利用进化算法进行随机模拟,优化育种程序设计。

IF 2.1 3区 生物学 Q3 GENETICS & HEREDITY
Azadeh Hassanpour, Johannes Geibel, Henner Simianer, Antje Rohde, Torsten Pook
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引用次数: 0

摘要

现代育种计划中资源的有效规划和分配是一项复杂的任务。育种计划的设计和运营管理对育种计划的成功与否有着重大影响,而育种计划中被选育/表型/基因分型个体数量等参数的变化将影响遗传增益、遗传多样性和成本。因此,考虑到不同育种目标和相关成本之间的权衡,仔细评估和平衡设计参数至关重要。在之前的一项研究中,我们通过随机模拟和核回归相结合的方法优化了奶牛育种计划中的资源分配策略,目的是在给定预算下最大化包含遗传增益和近交率的目标函数。然而,当使用所提出的核回归方法来优化具有许多参数的育种计划时,需要进行大量的模拟,这削弱了这种方法的有效性。在这项工作中,我们提出了一个优化框架,它建立在核回归概念的基础上,但又利用了进化算法,以实现更有效、更通用的优化。其主要思路是考虑育种程序的一组潜在参数设置,根据随机模拟评估其性能,并利用这些输出得出新的参数设置,在迭代过程中进行测试。进化算法在 Snakemake 工作流管理系统中实施,以便在大型分布式计算平台上高效扩展。在大量减少模拟次数的情况下,该算法在同一最佳值附近实现了稳定。因此,在优化框架中加入类变量并考虑更多参数,可大幅减少计算时间,并更好地扩展,以实现育种计划的优化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimization of breeding program design through stochastic simulation with evolutionary algorithms.

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.

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来源期刊
G3: Genes|Genomes|Genetics
G3: Genes|Genomes|Genetics GENETICS & HEREDITY-
CiteScore
5.10
自引率
3.80%
发文量
305
审稿时长
3-8 weeks
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.
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