Prediction by simulation in plant breeding

文献类型： 外文期刊

第一作者： Li, Huihui

作者： Li, Huihui;Zhang, Luyan;Gao, Shang;Wang, Jiankang;Li, Huihui;Zhang, Luyan;Gao, Shang;Wang, Jiankang;Li, Huihui;Gao, Shang;Wang, Jiankang

作者机构：

关键词： Prediction by simulation; Plant breeding; Modeling; Genetic model; Breeding method

期刊名称：CROP JOURNAL （ 影响因子：5.6； 五年影响因子：6.0 ）

ISSN： 2095-5421

年卷期： 2025 年 13 卷 2 期

页码：

收录情况： SCI

摘要： Computer simulation permits answering theoretical and applied questions in animal and plant breeding. Blib is a novel multi-module simulation platform, which is able to handle more complicated genetic effects and models than most existing tools. In this study, we describe one major and unified application module of Blib, i.e., ISB (abbreviated from in silico breeding), for simulating the three categories of breeding programs for developing clonal, pure-line and hybrid cultivars in plants. Genetic models on environments and breeding-targeted traits, one or several parental populations, and a number of breeding methods are key elements to run simulation experiments in ISB, which are arranged in three external input files by given formats. Applications of ISB are illustrated by three case studies, representing the three categories of plant breeding programs. Under the condition that 5000 F1 progenies were generated and tested from 50 heterozygous parents, Case study I showed that 50 crosses, each of 100 progenies, made the best balance between genetic achievement and field cost. In Case study II, one optimum breeding method was identified by which the pure lines with high yield and medium maturity could be developed. Case study III investigated the genetic consequence in hybrid breeding from five testers. One tester was identified for the simultaneous improvement in F1 hybrids and inbred lines. In summary, ISB identified a balanced crossing scheme, an optimum pure-line selection method, and an optimized tester in three case studies which are relevant to plant breeding. We believe the prediction by simulation would be highly required in front of the next generation of breeding to be driven by informatics and intelligence. (c) 2025 Crop Science Society of China and Institute of Crop Science, CAAS. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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