Identification of quantitative trait loci for milk protein percentage in Murrah buffaloes


348 / 220 / 36

Authors

  • UPASNA SHARMA Research Associate, ICAR-National Bureau of Animal Genetic Resources, Karnal, Haryana 132 001 India
  • PRIYANKA BANERJEE Post Doctoral Fellow, Technical University of Denmark, Lyngby, Kobenhavn, Denmark
  • JYOTI JOSHI Post Doctoral Fellow, Dalhousie University, Nova Scotia, Canada.
  • PRERNA KAPOOR Senior Research Fellow, ICAR-National Bureau of Animal Genetic Resources, Karnal, Haryana 132 001 India
  • RAMESH KUMAR VIJH Principal Scientist, ICAR-National Bureau of Animal Genetic Resources, Karnal, Haryana 132 001 India

https://doi.org/10.56093/ijans.v89i5.90021

Keywords:

Buffaloes, Candidate genes, Milk protein percentage, QTLs

Abstract

Milk protein is an important constituent of milk in buffaloes and is moderately heritable. The milk protein percentage varies significantly between breeds/herds/species. Buffaloes can be selected for higher milk protein percentage and this paper provides QTLs for marker assisted selection in buffaloes. The milk protein percentage records on 2,028 daughters belonging to 12 half sib families were analyzed for the identification of QTLs on 8 chromosomes in buffaloes using chromosome scans. The single marker analysis revealed 74 markers to be associated with milk protein percentage in 12 sire families. When common markers were removed from the analysis, 51 markers remained. The Interval mapping using R/qtl identified 69 QTLs in 12 half sib families on 8 chromosomes of buffalo. The meta QTL analysis defined 25 consensus QTL regions in buffaloes for milk protein percentage. Most of the QTLs identified have been reported for cattle however few new chromosomal locations were also identified to be associated with milk protein percentage in buffaloes. Comparative genomics revealed 1117 genes underlying the QTL regions associated with milk protein percentage. Among these, 109 genes were directly associated with protein metabolism. The protein-protein interaction among the genes and gene ontology analysis and pathways have been identified. These 109 genes have potential to be candidate genes for milk protein percentage in buffaloes.

Downloads

Download data is not yet available.

References

Alain K, Karrow N A, Thibault C, St-Pierre J, Lessard M and Bissonnette N. 2009. Osteopontin: an early innate immune marker of Escherichia coli mastitis harbors genetic polymorphisms with possible links with resistance to mastitis. BMC Genomics 10: 444. DOI: https://doi.org/10.1186/1471-2164-10-444

Amaral M E J, Grant J R, Riggs P K, Filho N B S E A R, Goldammer T, Weikard R, Brunner R M, Kochan K J, Greco A J, Jeong J, Cai Z, Lin G, Prasad A, Kumar S, Mathew G P S B, Kumar M A, Miziara M N, Mariani P, Caetano A R, Galvão S R, Tantia M S, Vijh R K, Mishra B, Bharani Kumar S T, Pelai V A, Santana A M, Fornitano L C, Jones B C, Tonhati H, Moore S, Stothard P and Womack J E. 2008. A first generation whole genome RH map of the river buffalo with comparison to domestic cattle. BMC Genomics 9: 631–41. DOI: https://doi.org/10.1186/1471-2164-9-631

Bagnato A, Schiavini F, Rossoni A, Maltecca C, Dolezal M, Medugorac I, Sölkner J, Russo V, Fontanesi L and Friedmann A. 2008. Quantitative trait loci affecting milk yield and protein percentage in a three-country Brown Swiss population. Journal of Dairy Science 91(2): 767–83. DOI: https://doi.org/10.3168/jds.2007-0507

Buitenhuis B, Poulsen N A, Gebreyesus G and Larsen L B. 2016. Estimation of genetic parameters and detection of chromosomal regions affecting the major milk proteins and their post translational modifications in Danish Holstein and Danish Jersey cattle. BMC Genetics 17: 114. DOI: https://doi.org/10.1186/s12863-016-0421-2

Chamberlain A J, Hayes B J, Savin K, Bolormaa S, McPartlan H C, Bowman P J, Van der Jagt C, MacEachern S, Goddard M E. 2012. Validation of single nucleotide polymorphisms associated with milk production traits in dairy cattle. Journal of Dairy Science 95(2): 864–75. DOI: https://doi.org/10.3168/jds.2010-3786

Churchill G A and Doerge R W. 1994. Empirical threshold values for quantitative trait mapping. Genetics 138: 963–71. DOI: https://doi.org/10.1093/genetics/138.3.963

Coffman C J, Doerge R W, Wayne M and McIntyre L M. 2003. Intersection tests for single marker QTL analysis can be more powerful than two marker QTL analysis. BMC Genetics 4: 10. DOI: https://doi.org/10.1186/1471-2156-4-10

Cole J B, Null D J and VanRaden P M. 2016. Phenotypic and genetic effects of recessive haplotypes on yield, longevity, and fertility. Journal of Dairy Science 99(9): 7274–88. DOI: https://doi.org/10.3168/jds.2015-10777

Cole J B, Wiggans G R, Ma L, Sonstegard T S, Lawlor T J Jr, Crooker B A, Van Tassell C P, Yang J, Wang S, Matukumalli L K and Da Y. 2011. Genome-wide association analysis of thirty one production, health, reproduction and body conformation traits in contemporary US Holstein cows. BMC Genomics 12: 408. DOI: https://doi.org/10.1186/1471-2164-12-408

Collard B C Y, Jahufer M Z Z, Brouwer J B and Pang E C K. 2005. An introduction to marker, quantitative trait loci (QTL) mapping and marker-assisted selection for crop improvement. The basic concepts. Euphytica 142: 169–96. DOI: https://doi.org/10.1007/s10681-005-1681-5

Corva P M and Medrano J F. 2001. Quantitative trait loci (QTLs) mapping for growth traits in the mouse. A review. Genetic Selection Evolution 33: 105–32. DOI: https://doi.org/10.1186/1297-9686-33-2-105

Croft D, Mundo A F, Haw R, Milacic M, Weiser J, Wu G, Caudy M, Garapati P, Gillespie M, Kamdar M R, Jassal B, Jupe S, Matthews L, May B, Palatnik S, Rothfels K, Shamovsky V, Song H, Williams M, Birney E, Hermjakob H, Stein L and D’Eustachio P. 2014. The Reactome pathway knowledgebase. Nucleic Acids Research 42: D472–7. DOI: https://doi.org/10.1093/nar/gkt1102

Doerge R W, Zeng Z B and Weir B S. 1997. Statistical issues in the search for genes affecting quantitative traits in experimental populations. Statistical Science 13: 195–219. DOI: https://doi.org/10.1214/ss/1030037909

Fabregat A, Sidiropoulos K, Garapati P, Gillespie M, Hausmann K, Haw R, Jassal B, Jupe S, Korninger F, McKay S, Matthews L, May B, Milacic M, Rothfels K, Shamovsky V, Webber M, Weiser J, Williams M, Wu G, Stein L, Hermjakob H, D’Eustachio P. 2016. The Reactome pathway Knowledgebase. Nucleic Acids Research 44(D1): D481–87. DOI: https://doi.org/10.1093/nar/gkv1351

Fang M, Fu W, Jiang D, Zhang Q, Sun D, Ding X and Liu J. 2014. A multiple-SNP approach for genome-wide association study of milk production traits in Chinese Holstein cattle. PLoS ONE 9(8): e99544. DOI: https://doi.org/10.1371/journal.pone.0099544

Goffinet B and Gerber S. 2000. Quantitative trait loci: a meta- analysis. Genetics 155(1): 463–73. DOI: https://doi.org/10.1093/genetics/155.1.463

Haley C and Knott S. 1992. A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69: 315–24. DOI: https://doi.org/10.1038/hdy.1992.131

Hayes B and Goddard M E. 2001. The distribution of the effects of genes affecting quantitative traits in livestock. Genetic Selection Evolution 33: 209–29. DOI: https://doi.org/10.1186/1297-9686-33-3-209

Heyen D W, Weller J I, Ron M, Band M, Beever J E, Feldmesser E, Da Y, Wiggans G R, VanRaden P M and Lewin H A. 1999. A genome scan for QTL influencing milk production and health traits in dairy cattle. Physiology Genomics 1(3): 165– 75. DOI: https://doi.org/10.1152/physiolgenomics.1999.1.3.165

Jiang J, Shen B, O’Connell J R, VanRaden P M, Cole J B and Ma L. 2017. Dissection of additive, dominance, and imprinting effects for production and reproduction traits in Holstein cattle. BMC Genomics 18(1): 425. DOI: https://doi.org/10.1186/s12864-017-3821-4

Jiang L, Liu J, Sun D, Ma P, Ding X, Yu Y and Zhang Q. 2010. Genome wide association studies for milk production traits in Chinese Holstein population. PLoS ONE 5(10): e13661. DOI: https://doi.org/10.1371/journal.pone.0013661

Kao C H. 2000. On the differences between maximum likelihood and regression interval mapping in the analysis of quantitative trait loci. Genetics 156: 855–65. DOI: https://doi.org/10.1093/genetics/156.2.855

Khatkar M, Randhawa I and Raadsma H. 2014. Meta-assembly of genomic regions and variants associated with female reproductive efficiency in cattle. Livestock Science 166(1): 144–57. DOI: https://doi.org/10.1016/j.livsci.2014.05.015

Knott S and Haley C. 1992. Aspects of maximum likelihood methods for mapping of quantitative trait loci in line crosses. Genetical Research 60: 139–52. DOI: https://doi.org/10.1017/S0016672300030822

Mackay T F C. 2001. The genetic architecture of quantitative traits. Annual Review of Genetics 35: 303–39. DOI: https://doi.org/10.1146/annurev.genet.35.102401.090633

Mosig M O, Lipkin E, Khutoreskaya G, Tchourzyna E, Soller M and 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): 1683–98. DOI: https://doi.org/10.1093/genetics/157.4.1683

Pimentel E C, Bauersachs S, Tietze M, Simianer H, Tetens J, Thaller G, Reinhardt F, Wolf E and Konig S. 2011. Exploration of relationships between production and fertility traits in dairy cattle via association studies of SNPs within candidate genes derived by expression profiling. Animal Genetic 42(3): 251– 62. DOI: https://doi.org/10.1111/j.1365-2052.2010.02148.x

Plante Y, Gibson J P, Nadesalingam J, Mehrabani-Yeganeh H, Lefebvre S,Vandervoort G and Jansen G B. 2001. Detection of quantitative trait loci affecting milk production traits on 10 chromosomes in Holstein cattle. Journal of Dairy Science 84(6): 1516–24. DOI: https://doi.org/10.3168/jds.S0022-0302(01)70185-3

Pryce J E, Bolormaa S, Chamberlain A J, Bowman P J, Savin K, Goddard M E and Hayes B J. 2010. A validated genome-wide association study in 2 dairy cattle breeds for milk production and fertility traits using variable length haplotypes. Journal of Dairy Science 93(7): 3331–45. DOI: https://doi.org/10.3168/jds.2009-2893

Rebaï A, Goffinet B and Mangin B. 1995. Comparing powers of different methods for QTL detection. Biometrics 51: 87–99. DOI: https://doi.org/10.2307/2533317

Ron M, Feldmesser E, Golik M, Tager-Cohen I, Kliger D, Reiss V, Domochovsky R, Alus O, Seroussi E, Ezra E and Weller J I. 2004. A complete genome scan of the Israeli Holstein population for quantitative trait loci by a daughter design. Journal of Dairy Science 2(87): 476–90. DOI: https://doi.org/10.3168/jds.S0022-0302(04)73187-2

Russo V, Fontanesi L, Dolezal M, Lipkin E, Scotti E, Zambonelli P, Dall’Olio S, Bigi D, Davoli R, Canavesi F, Medugorac I, Foster M, Solkner J, Schiavini F, Bagnato A and Soller M. 2012. A whole genome scan for QTL affecting milk protein percentage in Italian Holstein cattle, applying selective milk DNA pooling and multiple marker mapping in a daughter design. Animal Genetics 43(Suppl 1): 72–86. DOI: https://doi.org/10.1111/j.1365-2052.2012.02353.x

Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, Simonovic M, Roth A, Santos A, Tsafou K P, Kuhn M, Bork P, Jensen L J and von Mering C. 2015. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Research 43: D447–52. DOI: https://doi.org/10.1093/nar/gku1003

Viale E, Tiezzi F, Maretto F, De Marchi M, Penasa M and Cassandro M. 2017. Association of candidate gene polymorphisms with milk technological traits, yield, composition, and somatic cell score in Italian Holstein-Friesian sires. Journal of Dairy Science 100(9): 7271–81. DOI: https://doi.org/10.3168/jds.2017-12666

Vijh R K. 2013. Final Report of ‘Quantitative trait loci for milk yield, fat and protein percentage in buffaloes’ of World Bank Funded Project ‘National Agriculture Innovation Project’ of Indian Council of Agricultural Research, Grant No. 415401-02. ICAR-National Bureau of Animal Genetic Resources.

Vijh R K. 2014. Identification of quantitative trait loci for milk yield, fat and protein percentage in buffaloes. Buffalo Reference Family Germplasm Catalogue. Published by Indian Council of Agricultural Research. pp 671.

Vijh R K, Upasna S and Gokhle S B. 2018. Creation of a large reference family with phenotype recording and genotype data generation in buffaloes. Indian Journal of Animal Sciences 88(2): 59–65.

Submitted

2019-05-23

Published

2019-05-23

Issue

Section

Articles

How to Cite

SHARMA, U., BANERJEE, P., JOSHI, J., KAPOOR, P., & VIJH, R. K. (2019). Identification of quantitative trait loci for milk protein percentage in Murrah buffaloes. The Indian Journal of Animal Sciences, 89(5), 528–538. https://doi.org/10.56093/ijans.v89i5.90021
Citation