A review on Bioinformatics in Animal Breeding and Research on Disease Resistance
Bioinformatics in Animal Breeding and Research on Disease Resistance
Keywords:
Bioinformatics, Animal Breeding, Disease Resistance, Genomic Selection, Quantitative Trait Loci (QTLs)
Abstract
The integration of bioinformatics in animal breeding has transformed the way genetic information is utilized to improve disease resistance and overall productivity in livestock. This review examines the roles of bioinformatics in animal breeding, with a particular focus on research related to disease resistance. The article explores the latest advances in genome sequencing, genomic selection, and the identification of quantitative trait loci (QTLs) that contribute to disease resistance. By leveraging recent literature, it,s highlights the potential of bioinformatics tools and techniques to accelerate breeding programs, improve animal health, and enhance the efficiency of livestock production. It also discusses the challenges associated with bioinformatics in animal breeding, such as data management and the need for interdisciplinary collaboration.References
Afgan, E., Baker, D., Batut, B., van den Beek, M., Bouvier, D., Čech, M., &Blankenberg, D. (2018). The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Research, 46(W1), W537-W544.
Banos, G., Woolliams, J. A., Woodward, B. W., Mrode, R., & Coffey, M. P. (2020). Genetic and genomic resistance to bovine tuberculosis in dairy cattle. Animal Genetics, 51(3), 342-350.
Bishop, S. C., &Woolliams, J. A. (2014). Genomics and disease resistance studies in livestock. Livestock Science, 166, 190-198.
Huang, D. W., Sherman, B. T., &Lempicki, R. A. (2009). Bioinformatics enrichment tools: Paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Research, 37(1), 1-13.
Li, H., & Durbin, R. (2009). Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics, 25(14), 1754-1760.
Li, W., Zhu, X., Liu, Y., Shi, Y., Wang, H., & Li, X. (2019). Integrated analysis of transcriptomic and genomic data to improve resistance to salmonella in pigs. Frontiers in Genetics, 10, 836.
Meuwissen, T. H., Hayes, B. J., & Goddard, M. E. (2016). Genomic selection: A paradigm shift in animal breeding. Animal Frontiers, 6(1), 6-14.
Sun, H., Shi, Y., Zhang, Q., Wang, C., Wang, Y., & Li, X. (2021). Identification of QTLs associated with resistance to avian influenza virus in chickens using GWAS. Poultry Science, 100(8), 101013.
Zhang, H., Liu, Y., Wang, L., Shi, X., Li, W., & Liu, G. (2018). Fine mapping and candidate gene identification for resistance to Marek's disease in chickens. Frontiers in Genetics, 9, 32.
Ajayi, O. O., Peters, S. O., De Donato, M., Sowande, S. O., Mujibi, F. D. N., Morenikeji, O. B., ... &Imumorin, I. G. (2018). Computational genome-wide identification of heat shock protein genes in the bovine genome. F1000Research, 7, 1504. https://doi.org/10.12688/f1000research.15580.1
Altschul, S. F., Gish, W., Miller, W., Myers, E. W., &Lipman, D. J. (1990). Basic local alignment search tool. Journal of Molecular Biology, 215(3), 403-410. https://doi.org/10.1016/S0022-2836(05)80360-2
Arya, D., Tyagi, R., Sharma, S., & Sharma, M. (2023). Comparative study of antibacterial activity of Tulsi, garlic and commercially used antibiotics against bovine bacterial endometritis using molecular docking analysis. Indian Journal of Animal Reproduction, 44(2), 69-74. https://doi.org/10.5958/0974-1710.2023.00020.4
Dong, Z., Liu, M., Zou, X., Sun, W., Liu, X., Zeng, J., ... & Liu, X. (2022). Integrating network pharmacology and molecular docking to analyse the potential mechanism of action of Macleayacordata (Willd.) R. Br. in the treatment of bovine hoof disease. Veterinary Sciences, 9(1), 1-16. https://doi.org/10.3390/vetsci9010016
Cooper, N. G. (1994). The human genome project: Deciphering the blueprint of heredity. University Science Books.
D’Cruz, S. C., Jubendradass, R., Jayakanthan, M., Rani, S. J. A., &Mathur, P. P. (2012). Bisphenol A impairs insulin signaling and glucose homeostasis and decreases steroidogenesis in rat testis: An in vivo and in silico study. Food and Chemical Toxicology, 50(3-4), 1124-1133. https://doi.org/10.1016/j.fct.2012.01.045
Daetwyler, H. D., Calus, M. P., Pong-Wong, R., de los Campos, G., & Hickey, J. M. (2013). Genomic prediction in animals and plants: Simulation of data, validation, reporting, and benchmarking. Genetics, 193(2), 347-365. https://doi.org/10.1534/genetics.112.147983
Dawson, H. D., Chen, C., Gaynor, B., Shao, J., & Urban, J. F., Jr. (2017). The porcine translational research database: A manually curated, genomics and proteomics-based research resource. BMC Genomics, 18, 643. https://doi.org/10.1186/s12864-017-4031-7
Ganguly, B., Rastogi, S. K., & Prasad, S. (2013). Computational designing of a polyepitope fecundity vaccine for multiple species of livestock. Vaccine, 32(1), 11-18. https://doi.org/10.1016/j.vaccine.2013.10.059
Elrashedy, A., Nayel, M., Salama, A., Salama, M. M., & Hasan, M. E. (2024). Bioinformatics approach for structure modeling, vaccine design, and molecular docking of Brucella candidate proteins BvrR, OMP25, and OMP31. Scientific Reports, 14(1), 11951. https://doi.org/10.1038/s41598-024-70144-w
Gebre, M. S., Brito, L. A., Tostanoski, L. H., Edwards, D. K., Carfi, A., &Barouch, D. H. (2021). Novel approaches for vaccine development. Cell, 184(6), 1589-1603. https://doi.org/10.1016/j.cell.2021.02.044
Greener, J. G., Kandathil, S. M., Moffat, L., & Jones, D. T. (2022). A guide to machine learning for biologists. Nature Reviews Molecular Cell Biology, 23, 40-55. https://doi.org/10.1038/s41580-021-00407-0
Jhala, M. K., Joshi, C. G., Purohit, T. J., & Patel, N. P. (2011). Role of bioinformatics in biotechnology. Information Technology Centre, GAU, Anand. https://doi.org/10.2139/ssrn.1889433
Jovanović, S., Savić, M., &Živković, D. (2009). Genetic variation in disease resistance among farm animals. Biotechnology in Animal Husbandry, 25(5-6-1), 339-347. https://doi.org/10.2298/BAH0906339J
Kadarmideen, H. N. (2014). Genomics to systems biology in animal and veterinary sciences: Progress, lessons and opportunities. Livestock Science, 166, 232-248. https://doi.org/10.1016/j.livsci.2014.04.028
Kaikabo, A. A., &Kalshingi, H. A. (2007). Concepts of bioinformatics and its application in veterinary research and vaccines development. Nigerian Veterinary Journal, 28(2), 39-46.
Khalid, M. N., Abdullah, A., Ijaz, Z., Naheed, N., Hamad, A., &Sheir, M. A. (2021). Application and potential use of advanced bioinformatics techniques in agriculture and animal sciences. Indian Journal of Pure & Applied Biosciences, 9(3), 237-246. https://doi.org/10.18782/2582-2845.833
Kim, S., Lim, B., Cho, J., Lee, S., Dang, C. G., & Jeon, J. H. (2021). Genome-wide identification of candidate genes for milk production traits in Korean Holstein cattle. Animals, 11(5), 1392. https://doi.org/10.3390/ani11051392
Kumar, D. (2003). Identification of promiscuous MHC Class-1 and MHC Class-II binding epitopes of rabies virus glycoprotein (Unpublished doctoral dissertation). Deemed University, Indian Veterinary Research Institute, Izatnagar, Uttar Pradesh, India.
Latchman, D. (2005). Gene regulation. Taylor & Francis.
Lee, J., Lee, S. M., Lim, B., Park, J., Song, K. L., & Jeon, J. H. (2020). Estimation of variance components and genomic prediction for individual birth weight using three different genome-wide SNP platforms in Yorkshire pigs. Animals, 10(12), 2219. https://doi.org/10.3390/ani10122219
Banos, G., Woolliams, J. A., Woodward, B. W., Mrode, R., & Coffey, M. P. (2020). Genetic and genomic resistance to bovine tuberculosis in dairy cattle. Animal Genetics, 51(3), 342-350.
Bishop, S. C., &Woolliams, J. A. (2014). Genomics and disease resistance studies in livestock. Livestock Science, 166, 190-198.
Huang, D. W., Sherman, B. T., &Lempicki, R. A. (2009). Bioinformatics enrichment tools: Paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Research, 37(1), 1-13.
Li, H., & Durbin, R. (2009). Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics, 25(14), 1754-1760.
Li, W., Zhu, X., Liu, Y., Shi, Y., Wang, H., & Li, X. (2019). Integrated analysis of transcriptomic and genomic data to improve resistance to salmonella in pigs. Frontiers in Genetics, 10, 836.
Meuwissen, T. H., Hayes, B. J., & Goddard, M. E. (2016). Genomic selection: A paradigm shift in animal breeding. Animal Frontiers, 6(1), 6-14.
Sun, H., Shi, Y., Zhang, Q., Wang, C., Wang, Y., & Li, X. (2021). Identification of QTLs associated with resistance to avian influenza virus in chickens using GWAS. Poultry Science, 100(8), 101013.
Zhang, H., Liu, Y., Wang, L., Shi, X., Li, W., & Liu, G. (2018). Fine mapping and candidate gene identification for resistance to Marek's disease in chickens. Frontiers in Genetics, 9, 32.
Bishop, S.C. and Morris, C.A. (2007). Genetics of disease resistance in sheep and goats. Small Ruminant Reasearch. 70: 48-59.
Buniello, A., MacArthur, J.A.L., Cerezo, M., Harris, L.W., (2019). The NHGRI-EBI GWAS catalog of published genome-wide association studies, targeted arrays and summary statistics. Nucleic Acids Research. 47: 1005-1012.
Burkard, C., Lillico, S.G., Reid, E., Jackson, B., Mileham, A.J., AitAli, T., et al. (2017) Precision engineering for PRRSV resistance in pigs: Macrophages from genome edited pigs lacking CD163 SRCR5 domain are fully resistant to both PRRSV genotypes while maintaining biological function. PLoS Pathogens. 13(2): e1006206.
Cole, R.K. (1968). Studies on genetic resistance to Marek’s disease. Avian Diseases. 12: 9-28.
Fire, A., Xu, S., Montgomery, M. et al. (1998). Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans. Nature. 391: 806-811.
Fries, R., Hediger, R. and Stranzinger, G. (1986). Tentative chromosomal localization of the bovine major histocompatibility complex by in situ hybridization. Animal Genetics. 17(4): 287-294.
Gibbons, R. A., Sellwood, R., Burrows, M. et al. (1977). Inheritance of resistance to neonatal E. coli diarrhoea in the pig: examination of the genetic system. Theoretical and Applied Genetics. 51: 65-70.
Gowane, G.R., Akram, N., Misra, S.S., Prakash, V. and Kumar, A. (2018). Genetic diversity of Cahi DRB and DQB genes of caprine MHC class II in Sirohi goat. Journal of Genetics. 97(2): 483-492.
Heringstad, B., Klemetsdal, G. and Ruane, J. (2000). Selection for mastitis resistance in dairy cattle: a review with focus on the situation in the Nordic countries. Livestock Production Science. 64: 95-106.
Hübner, A., Petersen, B., Keil, G.M., Niemann, H., Mettenleiter, T.C. and Fuchs, W. (2018). Efficient inhibition of African swine fever virus replication by CRISPR/Cas9 targeting of the viral p30 gene (CP204L). Scientific Reports. 8: 1449.
Jinek, M., Chylinski, K., Fonfara, I., Hauer, M., Doudna, J. A. and Charpentier, E. (2012). A programmable dual-RNAguided DNA endonuclease in adaptive bacterial immunity. Science. 17: 816-21.
Kannaki, T.R., Reddy, M.R., Raja Ravindra, K.S. and Chatterjee R.N. (2017). Genetic diversity analysis of the major histocompatibility complex (MHC) region in Indian native chicken breeds and pureline chickensusing the LEI0258 microsatellite marker. Indian Journal of Animal Research. 51: 998-1001.
Kannaki, T.R., Verma, P.C., Reddy, M. R. and Shanmugam, M. (2018). Molecular characterization of duck (Anas platyrynhos) Toll-like receptors, mRNA expressions profile in day-old duckling’s tissues and cytokine response to in vitro TLR agonsists stimulation. Indian Journal of Animal Research. 52: 851-857.
Kizilkaya, K., Tait, R.G., Garrick, D.J., Fernando, R.L. and Reecy, J.M. (2013). Genome-wide association study of infectious bovine keratoconjunctivitis in Angus cattle. BMC Genetics. 14-23.
Landsteiner, K. (1901). Ueber Agglutinationserscheinungen normalen menschlichen Blutes. Wiener Klinische Wochenschrift. 46: 1132-1134. (Translation: On agglutination phenomena of normal human blood).
Liu, W., Miller, M.M. and Lamont, S.J. (2002). Association of MHC class I and class II gene polymorphisms with vaccine or challenge response to Salmonella enteritidis in young chicks. Immunogenetics. 54: 582-590.
Matzinger, P. and R. Zamoyska. (1982). A beginner’s guide to major histocompatibility complex function. Nature. 297: 628. Mcdevitt, H.O. and Benacerraf, B. (1969). Genetic Control of Specific Immune. Advances in Immunology, Academic Press, 11: 31-74.
Miller, M.R., White, A. and Boots, M. (2005). The evolution of host resistance: Tolerance and control as distinct strategies. Journal of Theoretical Biology. 236: 198-207.
Mugambi, J.M., Wanyangu, S.W., Bain, R.K., Owango, M.O., Duncan, J.L. and Stear, M.J. (1996). Response of Dorper and Red Maasai lambs to trickle Haemonchus contortus infections. Research in Veterinary Science. 61: 218-221.
Pal, A. and Chatterjee, P.N. (2009). Molecular cloning and characterization of CD14 gene in goat, Small Ruminant Research. 82(2): 84-87.
Pal, A., Sharma, A., Bhattacharya, T.K., Chatterjee, P.N. and Chakravarty, A.K. (2011). Molecular Characterization and SNP Detection of CD14 Gene of Crossbred Cattle. Molecular Biology International. 507346: 1-13.
Pashmi, M., Qanbari, S., Ghorashi, S.A., Sharifi, A.R. and Simianer, H. (2009). Analysis of relationship between bovine lymphocyte antigen DRB3.2 alleles, somatic cell count and milk traits in Iranian Holstein cattle. Journal of Animal Breeding and Genetics. 126(4): 296-303.
Rebel, J.M., Balk, F.R. and Boersma, W.J. (2005). Cytokine responses in broiler lines that differ in susceptibility to malabsorption syndrome. British Poultry Science. 46: 679-686.
Richardson, L.A. (2016). Understanding Disease Tolerance and Resilience. PLoS Biology. 14(7): e1002513.
Roy, B.A. and Kirchner, J.W. (2000). Evolutionary dynamics of pathogen resistance and. Evolution. 54: 51–63.
Shook, G.E. and Schutz, M.M. (1994). Selection on somatic cell score to improve resistance to mastitis in the United States. Journal of Dairy Science. 77: 648-658.
Singh, I., McConnell, I. and Blacklaws, B. (2006). Immune response to individual Maedi-Visna Virus gag antigens. Journal of Virology. 80: 912–919.
Smith, J., Gheyas, A. and Burt, D. W. (2016). Animal genomics and infectious disease resistance in poultry. International Office of Epizootics. 35: 105-119.
Banos, G., Woolliams, J. A., Woodward, B. W., Mrode, R., & Coffey, M. P. (2020). Genetic and genomic resistance to bovine tuberculosis in dairy cattle. Animal Genetics, 51(3), 342-350.
Bishop, S. C., &Woolliams, J. A. (2014). Genomics and disease resistance studies in livestock. Livestock Science, 166, 190-198.
Huang, D. W., Sherman, B. T., &Lempicki, R. A. (2009). Bioinformatics enrichment tools: Paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Research, 37(1), 1-13.
Li, H., & Durbin, R. (2009). Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics, 25(14), 1754-1760.
Li, W., Zhu, X., Liu, Y., Shi, Y., Wang, H., & Li, X. (2019). Integrated analysis of transcriptomic and genomic data to improve resistance to salmonella in pigs. Frontiers in Genetics, 10, 836.
Meuwissen, T. H., Hayes, B. J., & Goddard, M. E. (2016). Genomic selection: A paradigm shift in animal breeding. Animal Frontiers, 6(1), 6-14.
Sun, H., Shi, Y., Zhang, Q., Wang, C., Wang, Y., & Li, X. (2021). Identification of QTLs associated with resistance to avian influenza virus in chickens using GWAS. Poultry Science, 100(8), 101013.
Zhang, H., Liu, Y., Wang, L., Shi, X., Li, W., & Liu, G. (2018). Fine mapping and candidate gene identification for resistance to Marek's disease in chickens. Frontiers in Genetics, 9, 32.
Ajayi, O. O., Peters, S. O., De Donato, M., Sowande, S. O., Mujibi, F. D. N., Morenikeji, O. B., ... &Imumorin, I. G. (2018). Computational genome-wide identification of heat shock protein genes in the bovine genome. F1000Research, 7, 1504. https://doi.org/10.12688/f1000research.15580.1
Altschul, S. F., Gish, W., Miller, W., Myers, E. W., &Lipman, D. J. (1990). Basic local alignment search tool. Journal of Molecular Biology, 215(3), 403-410. https://doi.org/10.1016/S0022-2836(05)80360-2
Arya, D., Tyagi, R., Sharma, S., & Sharma, M. (2023). Comparative study of antibacterial activity of Tulsi, garlic and commercially used antibiotics against bovine bacterial endometritis using molecular docking analysis. Indian Journal of Animal Reproduction, 44(2), 69-74. https://doi.org/10.5958/0974-1710.2023.00020.4
Dong, Z., Liu, M., Zou, X., Sun, W., Liu, X., Zeng, J., ... & Liu, X. (2022). Integrating network pharmacology and molecular docking to analyse the potential mechanism of action of Macleayacordata (Willd.) R. Br. in the treatment of bovine hoof disease. Veterinary Sciences, 9(1), 1-16. https://doi.org/10.3390/vetsci9010016
Cooper, N. G. (1994). The human genome project: Deciphering the blueprint of heredity. University Science Books.
D’Cruz, S. C., Jubendradass, R., Jayakanthan, M., Rani, S. J. A., &Mathur, P. P. (2012). Bisphenol A impairs insulin signaling and glucose homeostasis and decreases steroidogenesis in rat testis: An in vivo and in silico study. Food and Chemical Toxicology, 50(3-4), 1124-1133. https://doi.org/10.1016/j.fct.2012.01.045
Daetwyler, H. D., Calus, M. P., Pong-Wong, R., de los Campos, G., & Hickey, J. M. (2013). Genomic prediction in animals and plants: Simulation of data, validation, reporting, and benchmarking. Genetics, 193(2), 347-365. https://doi.org/10.1534/genetics.112.147983
Dawson, H. D., Chen, C., Gaynor, B., Shao, J., & Urban, J. F., Jr. (2017). The porcine translational research database: A manually curated, genomics and proteomics-based research resource. BMC Genomics, 18, 643. https://doi.org/10.1186/s12864-017-4031-7
Ganguly, B., Rastogi, S. K., & Prasad, S. (2013). Computational designing of a polyepitope fecundity vaccine for multiple species of livestock. Vaccine, 32(1), 11-18. https://doi.org/10.1016/j.vaccine.2013.10.059
Elrashedy, A., Nayel, M., Salama, A., Salama, M. M., & Hasan, M. E. (2024). Bioinformatics approach for structure modeling, vaccine design, and molecular docking of Brucella candidate proteins BvrR, OMP25, and OMP31. Scientific Reports, 14(1), 11951. https://doi.org/10.1038/s41598-024-70144-w
Gebre, M. S., Brito, L. A., Tostanoski, L. H., Edwards, D. K., Carfi, A., &Barouch, D. H. (2021). Novel approaches for vaccine development. Cell, 184(6), 1589-1603. https://doi.org/10.1016/j.cell.2021.02.044
Greener, J. G., Kandathil, S. M., Moffat, L., & Jones, D. T. (2022). A guide to machine learning for biologists. Nature Reviews Molecular Cell Biology, 23, 40-55. https://doi.org/10.1038/s41580-021-00407-0
Jhala, M. K., Joshi, C. G., Purohit, T. J., & Patel, N. P. (2011). Role of bioinformatics in biotechnology. Information Technology Centre, GAU, Anand. https://doi.org/10.2139/ssrn.1889433
Jovanović, S., Savić, M., &Živković, D. (2009). Genetic variation in disease resistance among farm animals. Biotechnology in Animal Husbandry, 25(5-6-1), 339-347. https://doi.org/10.2298/BAH0906339J
Kadarmideen, H. N. (2014). Genomics to systems biology in animal and veterinary sciences: Progress, lessons and opportunities. Livestock Science, 166, 232-248. https://doi.org/10.1016/j.livsci.2014.04.028
Kaikabo, A. A., &Kalshingi, H. A. (2007). Concepts of bioinformatics and its application in veterinary research and vaccines development. Nigerian Veterinary Journal, 28(2), 39-46.
Khalid, M. N., Abdullah, A., Ijaz, Z., Naheed, N., Hamad, A., &Sheir, M. A. (2021). Application and potential use of advanced bioinformatics techniques in agriculture and animal sciences. Indian Journal of Pure & Applied Biosciences, 9(3), 237-246. https://doi.org/10.18782/2582-2845.833
Kim, S., Lim, B., Cho, J., Lee, S., Dang, C. G., & Jeon, J. H. (2021). Genome-wide identification of candidate genes for milk production traits in Korean Holstein cattle. Animals, 11(5), 1392. https://doi.org/10.3390/ani11051392
Kumar, D. (2003). Identification of promiscuous MHC Class-1 and MHC Class-II binding epitopes of rabies virus glycoprotein (Unpublished doctoral dissertation). Deemed University, Indian Veterinary Research Institute, Izatnagar, Uttar Pradesh, India.
Latchman, D. (2005). Gene regulation. Taylor & Francis.
Lee, J., Lee, S. M., Lim, B., Park, J., Song, K. L., & Jeon, J. H. (2020). Estimation of variance components and genomic prediction for individual birth weight using three different genome-wide SNP platforms in Yorkshire pigs. Animals, 10(12), 2219. https://doi.org/10.3390/ani10122219
Banos, G., Woolliams, J. A., Woodward, B. W., Mrode, R., & Coffey, M. P. (2020). Genetic and genomic resistance to bovine tuberculosis in dairy cattle. Animal Genetics, 51(3), 342-350.
Bishop, S. C., &Woolliams, J. A. (2014). Genomics and disease resistance studies in livestock. Livestock Science, 166, 190-198.
Huang, D. W., Sherman, B. T., &Lempicki, R. A. (2009). Bioinformatics enrichment tools: Paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Research, 37(1), 1-13.
Li, H., & Durbin, R. (2009). Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics, 25(14), 1754-1760.
Li, W., Zhu, X., Liu, Y., Shi, Y., Wang, H., & Li, X. (2019). Integrated analysis of transcriptomic and genomic data to improve resistance to salmonella in pigs. Frontiers in Genetics, 10, 836.
Meuwissen, T. H., Hayes, B. J., & Goddard, M. E. (2016). Genomic selection: A paradigm shift in animal breeding. Animal Frontiers, 6(1), 6-14.
Sun, H., Shi, Y., Zhang, Q., Wang, C., Wang, Y., & Li, X. (2021). Identification of QTLs associated with resistance to avian influenza virus in chickens using GWAS. Poultry Science, 100(8), 101013.
Zhang, H., Liu, Y., Wang, L., Shi, X., Li, W., & Liu, G. (2018). Fine mapping and candidate gene identification for resistance to Marek's disease in chickens. Frontiers in Genetics, 9, 32.
Bishop, S.C. and Morris, C.A. (2007). Genetics of disease resistance in sheep and goats. Small Ruminant Reasearch. 70: 48-59.
Buniello, A., MacArthur, J.A.L., Cerezo, M., Harris, L.W., (2019). The NHGRI-EBI GWAS catalog of published genome-wide association studies, targeted arrays and summary statistics. Nucleic Acids Research. 47: 1005-1012.
Burkard, C., Lillico, S.G., Reid, E., Jackson, B., Mileham, A.J., AitAli, T., et al. (2017) Precision engineering for PRRSV resistance in pigs: Macrophages from genome edited pigs lacking CD163 SRCR5 domain are fully resistant to both PRRSV genotypes while maintaining biological function. PLoS Pathogens. 13(2): e1006206.
Cole, R.K. (1968). Studies on genetic resistance to Marek’s disease. Avian Diseases. 12: 9-28.
Fire, A., Xu, S., Montgomery, M. et al. (1998). Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans. Nature. 391: 806-811.
Fries, R., Hediger, R. and Stranzinger, G. (1986). Tentative chromosomal localization of the bovine major histocompatibility complex by in situ hybridization. Animal Genetics. 17(4): 287-294.
Gibbons, R. A., Sellwood, R., Burrows, M. et al. (1977). Inheritance of resistance to neonatal E. coli diarrhoea in the pig: examination of the genetic system. Theoretical and Applied Genetics. 51: 65-70.
Gowane, G.R., Akram, N., Misra, S.S., Prakash, V. and Kumar, A. (2018). Genetic diversity of Cahi DRB and DQB genes of caprine MHC class II in Sirohi goat. Journal of Genetics. 97(2): 483-492.
Heringstad, B., Klemetsdal, G. and Ruane, J. (2000). Selection for mastitis resistance in dairy cattle: a review with focus on the situation in the Nordic countries. Livestock Production Science. 64: 95-106.
Hübner, A., Petersen, B., Keil, G.M., Niemann, H., Mettenleiter, T.C. and Fuchs, W. (2018). Efficient inhibition of African swine fever virus replication by CRISPR/Cas9 targeting of the viral p30 gene (CP204L). Scientific Reports. 8: 1449.
Jinek, M., Chylinski, K., Fonfara, I., Hauer, M., Doudna, J. A. and Charpentier, E. (2012). A programmable dual-RNAguided DNA endonuclease in adaptive bacterial immunity. Science. 17: 816-21.
Kannaki, T.R., Reddy, M.R., Raja Ravindra, K.S. and Chatterjee R.N. (2017). Genetic diversity analysis of the major histocompatibility complex (MHC) region in Indian native chicken breeds and pureline chickensusing the LEI0258 microsatellite marker. Indian Journal of Animal Research. 51: 998-1001.
Kannaki, T.R., Verma, P.C., Reddy, M. R. and Shanmugam, M. (2018). Molecular characterization of duck (Anas platyrynhos) Toll-like receptors, mRNA expressions profile in day-old duckling’s tissues and cytokine response to in vitro TLR agonsists stimulation. Indian Journal of Animal Research. 52: 851-857.
Kizilkaya, K., Tait, R.G., Garrick, D.J., Fernando, R.L. and Reecy, J.M. (2013). Genome-wide association study of infectious bovine keratoconjunctivitis in Angus cattle. BMC Genetics. 14-23.
Landsteiner, K. (1901). Ueber Agglutinationserscheinungen normalen menschlichen Blutes. Wiener Klinische Wochenschrift. 46: 1132-1134. (Translation: On agglutination phenomena of normal human blood).
Liu, W., Miller, M.M. and Lamont, S.J. (2002). Association of MHC class I and class II gene polymorphisms with vaccine or challenge response to Salmonella enteritidis in young chicks. Immunogenetics. 54: 582-590.
Matzinger, P. and R. Zamoyska. (1982). A beginner’s guide to major histocompatibility complex function. Nature. 297: 628. Mcdevitt, H.O. and Benacerraf, B. (1969). Genetic Control of Specific Immune. Advances in Immunology, Academic Press, 11: 31-74.
Miller, M.R., White, A. and Boots, M. (2005). The evolution of host resistance: Tolerance and control as distinct strategies. Journal of Theoretical Biology. 236: 198-207.
Mugambi, J.M., Wanyangu, S.W., Bain, R.K., Owango, M.O., Duncan, J.L. and Stear, M.J. (1996). Response of Dorper and Red Maasai lambs to trickle Haemonchus contortus infections. Research in Veterinary Science. 61: 218-221.
Pal, A. and Chatterjee, P.N. (2009). Molecular cloning and characterization of CD14 gene in goat, Small Ruminant Research. 82(2): 84-87.
Pal, A., Sharma, A., Bhattacharya, T.K., Chatterjee, P.N. and Chakravarty, A.K. (2011). Molecular Characterization and SNP Detection of CD14 Gene of Crossbred Cattle. Molecular Biology International. 507346: 1-13.
Pashmi, M., Qanbari, S., Ghorashi, S.A., Sharifi, A.R. and Simianer, H. (2009). Analysis of relationship between bovine lymphocyte antigen DRB3.2 alleles, somatic cell count and milk traits in Iranian Holstein cattle. Journal of Animal Breeding and Genetics. 126(4): 296-303.
Rebel, J.M., Balk, F.R. and Boersma, W.J. (2005). Cytokine responses in broiler lines that differ in susceptibility to malabsorption syndrome. British Poultry Science. 46: 679-686.
Richardson, L.A. (2016). Understanding Disease Tolerance and Resilience. PLoS Biology. 14(7): e1002513.
Roy, B.A. and Kirchner, J.W. (2000). Evolutionary dynamics of pathogen resistance and. Evolution. 54: 51–63.
Shook, G.E. and Schutz, M.M. (1994). Selection on somatic cell score to improve resistance to mastitis in the United States. Journal of Dairy Science. 77: 648-658.
Singh, I., McConnell, I. and Blacklaws, B. (2006). Immune response to individual Maedi-Visna Virus gag antigens. Journal of Virology. 80: 912–919.
Smith, J., Gheyas, A. and Burt, D. W. (2016). Animal genomics and infectious disease resistance in poultry. International Office of Epizootics. 35: 105-119.
Published
2024-11-15
How to Cite
1.
Tella A, Adebayo FB, Oluwadele JF, Osunkey OJ, Dandara GB. A review on Bioinformatics in Animal Breeding and Research on Disease Resistance. Journal of Agricultural and Biomedical Sciences [Internet]. 15Nov.2024 [cited 9Jan.2025];8(1). Available from: https://nscme.unza.zm/index.php/JABS/article/view/1274
Section
Veterinary Medicine
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright: ©️ JABS. Articles in this journal are distributed under the terms of the Creative Commons Attribution License Creative Commons Attribution License (CC BY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.