Journal of Animal Science and Technology

  • 제60권12호
  • /
  • Pages.31.1-31.10
  • /
  • 2018
  • /
  • 2672-0191(pISSN)
  • /
  • 2055-0391(eISSN)
한국축산학회 (Korean Society of Animal Sciences and Technology)
권호 표지
DOI QR코드

DOI QR Code

Single nucleotide polymorphisms in candidate genes associated with milk yield in Argentinean Holstein and Holstein × Jersey cows

  • Raschia, Maria Agustina (Instituto Nacional de Tecnologia Agropecuaria (INTA), Centro de Investigacion en Ciencias Veterinarias y Agronomicas (CICVyA), Instituto de Genetica "Ewald A. Favret") ;
  • Nani, Juan Pablo (Instituto Nacional de Tecnologia Agropecuaria (INTA), Estacion Experimental Agropecuaria Rafaela) ;
  • Maizon, Daniel Omar (Instituto Nacional de Tecnologia Agropecuaria (INTA), Estacion Experimental Agropecuaria Anguil) ;
  • Beribe, Maria Jose (Instituto Nacional de Tecnologia Agropecuaria (INTA), Estacion Experimental Agropecuaria Pergamino) ;
  • Amadio, Ariel Fernando (Instituto Nacional de Tecnologia Agropecuaria (INTA), Estacion Experimental Agropecuaria Rafaela) ;
  • Poli, Mario Andres (Instituto Nacional de Tecnologia Agropecuaria (INTA), Centro de Investigacion en Ciencias Veterinarias y Agronomicas (CICVyA), Instituto de Genetica "Ewald A. Favret")
  • 투고 : 2018.07.26
  • 심사 : 2018.12.03
  • 발행 : 2018.12.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Background: Research on loci influencing milk production traits of dairy cattle is one of the main topics of investigation in livestock. Many genomic regions and polymorphisms associated with dairy production have been reported worldwide. In this context, the purpose of this study was to identify candidate loci associated with milk yield in Argentinean dairy cattle. A database of candidate genes and single nucleotide polymorphisms (SNPs) for milk production and composition was developed. Thirty-nine SNPs belonging to 22 candidate genes were genotyped on 1643 animals (Holstein and Holstein x Jersey). The genotypes obtained were subjected to association studies considering the whole population and discriminating the population by Holstein breed percentage. Phenotypic data consisted of milk production values recorded during the first lactation of 1156 Holstein and 462 Holstein ${\times}$ Jersey cows from 18 dairy farms located in the central dairy area of Argentina. From these records, 305-day cumulative milk production values were predicted. Results: Eight SNPs (rs43375517, rs29004488, rs132812135, rs137651874, rs109191047, rs135164815, rs43706485, and rs41255693), located on six Bos taurus autosomes (BTA4, BTA6, BTA19, BTA20, BTA22, and BTA26), showed suggestive associations with 305-day cumulative milk production (under Benjamini-Hochberg procedure with a false discovery rate of 0.1). Two of those SNPs (rs43375517 and rs135164815) were significantly associated with milk production (Bonferroni adjusted p-values < 0.05) when considering the Holstein population. Conclusions: The results obtained are consistent with previously reported associations in other Holstein populations. Furthermore, the SNPs found to influence bovine milk production in this study may be used as possible candidate SNPs for marker-assisted selection programs in Argentinean dairy cattle.

키워드

참고문헌

  1. La lecheria argentina: estado actual y su evolucion (2008 a 2011). https://inta.gob.ar/documentos/la-lecheria-argentina-estado-actual-y-su-evolucion-2008-a-2011. Accessed 10 Oct 2018.
  2. SENASA. Tambos. https://www.argentina.gob.ar/senasa/mercados-yestadisticas/estadisticas/animal-estadisticas/bovinos/bovinos-y-bubalinossector-primario. Accessed 10 Oct 2018.
  3. Produccion de leche a nivel nacional. https://datos.agroindustria.gob.ar/dataset?organization=subse-lecheria&groups=produccion-agroindustrial. Accessed 10 Oct 2018.
  4. Cappellini OR. Dairy development in Argentina. In: Dairy Reports. FAO. 2011.
  5. Dekkers JCM, Hospital F. The use of molecular genetics in the improvement of agricultural populations. Nat Rev Genet. 2002;3:22-32. https://doi.org/10.1038/nrg701
  6. Dekkers JCM. Application of genomics tools to animal breeding. Current Genomics. 2012;13:207-12. https://doi.org/10.2174/138920212800543057
  7. Boichard D, Fritz S, Rossignol MN, Boscher MY, Malafosse A, Colleau JJ. Implementation of Marker Assisted Selection in French Dairy Cattle Breeding. In: Proceedings of the 7th World Congress on Genetics Applied to Livestock Production, August 19-23, vol. 22. France, Session: Montpellier; 2002. p. 22.03.
  8. Bennewitz J, Reinsch N, Thomsen H, Szyda J, Reinhardt F, Kühn C, Tuchscherer A, Schwerin M, Weimann C, Erhardt G, Kalm E. Marker Assisted Selection in German Holstein Dairy Cattle Breeding: Outline of the Program and Marker Assisted Breeding Value Estimation. In: 54st Annual Meeting of the European Association for Animal Production, 31 August to 3 2003, Rome, Italy, Session G1.9.
  9. Spelman RJ. Utilisation of molecular information in dairy cattle breeding. In: Proceedings of the 7th World Congress on Genetics Applied to Livestock Production, August 19-23, 2002, Montpellier, France, Session 22, 22.02.
  10. Weller JI, Ezra E, Ron N. Invited review: a perspective on the future of genomic selection in dairy cattle. J Dairy Sci. 2017;100:1-12. https://doi.org/10.3168/jds.2016-11302
  11. Grisart B, Coppieters W, Farnir F, Karin L, Ford C, et al. Positional candidate cloning of a QTL in dairy cattle: identification of a missense mutation in the bovine DGAT1 gene with major effect on milk yield and composition. Genome Res. 2002;12:222-31. https://doi.org/10.1101/gr.224202
  12. Weikard R, Kuhn C, Goldammer T, Freyer G, Schwerin M. The bovine PPARGC1A gene: molecular characterization and association of an SNP with variation of milk fat synthesis. Physiol Genomics. 2005;21:1-13. https://doi.org/10.1152/physiolgenomics.00103.2004
  13. Roy R, Ordovas L, Zaragoza P, Romero A, Moreno C, Altarriba J, Rodellar C. Association of polymorphisms in the bovine FASN gene with milk-fat content. Anim Genet. 2006;37:215-8. https://doi.org/10.1111/j.1365-2052.2006.01434.x
  14. Ogorevc J, Kunej T, Razpet A, Dovc P. Database of cattle candidate genes and genetic markers for milk production and mastitis. Anim Genet. 2009;40:832-51. https://doi.org/10.1111/j.1365-2052.2009.01921.x
  15. You FM, Huo N, Gu YQ, Luo M-C, Ma Y, Hane D, Lazo GR, Dvorak J, Anderson OD. BatchPrimer3: a high throughput web application for PCR and sequencing primer design. BMC Bioinformatics. 2008;9:253. https://doi.org/10.1186/1471-2105-9-253
  16. Staden R, Beal KF, Bonfield JK. The Staden package. In: S Misener, SA Krawetz, editors. Computer methods in molecular biology, bioinformatics methods and protocols. The Humana press Inc., Totowa, New Jersey; 1998. Vol 132; p. 115-130.
  17. Zimin AV, Delcher AL, Florea L, Kelley DR, Schatz MC, et al. A wholegenome assembly of the domestic cow. Bos taurus Genome Biol. 2009. https://doi.org/10.1186/gb-2009-10-4-r42.
  18. Slater GSC, Birney E. Automated generation of heuristics for biological sequence comparison. BMC Bioinformatics. 2005;6:31. https://doi.org/10.1186/1471-2105-6-31
  19. Tobler AR, Short S, Andersen MR, Paner TM, Briggs JC, et al. The SNPlex genotyping system: a flexible and scalable platform for SNP genotyping. J Biomol Tech. 2005;16:396-404.
  20. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, 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
  21. Kirkpatrick M, Lofsvold D, Bulmer M. Analysis of the inheritance, selection and evolution of growth trajectories. Genetics. 1990;124:979-93.
  22. Jamrozik J, Schaeffer LR, Dekkers JCM. Genetic evaluation of dairy cattle using test day yields and random regression model. J Dairy Sci. 1997;80:1217-26. https://doi.org/10.3168/jds.S0022-0302(97)76050-8
  23. Cohen-Zinder M, Seroussi E, Larkin DM, Loor JJ, et al. Identification of a missense mutation in the bovine ABCG2 gene with a major effect on the QTL on chromosome 6 affecting milk yield and composition in Holstein cattle. Genome Res. 2005;15:936-44. https://doi.org/10.1101/gr.3806705
  24. Ikonen T, Bovenhuis H, Ojala M, Ruottinen O, Georges M. Associations between casein haplotypes and first lactation milk production traits in Finnish Ayrshire cows. J Dairy Sci. 2001;84:507-14. https://doi.org/10.3168/jds.S0022-0302(01)74501-8
  25. Nilsen H, Olsen H, Hayes B, Sehested E, Svendsen M, Nome T, Meuwissen T, Lien S. Casein haplotypes and their association with milk production traits in Norwegian red cattle. Genet Sel Evol. 2009. https://doi.org/10.1186/1297-9686-41-24.
  26. Khatib H, Zaitoun I, Wiebelhaus-Finger J, Chang YM, Rosa GJM. The association of bovine PPARGC1A and OPN genes with milk composition in two independent Holstein cattle populations. J Dairy Sci. 2007;90:2966-70. https://doi.org/10.3168/jds.2006-812
  27. Leonard S, Khatib H, Schutzkus V, Chang YM, Maltecca C. Effects of the osteopontin gene variants on milk production traits in dairy cattle. J Dairy Sci. 2005;88:4083-6. https://doi.org/10.3168/jds.S0022-0302(05)73092-7
  28. Schnabel RD, Kim J-J, Ashwell MS, Sonstegard TS, Van Tassell CP, Connor EE, Taylor JF. Fine-mapping milk production quantitative trait loci on BTA6: analysis of the bovine osteopontin gene. Proc Natl Acad Sci U S A. 2005;102:6896-901. https://doi.org/10.1073/pnas.0502398102
  29. Blott S, Kim J-J, Moisio S, Schmidt-Küntzel A, Cornet A, et al. Molecular dissection of a quantitative trait locus: a phenylalanine-to-tyrosine substitution in the transmembrane domain of the bovine growth hormone receptor is associated with a major effect on milk yield and composition. Genetics. 2003;163:253-66.
  30. Buchanan FC, Van Kessel AG, Waldner C, Christensen DA, Laarveld B, Schmutz SM. Hot topic: an association between a leptin single nucleotide polymorphism and Milk and protein yield. J Dairy Sci. 2003;86:3164-6. https://doi.org/10.3168/jds.S0022-0302(03)73918-6
  31. Komisarek J, Dorynek Z. Effect of ABCG2, PPARGC1A, OLR1 and SCD1 gene polymorphism on estimated breeding values for functional and production traits in polish Holstein-Friesian bulls. J Appl Genet. 2009;50:125-32. https://doi.org/10.1007/BF03195663
  32. Rincon G, Islas-Trejo A, Casellas J, Ronin Y, Soller M, Lipkin E, Medrano JF. Fine mapping and association analysis of a quantitative trait locus for milk production traits on Bos taurus autosome 4. J Dairy Sci. 2009;92:758-64. https://doi.org/10.3168/jds.2008-1395
  33. Viitala S, Szyda J, Blott S, Schulman N, Lidauer M, Mäki-Tanila A, Georges M, Vilkki J. The role of the bovine growth hormone receptor and prolactin receptor genes in milk, fat and protein production in Finnish Ayrshire dairy cattle. Genetics. 2006;173:2151-64. https://doi.org/10.1534/genetics.105.046730
  34. Kuss AW, Gogol J, Geldermann H. Associations of a polymorphic AP-2 binding site in the 5'-flanking region of the bovine ${\beta}$-Lactoglobulin gene with milk proteins. J Dairy Sci. 2003;86:2213-8. https://doi.org/10.3168/jds.S0022-0302(03)73811-9
  35. Brym P, Kaminski S, Wojcik E. Nucleotide sequence polymorphism within exon 4 of the bovine prolactin gene and its associations with milk performance traits. J Appl Genet. 2005;46:179-85.
  36. Khatib H, Schutzkus V, Chang YM, Rosa GJM. Pattern of expression of the uterine Milk protein gene and its association with productive life in dairy cattle. J Dairy Sci. 2007;90:2427-33. https://doi.org/10.3168/jds.2006-722
  37. Liao Y, Du X, Lonnerdal B. miR-214 regulates lactoferrin expression and proapoptotic function in mammary epithelial cells. J Nutr. 2010;140:1552-6. https://doi.org/10.3945/jn.110.124289
  38. Farrell HM, Jimenez-Flores R, Bleck GT, Brown EM, Butler JE, et al. Nomenclature of the proteins of cows' Milk - sixth revision. J Dairy Sci. 2004;87:1641-74. https://doi.org/10.3168/jds.S0022-0302(04)73319-6
  39. Schennink A, Bovenhuis H, Leon-Kloosterziel KM, van Arendonk AM, Visker MHPW. Effect of polymorphisms in the FASN, OLR1, PPARGC1A, PRL and STAT5A genes on bovine milk-fat composition. Anim Genet. 2009;40:909-16. https://doi.org/10.1111/j.1365-2052.2009.01940.x
  40. Olsen HG, Nilsen H, Hayes B, Berg PR, Svendsen M, Lien S, Meuwissen T. Genetic support for a quantitative trait nucleotide in the ABCG2 gene affecting milk composition of dairy cattle. BMC Genet. 2007;8:32.
  41. Mullen MP, Berry DP, Howard DJ, Diskin MG, Lynch CO, Berkowicz EW, Magee DA, MacHugh DE, Waters SM. Associations between novel single nucleotide polymorphisms in the Bos taurus growth hormone gene and performance traits in Holstein-Friesian dairy cattle. J Dairy Sci. 2010;93:5959-69. https://doi.org/10.3168/jds.2010-3385
  42. Huang W, Penagaricano F, Ahmad KR, Lucey JA, Weigel KA, Khatib H. Association between milk protein gene variants and protein composition traits in dairy cattle. J Dairy Sci. 2012;95:440-9. https://doi.org/10.3168/jds.2011-4757
  43. Chang SC, Chang PY, Butler B, Goldstein BY, Mu L, et al. Single nucleotide polymorphisms of one-carbon metabolism and cancers of the esophagus, stomach, and liver in a Chinese population. PLoS One. 2014. https://doi.org/10.1371/journal.pone.0109235.
  44. Davidson CM, Northrup H, King TM, Fletcher JM, Townsend I, Tyerman GH, Au KS. Genes in glucose metabolism and association with spina bifida. Reprod Sci. 2008;15:51-8. https://doi.org/10.1177/1933719107309590
  45. Tran PX, Au KS, Morrison AC, Fletcher JM, et al. Association of retinoic acid receptor genes with meningomyelocele. Birth defects research. Part a. Clinical and molecular teratology. 2011;91:39-43. https://doi.org/10.1002/bdra.20744
  46. Jimenez-Sousa MA, Lopez E, Fernandez-Rodriguez A, Tamayo E, et al. Genetic polymorphisms located in genes related to immune and inflammatory processes are associated with end-stage renal disease: a preliminary study. BMC Med Genet. 2012. https://doi.org/10.1186/1471-2350-13-58.
  47. O'Byrne MR, Au KS, Morrison AC, Lin JI, Fletcher JM, et al. Association of folate receptor (FOLR1, FOLR2, FOLR3) and reduced folate carrier (SLC19A1) genes with meningomyelocele. Birth defects research. Part A, Clinical and molecular teratology. 2010;88:689-94. https://doi.org/10.1002/bdra.20706
  48. Kgwatalala PM, Ibeagha-Awemu EM, Hayes JF, Zhao X. Stearoyl-CoA desaturase 1 3'UTR SNPs and their influence on milk fatty acid composition of Canadian Holstein cows. J Anim Breed Genet. 2009;126:394-403. https://doi.org/10.1111/j.1439-0388.2008.00796.x
  49. Fontanesi L, Calo DG, Galimberti G, Negrini R, Marino R, et al. A candidate gene association study for nine economically important traits in Italian Holstein cattle. Anim Genet. 2014;45:576-80. https://doi.org/10.1111/age.12164
  50. Casas E, Montaldo H, Nonneman D, Poli M. Potential contribution of genomics and biotechnology in animal production. In: Nunez R, Ramírez R, Fernandez S, et al., editors. La ganaderia en America Latina y el Caribe: alternativas para la producción competitiva, sustentable e incluyente de alimentos de origen animal. 1st ed. Fundacion Colegio de Postgraduados, Mexico: Biblioteca Basica de Agricultura; 2015. p. 761-88.
  51. Meuwissen T, Hayes B, Goddard M. Genomic selection: a paradigm shift in animal breeding. Animal Frontiers. 2016;6:6.

피인용 문헌

  1. Survival analyses in Holstein cows considering direct disease diagnoses and specific SNP marker effects vol.103, pp.9, 2018, https://doi.org/10.3168/jds.2020-18174
  2. Weighted single-step genome-wide association analyses for milk traits in Holstein and Holstein x Jersey crossbred dairy cattle. vol.242, pp.None, 2018, https://doi.org/10.1016/j.livsci.2020.104294
  3. Unraveling Admixture, Inbreeding, and Recent Selection Signatures in West African Indigenous Cattle Populations in Benin vol.12, pp.None, 2021, https://doi.org/10.3389/fgene.2021.657282
  4. Milk Consumption and Respiratory Function in Asthma Patients: NHANES Analysis 2007-2012 vol.13, pp.4, 2018, https://doi.org/10.3390/nu13041182
  5. Genetic Markers Associated with Milk Production Traits in Dairy Cattle vol.11, pp.10, 2021, https://doi.org/10.3390/agriculture11101018