Azadeh Hassanpour, Johannes Geibel, Henner Simianer, Antje Rohde, Torsten Pook
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引用次数: 0
Abstract
The effective planning and allocation of resources in modern breeding programs is a complex task. Breeding program design and operational management have a major impact on the success of a breeding program and changing parameters such as the number of selected/phenotyped/genotyped individuals in the breeding program will impact genetic gain, genetic diversity, and costs. As a result, careful assessment and balancing of design parameters is crucial, taking into account the trade-offs between different breeding goals and associated costs. In a previous study, we optimized the resource allocation strategy in a dairy cattle breeding scheme via the combination of stochastic simulations and kernel regression, aiming to maximize a target function containing genetic gain and the inbreeding rate under a given budget. However, the high number of simulations required when using the proposed kernel regression method to optimize a breeding program with many parameters weakens the effectiveness of such a method. In this work, we are proposing an optimization framework that builds on the concepts of kernel regression but additionally makes use of an evolutionary algorithm to allow for a more effective and general optimization. The key idea is to consider a set of potential parameter settings of the breeding program, evaluate their performance based on stochastic simulations, and use these outputs to derive new parameter settings to test in an iterative procedure. The evolutionary algorithm was implemented in a Snakemake workflow management system to allow for efficient scaling on large distributed computing platforms. The algorithm achieved stabilization around the same optimum with a massively reduced number of simulations. Thereby, the incorporation of class variables and accounting for a higher number of parameters in the optimization framework leads to substantially reduced computing time and better scaling for the desired optimization of a breeding program.
期刊介绍:
G3: Genes, Genomes, Genetics provides a forum for the publication of high‐quality foundational research, particularly research that generates useful genetic and genomic information such as genome maps, single gene studies, genome‐wide association and QTL studies, as well as genome reports, mutant screens, and advances in methods and technology. The Editorial Board of G3 believes that rapid dissemination of these data is the necessary foundation for analysis that leads to mechanistic insights.
G3, published by the Genetics Society of America, meets the critical and growing need of the genetics community for rapid review and publication of important results in all areas of genetics. G3 offers the opportunity to publish the puzzling finding or to present unpublished results that may not have been submitted for review and publication due to a perceived lack of a potential high-impact finding. G3 has earned the DOAJ Seal, which is a mark of certification for open access journals, awarded by DOAJ to journals that achieve a high level of openness, adhere to Best Practice and high publishing standards.