Hostname: page-component-cd9895bd7-lnqnp Total loading time: 0 Render date: 2024-12-26T19:54:31.915Z Has data issue: false hasContentIssue false
  • English
  • Français

Analyses of reaction norms reveal new chromosome regions associated with tick resistance in cattle

Published online by Cambridge University Press:  13 July 2017

R. R. Mota ,
F. F. Silva ,
P. S. Lopes ,
R. J. Tempelman ,
B. P. Sollero ,
I. Aguilar  and
F. F. Cardoso
R. R. Mota*
Affiliation:
Gembloux Agro-Bio Tech Faculty, TERRA Teaching and Research Centre, University of Liège, B-5030 Gembloux, Belgium
F. F. Silva
Affiliation:
Department of Animal Science, Universidade Federal de Viçosa, 36570-900 Viçosa, Minas Gerais, Brazil
P. S. Lopes
Affiliation:
Department of Animal Science, Universidade Federal de Viçosa, 36570-900 Viçosa, Minas Gerais, Brazil
R. J. Tempelman
Affiliation:
Department of Animal Science, Michigan State University, 48824 East Lansing, MI, USA
B. P. Sollero
Affiliation:
Embrapa Pecuária Sul, 96401-970 Bagé, Rio Grande do Sul, Brazil
I. Aguilar
Affiliation:
Instituto Nacional de Investigación Agropecuaria – INIA Las Brujas-Canelones, Ruta 48 km 10, Rincon del Colorado, Departamento de Canelones, Uruguay
F. F. Cardoso
Affiliation:
Embrapa Pecuária Sul, 96401-970 Bagé, Rio Grande do Sul, Brazil Programa de Pós-graduação em Zootecnia, Universidade Federal de Pelotas, 96010-610 Pelotas, Rio Grande do Sul, Brazil
*
†E-mail: rrmota@ulg.ac.be
Get access

Abstract

Despite single nucleotide polymorphism (SNP) availability and frequent cost reduction has allowed genome-wide association studies even in complex traits as tick resistance, the use of this information source in SNP by environment interaction context is unknown for many economically important traits in cattle. We aimed at identifying putative genomic regions explaining differences in tick resistance in Hereford and Braford cattle under SNP by environment point of view as well as to identify candidate genes derived from outliers/significant markers. The environment was defined as contemporary group means of tick counts, since they seemed to be the most appropriate entities to describe the environmental gradient in beef cattle. A total of 4363 animals having tick counts (n=10 673) originated from 197 sires and 3966 dams were used. Genotypes were acquired on 3591 of these cattle. From top 1% SNPs (410) having the greatest effects in each environment, 75 were consistently relevant in all environments, which indicated SNP by environment interaction. The outliers/significant SNPs were mapped on chromosomes 1, 2, 5, 6, 7, 9, 11, 13, 14, 15, 16, 18, 21, 23, 24, 26 and 28, and potential candidate genes were detected across environments. The presence of SNP by environment interaction for tick resistance indicates that genetic expression of resistance depends upon tick burden. Markers with major portion of genetic variance explained across environments appeared to be close to genes with different direct or indirect functions related to immune system, inflammatory process and mechanisms of tissue destruction/repair, such as energy metabolism and cell differentiation.

Keywords

beef cattlecandidate genesenvironmental gradientgene functionsingle-step
Type
Research Article
Information
animal , Volume 12 , Issue 2 , February 2018 , pp. 205 - 214
Copyright
© The Animal Consortium 2017 

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

Astrup, E, Lekva, T, Davì, G, Otterdal, K, Santilli, F, Øie, E, Halvorsen, B, Damås, JK, Raoult, D and Vitale, G 2012. A complex interaction between Rickettsia conorii and Dickkopf-1–potential role in immune evasion mechanisms in endothelial cells. PloS one 7, e43638.CrossRefGoogle ScholarPubMed
Bagnall, N, Gough, J, Cadogan, L, Burns, B and Kongsuwan, K 2009. Expression of intracellular calcium signalling genes in cattle skin during tick infestation. Parasite Immunology 31, 177–187.CrossRefGoogle ScholarPubMed
Biegelmeyer, P, Gulias-Gomes, CC, Caetano, AR, Steibel, JP and Cardoso, FF 2016. Linkage disequilibrium, persistence of phase and effective population size estimates in Hereford and Braford cattle. BMC Genetics 17, 1.CrossRefGoogle ScholarPubMed
Cardoso, F, Gomes, C, Sollero, B, Oliveira, M, Roso, V, Piccoli, M, Higa, R, Yokoo, M, Caetano, A and Aguilar, I 2015. Genomic prediction for tick resistance in Braford and Hereford cattle. Journal of Animal Science 93, 2693–2705.CrossRefGoogle ScholarPubMed
Cardoso, F and Tempelman, R 2012. Linear reaction norm models for genetic merit prediction of Angus cattle under genotype by environment interaction. Journal of Animal Science 90, 2130–2141.CrossRefGoogle ScholarPubMed
Carvalho, WA, Domingues, R, de Azevedo Prata, MC, da Silva, MVG, de Oliveira, GC, Guimarães, SEF and Machado, MA 2014. Microarray analysis of tick-infested skin in resistant and susceptible cattle confirms the role of inflammatory pathways in immune activation and larval rejection. Veterinary Parasitology 205, 307–317.CrossRefGoogle ScholarPubMed
Choi, S-C, Titov, AA, Sivakumar, R, Li, W and Morel, L 2016. Immune cell metabolism in systemic lupus erythematosus. Current Rheumatology Reports 18, 66.CrossRefGoogle ScholarPubMed
Ellis, SA and Codner, G 2012. The impact of MHC diversity on cattle T cell responses. Veterinary Immunology and Immunopathology 148, 74–77.CrossRefGoogle ScholarPubMed
Fernández, J and Toro, M 2006. A new method to estimate relatedness from molecular markers. Molecular Ecology 15, 1657–1667.CrossRefGoogle ScholarPubMed
Fischer, D, Laiho, A, Gyenesei, A and Sironen, A 2015. Identification of reproduction-related gene polymorphisms using whole transcriptome sequencing in the large white pig population. G3: Genes, Genomes, Genetics 5, 1351–1360.CrossRefGoogle ScholarPubMed
Flandez, M, Cendrowski, J, Cañamero, M, Salas, A, Del Pozo, N, Schoonjans, K and Real, FX 2014. Nr5a2 heterozygosity sensitises to, and cooperates with, inflammation in KRasG12V-driven pancreatic tumourigenesis. Gut 63, 647–655.CrossRefGoogle Scholar
Forristal, C, Henley, SA, MacDonald, JI, Bush, JR, Ort, C, Passos, DT, Talluri, S, Ishak, CA, Thwaites, MJ and Norley, CJ 2014. Loss of the mammalian DREAM complex deregulates chondrocyte proliferation. Molecular and Cellular Biology 34, 2221–2234.CrossRefGoogle ScholarPubMed
Gasparin, G, Miyata, M, Coutinho, L, Martinez, M, Teodoro, R, Furlong, J, Machado, M, Silva, M, Sonstegard, T and Regitano, L 2007. Mapping of quantitative trait loci controlling tick [Riphicephalus (Boophilus) microplus] resistance on bovine chromosomes 5, 7 and 14. Animal Genetics 38, 453–459.CrossRefGoogle Scholar
Gras, R, Almonacid, L, Ortega, P, Serramia, MJ, Gomez, R, de la Mata, FJ, Lopez-Fernandez, LA and Muñoz-Fernandez, MA 2009. Changes in gene expression pattern of human primary macrophages induced by carbosilane dendrimer 2G-NN16. Pharmaceutical Research 26, 577–586.CrossRefGoogle ScholarPubMed
Hanna, LLH and Riley, DG 2014. Mapping genomic markers to closest feature using the R package Map2NCBI. Livestock Science 162, 59–65.CrossRefGoogle Scholar
Kariuki, SN, Ghodke-Puranik, Y, Dorschner, JM, Chrabot, BS, Kelly, JA, Tsao, BP, Kimberly, RP, Alarcón-Riquelme, ME, Jacob, CO and Criswell, LA 2015. Genetic analysis of the pathogenic molecular sub-phenotype interferon-alpha identifies multiple novel loci involved in systemic lupus erythematosus. Genes and Immunity 16, 15–23.CrossRefGoogle ScholarPubMed
Lee, H-C, Chang, D-E, Yeom, M, Kim, G-H, Choi, K-D, Shim, I, Lee, H-J and Hahm, D-H 2005. Gene expression profiling in hypothalamus of immobilization-stressed mouse using cDNA microarray. Molecular Brain Research 135, 293–300.CrossRefGoogle ScholarPubMed
Lee, H-J, Su, Y, Yin, P-H, Lee, H-C and Chi, C-W 2009. PPARγ/PGC-1α pathway in E-cadherin expression and motility of HepG2 cells. Anticancer Research 29, 5057–5063.Google ScholarPubMed
Lemos, MV, Chiaia, HLJ, Berton, MP, Feitosa, FL, Aboujaoud, C, Camargo, GM, Pereira, AS, Albuquerque, LG, Ferrinho, AM and Mueller, LF 2016. Genome-wide association between single nucleotide polymorphisms with beef fatty acid profile in Nellore cattle using the single step procedure. BMC Genomics 17, 213.CrossRefGoogle ScholarPubMed
Liao, Y-W, Wu, X-M, Jia, J, Wu, X-L, Hong, T, Meng, L-X, and Wu, X-Y 2013. Myosin VI contributes to maintaining epithelial barrier function. Journal of biomedical science 20, 68.CrossRefGoogle ScholarPubMed
Machado, MA, Azevedo, ALS, Teodoro, RL, Pires, MA, Peixoto, MGC, de Freitas, C, Prata, MCA, Furlong, J, da Silva, MVG and Guimarães, SE 2010. Genome wide scan for quantitative trait loci affecting tick resistance in cattle (Bos taurus×Bos indicus). BMC Genomics 11, 1.CrossRefGoogle Scholar
Mapholi, N, Maiwashe, A, Matika, O, Riggio, V, Bishop, S, MacNeil, M, Banga, C, Taylor, J and Dzama, K 2016. Genome-wide association study of tick resistance in South African Nguni cattle. Ticks and Tick-Borne Diseases 7, 487–497.CrossRefGoogle ScholarPubMed
Martinez, M, Machado, M, Nascimento, C, Silva, M, Teodoro, R, Furlong, J, Prata, M, Campos, A, Guimarães, M and Azevedo, A 2006. Association of BoLA-DRB3. 2 alleles with tick (Boophilus microplus) resistance in cattle. Genetics and Molecular Research 5, 513–524.Google ScholarPubMed
Marufu, MC, Qokweni, L, Chimonyo, M and Dzama, K 2011. Relationships between tick counts and coat characteristics in Nguni and Bonsmara cattle reared on semiarid rangelands in South Africa. Ticks and Tick-Borne Diseases 2, 172–177.CrossRefGoogle ScholarPubMed
Melo, TP, Takada, L, Baldi, F, Oliveira, HN, Dias, MM, Neves, HH, Schenkel, FS, Albuquerque, LG and Carvalheiro, R 2016. Assessing the value of phenotypic information from non-genotyped animals for QTL mapping of complex traits in real and simulated populations. BMC Genetics 17, 89.CrossRefGoogle ScholarPubMed
Moore, KJ, Rosen, ED, Fitzgerald, ML, Randow, F, Andersson, LP, Altshuler, D, Milstone, DS, Mortensen, RM, Spiegelman, BM and Freeman, MW 2001. The role of PPAR-γ in macrophage differentiation and cholesterol uptake. Nature Medicine 7, 41–47.CrossRefGoogle ScholarPubMed
Morota, G, Beissinger, TM and Peñagaricano, F 2016. MeSH-informed enrichment analysis and MeSH-guided semantic similarity among functional terms and gene products in chicken. G3: Genes, Genomes, Genetics 6, 2447–2453.CrossRefGoogle ScholarPubMed
Morota, G, Peñagaricano, F, Petersen, J, Ciobanu, DC, Tsuyuzaki, K and Nikaido, I 2015. An application of MeSH enrichment analysis in livestock. Animal Genetics 46, 381–387.CrossRefGoogle ScholarPubMed
Mota, R, Lopes, P, Tempelman, R, Silva, F, Aguilar, I, Gomes, C and Cardoso, F 2016a. Genome-enabled prediction for tick resistance in Hereford and Braford beef cattle via reaction norm models. Journal of Animal Science 94, 1834–1843.CrossRefGoogle ScholarPubMed
Mota, RR, Tempelman, RJ, Lopes, PS, Aguilar, I, Silva, FF and Cardoso, FF 2016b. Genotype by environment interaction for tick resistance of Hereford and Braford beef cattle using reaction norm models. Genetics Selection Evolution 48, 1.CrossRefGoogle ScholarPubMed
Mulenga, A, Sugino, M, Nakajima, M, Sugimoto, C and Onuma, M 2001. Tick-encoded serine proteinase inhibitors (serpins); potential target antigens for tick vaccine development. Journal of Veterinary Medical Science 63, 1063–1069.CrossRefGoogle Scholar
Neels, JG and Grimaldi, PA 2014. Physiological functions of peroxisome proliferator-activated receptor β. Physiological Reviews 94, 795–858.CrossRefGoogle ScholarPubMed
Nelson, SJ, Schopen, M, Savage, AG, Schulman, J-L and Arluk, N 2004. The MeSH translation maintenance system: structure, interface design, and implementation. Medinfo 11, 67–69.Google Scholar
Perry, DJ, Yin, Y, Telarico, T, Baker, HV, Dozmorov, I, Perl, A and Morel, L 2012. Murine lupus susceptibility locus Sle1c2 mediates CD4+ T cell activation and maps to estrogen-related receptor γ. The Journal of Immunology 189, 793–803.CrossRefGoogle ScholarPubMed
Porto Neto, L, Bunch, R, Harrison, B and Barendse, W 2011a. DNA variation in the gene ELTD1 is associated with tick burden in cattle. Animal Genetics 42, 50–55.CrossRefGoogle ScholarPubMed
Porto Neto, LR, Bunch, RJ, Harrison, BE, Prayaga, KC and Barendse, W 2010. Haplotypes that include the integrin alpha 11 gene are associated with tick burden in cattle. BMC Genetics 11, 55.CrossRefGoogle ScholarPubMed
Porto Neto, LR, Jonsson, NN, Ingham, A, Bunch, RJ, Harrison, BE and Barendse, W, Technologies CRCfBG 2012. The RIPK2 gene: a positional candidate for tick burden supported by genetic associations in cattle and immunological response of knockout mouse. Immunogenetics 64, 379–388.CrossRefGoogle ScholarPubMed
Porto Neto, LR, Jonsson, NN, Michael, J and Barendse, W 2011b. Molecular genetic approaches for identifying the basis of variation in resistance to tick infestation in cattle. Veterinary Parasitology 180, 165–172.CrossRefGoogle ScholarPubMed
Porto-Neto, LR, Sonstegard, TS, Liu, GE, Bickhart, DM, Da Silva, MV, Machado, MA, Utsunomiya, YT, Garcia, JF, Gondro, C and Van Tassell, CP 2013. Genomic divergence of zebu and taurine cattle identified through high-density SNP genotyping. BMC Genomics 14, 876.CrossRefGoogle ScholarPubMed
Prayaga, K and Henshall, J 2005. Adaptability in tropical beef cattle: genetic parameters of growth, adaptive and temperament traits in a crossbred population. Animal Production Science 45, 971–983.CrossRefGoogle Scholar
Sharma, N and Jeong, DK 2013. Stem cell research: a novel boulevard towards improved bovine mastitis management. International Journal of Biological Science 9, 818–829.CrossRefGoogle ScholarPubMed
Silva, F, Mulder, H, Knol, E, Lopes, M, Guimarães, S, Lopes, P, Mathur, P, Viana, J and Bastiaansen, J 2014. Sire evaluation for total number born in pigs using a genomic reaction norms approach. Journal of Animal Science 92, 3825–3834.CrossRefGoogle ScholarPubMed
Silva-García, O, Valdez-Alarcón, JJ and Baizabal-Aguirre, VM 2014. The Wnt/β-catenin signaling pathway controls the inflammatory response in infections caused by pathogenic bacteria. Mediators of Inflammation 2014, 1–7.CrossRefGoogle ScholarPubMed
Singh, SK and Girschick, HJ 2003. Tick-host interactions and their immunological implications in tick-borne diseases. Current Science 85, 1284–1298.Google Scholar
Stear, M, Bishop, S, Mallard, B and Raadsma, H 2001. The sustainability, feasibility and desirability of breeding livestock for disease resistance. Research in Veterinary Science 71, 1–7.CrossRefGoogle ScholarPubMed
Streit, M, Wellmann, R, Reinhardt, F, Thaller, G, Piepho, H-P and Bennewitz, J 2013. Using genome-wide association analysis to characterize environmental sensitivity of milk traits in dairy cattle. G3: Genes, Genomes, Genetics 3, 1085–1093.CrossRefGoogle ScholarPubMed
Theilgaard-Mönch, K, Knudsen, S, Follin, P and Borregaard, N 2004. The transcriptional activation program of human neutrophils in skin lesions supports their important role in wound healing. The Journal of Immunology 172, 7684–7693.CrossRefGoogle ScholarPubMed
Thompson-Crispi, KA, Sargolzaei, M, Ventura, R, Abo-Ismail, M, Miglior, F, Schenkel, F and Mallard, BA 2014. A genome-wide association study of immune response traits in Canadian Holstein cattle. BMC Genomics 15, 559.CrossRefGoogle ScholarPubMed
Utsunomiya, YT, Carmo, AS, Neves, HH, Carvalheiro, R, Matos, MC, Zavarez, LB, Ito, PK, O’Brien, AMP, Sölkner, J and Porto-Neto, LR 2014. Genome-wide mapping of loci explaining variance in scrotal circumference in Nellore cattle. PLoS One 9, e88561.CrossRefGoogle ScholarPubMed
Valente, TS, Baldi, F, Sant’Anna, AC, Albuquerque, LG and da Costa, MJRP 2016. Genome-Wide Association Study between single nucleotide polymorphisms and flight speed in Nellore cattle. PloS One 11, e0156956.CrossRefGoogle ScholarPubMed
Verardo, L, Lopes, M, Wijga, S, Madsen, O, Silva, F, Groenen, M, Knol, E, Lopes, P and Guimarães, S 2016. After genome-wide association studies: gene networks elucidating candidate genes divergences for number of teats across two pig populations. Journal of Animal Science 94, 1446–1458.CrossRefGoogle ScholarPubMed
Vivier, E, Tomasello, E, Baratin, M, Walzer, T and Ugolini, S 2008. Functions of natural killer cells. Nature Immunology 9, 503–510.CrossRefGoogle ScholarPubMed
Wambura, P, Gwakisa, P, Silayo, R and Rugaimukamu, E 1998. Breed-associated resistance to tick infestation in Bos indicus and their crosses with Bos taurus. Veterinary Parasitology 77, 63–70.CrossRefGoogle ScholarPubMed
Wang, H, Misztal, I, Aguilar, I, Legarra, A and Muir, W 2012. Genome-wide association mapping including phenotypes from relatives without genotypes. Genetics Research 94, 73–83.CrossRefGoogle ScholarPubMed
Wei, J, Wang, H, Yang, X, Dong, M and Wang, Z 2010. Altered gene profile of placenta from women with intrahepatic cholestasis of pregnancy. Archives of Gynecology and Obstetrics 281, 801–810.CrossRefGoogle ScholarPubMed
Wiggans, G, Sonstegard, T, VanRaden, P, Matukumalli, L, Schnabel, R, Taylor, J, Schenkel, F and Van Tassell, C 2009. Selection of single-nucleotide polymorphisms and quality of genotypes used in genomic evaluation of dairy cattle in the United States and Canada. Journal of Dairy Science 92, 3431–3436.CrossRefGoogle Scholar
Wikel, S and Allen, J 1976. Acquired resistance to ticks. I. Passive transfer of resistance. Immunology 30, 311–316.Google ScholarPubMed
Zhang, X, Lourenco, D, Aguilar, I, Legarra, A and Misztal, I 2016. Weighting strategies for single-step genomic BLUP: an iterative approach for accurate calculation of GEBV and GWAS. Frontiers in Genetics 7, 1–14.CrossRefGoogle ScholarPubMed
Supplementary material: PDF

Mota supplementary material S1

Supplementary Figure

Download Mota supplementary material S1(PDF)
PDF 230.4 KB
Supplementary material: File

Mota supplementary material S2

Supplementary Table

Download Mota supplementary material S2(File)
File 23.4 KB
Supplementary material: File

Mota supplementary material S3

Supplementary Table

Download Mota supplementary material S3(File)
File 214 KB