Bibliometric Analysis for Genomic Selection Studies in Animal Science

The animal breeding studies rapidly increased over the last century. When the genomic selection tool introduced, scientists and animal breeders have a new area to work with increasing computer power and genomic tolls. In this study, it was aimed to show the situation of last 25 years of the genomic selection studies. Results showed that the number of authors per article showed that the genomic selection is a collobtrative work that its tasks should be shared by gruoup of scientists. Only about 1/3 of the genomic seelction studies related to animal science. Its reason may be hardeness of working with animals ant generation interval which is more easy for plant breeding. When the article issues examined it can be seen that most of the articles were related to dairy science because this method widely use for dairy industry especially to determine candidate sire. The keyword “genomic selection” is widely used even this is a prediction method. It is the proof that the genomic selection is generally accepted idiom. Citation values of the most cited articles also showed that this method mostly affect the dairy science.

Keywords:

Genomic selection, genomic prediction, animal breeding bibliometry, citation,

PDF

___

Abacı SH, Önder H, 2020. Comparison of BLUP and Bayesian methods for different sizes of training population in genomic selection. Turkish Journal of Veterinary & Animal Sciences, 44(5): 994-1002. doi:10.3906/vet-2001-52.
Aria M, Cuccurullo C, 2017. bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of informetrics, 11(4): 959-975.
Ding X, Zhang Z, Li X, Wang S, Wu X, Sun D, Yu Y, Liu J, Wang Y, Zhang Y, Zhang S, Zhang Y, Zhang Q, 2013. Accuracy of genomic prediction for milk production traits in the Chinese Holstein population using a reference population consisting of cows. Journal of Dairy Science, 96(8): 5315-5323. doi:10.3168/jds.2012-6194.
Goddard ME, Hayes BJ, 2007. Genomic selection. Journal of Animal breeding and Genetics, 124(6): 323-330.
Han J, Kang HJ, Kim M, Kwon GH, 2020. Mapping the intellectual structure of research on surgery with mixed reality: Bibliometric network analysis (2000–2019). Journal of Biomedical Informatics, 109: 103516.
Meuwissen THE, Hayes BJ, Goddard ME, 2001 Prediction of total genetic value using genome-wide dense marker maps. Genetics, 157(4): 1819-1829.
Olfaz M, Tırınk C, Önder, H, 2019. Use of CART and CHAID algorithms in Karayaka sheep breeding. Kafkas Üniversitesi Veteriner Fakültesi Dergisi, 25 (1): 105-110. doi: 10.9775/kvfd.2018.20388.
Önder H, Abaci SH, 2015. Path Analysis for Body Measurements on Body Weight of Saanen Kids. Kafkas Üniversitesi Veteriner Fakültesi Dergisi, 21(3): 351-354. DOI: 10.9775/kvfd.2014.12500.
Önder H, Şen U, Takma Ç, Ocak S, Abacı SH, 2015. Genetic Parameter Estimates for Growth Traits in Saanen Kids. Kafkas Üniversitesi Veteriner Fakültesi Dergisi, 21(6): 799-804. DOI: 10.9775/kvfd.2015.13407.
R Core Team, 2020. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
Seyedsharifi R, Azizyan S, Boustan A, Seifdavati J, Mojtahedin A, 2018. Investigating the Bias Resulted from Ignoring Bulmer Effect on the Genetic and Economic Output in Progeny Test and Genomic Selection Program. Kafkas Üniversitesi Veteriner Fakültesi Dergisi, 24(1): 137-144. DOI: 10.9775/kvfd.2017.18618.
Taylor JF, Taylor KH, Decker JE, 2016. Holsteins are the genomic selection poster cows. PNAS, 113: 28. doi: 10.1073/pnas.1608144113.
Wan C, Huang Y, Shi S, Fu G, Cheng L, Chen H, Fu Q, Chen C, Hu K, 2016. Complete Genome Sequence of Goose Parvovirus Isolated from Anser cygnoides in China. Kafkas Üniversitesi Veteriner Fakültesi Dergisi, 22(1): 155-158, DOI: 10.9775/kvfd.2015.14242.
Weller JI, Ezra E, Ron M, 2017. Invited review: A perspective on the future of genomic selection in dairy cattle. Journal of dairy science, 100(11): 8633-8644. doi: 10.3168/jds.2017-12879.