[en] With the purpose to organize methodologies found in (recent) papers focusing on the development of genomic breed/population assignment tools, this review proposes to highlight good practice for the development of such tools. After an appropriate quality control of markers and the building of a representative reference population, three main steps can be followed to develop a genomic breed/population assignment tool: 1) The selection of discriminant markers, 2) The development of a model that allows accurate assignment of animals to their breed/population of origin, the so-called classification step, and, 3) The validation of the developed model on new animals to evaluate its performances in real conditions. The first step can be avoided when a mid- or low-density chip is used, depending on the methodology used for assignment. In the case selection of SNPs is necessary, we advise the use of one stage methodologies and to define a threshold for this selection. Then, machine learning can be used to develop the model per se, based on the selected or available markers. To tune the model, we recommend the use of cross-validation. Finally, new animals, not used in the first two steps, should be used to evaluate the performances of the model (e.g., with balanced accuracy and probabilities), also in terms of computation time.
Disciplines :
Agriculture & agronomy
Author, co-author :
Wilmot, Hélène ; Université de Liège - ULiège > Département GxABT > Animal Sciences (AS)
Gengler, Nicolas ; Université de Liège - ULiège > TERRA Research Centre > Animal Sciences (AS)
Language :
English
Title :
Good practice for assignment of breeds and populations—a review
F.R.S.-FNRS - Fonds de la Recherche Scientifique SPW DG03-DGARNE - Service Public de Wallonie. Direction Générale Opérationnelle Agriculture, Ressources naturelles et Environnement EU - European Union
Funding text :
The author(s) declare that financial support was received for the
research, authorship, and/or publication of this article. H. Wilmot, as
a former Research Fellow andN. Gengler, as a former Senior Research
Associate, acknowledge the support of the Fonds de la Recherche
Scientifique – FNRS (Brussels, Belgium). The Walloon Government
(Service Public de Wallonie – Direction Générale Opérationnelle
Agriculture, Ressources Naturelles et Environnement, SPWDGARNE;
Namur, Belgium) is acknowledged for its financial
support. The authors gratefully acknowledge the support of the
project 'Rotbunt DN' funded under the European Innovation
Partnership (EIP Agri) Schleswig-Holstein, Germany, through the
European Agricultural Fund for Rural Development (EAFRD).
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