Big data and the role of high-throughput technologies in livestock and poultry breeding

Document Type : Review Article

Authors

1 Ph.D. Student of Animal and Poultry Breeding & Genetics, Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran

2 Associate Professor of Animal Breeding and Genetics, Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran

3 M.Sc. of Animal Breeding and Genetics, Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran

Abstract

The general perspective of livestock and poultry breeding is being transferred to the digital era with high operational capacity, in which high-throughput technologies are utilized to boost the accuracy of phenotypic records collection and estimation of breeding values. Then, using advanced software and large computers, high amount of data is processed. The advent of NGS and the identification of SNPs along with new statistical methods for using this data to estimate the breeding value has led to the widespread use of genomic selection in dairy cattles and poultry. The development of data mining algorithms related to big data plays a significant role in estimating breeding values. A range of novel technologies, such as artificial intelligence, machine learning and deep learning, provide proper opportunities compared to traditional methods for examining economic traits with complex architecture. These approaches have made it possible to analyze large data sets and large genomic information in order to achieve desirable results. The purpose of this study is to provide a brief explanation of the new methods and novel technologies in animal sciences which are widely used in phenotype data collection and data registration in order to estimate accurate breeding values, in such a way as to lead to a digital future. Therefore, increasing the potential of big data analysis, along with new methods for recording phenotypic traits and estimating the breeding values, will dramatically augment genetic improvement.

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References

Britt, J. H., Cushman, R. A., Dechow, C. D., Dobson, H., Humblot, P., Hutjens, M. F., Jones, G.A., Ruegg, P.S., Sheldon, I.M. and Stevenson, J. S. (2018). Invited review: Learning from the future—A vision for dairy farms and cows in 2067. Journal of Dairy Science, 101(5), 3722-3741.
Chu, Y., and Corey, D. R. (2012). RNA sequencing: platform selection, experimental design, and data interpretation. Nucleic Acid Therapeutics, 22(4), 271-274.
CM Dekkers, J. (2012). Application of genomics tools to animal breeding. Current Genomics, 13(3), 207-212.
Cole, J. B., Newman, S., Foertter, F., Aguilar, I., and Coffey, M. (2012). Breeding and genetics symposium: Really big data: Processing and analysis of very large data sets. Journal of Animal Science, 90(3), 723-733.
Kumar, S., Banks, T. W., and Cloutier, S. (2012). SNP discovery through next-generation sequencing and its applications. International Journal of Plant Genomics, 2012, 1-15.
Murase, H. (2000). Artificial intelligence in agriculture. Computers and Electronics in Agriculture, 29(1/2).
Nayeri, S., Sargolzaei, M., and Tulpan, D. (2019). A review of traditional and machine learning methods applied to animal breeding. Animal Health Research Reviews, 20(1), 31-46.
Perera, C., Liu, C. H., and Jayawardena, S. (2015). The emerging internet of things marketplace from an industrial perspective: A survey. IEEE Transactions on Emerging Topics in Computing, 3(4), 585-598.
Pérez‐Enciso, M. (2017). Animal breeding learning from machine learning. Journal of Animal Breeding and Genetics, 134(2), 85-86.
YANG, Y. L., Rong, Z., and Kui, L. (2017). Future livestock breeding: Precision breeding based on multi-omics information and population personalization. Journal of Integrative Agriculture, 16(12), 2784-2791.
Zhang, H., Wang, Z., Wang, S., and Li, H. (2012). Progress of genome wide association study in domestic animals. Journal of Animal Science and Biotechnology, 3(1), 26.

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