Good practice for assignment of breeds and populations—a review

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.