Identification of QTLs for low somatic cell count in Murrah buffaloes
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Authors
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UPASNA SHARMA
Research Associate, ICAR-National Bureau of Animal Genetic Resources, Karnal, Haryana 132 001 India
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PRIYANKA BANERJEE
Post Doctoral Fellow, Technical University of Denmark, Kobenhavn, Denmark.
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JYOTI JOSHI
Post Doctoral Fellow, Dalhousie University, Nova Scotia, Canada
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PRERNA KAPOOR
Senior Research Fellow, ICAR-National Bureau of Animal Genetic Resources, Karnal, Haryana 132 001 India
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RAMESH KUMAR VIJH
Principal Scientist, ICAR-National Bureau of Animal Genetic Resources, Karnal, Haryana 132 001 India
Keywords:
Buffalo, Interval mapping, QTLs, Mastitis, Murrah, Somatic cell countAbstract
Mastitis, the most frequent and costly disease in buffalo, is the major cause of morbidity. The somatic cell count, an indirect indicator of susceptibility/resistance to mastitis, is a low heritable trait and thus a perfect candidate for marker assisted selection. Half sib families (12) were created and the somatic cell count was recorded at 3 stages of lactation during the first lactation of the 2,422 daughters belonging to 12 sires. Partial genome scan was carried out using interval mapping with different algorithms. The QTLs obtained for each half sib family were further subjected to meta analysis to identify chromosomal regions associated with somatic cell count on 8 chromosomes of buffalo. Four metaQTL regions were identified on chromosomes BBU1q, BBU8, and BBU10; 3 metaQTL regions on BBU2q, BBU9 and BBU15; 2 metaQTL regions on BBU6 and 1 on BBU7 of buffalo. Comparative genomics was used for finding out genes underlying the metaQTL regions; 1,065 genes were underlying the metaQTL regions in buffaloes assuming buffalo–cattle–human synteny. Genes (78) mapped to immune response. These genes are supposedly important candidate genes for further analysis. Gene ontology and network analysis was carried out on these genes. The genes identified belonged to immune response and defense mechanism. The QTL markers identified in the present analysis can be used in the breeding programs of buffalo to select the bulls, which are less susceptible to mastitis.Downloads
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 NBSEAR, Goldammer T, Weikard R, Brunner R M, Kochan K J, Greco A J, Jeong J, Cai Z, Lin G, Prasad A, Kumar S, Mathew GPSB, 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
Baes C, Goertz I, Mayer M, Weimann C, Liu Z, Reinhardt F, Erhardt G and Reinsch N. 2010. Refined mapping of quantitative trait loci for somatic cell score on BTA02 in the German Holstein. Journal of Animal Breeding and Genetics 127(3): 180–88. DOI: https://doi.org/10.1111/j.1439-0388.2009.00838.x
Bennewitz J, Reinsch N, Grohs C, Leveziel H, Malafosse A, Thomsen H, Xu N and Looft C. 2003. Combined analysis of data from two granddaughter designs: A simple strategy for QTL confirmation and increasing experimental power in dairy cattle. Genetics Selection Evolution 3(35): 319–38. DOI: https://doi.org/10.1186/1297-9686-35-3-319
Boichard D, Grohs C, Bourgeois F, Cerqueira F, Faugeras R, Neau A, Rupp R, Amigues Y, Boscher M Y and Leveziel H. 2003. Detection of genes influencing economic traits in three French dairy cattle breeds. Genetics Selection Evolution 35: 77–101. DOI: https://doi.org/10.1186/1297-9686-35-1-77
Chan J K C, NG C S and Hui P K. 1988. A simple guide to the terminology and application of leucocyte monoclonal antibodies. Histopathology 12(5): 461–80. DOI: https://doi.org/10.1111/j.1365-2559.1988.tb01967.x
Chen R, Yang Z, Ji D, Mao Y, Chen Y, Zhang Y, Hamza, Wang X and Li Y. 2011. SNPs of CXCR1 gene and its associations with somatic cell score in Chinese Holstein cattle. Animal Biotechnology 22(3): 133–42. DOI: https://doi.org/10.1080/10495398.2011.582804
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
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
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(Database issue): D472–77. DOI: https://doi.org/10.1093/nar/gkt1102
Daetwyler H D, Schenkel F S, Sargolzaei M and Robinson J A B. 2008. A genome scan to detect quantitative trait loci for economically important traits in Holstein cattle using two methods and a dense single nucleotide polymorphism map. Journal of Dairy Science 91(8): 3225–36. DOI: https://doi.org/10.3168/jds.2007-0333
Doran Anthony G, Berry Donagh P and Creevey Christopher J. 2014. Whole genome association study identifies regions of the bovine genome and biological pathways involved in carcass trait performance in Holstein-Friesian cattle. BMC Genomics 15: 837. DOI: https://doi.org/10.1186/1471-2164-15-837
Doye V and Hurt E. 1997. From nucleoporins to nuclearpore complexes. Current Opinion in Cell Biology 9(3): 401–11. DOI: https://doi.org/10.1016/S0955-0674(97)80014-2
El-Halawany N, Abd-El-Monsif S A, Al-Tohamy Ahmed F M, Hegazy L, Abdel-Shafy H, Abdel-Latif M A, Ghazi Y A, Neuhoff C, Salilew-Wondim D and Schellander K. 2017. Complement component 3: characterization and association with mastitis resistance in Egyptian water buffalo and cattle. Journal of Genetics 96(1): 65–73. DOI: https://doi.org/10.1007/s12041-017-0740-8
ElKassar N and Gress R E. 2010. An overview of IL-7 biology and its use in immunotherapy. Journal of Immunotoxicology 7(1): 1–7. DOI: https://doi.org/10.3109/15476910903453296
Emanuelson U, Danell B and Philipsson J. 1988. Genetic parameters for clinical mastitis, somatic cell count and milk production estimates by multiple-trait restricted maximum likelihood. Journal of Dairy Science 71: 467–76. DOI: https://doi.org/10.3168/jds.S0022-0302(88)79576-4
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 and D ’Eustachio P. 2016. The Reactome pathway Knowledgebase. Nucleic Acids Research 44(D1): D481-7. DOI: https://doi.org/10.1093/nar/gkv1351
Georges M. 1999. Towards marker assisted selection in livestock. Reproduction Nutrition Development 39: 555–61. DOI: https://doi.org/10.1051/rnd:19990504
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
Gomez-Raya L, Klungland H, Våge D I, Olsaker I and Fimland E. 1998. Mapping QTL for milk production traits in Norwegian cattle. Proceedings of the 6th World Congresson Genetics Applied to Livestock Production, Armidale. pp 429–32.
Harmon R J. 1994. Physiology of mastitis and factors affecting somatic cell counts. Journal of Dairy Science 77: 2103–12. DOI: https://doi.org/10.3168/jds.S0022-0302(94)77153-8
Heringstad B, Klemetsdal G and Ruane J. 1999. Clinical mastitis in the Norwegian cattle: Frequency, variance, components and genetic correlation with protein yield. Journal of Dairy Science 82: 1325–30. DOI: https://doi.org/10.3168/jds.S0022-0302(99)75356-7
Heringstad B, Klemetsdal G and Ruane J. 2001. Response to selection against clinical mastitis in the Norwegian cattle population. Acta Agriculturae Scandinavica 51: 155–60. DOI: https://doi.org/10.1080/090647001750193503
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. Physiological Genomics 1(3): 165–75. DOI: https://doi.org/10.1152/physiolgenomics.1999.1.3.165
Jordan M A, Field J, Butzkueven H and Baxter A G. 2014. Genetic Predisposition, Humans. The Autoimmune Diseases. Fifth Edition. pp. 341–64. DOI: https://doi.org/10.1016/B978-0-12-384929-8.00026-5
Kato-Kogoe N, Ohyama H, Okano S, Yamanegi K, Yamada N, Hata M, Nishiura H, Abiko Y, Terada N and Nakasho K. 2016. Functional analysis of differences in transcriptional activity conferred by genetic variants in the 5’ flanking region of the IL12RB2 gene. Immunogenetics 68(1): 55–65. DOI: https://doi.org/10.1007/s00251-015-0882-x
Khatib H, Zaitoun I, Wiebelhaus-Finger J, Chang Y M and Rosa G J M. 2007. The association of bovine PPARGC1A and OPN genes with milk composition in two holstien cattle populations. Journal of Dairy Science 90(6): 2966–70. DOI: https://doi.org/10.3168/jds.2006-812
Kitchen B J. 1981. Review of the progress of dairy science: Bovine mastitis: Milk compositional changes and related diagnostic tests. Journal of Dairy Science 64: 167–88. DOI: https://doi.org/10.1017/S0022029900021580
Klungland H, Sabry A, Heringstad B, Olsen H G, Gomez-Raya L, Vage D I, Olsaker I. Ødegard J, Klemetsdal G, Schulman N, Vilkki J, Ruane J, Aasland M, Ronningen K and Lien S. 2001. Quantitative trait loci affecting clinical mastitis and somatic cell count in dairy cattle. Mammalian Genome 12(11): 837–42. DOI: https://doi.org/10.1007/s00335001-2081-3
Lecerf F, Bretaudeau A, Sallou O, Desert C, Blum Y, lagarrigue S and Dewere O. 2011. AnnotQTL: a new tool to gather functional and comparative information on a genomic region. Nucelc Acid Research 39: W328–33. DOI: https://doi.org/10.1093/nar/gkr361
Liu Y, Jansen G B and Lin C Y. 2004. QTL mapping for dairy cattle production traits using maximum likelihood method. Journal of Dairy Science 87(2): 491–500. DOI: https://doi.org/10.3168/jds.S0022-0302(04)73188-4
Lund M S, Jenson J and Peterson P H. 1999. Estimation of genetic and phenotypic parameters for clinical mastitis, somatic cell production, deviance, and protein yield in dairy cattle using Gibbs sampling. Journal of Diary Science 82: 1045–51. DOI: https://doi.org/10.3168/jds.S0022-0302(99)75325-7
Lund M S, Sahana G, Anderson-Eklund L, Hastings N, Fernandez A, Schulman N, Thomsen B, Viitala S, Williams J L, Sabry A, Viinalaas H and Vilkki J. 2007. Joint analysis of quantitative trait loci for clinical mastitis and somatic cell score on five chromosomes in three Nordic dairy cattle breeds. Journal of Diary Science 90: 5282–90. DOI: https://doi.org/10.3168/jds.2007-0177
Meredith B K, Kearney F J, Finlay E K, Bradley D G, Fahey A G, Berry D P and Lynn D J. 2012. Genome-wide association for milk production and somatic cell score in Holstein-Friesian cattle in Ireland. BMC Genetics 13: 21. DOI: https://doi.org/10.1186/1471-2156-13-21
Miller R H and Paape M J. 1985. Relationship between milk somatic cell count and milk yield. Proceedings of Annual Meeting of National Mastitis Council. p. 60.
Mitchell R E, Hassan M, Burton B R, Britton G, Hill E V, Verhagen J and Wraith D C. 2017. IL-4 enhances IL-10 production in Th1 cells: implications for Th1 and Th2 regulation. Scientific Reports 7: 11315. DOI: https://doi.org/10.1038/s41598-017-11803-y
Ødegard J, Klemetsdal G and Heringstad B. 2003. Variance components and genetic trend or somatic cell count in Norwegian cattle. Livestock Production Science 79: 135–44 DOI: https://doi.org/10.1016/S0301-6226(02)00148-3
Patil M P, Nagvekar A S, Ingole S D, Bharucha S V and Palve V T. 2015. Somatic cell count and alkaline phosphate activity in milk foe evaluation of mastitis in buffalo. Veterinary World 8(3): 363–66. DOI: https://doi.org/10.14202/vetworld.2015.363-366
Rodriguez-Zas S L, Southey B R, Heyen D W and Lewin H A. 2002. Detection of quantitative trait loci influencing dairy traits using a model for longitudinal data. Journal of Diary Science 85(10): 2681–91. DOI: https://doi.org/10.3168/jds.S0022-0302(02)74354-3
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
Rupp R and Boichard D. 1999. Genetic parameters for clinical mastitis, somatic cell score, production, udder type traits and milking ease in first lactation Holsteins. Journal of Diary Science 82: 2198–204. DOI: https://doi.org/10.3168/jds.S0022-0302(99)75465-2
Rupp R and Boichard D. 2003. Genetics of resistance to mastitis in dairy cattle. Veterinary Research 34: 671–88. DOI: https://doi.org/10.1051/vetres:2003020
Sahana G, Gulbrandsten B, Thompsen B and Lund M S. 2013. Confirmation and fine mapping of clinical mastitis and somatic cell score QTL in Nordic Holstein cattle. Animal Genetics 44(6): 620–26. DOI: https://doi.org/10.1111/age.12053
Sahana G, Lund M S, Anderson-Eklund L, Hastings N, Fernandez A, Iso-Touru T, Thomsen B, ViItala S, Sorensen P, Williams J L and Vilkki J. 2008. Fine-mapping QTL for mastitis resistance on BTA9 in three Nordic red cattle breeds. Animal Genetics 3: 354–62. DOI: https://doi.org/10.1111/j.1365-2052.2008.01729.x
Sharma N, Singh N K and Bhadwal M S. 2011. Relationship of somatic cell count and mastitis: an overview. Asian Australasian Journal of Animal Science 24(3): 429–38. DOI: https://doi.org/10.5713/ajas.2011.10233
Spelman R J and Bovenhuis H. 1998. Moving from QTL experiments results to the utilization of QTL in breeding programmes. Animal Genetics 29: 77–84. DOI: https://doi.org/10.1046/j.1365-2052.1998.00238.x
Strillacci M G, Frigo E, Schiavini F, Samoré A B, Canavesi F, Vevey M, Cozzi M C, Soller M, Lipkin E and Bagnato A. 2014. Genome-wide association study for somatic cell score in Valdostana Red Pied cattle breed using pooled DNA. BMC Genetics 15: 106. DOI: https://doi.org/10.1186/s12863-014-0106-7
Supek F, Bošnjak M, Škunca N and Šmuc T. 2011. REVIGO summarizes and visualizes long lists of Gene Ontology terms. PLoS ONE 6(7): e21800.. DOI: https://doi.org/10.1371/journal.pone.0021800
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(Database issue): D447-52. DOI: https://doi.org/10.1093/nar/gku1003
Tal-Stein R, Fontanesi L, Dolezal M, Scotti E, Bagnato A, Russo V, Canavesi F, Friedmann A, Soller M and Lipkin E. 2010. A genome scan for quantitative trait loci affecting milk somatic cell score in Israeli and Italian Holstein cows by means of selective DNA pooling with single- and multiple-marker mapping. Journal of Diary Science 93(10): 4913–27. DOI: https://doi.org/10.3168/jds.2010-3254
Tanaka T, Narazaki M and Kishimoto T. 2014. IL-6 in inflammation, immunity and disease. Cold Spring Harbour Perspective in Biology 6(10): a016295. DOI: https://doi.org/10.1101/cshperspect.a016295
Vijh R K. 2013. Final Report of sub-project entitled ‘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 under Component IV Basic and Strategic Research in Agriculture. ICAR-National Bureau of Animal Genetic Resources, Karnal, Haryana.
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.
Wang X, Ma P, Liu J, Zhang Q, Zhang Y, Ding X, Jiang L, Wang Y, Zhang Y, Sun D, Zhang S, Su G and Yu Y. 2015. Genome- wide association study in Chinese Holstein cows reveal two candidate genes for somatic cell score as an indicator for mastitis susceptibility. BMC Genetics 16: 111. DOI: https://doi.org/10.1186/s12863-015-0263-3
Weller J I, Saran A and Zeliger Y. 1992. Genetic and environmental relationships among somatic cell count, bacterial infection and clinical mastitis. Journal of Diary Science 75: 2532–40. DOI: https://doi.org/10.3168/jds.S0022-0302(92)78015-1
Wijga S, Bastiaansen J W M, Wall E, Strandberg E, de Haas Y, Giblin L and Bovenhuis H. 2012. Genomic associations with somatic cell score in first-lactation Holstein cows. Journal of Dairy Science 95(2): 899–908. DOI: https://doi.org/10.3168/jds.2011-4717
Woo P and Humphires S E. 2013. IL-6 polymorphisms: a useful genetic tool for inflammation research. Journal of Clinical Investigation 123(4): 1413–14. DOI: https://doi.org/10.1172/JCI67221
Zhang Q, Boichard D, Hoeschele I, Ernst C, Eggen A, Murkve B, Pfister-Genskow M, Witte L A, Grignola F E, Uimari P, Thaller G and Bishop M D. 1998. Mapping quantitative trait loci for milk production and health of dairy cattle in a large out-bred pedigree. Genetics 149(4): 1959–73. DOI: https://doi.org/10.1093/genetics/149.4.1959
Zola H, Swart B, Banham A, Barry S, Beare A, Bensussan A, Boumsell L, D Buckley C, Buhring H J, Clark G, Engel P, Fox D, Jin B Q, Macardle P J, Malavasi F, Mason D, Stockinger H and Yang X. 2007. CD molecules 2006-human cell differentiation molecules. Journal of Immunological Methods 319: 1–2. DOI: https://doi.org/10.1016/j.jim.2006.11.001
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