可观察到的质量性状在不同环境中相对稳定,通常用于评估作物遗传多样性。最近,在多样性调查中,分子标记在很大程度上取代了描述表型。然而,定性描述符可用于对种质资源进行分类和描述专利中的新种质,出版物,和/或植物品种保护(PVP)系统。这项研究的重点是对国家棉花种质馆藏(NCGC)中代表的标准化棉花性状进行比较分析。棉花性状由具有非数字子类别(描述符状态)的“描述符”命名,反映了每个性状在植物中如何表现或不存在的细节。根据NCGC策展人的定义,我们对来自三个主要棉属群体的选定种质进行了统计评估:(1)“Stoneville种质(SA),\“主要包含陆地棉(陆地棉)品种;(2)\”德克萨斯州种质(TEX),\“主要含有陆地黄;和(3)巴巴多斯棉属(Gb),含有皮马棉(Gossypiumbarbadense)的品种或地方品种。对于33个棉花描述符,我们:(a)揭示了每个描述符在每个组内的字符状态分布;(b)分析了成对描述符之间的双变量关联;(c)根据其描述符对种质进行聚类。描述符之间最少的显著关联发生在SA数据集中,可能反映了品种发展的广泛育种。相比之下,TEX和Gb数据集显示了更多数量的描述符之间的显著关联,可能与育种工作的影响较小有关。所有三组都确定了三个显著的双变量关联,苞片蜜腺:棉铃蜜腺,叶毛:茎毛,和皮棉颜色:种子绒毛颜色。无监督聚类分析概括了约97%的种质的物种标签。意外的聚类结果表明,可能会从潜在的进一步调查中受益的种质。在未来,策展人可以使用标准化描述符之间的显着关联来确定新的外来/不寻常的种质是否与高地或皮马棉最相似。此外,该研究表明,大型种质数据集的现有描述符如何有助于为育种和研究中的下游目标提供信息,如确定具有特定性状组合的稀有个体,并通过育种瞄准剩余性状关联的细分,从而证明了分析方法在对种质资源多样性进行分类中的实用性。
Observable qualitative traits are relatively stable across environments and are commonly used to evaluate crop genetic diversity. Recently, molecular markers have largely superseded describing phenotypes in diversity surveys. However, qualitative descriptors are useful in cataloging germplasm collections and for describing new germplasm in patents, publications, and/or the Plant Variety Protection (PVP) system. This research focused on the comparative analysis of standardized
cotton traits as represented within the National
Cotton Germplasm Collection (NCGC). The
cotton traits are named by \'descriptors\' that have non-numerical sub-categories (descriptor states) reflecting the details of how each trait manifests or is absent in the plant. We statistically assessed selected accessions from three major groups of Gossypium as defined by the NCGC curator: (1) \"Stoneville accessions (SA),\" containing mainly Upland cotton (Gossypium hirsutum) cultivars; (2) \"Texas accessions (TEX),\" containing mainly G. hirsutum landraces; and (3) Gossypium barbadense (Gb), containing cultivars or landraces of Pima
cotton (Gossypium barbadense). For 33
cotton descriptors we: (a) revealed distributions of character states for each descriptor within each group; (b) analyzed bivariate associations between paired descriptors; and (c) clustered accessions based on their descriptors. The fewest significant associations between descriptors occurred in the SA dataset, likely reflecting extensive breeding for cultivar development. In contrast, the TEX and Gb datasets showed a higher number of significant associations between descriptors, likely correlating with less impact from breeding efforts. Three significant bivariate associations were identified for all three groups, bract nectaries:boll nectaries, leaf hair:stem hair, and lint color:seed fuzz color. Unsupervised clustering analysis recapitulated the species labels for about 97% of the accessions. Unexpected clustering results indicated accessions that may benefit from potential further investigation. In the future, the significant associations between standardized descriptors can be used by curators to determine whether new exotic/unusual accessions most closely resemble Upland or Pima cotton. In addition, the study shows how existing descriptors for large germplasm datasets can be useful to inform downstream goals in breeding and research, such as identifying rare individuals with specific trait combinations and targeting breakdown of remaining trait associations through breeding, thus demonstrating the utility of the analytical methods employed in categorizing germplasm diversity within the collection.