Huang Yen-Hsiang, Ku Hsin-Mei, Wang Chong-An, Chen Ling-Yu, He Shan-Syue, Chen Shu, Liao Po-Chun, Juan Pin-Yuan, Kao Chung-Feng
Department of Agronomy, College of Agriculture and Natural Resources, National Chung Hsing University, Taichung, Taiwan.
Department of Agronomy, College of Bioresources and Agriculture, National Taiwan University, Taipei, Taiwan.
Front Plant Sci. 2022 Sep 2;13:948349. doi: 10.3389/fpls.2022.948349. eCollection 2022.
Establishment of vegetable soybean (edamame) [ (L.) Merr.] germplasms has been highly valued in Asia and the United States owing to the increasing market demand for edamame. The idea of core collection (CC) is to shorten the breeding program so as to improve the availability of germplasm resources. However, multidimensional phenotypes typically are highly correlated and have different levels of missing rate, often failing to capture the underlying pattern of germplasms and select CC precisely. These are commonly observed on correlated samples. To overcome such scenario, we introduced the "multiple imputation" (MI) method to iteratively impute missing phenotypes for 46 morphological traits and jointly analyzed high-dimensional imputed missing phenotypes (EC ) to explore population structure and relatedness among 200 Taiwanese vegetable soybean accessions. An advanced maximization strategy with a heuristic algorithm and PowerCore was used to evaluate the morphological diversity among the EC . In total, 36 accessions (denoted as CC ) were efficiently selected representing high diversity and the entire coverage of the EC . Only 4 (8.7%) traits showed slightly significant differences between the CC and EC . Compared to the EC , 96% traits retained all characteristics or had a slight diversity loss in the CC . The CC exhibited a small percentage of significant mean difference (4.51%), and large coincidence rate (98.1%), variable rate (138.76%), and coverage (close to 100%), indicating the representativeness of the EC . We noted that the CC outperformed the CC in evaluation properties, suggesting that the multiple phenotype imputation method has the potential to deal with missing phenotypes in correlated samples efficiently and reliably without re-phenotyping accessions. Our results illustrated a significant role of imputed missing phenotypes in support of the MI-based framework for plant-breeding programs.
由于市场对毛豆的需求不断增加，毛豆[（L.）Merr.]种质资源的建立在亚洲和美国受到高度重视。核心种质（CC）的理念是缩短育种计划，以提高种质资源的可用性。然而，多维表型通常高度相关且缺失率不同，常常无法捕捉种质的潜在模式并精确选择核心种质。这些情况在相关样本中很常见。为了克服这种情况，我们引入了“多重填补”（MI）方法，对46个形态性状的缺失表型进行迭代填补，并联合分析高维填补后的缺失表型（EC），以探索200份台湾毛豆种质的群体结构和相关性。使用一种带有启发式算法和PowerCore的先进最大化策略来评估EC中的形态多样性。总共有效选择了36份种质（记为CC），它们代表了高多样性和EC的全部覆盖范围。CC和EC之间只有4个（8.7%）性状表现出轻微显著差异。与EC相比，CC中96%的性状保留了所有特征或仅有轻微的多样性损失。CC表现出较小的显著平均差异百分比（4.51%）、较大的符合率（98.1%）、变异率（138.76%）和覆盖率（接近100%），表明EC具有代表性。我们注意到CC在评估特性方面优于CC，这表明多重表型填补方法有潜力在不重新对种质进行表型鉴定的情况下，高效且可靠地处理相关样本中的缺失表型。我们的结果说明了填补后的缺失表型在支持基于MI的植物育种计划框架中的重要作用。
