MoBPSweb: A web-based framework to simulate and compare breeding programs

Pook, Torsten; Büttgen, Lisa; Ganesan, Amudha; Ha, Ngoc-Thuy; Simianer, Henner

doi:10.1093/g3journal/jkab023

Cited by 3 publications

(3 citation statements)

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“…Note that a variety of other features have been added since the initial release of MoBPS (Pook et al 2020) to handle subpopulations, calculate average offspring performance, use different arrays for genotyping or features to automatically avoid fullsib/halfsib matings that are not relevant for the present study but further enhance the design options in the MoBPS framework. In combination with the graphical user interface (Pook et al 2021), the variety of preimplemented methods allows the simple use of the MoBPS framework not only for research but also for users without scripting experience, in industry, or to assist teaching of breeding program design. At the same time, the flexibility and modular structure allows researchers to assess new and experimental methodology, such as new selection techniques or a new breeding value estimation approach.…”

Section: Discussionmentioning

confidence: 99%

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The Modular Breeding Program Simulator (MoBPS) allows efficient simulation of complex breeding programs

et al. 2021

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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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Section: Discussionmentioning

confidence: 99%

“…1. The given breeding scheme can also be found as a template in the MoBPSweb application at www.mobps.de (Pook et al 2021). In the following, we will consider five In case a breeding value estimation was performed, the animals of the last two generations were used with a pedigreedepth of 7.…”

Section: Methodsmentioning

confidence: 99%

The Modular Breeding Program Simulator (MoBPS) allows efficient simulation of complex breeding programs

et al. 2021

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“…Is important to notice that a RACI chart can and should be employed during the management of the different tasks of the breeding process and not only for the continuous improvement of breeding schemes. We propose that the future state and actions included in the improvement plan should be guided by sound quantitative genetics principles and recommendations coming from stateof-the-art tools, such as evaluation of new strategies by genetic simulation (Mi et al, 2014;Gaynor et al, 2017;Pook et al, 2020Pook et al, , 2021. We expect that results obtained through simulation can identify close-to-optimal solutions and changes to the breeding CES tasks and decisions.…”

Section: Improving the Processmentioning

confidence: 99%

Breeding Schemes: What Are They, How to Formalize Them, and How to Improve Them?

et al. 2022

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Formalized breeding schemes are a key component of breeding program design and a gateway to conducting plant breeding as a quantitative process. Unfortunately, breeding schemes are rarely defined, expressed in a quantifiable format, or stored in a database. Furthermore, the continuous review and improvement of breeding schemes is not routinely conducted in many breeding programs. Given the rapid development of novel breeding methodologies, it is important to adopt a philosophy of continuous improvement regarding breeding scheme design. Here, we discuss terms and definitions that are relevant to formalizing breeding pipelines, market segments and breeding schemes, and we present a software tool, Breeding Pipeline Manager, that can be used to formalize and continuously improve breeding schemes. In addition, we detail the use of continuous improvement methods and tools such as genetic simulation through a case study in the International Institute of Tropical Agriculture (IITA) Cassava east-Africa pipeline. We successfully deploy these tools and methods to optimize the program size as well as allocation of resources to the number of parents used, number of crosses made, and number of progeny produced. We propose a structured approach to improve breeding schemes which will help to sustain the rates of response to selection and help to deliver better products to farmers and consumers.

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Optimization Strategies to Adapt Sheep Breeding Programs to Pasture-Based Production Environments: A Simulation Study

Martin,

Pook,

Bennewitz

et al. 2023

Animals

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Strong differences between the selection (indoor fattening) and production environment (pasture fattening) are expected to reduce genetic gain due to possible genotype-by-environment interactions (G × E). To investigate how to adapt a sheep breeding program to a pasture-based production environment, different scenarios were simulated for the German Merino sheep population using the R package Modular Breeding Program Simulator (MoBPS). All relevant selection steps and a multivariate pedigree-based BLUP breeding value estimation were included. The reference scenario included progeny testing at stations to evaluate the fattening performance and carcass traits. It was compared to alternative scenarios varying in the progeny testing scheme for fattening traits (station and/or field). The total merit index (TMI) set pasture-based lamb fattening as a breeding goal, i.e., field fattening traits were weighted. Regarding the TMI, the scenario with progeny testing both in the field and on station led to a significant increase in genetic gain compared with the reference scenario. Regarding fattening traits, genetic gain was significantly increased in the alternative scenarios in which field progeny testing was performed. In the presence of G × E, the study showed that the selection environment should match the production environment (pasture) to avoid losses in genetic gain. As most breeding goals also contain traits not recordable in field testing, the combination of both field and station testing is required to maximize genetic gain.

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MoBPSweb: A web-based framework to simulate and compare breeding programs

Cited by 3 publications

References 27 publications

The Modular Breeding Program Simulator (MoBPS) allows efficient simulation of complex breeding programs

The Modular Breeding Program Simulator (MoBPS) allows efficient simulation of complex breeding programs

Breeding Schemes: What Are They, How to Formalize Them, and How to Improve Them?

Optimization Strategies to Adapt Sheep Breeding Programs to Pasture-Based Production Environments: A Simulation Study

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