A genomic breed assignment test for traceability of meat of Dual-Purpose Blue

[en] Assigning meat to its breed of origin for traceability purposes is not always straightforward if the breed from which products are derived is closely related to another one. The objective of this study was to determine if a genomic breed assignment test could distinguish meat of Dual-Purpose Blue, a local endangered breed, from meat of Beef Belgian Blue, a heavily used breed in the Belgian meat industry which is related to Dual-Purpose Blue. For this purpose, a genomic breed assignment test based on a panel of 2,005 SNPs and the nearest shrunken centroids method was used to classify 32 meat samples from Dual-Purpose Blue (n = 16), Beef Belgian Blue (n = 8) and Holstein (n = 8) into their breed of origin. From this SNP panel, 167 SNPs allowed to detect meat of Dual-Purpose Blue and 173 SNPs allowed to detect meat of Beef Belgian Blue. The genomic breed assignment test correctly allocated all the meat samples to their breed of origin with a probability of one. Therefore, the use of the genomic breed assignment test in routine as one step of the certification process of Dual- Purpose Blue meat seemed possible.

Disciplines :

Animal production & animal husbandry

Author, co-author :

Wilmot, Hélène ; Université de Liège - ULiège > TERRA Research Centre > Ingénierie des productions animales et nutrition

Glorieux, G.

Hubin, X.

Gengler, Nicolas ; Université de Liège - ULiège > Département GxABT > Ingénierie des productions animales et nutrition

Language :

English

Title :

A genomic breed assignment test for traceability of meat of Dual-Purpose Blue

Publication date :

September 2022

Journal title :

Livestock Science

ISSN :

1871-1413

eISSN :

1878-0490

Publisher :

Elsevier BV

Volume :

263

Pages :

104996

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

https://api.elsevier.com/content/article/PII:S1871141322001755?httpAccept=text/xml

Funders :

F.R.S.-FNRS - Fonds de la Recherche Scientifique

Available on ORBi :

since 07 July 2022

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Bibliography

Alexander, D.H., Novembre, J., Lange, K., Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19:9 (2009), 1655–1664, 10.1101/gr.094052.109.
Baumung, R., Cubric-Curik, V., Schwend, K., Achmann, R., Sölkner, J., Genetic characterisation and breed assignment in Austrian sheep breeds using microsatellite marker information. J. Anim. Breed. Genet. 123:4 (2006), 265–271, 10.1111/j.1439-0388.2006.00583.x.
Bertolini, F., Galimberti, G., Calò, D.G., Schiavo, G., Matassino, D., Fontanesi, L., Combined use of principal component analysis and random forests identify population-informative single nucleotide polymorphisms: application in cattle breeds. J. Anim. Breed. Genet. 132:5 (2015), 346–356, 10.1111/jbg.12155.
BlueSter. (2021). BlueSter. https://www.projet-bluester.eu/.
Charlier, C., Coppieters, W., Farnir, F., Grobet, L., Leroy, P.L., Michaux, C., Mni, M., Schwers, A., Vanmanshoven, P., Hanset, R., Georges, M., The mh gene causing double-muscling in cattle maps to bovine Chromosome 2. Mamm. Genome 6:11 (1995), 788–792, 10.1007/BF00539005.
Colinet, F., The dual purpose Belgian Blue. Brochure, 2010, University of Liege.
Dalvit, C., De Marchi, M., Cassandro, M., Genetic traceability of livestock products: a review. Meat Sci. 77:4 (2007), 437–449, 10.1016/j.meatsci.2007.05.027.
Dalvit, C., De Marchi, M., Targhetta, C., Gervaso, M., Cassandro, M, Genetic traceability of meat using microsatellite markers. Food Res. Int. 41 (2008), 301–307, 10.1016/j.foodres.2007.12.010.
Funkhouser, S.A., Bates, R.O., Ernst, C.W., Newcom, D., Steibel, J.P., Estimation of genome-wide and locus-specific breed composition in pigs. Transl. Anim. Sci. 1:1 (2017), 36–44, 10.2527/tas2016.0003.
He, J., Guo, Y., Xu, J., Li, H., Fuller, A., Tait, R.G., Wu, X.L., Bauck, S., Comparing SNP panels and statistical methods for estimating genomic breed composition of individual animals in ten cattle breeds. BMC Genet., 19, 2018, 10.1186/s12863-018-0654-3.
Hulsegge, B., Calus, M.P.L., Windig, J.J., Hoving-Bolink, A.H., Maurice-van Eijndhoven, M.H.T., Hiemstra, S.J., Selection of SNP from 50K and 777K arrays to predict breed of origin in cattle. J. Anim. Sci. 91:11 (2013), 5128–5134, 10.2527/jas.2013-6678.
Iquebal, M.A., Ansari, M.S., Sarika, S., Dixit, S.P., Verma, N.K., Aggarwal, R.A.K., Jayakumar, S., Rai, A., Kumar, D., Locus minimization in breed prediction using artificial neural network approach. Anim. Genet. 45:6 (2014), 898–902, 10.1111/age.12208.
Josse, J., Husson, F., Handling missing values in exploratory multivariate data analysis methods. J. Soc. fr. Stat. 153:2 (2012), 77–99.
Judge, M.M., Kelleher, M.M., Kearney, J.F., Sleator, R.D., Berry, D.P., Ultra-low-density genotype panels for breed assignment of Angus and Hereford cattle. Animal 11:6 (2017), 938–947, 10.1017/S1751731116002457.
Kuehn, L.A., Keele, J.W., Bennett, G.L., McDaneld, T.G., Smith, T.P.L., Snelling, W.M., Sonstegard, T.S., Thallman, R.M., Predicting breed composition using breed frequencies of 50,000 markers from the US Meat Animal Research Center 2,000 bull project. J. Anim. Sci. 89:6 (2011), 1742–1750, 10.2527/jas.2010-3530.
Márquez, G.C., Speidel, S.E., Enns, R.M., Garrick, D.J., Genetic diversity and population structure of American Red Angus cattle. J. Anim. Sci. 88:1 (2010), 59–68, 10.2527/jas.2008-1292.
Maudet, C., Luikart, G., Taberlet, P., Genetic diversity and assignment tests among seven French cattle breeds based on microsatellite DNA analysis. J. Anim. Sci. 80:4 (2002), 942–950, 10.2527/2002.804942x.
Mota, R.R., Mayeres, P., Bastin, C., Glorieux, G., Bertozzi, C., Vanderick, S., Hammami, H., Colinet, F.G., Gengler, N., Genetic evaluation for birth and conformation traits in dual-purpose Belgian Blue cattle using a mixed inheritance model. J. Anim. Sci. 95:10 (2017), 4288–4299, 10.2527/jas2017.1748.
Putnová, L., Štohl, R., Comparing assignment-based approaches to breed identification within a large set of horses. J. Appl. Genet. 60:2 (2019), 187–198, 10.1007/s13353-019-00495-x.
R Core Team. (2021). R: alanguage and environment for statistical computing. (3.6.3). R Foundation for Statistical Computing. http://www.r-project.org/.
R Studio Team. (2020). RStudio: integrated development for R. RStudio. PBC. http://www.rstudio.com/.
Schiavo, G., Bertolini, F., Galimberti, G., Bovo, S., Dall'olio, S., Nanni Costa, L., Gallo, M., Fontanesi, L, A machine learning approach for the identification of population-informative markers from high-throughput genotyping data: application to several pig breeds. Animal, 2019, 223–232, 10.1017/S1751731119002167.
Tibshirani, R., Hastie, T., Narasimhan, B., Chu, G., Diagnosis of multiple cancer types by shrunken centroids of gene expression. PNAS 99:10 (2002), 6567–6572, 10.1073/pnas.082099299.
Wilkinson, S., Wiener, P., Archibald, A.L., Law, A., Schnabel, R.D., McKay, S.D., Taylor, J.F., Ogden, R., Evaluation of approaches for identifying population informative markers from high density SNP Chips. BMC Genet., 12, 2011, 10.1186/1471-2156-12-45.
Wilmot, H., Bormann, J., Soyeurt, H., Hubin, X., Glorieux, G., Mayeres, P., Bertozzi, C., Gengler, N., Development of a genomic tool for breed assignment by comparison of different classification models - Application to three local cattle breeds. J. Anim. Breed. Genet. 139:1 (2022), 40–61, 10.1111/jbg.12643.