Genetic and genomic analyses underpin the feasibility of concomitant genetic improvement of milk yield and mastitis resistance in dairy sheep

Autoři: Georgios Banos aff001;  Emily L. Clark aff002;  Stephen J. Bush aff002;  Prasun Dutta aff002;  Georgios Bramis aff003;  Georgios Arsenos aff003;  David A. Hume aff002;  Androniki Psifidi aff002
Působiště autorů: Scotland’s Rural College, Edinburgh, Easter Bush, Midlothian, Scotland, United Kingdom aff001;  The Roslin Institute, University of Edinburgh, Easter Bush, Midlothian, Scotland, United Kingdom aff002;  School of Veterinary Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece aff003;  Nuffield Department of Clinical Medicine, University of Oxford, John Radcliffe Hospital, Headington, Oxford, England, United Kingdom aff004;  Mater Research Institute-University of Queensland, Translational Research Institute, Woolloongabba, Australia aff005;  Royal Veterinary College, University of London, Hatfield, England, United Kingdom aff006
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: 10.1371/journal.pone.0214346


Milk yield is the most important dairy sheep trait and constitutes the key genetic improvement goal via selective breeding. Mastitis is one of the most prevalent diseases, significantly impacting on animal welfare, milk yield and quality, while incurring substantial costs. Our objectives were to determine the feasibility of a concomitant genetic improvement programme for enhanced milk production and resistance to mastitis. Individual records for milk yield, and four mastitis-related traits (milk somatic cell count, California Mastitis Test score, total viable bacterial count in milk and clinical mastitis presence) were collected monthly throughout lactation for 609 ewes of the Chios breed. All ewes were genotyped with a mastitis specific custom-made 960 single nucleotide polymorphism (SNP) array. We performed targeted genomic association studies, (co)variance component estimation and pathway enrichment analysis, and characterised gene expression levels and the extent of allelic expression imbalance. Presence of heritable variation for milk yield was confirmed. There was no significant genetic correlation between milk yield and mastitis traits. Environmental factors appeared to favour both milk production and udder health. There were no overlapping of SNPs associated with mastitis resistance and milk yield in Chios sheep. Furthermore, four distinct Quantitative Trait Loci (QTLs) affecting milk yield were detected on chromosomes 2, 12, 16 and 19, in locations other than those previously identified to affect mastitis resistance. Five genes (DNAJA1, GHR, LYPLA1, NUP35 and OXCT1) located within the QTL regions were highly expressed in both the mammary gland and milk transcriptome, suggesting involvement in milk synthesis and production. Furthermore, the expression of two of these genes (NUP35 and OXCT1) was enriched in immune tissues implying a potentially pleiotropic effect or likely role in milk production during udder infection, which needs to be further elucidated in future studies. In conclusion, the absence of genetic antagonism between milk yield and mastitis resistance suggests that simultaneous genetic improvement of both traits be achievable.

Klíčová slova:

Gene expression – Mammary glands – Mastitis – Milk – Molecular genetics – Quantitative trait loci – Sheep


1. Miltiadou D, Hager-Theodorides AL, Symeou S, Constantinou C, Psifidi A, Banos G, et al. Variants in the 3′ untranslated region of the ovine acetyl-coenzyme A acyltransferase 2 gene are associated with dairy traits and exhibit differential allelic expression. Journal of Dairy Science. 2017;100(8):6285–97. doi: 10.3168/jds.2016-12326 28624287

2. Barillet F. Genetic improvement for dairy production in sheep and goats. Small Ruminant Research. 2007;70(1):60–75.

3. Legarra A, Ugarte E. Genetic Parameters of Udder Traits, Somatic Cell Score, and Milk Yield in Latxa Sheep. Journal of Dairy Science. 2005;88(6):2238–45. doi: 10.3168/jds.S0022-0302(05)72899-X 15905453

4. Baro J, San Primitivo F, Facultad De Veterinaria I, Spa L. Breeding programme for the Spanish Churra sheep breed1995.

5. Mavrogenis AP, Papachristoforou C. Genetic and phenotypic relationships between milk production and body weight in Chios sheep and Damascus goats. Livestock Production Science. 2000;67(1/2):81–7. doi: 10.1016/S0301-6226(00)00187-1

6. Davies G, Genini S, Bishop SC, Giuffra E. An assessment of opportunities to dissect host genetic variation in resistance to infectious diseases in livestock. Animal: an international journal of animal bioscience. 2009;3(3):415–36. Epub 2009/03/01. doi: 10.1017/s1751731108003522 22444313.

7. Bishop SC, Axford RFE, Nicholas FW, Owen JB. Breeding for disease resistance in farm animals: CABI Publishing; 2010.

8. Merz A, Stephan R, Johler S. Staphylococcus aureus Isolates from Goat and Sheep Milk Seem to Be Closely Related and Differ from Isolates Detected from Bovine Milk. Frontiers in Microbiology. 2016;7:319. doi: 10.3389/fmicb.2016.00319 PMC4789554. 27014240

9. Authority EFS. Scientific opinion on the welfare risks related to the farming of sheep for wool, meat and milk production. EFSA J. 2014;12:128

10. Banos G, Bramis G, Bush SJ, Clark EL, McCulloch MEB, Smith J, et al. The genomic architecture of mastitis resistance in dairy sheep. BMC Genomics. 2017;18(1):624. Epub 2017/08/18. doi: 10.1186/s12864-017-3982-1 28814268; PubMed Central PMCID: PMC5559839.

11. Rupp R, Senin P, Sarry J, Allain C, Tasca C, Ligat L, et al. A Point Mutation in Suppressor of Cytokine Signalling 2 (Socs2) Increases the Susceptibility to Inflammation of the Mammary Gland while Associated with Higher Body Weight and Size and Higher Milk Production in a Sheep Model. PLoS genetics. 2015;11(12):e1005629. Epub 2015/12/15. doi: 10.1371/journal.pgen.1005629 26658352; PubMed Central PMCID: PMC4676722.

12. Gutiérrez-Gil B G-GE, Suárez-Vega A., Arranz JJ Detection of QTL influencing somatic cell score in Churra sheep employing the OvineSNP50 BeadChip. EAAP, 64th Annual Meeting, Nantes 2013;

13. Sechi S CS, Casula M, Congiu GB, Miari S, Mulas G, Salaris S, et al. Genome -wide association analysis of resistance to paratuberculosis and mastitis in dairy sheep. EAAP, 64th Annual Meeting, Nantes 2013. 2013.

14. Gilmour AR, Cullis B.R. and Thompson R. ASREML User Guide, Release 3.0, NSW Department of Primary Industries, Australia. 2009.

15. Psifidi A, Dovas CI, Bramis G, Lazou T, Russel CL, Arsenos G, et al. Comparison of eleven methods for genomic DNA extraction suitable for large-scale whole-genome genotyping and long-term DNA banking using blood samples. PLoS One. 2015;10(1):e0115960. doi: 10.1371/journal.pone.0115960 25635817

16. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81(3):559–75. Epub 2007/08/19. doi: 10.1086/519795 17701901; PubMed Central PMCID: PMC1950838.

17. Aulchenko YS, Ripke S, Isaacs A, van Duijn CM. GenABEL: an R library for genome-wide association analysis. Bioinformatics (Oxford, England). 2007;23(10):1294–6. Epub 2007/03/27. doi: 10.1093/bioinformatics/btm108 17384015.

18. Zhou X, Stephens M. Efficient multivariate linear mixed model algorithms for genome-wide association studies. Nat Methods. 2014;11(4):407–9. Epub 2014/02/18. doi: 10.1038/nmeth.2848 24531419; PubMed Central PMCID: PMC4211878.

19. Barrett JC, Fry B, Maller J, Daly MJ. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics (Oxford, England). 2005;21(2):263–5. Epub 2004/08/07. doi: 10.1093/bioinformatics/bth457 15297300.

20. Krämer A, Green J, Pollard J, Tugendreich S. Causal analysis approaches in Ingenuity Pathway Analysis. Bioinformatics (Oxford, England). 2014;30(4):523–30. doi: 10.1093/bioinformatics/btt703 PMC3928520. 24336805

21. Suarez-Vega A, Gutierrez-Gil B, Klopp C, Tosser-Klopp G, Arranz JJ. Comprehensive RNA-Seq profiling to evaluate lactating sheep mammary gland transcriptome. Scientific data. 2016;3:160051. Epub 2016/07/06. doi: 10.1038/sdata.2016.51 27377755; PubMed Central PMCID: PMC4932878.

22. Suarez-Vega A, Gutierrez-Gil B, Klopp C, Robert-Granie C, Tosser-Klopp G, Arranz JJ. Characterization and Comparative Analysis of the Milk Transcriptome in Two Dairy Sheep Breeds using RNA Sequencing. Scientific reports. 2015;5:18399. Epub 2015/12/19. doi: 10.1038/srep18399 26677795; PubMed Central PMCID: PMC4683406.

23. Clark EL, Bush SJ, McCulloch MEB, Farquhar IL, Young R, Lefevre L, et al. A high resolution atlas of gene expression in the domestic sheep (Ovis aries). PLoS genetics. 2017;13(9):e1006997. doi: 10.1371/journal.pgen.1006997 28915238

24. Jiang Y, Xie M, Chen W, Talbot R, Maddox JF, Faraut T, et al. The sheep genome illuminates biology of the rumen and lipid metabolism. Science. 2014;344(6188):1168–73. Epub 2014/06/07. doi: 10.1126/science.1252806 24904168; PubMed Central PMCID: PMC4157056.

25. Bray NL, Pimentel H, Melsted P, Pachter L. Near-optimal probabilistic RNA-seq quantification. Nat Biotech. 2016;34(5):525–7. doi: 10.1038/nbt.3519 27043002

26. Lahens NF, Kavakli IH, Zhang R, Hayer K, Black MB, Dueck H, et al. IVT-seq reveals extreme bias in RNA sequencing. Genome biology. 2014;15(6):R86–R. doi: 10.1186/gb-2014-15-6-r86 24981968.

27. Bush SJ, Freem L, MacCallum AJ, O'Dell J, Wu C, Afrasiabi C, et al. Combination of novel and public RNA-seq datasets to generate an mRNA expression atlas for the domestic chicken. BMC genomics. 2018;19(1):594–. doi: 10.1186/s12864-018-4972-7 30086717.

28. Bush SJ, McCulloch MEB, Summers KM, Hume DA, Clark EL. Integration of quantitated expression estimates from polyA-selected and rRNA-depleted RNA-seq libraries. BMC bioinformatics. 2017;18(1):301–. doi: 10.1186/s12859-017-1714-9 28610557.

29. Soneson C, Love MI, Robinson MD. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Res. 2015;4:1521. Epub 2016/03/01. doi: 10.12688/f1000research.7563.2 26925227; PubMed Central PMCID: PMC4712774.

30. Nicolae DL, Gamazon E, Zhang W, Duan S, Dolan ME, Cox NJ. Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS. PLoS genetics. 2010;6(4):e1000888. Epub 2010/04/07. doi: 10.1371/journal.pgen.1000888 20369019; PubMed Central PMCID: PMC2848547.

31. Morley M, Molony CM, Weber TM, Devlin JL, Ewens KG, Spielman RS, et al. Genetic analysis of genome-wide variation in human gene expression. Nature. 2004;430(7001):743–7. Epub 2004/07/23. doi: 10.1038/nature02797 15269782; PubMed Central PMCID: PMC2966974.

32. Stranger BE, Forrest MS, Clark AG, Minichiello MJ, Deutsch S, Lyle R, et al. Genome-wide associations of gene expression variation in humans. PLoS genetics. 2005;1(6):e78. Epub 2005/12/20. doi: 10.1371/journal.pgen.0010078 16362079; PubMed Central PMCID: PMC1315281.

33. Bray NJ, Buckland PR, Owen MJ, O'Donovan MC. Cis-acting variation in the expression of a high proportion of genes in human brain. Human genetics. 2003;113(2):149–53. Epub 2003/05/03. doi: 10.1007/s00439-003-0956-y 12728311.

34. Chamberlain AJ, Vander Jagt CJ, Hayes BJ, Khansefid M, Marett LC, Millen CA, et al. Extensive variation between tissues in allele specific expression in an outbred mammal. BMC Genomics. 2015;16:993. Epub 2015/11/26. doi: 10.1186/s12864-015-2174-0 26596891; PubMed Central PMCID: PMC4657355.

35. Kim D, Langmead B, Salzberg SL. HISAT: a fast spliced aligner with low memory requirements. Nat Methods. 2015;12(4):357–60. Epub 2015/03/10. doi: 10.1038/nmeth.3317 25751142; PubMed Central PMCID: PMC4655817.

36. Li H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics (Oxford, England). 2011;27(21):2987–93. Epub 2011/09/10. doi: 10.1093/bioinformatics/btr509 21903627; PubMed Central PMCID: PMC3198575.

37. Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics (Oxford, England). 2010;26(6):841–2. Epub 2010/01/30. doi: 10.1093/bioinformatics/btq033 20110278; PubMed Central PMCID: PMC2832824.

38. Cingolani P, Platts A, Wang le L, Coon M, Nguyen T, Wang L, et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly. 2012;6(2):80–92. Epub 2012/06/26. doi: 10.4161/fly.19695 22728672; PubMed Central PMCID: PMC3679285.

39. Mayba O, Gilbert HN, Liu J, Haverty PM, Jhunjhunwala S, Jiang Z, et al. MBASED: allele-specific expression detection in cancer tissues and cell lines. Genome Biol. 2014;15(8):405. Epub 2014/10/16. doi: 10.1186/s13059-014-0405-3 25315065; PubMed Central PMCID: PMC4165366.

40. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society Series B (Methodological). 1995;57(1):289–300.

41. Hagnestam-Nielsen C, Emanuelson U, Berglund B, Strandberg E. Relationship between somatic cell count and milk yield in different stages of lactation. J Dairy Sci. 2009;92(7):3124–33. Epub 2009/06/17. doi: 10.3168/jds.2008-1719 19528590.

42. Rupp R, Boichard D. Genetics of resistance to mastitis in dairy cattle. Veterinary research. 2003;34(5):671–88. Epub 2003/10/15. doi: 10.1051/vetres:2003020 14556700.

43. Tolone M, Riggio V, Portolano B. Estimation of genetic and phenotypic parameters for bacteriological status of the udder, somatic cell score, and milk yield in dairy sheep using a threshold animal model. Livestock Science. 2013;151(2–3):134–9. doi: 10.1016/j.livsci.2012.11.014

44. Juan José Arranz BGr-G. Detection of QTL Underlying Milk Traits in Sheep: An Update, Milk Production. In: Chaiyabutr PN, editor. Advanced Genetic Traits, Cellular Mechanism, Animal Management and Health: InTech; 2012.

45. Ligda C, Papadopoulos T, Mavrogenis A, Georgoudis A. Genetic parameters for test day milk traits and somatic cell counts in Chios dairy sheep. In: Gabiña D, Sanna S, editors. Breeding programmes for improving the quality and safety of products New traits, tools, rules and organization? Options Méditerranéennes: Série A. Séminaires Méditerranéens. 55: Zaragoza: CIHEAM; 2003. p. 55–9.

46. Gutierrez-Gil B, Arranz JJ, Pong-Wong R, Garcia-Gamez E, Kijas J, Wiener P. Application of selection mapping to identify genomic regions associated with dairy production in sheep. PLoS One. 2014;9(5):e94623. Epub 2014/05/03. doi: 10.1371/journal.pone.0094623 24788864; PubMed Central PMCID: PMC4006912.

47. Mateescu RG, Thonney ML. Genetic mapping of quantitative trait loci for milk production in sheep. Anim Genet. 2010;41(5):460–6. Epub 2010/04/17. doi: 10.1111/j.1365-2052.2010.02045.x 20394603.

48. Garcia-Gamez E, Gutierrez-Gil B, Sahana G, Sanchez JP, Bayon Y, Arranz JJ. GWA analysis for milk production traits in dairy sheep and genetic support for a QTN influencing milk protein percentage in the LALBA gene. PLoS One. 2012;7(10):e47782. Epub 2012/10/25. doi: 10.1371/journal.pone.0047782 23094085; PubMed Central PMCID: PMC3475704.

49. Suárez-Vega A, Gutiérrez-Gil B, Klopp C, Tosser-Klopp G, Arranz JJ. Variant discovery in the sheep milk transcriptome using RNA sequencing. BMC Genomics. 2017;18(1):170. doi: 10.1186/s12864-017-3581-1 28202015

50. Gotoh T, Terada K, Oyadomari S, Mori M. hsp70-DnaJ chaperone pair prevents nitric oxide- and CHOP-induced apoptosis by inhibiting translocation of Bax to mitochondria. Cell death and differentiation. 2004;11(4):390–402. Epub 2004/01/31. doi: 10.1038/sj.cdd.4401369 14752510.

51. Stefanon B, Colitti M, Gabai G, Knight CH, Wilde CJ. Mammary apoptosis and lactation persistency in dairy animals. The Journal of dairy research. 2002;69(1):37–52. Epub 2002/06/06. doi: 10.1017/s0022029901005246 12047109.

52. Bionaz M, Loor JJ. Gene networks driving bovine milk fat synthesis during the lactation cycle. BMC Genomics. 2008;9:366. Epub 2008/08/02. doi: 10.1186/1471-2164-9-366 18671863; PubMed Central PMCID: PMC2547860.

53. Zarrin M, Wellnitz O, van Dorland HA, Gross JJ, Bruckmaier RM. Hyperketonemia during lipopolysaccharide-induced mastitis affects systemic and local intramammary metabolism in dairy cows. Journal of Dairy Science. 2014;97(6):3531–41. doi: 10.3168/jds.2013-7480 24679930

54. Tiezzi F, Parker-Gaddis KL, Cole JB, Clay JS, Maltecca C. A Genome-Wide Association Study for Clinical Mastitis in First Parity US Holstein Cows Using Single-Step Approach and Genomic Matrix Re-Weighting Procedure. PLoS ONE. 2015;10(2):e0114919. doi: 10.1371/journal.pone.0114919 PMC4319771. 25658712

55. Raven LA, Cocks BG, Goddard ME, Pryce JE, Hayes BJ. Genetic variants in mammary development, prolactin signalling and involution pathways explain considerable variation in bovine milk production and milk composition. Genet Sel Evol. 2014;46:29. Epub 2014/05/02. doi: 10.1186/1297-9686-46-29 24779965; PubMed Central PMCID: PMC4036308.

56. Sun D, Jia J, Ma Y, Zhang Y, Wang Y, Yu Y, et al. Effects of DGAT1 and GHR on milk yield and milk composition in the Chinese dairy population. Animal Genetics. 2009;40(6):997–1000. doi: 10.1111/j.1365-2052.2009.01945.x 19781040

57. Rahmatalla SA, Muller U, Strucken EM, Reissmann M, Brockmann GA. The F279Y polymorphism of the GHR gene and its relation to milk production and somatic cell score in German Holstein dairy cattle. Journal of applied genetics. 2011;52(4):459–65. Epub 2011/06/11. doi: 10.1007/s13353-011-0051-3 21660490.

58. 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(6):832–51. Epub 2009/06/11. doi: 10.1111/j.1365-2052.2009.01921.x 19508288; PubMed Central PMCID: PMC2779988.

59. Gutiérrez-Gil B, Arranz JJ, Wiener P. An interpretive review of selective sweep studies in Bos taurus cattle populations: identification of unique and shared selection signals across breeds. Frontiers in genetics. 2015;6(167). doi: 10.3389/fgene.2015.00167 26029239

60. Chatziplis DG, Tzamaloukas O, Miltiadou D, Ligda C, Koumas A, Mavrogenis AP, et al. Evidence of major gene(s) affecting milk traits in the Chios sheep breed. Small Ruminant Research. 2012;105(1–3):61–8.

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