Modern animal breeding programmes are constantly evolving with advances in breeding theory, biotechnology and genetics. Surprisingly, there seems to be no generally accepted succinct definition of what exactly a breeding programme is, neither is there a unified language to describe breeding programmes in a comprehensive, unambiguous and reproducible way. In this work, we try to fill this gap by suggesting a general definition of breeding programmes that also pertains to cases where genetic progress is not achieved through selection, but, for example, through transgenic technologies, or the aim is not to generate genetic progress, but, for example, to maintain genetic diversity. The key idea of the underlying concept is to represent a breeding programme in modular form as a directed graph that is composed of nodes and edges, where nodes represent cohorts of breeding units, usually individuals, and edges represent breeding activities, like “selection” or “reproduction.” We claim, that by defining a comprehensive set of nodes and edges, it is possible to represent any breeding programme of arbitrary complexity by such a graph, which thus comprises a full description of the breeding programme. This concept is implemented in a web‐based tool (MoBPSweb, available at http://www.mobps.de) and has a link to the R‐package MoBPS (Modular Breeding Program Simulator) to simulate the described breeding programmes. The approach is illustrated by showcasing three different breeding programmes of increasing complexity. The concept allows a formal description of breeding programmes, which is requested, for example, in legal regulations of the European Union, but so far cannot be provided in a standardized format. In the discussion, we point out potential limitations of the concept and argue that the general approach can be easily extended to account for novel breeding technologies, to breeding of crops or experimental species, but also to modelling diversity dynamics in natural populations.
In this work, we introduce a new web-based simulation framework (”MoBPSweb”) that combines a unified language to describe breeding programs with the simulation software MoBPS, standing for ’Modular Breeding Program Simulator’. Thereby, MoBPSweb provides a flexible environment to log, simulate, evaluate, and compare breeding programs. Inputs can be provided via modules ranging from a Vis.js-based environment for” drawing” the breeding program to a variety of modules to provide phenotype information, economic parameters, and other relevant information. Similarly, results of the simulation study can be extracted and compared to other scenarios via output modules (e.g. observed phenotypes, the accuracy of breeding value estimation, inbreeding rates), while all simulations and downstream analysis are executed in the highly efficient R-package MoBPS.
Context.Breeding programs aim at improving the genetic characteristics of livestock populations with respect to productivity, fitness and adaptation, while controlling negative effects such as inbreeding or health and welfare issues. As breeding is affected by a variety of interdependent factors, the analysis of the effect of certain breeding actions and the optimisation of a breeding program are highly complex tasks.Aims. This study was conducted to display the potential of using stochastic simulation to analyse, evaluate and compare breeding programs and to show how the Modular Breeding Program Simulator (MoBPS) simulation framework can further enhance this.Methods. In this study, a simplified version of the breeding program of Göttingen Minipigs was simulated to analyse the impact of genotyping and optimum contribution selection in regard to both genetic gain and diversity. The software MoBPS was used as the backend simulation software and was extended to allow for a more realistic modelling of pig breeding programs. Among others, extensions include the simulation of phenotypes with discrete observations (e.g. teat count), variable litter sizes, and a breeding value estimation in the associated R-package miraculix that utilises a graphics processing unit.Key results. Genotyping with the subsequent use of genomic best linear unbiased prediction (GBLUP) led to substantial increases in genetic gain (15.3%) compared with a pedigree-based BLUP, while reducing the increase of inbreeding by 24.8%. The additional use of optimum genetic selection was shown to be favourable compared with the plain selection of top boars. The use of graphics processing unit-based breeding value estimation with known heritability was~100 times faster than the state-of-the-art R-package rrBLUP.Conclusions. The results regarding the effect of both genotyping and optimal contribution selection are in line with well established results. Paired with additional new features such as the modelling of discrete phenotypes and adaptable litter sizes, this confirms MoBPS to be a unique tool for the realistic modelling of modern breeding programs.Implications. The MoBPS framework provides a powerful tool for scientists and breeders to perform stochastic simulations to optimise the practical design of modern breeding programs to secure standardised breeding of highquality animals and answer associated research questions.
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