Abstract：
Regular measurement of realized genetic gain allows plant breeders to assess and review the effectiveness of their strategies, allocate resources efficiently, and make informed decisions throughout the breeding process. Realized genetic gain estimation requires separating genetic trends from nongenetic trends using the linear mixed model (LMM) on historical multi-environment trial data. The LMM, accounting for the year effect, experimental designs, and heterogeneous residual variances, estimates best linear unbiased estimators of genotypes and regresses them on their years of origin. An illustrative example of estimating realized genetic gain was provided by analyzing historical data on fresh cassava (Manihot esculenta Crantz) yield in West Africa (https://github.com/Biometrics-IITA/Estimating-Realized-Genetic-Gain). This approach can serve as a model applicable to other crops and regions. Modernization of breeding programs is necessary to maximize the rate of genetic gain. This can be achieved by adopting genomics to enable faster breeding, accurate selection, and improved traits through genomic selection and gene editing. Tracking operational costs, establishing robust, digitalized data management and analytics systems, and developing effective varietal selection processes based on customer insights are also crucial for success. Capacity building and collaboration of breeding programs and institutions also play a significant role in accelerating genetic gains.
摘要：
定期测量已实现的遗传增益使植物育种者能够评估和审查其策略的有效性，有效地分配资源，并在整个育种过程中做出明智的决定。实现遗传增益估计需要在历史多环境试验数据上使用线性混合模型（LMM）将遗传趋势与非遗传趋势分开。LMM,核算年度效应，实验设计,和异质残差方差，估计基因型的最佳线性无偏估计量，并根据其起源年份对其进行回归。通过分析西非新鲜木薯（ManihotesculentaCrantz）产量的历史数据，提供了估算已实现遗传增益的说明性示例（https://github.com/Biometrics-IITA/Estimating-Realized-Genetic-Gain）。这种方法可以作为适用于其他作物和地区的模型。育种计划的现代化对于最大程度地提高遗传增益率是必要的。这可以通过采用基因组学来实现更快的育种，准确选择,并通过基因组选择和基因编辑改善性状。跟踪运营成本，建立健壮的，数字化数据管理和分析系统，基于客户洞察开发有效的品种选择流程对于成功也至关重要。育种计划和机构的能力建设和合作在加速遗传收获方面也发挥着重要作用。
