Hostname: page-component-586b7cd67f-2plfb Total loading time: 0 Render date: 2024-11-29T17:14:48.088Z Has data issue: false hasContentIssue false
  • English
  • Français

The genetic background of clinical mastitis in Holstein-Friesian cattle

Published online by Cambridge University Press:  05 March 2019

J. Szyda Open the ORCID record for J. Szyda [Opens in a new window] ,
M. Mielczarek ,
M. Frąszczak ,
G. Minozzi ,
J. L. Williams  and
K. Wojdak-Maksymiec
J. Szyda*
Affiliation:
Biostatistics Group, Department of Genetics, Wroclaw University of Environmental and Life Sciences, Kozuchowska 7, 51-631 Wroclaw, Poland Institute of Animal Breeding, Krakowska 1, 32-083 Balice, Poland
M. Mielczarek
Affiliation:
Biostatistics Group, Department of Genetics, Wroclaw University of Environmental and Life Sciences, Kozuchowska 7, 51-631 Wroclaw, Poland Institute of Animal Breeding, Krakowska 1, 32-083 Balice, Poland
M. Frąszczak
Affiliation:
Biostatistics Group, Department of Genetics, Wroclaw University of Environmental and Life Sciences, Kozuchowska 7, 51-631 Wroclaw, Poland
G. Minozzi
Affiliation:
Department of Veterinary Medicine, Università degli Studi di Milano, Via Giovanni Celoria 10, 20133 Milano, Italy
J. L. Williams
Affiliation:
Davies Research Centre, School of Animal and Veterinary Sciences, University of Adelaide, Roseworthy, SA 5371, Australia
K. Wojdak-Maksymiec
Affiliation:
Department of Animal Genetics, West Pomeranian University of Technology, Doktora Judyma 26, 71-466 Szczecin, Poland
*
†E-mail: [email protected]
Get access

Abstract

Mastitis is an inflammatory disease of the mammary gland, which has a significant economic impact and is an animal welfare concern. This work examined the association between single nucleotide polymorphisms (SNPs) and copy number variations (CNVs) with the incidence of clinical mastitis (CM). Using information from 16 half-sib pairs of Holstein-Friesian cows (32 animals in total) we searched for genomic regions that differed between a healthy (no incidence of CM) and a mastitis-prone (multiple incidences of CM) half-sib. Three cows with average sequence depth of coverage below 10 were excluded, which left 13 half-sib pairs available for comparisons. In total, 191 CNV regions were identified, which were deleted in a mastitis-prone cow, but present in its healthy half-sib and overlapped in at least nine half-sib pairs. These regions overlapped with exons of 46 genes, among which APP (BTA1), FOXL2 (BTA1), SSFA2 (BTA2), OTUD3 (BTA2), ADORA2A (BTA17), TXNRD2 (BTA17) and NDUFS6 (BTA20) have been reported to influence CM. Moreover, two duplicated CNV regions present in nine healthy individuals and absent in their mastitis-affected half-sibs overlapped with exons of a cholinergic receptor nicotinic α 10 subunit on BTA15 and a novel gene (ENSBTAG00000008519) on BTA27. One CNV region deleted in nine mastitis-affected sibs overlapped with two neighbouring long non-coding RNA sequences located on BTA12. Single nucleotide polymorphisms with differential genotypes between a healthy and a mastitis-affected sib included 17 polymorphisms with alternate alleles in eight affected and healthy half-sib families. Three of these SNPs were located introns of genes: MET (BTA04), RNF122 (BTA27) and WRN (BTA27). In summary, structural polymorphisms in form of CNVs, putatively play a role in susceptibility to CM. Specifically, sequence deletions have a greater effect on reducing resistance against mastitis, than sequence duplications have on increasing resistance against the disease.

Keywords

copy number variationgenomic annotationsingle nucleotide polymorphismsomatic mutationswhole genome sequence
Type
Research Article
Information
animal , Volume 13 , Issue 10 , October 2019 , pp. 2156 - 2163
Copyright
© The Animal Consortium 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abyzov, A, Urban, AE, Snyder, M and Gerstein, M 2011. CNVnator: an approach to discover, genotype, and characterize typical and atypical CNVs from family and population genome sequencing. Genome Research 21, 974–984.CrossRefGoogle ScholarPubMed
Billerey, C, Boussaha, M, Esquerré, D, Rebours, E, Djari, A, Meersseman, C, Klopp, C, Gautheret, D and Rocha, D 2014. Identification of large intergenic non-coding RNAs in bovine muscle using next-generation transcriptomic sequencing. BMC Genomics 15, 499.CrossRefGoogle ScholarPubMed
Boussaha, M, Esquerré, D, Barbieri, J, Djari, A, Pinton, A and Letaief, R 2015. Genome-wide study of structural variants in Bovine Holstein, Montbéliarde and Normande dairy breeds. PLoS One 10, 8.CrossRefGoogle ScholarPubMed
Choi, J-W, Liao, X, Stothard, P, Chung, WH, Jeon, HJ, Miller, SP, Choi, S, Lee, JK, Yang, B, Lee, KT, Han, KJ, Kim, HC, Jeong, D, Oh, JD, Kim, N, Kim, TH, Lee, HK and Lee, SJ 2014. Whole-genome analyses of Korean native and Holstein cattle breeds by massively parallel sequencing. PLoS One 9, 101127.CrossRefGoogle ScholarPubMed
Detilleux, JC 2009. Genetic factors affecting susceptibility to udder pathogens. Veterinary Microbiology 134, 157–164.CrossRefGoogle ScholarPubMed
Domínguez-Gerpe, L and Araújo-Vilar, D 2008. Prematurely aged children: molecular alterations leading to Hutchinson-Gilford Progeria and Werner syndromes. Current Aging Science 1, 202–212.CrossRefGoogle ScholarPubMed
Durán Aguilar, M, Román Ponce, SI, Ruiz López, FJ, González Padilla, E, Vásquez Peláez, CG, Bagnato, A and Strillacci, MG 2016. Genome-wide association study for milk somatic cell score in Holstein cattle using copy number variation as markers. Journal of Animal Breeding and Genetics 134, 49–59.CrossRefGoogle ScholarPubMed
Fang, L, Sahana, G, Su, G, Yu, Y, Zhang, S, Lund, MS and Sørensen, P 2017. Integrating sequence-based GWAS and RNA-Seq provides Novel insights into the genetic basis of mastitis and milk production in dairy cattle. Scientific Reports 7, 45560.CrossRefGoogle ScholarPubMed
Ghorbani, S, Tahmoorespur, M, Masoudi Nejad, A, Nasiri, M and Asgari, Y 2015. Analysis of the enzyme network involved in cattle milk production using graph theory. Molecular Biology Research Communications 4, 93–103.Google ScholarPubMed
Hou, Y, Liu, GE, Bickhart, DM, Cardone, MF, Wang, K, Kim, ES, Matukumalli, LK, Ventura, M, Song, J, VanRaden, PM, Sonstegard, TS and Van Tassell, CP 2011. Genomic characteristics of cattle copy number variations. BMC Genomics 12, 127.CrossRefGoogle ScholarPubMed
Keel, BN, Keele, JW and Snelling, WM 2017. Genome-wide copy number variation in the bovine genome detected using low coverage sequence of popular beef breeds. Animal Genetics 48, 141–150.CrossRefGoogle ScholarPubMed
Koboldt, D, Zhang, Q, Larson, D, Shen, D, McLellan, M, Lin, L, Miller, C, Mardis, E, Ding, L and Wilson, R 2012. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Research 22, 568–576.CrossRefGoogle ScholarPubMed
Koufariotis, LT, Chen, YP, Chamberlain, A, van der Jagt, C and Hayes, BJ 2015. A catalogue of novel bovine long noncoding RNA across 18 tissues. PLoS One 10, e0141225.10.1371/journal.pone.0141225CrossRefGoogle ScholarPubMed
Li, H and Durbin, R 2009. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760.CrossRefGoogle ScholarPubMed
Li, H, Handsaker, B, Wysoker, A, Fennell, T, Ruan, J, Homer, N, Marth, G, Abecasis, G and Durbin, R 2009. The sequence alignment/map (SAM) format and SAMtools. Bioinformatics 25, 2078–2079.CrossRefGoogle ScholarPubMed
McGovern, DP, Gardet, A, Törkvist, L, Goyette, P, Essers, J, Taylor, KD, Neale, BM, Ong, RT, Lagacé, C, Li, C, Green, T, Stevens, CR, Beauchamp, C, Fleshner, PR, Carlson, M, D’Amato, M, Halfvarson, J, Hibberd, ML, Lördal, M, Padyukov, L, Andriulli, A, Colombo, E, Latiano, A, Palmieri, O, Bernard, EJ, Deslandres, C, Hommes, DW, de Jong, DJ, Stokkers, PC, Weersma, RK, NIDDK, IBD, Consortium, G, Sharma, Y, Silverberg, MS, Cho, JH, Wu, J, Roeder, K, Brant, SR, Schumm, LP, Duerr, RH, Dubinsky, MC, Glazer, NL, Haritunians, T, Ippoliti, A, Melmed, GY, Siscovick, DS, Vasiliauskas, EA, Targan, SR, Annese, V, Wijmenga, C, Pettersson, S, Rotter, JI, Xavier, RJ, Daly, MJ, Rioux, JD and Seielstad, M 2010. Genome-wide association identifies multiple ulcerative colitis susceptibility loci. Nature Genetics 42, 332–337.CrossRefGoogle ScholarPubMed
McLaren, W, Pritchard, B, Rios, D, Chen, Y, Flicek, P and Cunningham, F 2010. Deriving the consequences of genomic variants with the Ensembl API and SNP effect predictor. Bioinformatics 26, 2069–2070.CrossRefGoogle ScholarPubMed
Mielczarek, M, Fraszczak, M, Giannico, R, Minozzi, G, Williams, JL, Wojdak-Maksymiec, K and Szyda, J 2017. Analysis of copy number variations in Holstein-Friesian cow genomes based on whole genome sequence data. Journal of Dairy Science 100, 5515–5525.CrossRefGoogle ScholarPubMed
Miglio, A, Moscati, L, Fruganti, G, Pela, M, Scoccia, E, Valiani, A and Maresca, C 2013. Use of milk amyloid A in the diagnosis of subclinical mastitis in dairy ewes. Journal of Dairy Research 80, 496–502.CrossRefGoogle Scholar
Moumné, L, Batista, F, Benayoun, B, Nallathambi, J, Fellous, M, Sundaresan, P and Veitia, RA 2008. The mutations and potential targets of the forkhead transcription factor FOXL2. Molecular and Cellular Endocrinology 282, 2–11.CrossRefGoogle ScholarPubMed
Nelson, CL, Pelak, K, Podgoreanu, MV, Ahn, SH, Scott, WK, Allen, AS, Cowell, LG, Rude, TH, Zhang, Y, Tong, A, Ruffin, F, Sharma-Kuinkel, BK and Fowler, VG Jr 2014. A genome-wide association study of variants associated with acquisition of Staphylococcus aureus bacteremia in a healthcare setting. BMC Infectious Diseases 14, 83.CrossRefGoogle Scholar
Olsen, HG, Knutsen, TM, Lewandowska‑Sabat, AM, Grove, H, Nome, T, Svendsen, M, Arnyasi, M, Sodeland, M, Sundsaasen, KK, Dahl, SR, Heringstad, B, Hansen, HH, Olsaker, I, Kent, MP and Lien, S 2016. Fine mapping of a QTL on bovine chromosome 6 using imputed full sequence data suggests a key role for the group‑specific component (GC) gene in clinical mastitis and milk production. Genetics Selection Evolution 48, 79.CrossRefGoogle Scholar
Sahana, G, Guldbrandtsen, B, Thomsen, B, Holm, LE, Panitz, F, Brondum, RF, Bendixen, C and Lund, MS 2014. Genome-wide association study using high-density single nucleotide polymorphism arrays and whole-genome sequences for clinical mastitis traits in dairy cattle. Journal of Dairy Science 97, 7258–7275.CrossRefGoogle ScholarPubMed
Salmon, JE, Brogle, N, Brownlie, C, Edberg, JC, Kimberly, RP, Chen, BX and Erlanger, BF 1993. Human mononuclear phagocytes express adenosine A1 receptors. A novel mechanism for differential regulation of Fc gamma receptor function. Journal of Immunology 151, 2775–2785.Google ScholarPubMed
Sanchez, M-P, Govignon-Gion, A, Croiseau, P, Fritz, S, Hozé, C, Miranda, G, Martin, P, Barbat-Leterrier, A, Letaïef, R, Rocha, D, Brochard, M, Boussaha, M and Boichard, D 2017. Within-breed and multi-breed GWAS on imputed whole-genome sequence variants reveal candidate mutations affecting milk protein composition in dairy cattle. Genetics Selection Evolution 49, 68.CrossRefGoogle ScholarPubMed
Sanchez, M-P, Govignon-Gion, A, Ferrand, M, Gelé, M, Pourchet, D, Amigues, Y, Fritz, S, Boussaha, M, Capitan, A, Rocha, D, Miranda, G, Martin, P, Brochard, M and Boichard, D 2016. Whole-genome scan to detect quantitative trait loci associated with milk protein composition in 3 French dairy cattle breeds. Journal of Dairy Science 99, 8203–8215.CrossRefGoogle ScholarPubMed
Schukken, YH, Günther, J, Fitzpatrick, J, Fontaine, MC, Goetze, L, Holst, O, Leigh, J, Petzl, W, Schuberth, HJ, Sipka, A, Smith, DG, Quesnell, R, Watts, J, Yancey, R, Zerbe, H, Gurjar, A, Zadoks, RN and Seyfert, HM 2011. Host-response patterns of intramammary infections in dairy cows. Veterinary Immunology and Immunopathology 144, 270–289.CrossRefGoogle ScholarPubMed
Sodeland, M, Kent, MP, Olsen, HG, Opsal, MA, Svendsen, M, Sehested, E, Hayes, BJ and Lien, S 2011. Quantitative trait loci for clinical mastitis on chromosomes 2, 6, 14 and 20 in Norwegian Red cattle. Animal Genetics 42, 457–465.CrossRefGoogle ScholarPubMed
Strillacci, MG, Frigo, E, Schiavini, F, Samoré, AB, Canavesi, F, Vevey, M, Cozzi, MC, 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.CrossRefGoogle ScholarPubMed
Szyda, J, Frąszczak, M, Mielczarek, M, Giannico, R, Minozzi, G, Nicolazzi, EL, Kamiński, S and Wojdak-Maksymiec, K 2015. The assessment of inter-individual variation of whole-genome DNA sequence in 32 cows. Mammalian Genome 26, 658–665.CrossRefGoogle ScholarPubMed
Thompson-Crispi, K, Atalla, H, Miglior, F and Mallard, BA 2014. Bovine mastitis: frontiers in immunogenetics. Frontiers in Immunology 7, 493.Google Scholar
VanRaden, PM, Tooker, ME, O’Connell, JR, Cole, JB and Bickhart, DM 2017. Selecting sequence variants to improve genomic predictions for dairy cattle. Genetics Selection Evolution 49, 32.CrossRefGoogle ScholarPubMed
Wang, W, Jiang, M, Liu, S, Zhang, S, Liu, W, Ma, Y, Zhang, L, Zhang, J and Cao, X 2016. RNF122 suppresses antiviral type I interferon production by targeting RIG-I CARDs to mediate RIG-I degradation. PNAS 113, 9581–9586.CrossRefGoogle ScholarPubMed
Wang, XG, Ju, ZH, Hou, MH, Jiang, Q, Yang, CH, Zhang, Y, Sun, Y, Li, RL, Wang, CF, Zhong, JF and Huang, JM 2016. Deciphering transcriptome and complex alternative splicing transcripts in mammary gland tissues from cows naturally infected with Staphylococcus aureus mastitis. PLoS One 11, e0159719.CrossRefGoogle ScholarPubMed
Ye, Z, Vasco, DA, Carter, TC, Brilliant, MH, Schrodi, SJ and Shukla, SK 2014. Genome wide association study of SNP-, gene-, and pathway-based approaches to identify genes influencing susceptibility to Staphylococcus aureus infections. Frontiers in Genetics 5, 125.CrossRefGoogle ScholarPubMed
Yue, F, Cheng, Y, Breschi, A, Vierstra, J, Wu, W, Ryba, T, Sandstrom, R, Ma, Z, Davis, C, Pope, BD, Shen, Y, Pervouchine, DD, Djebali, S, Thurman, RE, Kaul, R, Rynes, E, Kirilusha, A, Marinov, GK, Williams, BA, Trout, D, Amrhein, H, Fisher-Aylor, K, Antoshechkin, I, DeSalvo, G, See, LH, Fastuca, M, Drenkow, J, Zaleski, C, Dobin, A, Prieto, P, Lagarde, J, Bussotti, G, Tanzer, A, Denas, O, Li, K, Bender, MA, Zhang, M, Byron, R, Groudine, MT, McCleary, D, Pham, L, Ye, Z, Kuan, S, Edsall, L, Wu, YC, Rasmussen, MD, Bansal, MS, Kellis, M, Keller, CA, Morrissey, CS, Mishra, T, Jain, D, Dogan, N, Harris, RS, Cayting, P, Kawli, T, Boyle, AP, Euskirchen, G, Kundaje, A, Lin, S, Lin, Y, Jansen, C, Malladi, VS, Cline, MS, Erickson, DT, Kirkup, VM, Learned, K, Sloan, CA, Rosenbloom, KR, Lacerda de Sousa, B, Beal, K, Pignatelli, M, Flicek, P, Lian, J, Kahveci, T, Lee, D, Kent, WJ, Ramalho Santos, M, Herrero, J, Notredame, C, Johnson, A, Vong, S, Lee, K, Bates, D, Neri, F, Diegel, M, Canfield, T, Sabo, PJ, Wilken, MS, Reh, TA, Giste, E, Shafer, A, Kutyavin, T, Haugen, E, Dunn, D, Reynolds, AP, Neph, S, Humbert, R, Hansen, RS, De Bruijn, M, Selleri, L, Rudensky, A, Josefowicz, S, Samstein, R, Eichler, EE, Orkin, SH, Levasseur, D, Papayannopoulou, T, Chang, KH, Skoultchi, A, Gosh, S, Disteche, C, Treuting, P, Wang, Y, Weiss, MJ, Blobel, GA, Cao, X, Zhong, S, Wang, T, Good, PJ, Lowdon, RF, Adams, LB, Zhou, XQ, Pazin, MJ, Feingold, EA, Wold, B, Taylor, J, Mortazavi, A, Weissman, SM, Stamatoyannopoulos, JA, Snyder, MP, Guigo, R, Gingeras, TR, Gilbert, DM, Hardison, RC, Beer, MA and Ren, B, Mouse ENCODE Consortium 2014. A comparative encyclopedia of DNA elements in the mouse genome. Nature 515, 3553–3564.CrossRefGoogle ScholarPubMed
Supplementary material: File

Szyda et al. supplementary material

Szyda et al. supplementary material 1

Download Szyda et al. supplementary material(File)
File 132.2 KB