doi.org/10.3168/jds.2007-0367CrossRefPubMedGoogle Scholar
  51. Weller JI, Golik M, Seroussi E, Ezra E, Ron M (2003) Population-wide analysis of a QTL affecting milk-fat production in the Israeli Holstein population. J Dairy Sci 86(6):2219–2227CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Birbal Singh
    • 1
    Email author
  • Gorakh Mal
    • 1
  • Sanjeev K. Gautam
    • 2
  • Manishi Mukesh
    • 3
  1. 1.ICAR-Indian Veterinary Research Institute, Regional StationPalampurIndia
  2. 2.Department of BiotechnologyKurukshetra UniversityKurukshetraIndia
  3. 3.Department of Animal BiotechnologyICAR-National Bureau of Animal Genetic ResourcesKarnalIndia

Personalised recommendations