保留特殊种质材料的核心库构建方法

本研究提出了能保留特殊种质材料的多次聚类构建种质资源核心库方法,对种质材料的表现型数据采用合适的遗传模型及混合线性模型统计分析方法无偏预测基因型值,用基因型值构建核心库,计算材料间的马氏距离,用不加权类平均法进行聚类,根据聚类图选取材料构建核心库时,优先保留特殊遗传材料,用方差F测验、均值t测验、极差比和变异系数评价核心库代表原有种质资源群体遗传多样性的程度,以168个棉花基因型的5个纤维性状构建核心库。     

应用混合遗传距离和基因型值遗传距离构建植物遗传资源核心子集

将连续性的基因型值数据和间断性的分子标记数据整合建立混合遗传距离,对比了应用混合遗传距离和单纯应用基因型遗传距离构建植物遗传资源核心子集的效果．应用混合线性模型中的调整无偏预测法（AUP）预测基因型值,结合不加权类平均法（UPGMA）逐步聚类构建遗传资源群体的核心子集,并检测一系列核心子集的代表性评价参数．采用包含8个农艺性状和60个SSR标记信息的水稻群体数据验证混合遗传距离的有效性．结果表明,采用混合数据构建的核心子集比单纯的基因型值数据构建的核心子集更有代表性．主成分分析结果验证了该结论的可知陛．     

贵州核桃核心种质筛选与构建研究北大核心CSCD

为了更好地构建贵州核桃核心种质,该研究以245份贵州核桃种质资源为试材,利用数量性状的遗传变异数据,对其构建方法进行了探索;采用随机取样策略、偏离度取样策略和位点优先取样策略,结合8个取样比例(5%、10%、15%、20%、25%、30%、40%和50%),进行多次聚类构建核心种质;将核心种质与原种质和保留种质进行t检验比较来评价核心种质,并用主成分分析法和表型性状对核心种质进行确认。结果表明:(1)选取偏离度取样策略,总体取样比例为50%,构建出131份贵州核桃核心种质资源。(2)对种质资源核心库13个数量性状进行主成分分析,其累计贡献率达到76.48%,主成分图中核桃核心种质与原种质的分布较为相似,表明核心种质有效地保存了核桃原种质的遗传结构,并有效避免了种质的冗余。(3)在欧氏距离结合组间联接法的聚类条件下,偏离度取样策略是构建贵州核桃核心种质的最佳方法。     

中国芒(Miscanthus sinensis)初级核心种质的构建

以555份芒(Miscanthus sinensis)种质资源为研究对象,根据26个表型性状数据,按地理来源、植物区系和单一性状进行分组,分别采用简单比例法、平方根法和多样性指数法确定组内取样数,再根据聚类和随机2种方法进行组内个体选择。依照上述方案共构建出19个具有代表性的芒初选核心种质样本库。通过平均相似系数、性状符合度、数量性状变异系数和遗传多样性指数等4项检测指标对上述19种构建方案进行比较,最终确定了按"植物区划分组+多样性指数确定取样数+聚类选择个体"为芒初级核心种质构建的最佳方案。通过此方法建立起的芒初级核心种质资源共83份,占总资源的14.95%,且新构建的初级种质资源与总资源性状符合度达到100%。     

白桦核心种质构建的聚类方法研究

以白桦240个家系的胸径、树高、材积和纤维素含量数据为依据,采用马氏距离计算家系间距离、10%的取样比例和优先取样法,研究了最短距离法、最长距离法、中间距离法、重心法、类平均法、加权配对算术平均法、可变法和离差平方和聚类法建构的核心种质与原种质的遗传参数、性状相关性及分布格局.结果表明,最短距离法构建白桦初级核心种质均值差异百分率、极差符合率、方差差异百分率和极差符合率分别为0、100%、75%和143%,4个性状相关性显著、相关系数均超过0.5,保持了原种质资源的空间分布格局,是构建白桦核心种质最佳方法.     

内蒙古3种生态型扁蓿豆遗传多样性与亲缘关系的分析

采用SSR分子标记结合21个表型性状对来自内蒙古3种生态型扁蓿豆种质资源的遗传多样性和亲缘关系进行了分析.结果表明:3种生态型的22份扁蓿豆材料在考察的4个质量性状上差异明显;在17个数量性状上,总体变异程度为黄花型扁蓿豆>扁蓿豆>细叶扁蓿豆;表型性状的遗传相似系数在31.59～113.27间,变异系数为43.30%.SSR分析结果显示,18对引物平均多态性比率为80.09%,引物平均等位位点数为6.06,位点多态性信息含量平均为0.32,遗传相似系数在0.37～0.47间,变异系数为61.80%.表型性状聚类、SSR分子标记聚类及主成分分析结果均显示,黄花型扁蓿豆和扁蓿豆的亲缘关系较近,与细叶扁蓿豆的亲缘关系较远.     

长江春大豆核心种质构建及分析

利用长江春大豆初选核心种质SSR(simple sequence repeat）标记和农艺性状表型等基础数据,对用不同个体取样方法以及不同数据类型建立的核心种质进行评价,目的是确定中国大豆（Glycine max）核心种质的最佳取样策略提供依据,结果表明,根据SSR分子数据聚类,采用类内随机取样,类内以遗传相似性系数取样以及仅依据遗传相似性系数取样都可用于大豆核心种质构建,但是综合不同评价参数发现,以类内随机取样最佳,类内按遗传相似性系数取样次之,单独以遗传相似性系数取样较差。分析不同SSR等位变异保留比例的遗传多样性指数发现,当保留90%和80%的SSR等位变异时,核心种质具有更高的遗传多样性,由于与SSR分子数据种质遗传关系评价的不一致性,农艺性状等基础数据虽然可用来构建核心种质,但其SSR分子水平代表性相对较低,本研究结果还表明,用不同方法或同一方法不同重复次数取样建立的核心种质具有异质性,且这种异质性随核心种质取样比例的降低而增大,因此,虽然可依据不同数据类型确定相应的方法建立核心种质,但综合表型和分子数据建立的核心种质更具有代表性。     

基于农艺性状的榕江茶（Camellia yungkiangensis）核心种质构建

摘 要：为得出较为可靠的榕江茶初级核心种质群体,以加强榕江茶种质选育、开发利用和分子遗传学研究,解决其种质资源保存成本较高问题,促进榕江茶种质资源的鉴定和有效利用。以118份榕江茶种质资源为材料,对19个表型性状和4个基本品质成分共计23个农艺性状进行分析;基于2种遗传距离(标准化欧氏距离、马氏距离）、4种聚类方法（离差平方和法、非加权组平均法、最长距离法、最短距离法）和7种总体取样规模（10%、15%、20%、25%、30%、35%、40%）构建了56个候选榕江茶核心种质,利用筛选得到的最佳构建方案构建初级核心种质。通过对原种质、保留种质和核心种质的表型遗传多样性和变异程度,以及各种质不同数量性状间的t检验来评价核心种质的代表性,并用主成分分析法对核心种质进行确认。构建榕江茶核心种质时,2种遗传距离中,标准化欧氏距离优于马氏距离;4种聚类方法中,最短距离法优于另外3种;7种总体取样规模中,30%的取样比例较适宜榕江茶核心种质的构建。对构建的38份核心种质进行分析评价,结果表明,核心种质与原种质23个性状的6个特征值一致性较好,并且遗传多样性指数有一定程度的提升;t检验结果表明,核心种质平衡了稀有性状的分布,有效保留了原种质的遗传多样性;主成分分析结果表明,核心种质的主成分累计贡献率高于原种质;对核心种质进一步确认时,发现核心种质均匀分布在原始种质范围内,无重叠现象,有效的避免了核心种质的冗余。所构建的榕江茶核心种质资源可以很好地代表原种质,较好的保留了原种质的性状、遗传多样性和变异。标准化欧氏距离、最短距离法和30%总体取样规模的构建策略,是构建榕江茶核心种质的最佳方法。最终构建了38份榕江茶种质资源的初级核心种质,占原种质32.20%。核心种质评价表明初级核心种质构建有效且质量较高,能够在保证冗余较少的情况下充分代表原种质遗传多样性。     

用EST-SSR分子标记技术构建大白菜核心种质及其指纹图谱库

利用EST-SSR分子标记对大白菜种质资源基因库中686份样品所代表的1 900份大白菜种质资源进行分析研究.构建大白菜种质资源的核心种质并且形成核心种质的EST-SSR指纹图谱库.结果表明利用4组鉴定白菜品种的EST-SSR的特异性标记组合,获得近158个EST-SSR多态的标记,对大白菜种质资源基因库中686份样品所代表的1 900份大白菜种质资源进行核心种质的构建提供了分析数据.形成的核心种质包括168份样品,占库存资源的8.8%,它的多态位点百分率保持了原群体的100%.所构建的核心种质涵盖了原资源的绝大部分区域来源的品种,包含了早、中、晚熟品种中所有典型的大白菜类型和其相关的特征特性.并进一步进行了核心种质资源遗传多样性分析.核心种质的EST-SSR指纹图谱库中,每一份样品的指纹都是唯一的,为登记、评价、整理、分发、繁殖等种质资源库的管理和育种者对其材料的利用提供了重要的有价值的信息.EST-SSR标记组合是构建中国大白菜核心种质及其指纹图谱的经济、高效的方法.     

菜用和观赏甘薯种质资源遗传多样性分析

为了发掘菜用和观赏甘薯优异种质资源,通过对国家种质徐州甘薯试管苗库中1000余份资源材料进行鉴定,筛选了96份优异种质。利用30对SSR引物对入选材料进行了遗传多样性和群体结构分析,明确了这些材料遗传差异;并对入选材料12个表型质量性状进行主成分和聚类分析。结果表明:扩增的总条带数为275条,其中多态性条带为269条,多态率97.8%;利用DPS软件计算入选材料间的Nei72遗传距离为0.15~0.76,平均遗传距离0.66;群体结构分为3个组群,与分子标记聚类结果相似,表明入选材料有较大的遗传差异性;表型质量性状主成分分析得到5个主要成分,其累计贡献率达到80.50%;利用表型质量性状,可聚为8个组群。本研究通过分子标记与表型质量性状分析为下一步杂交选育菜用和观赏甘薯新品种提供了亲本选择信息。     

Methods of constructing core collections by stepwise clustering with three sampling strategies based on the genotypic values of crops

A genetic model with genotype×environment (GE) interactions for controlling systematical errors in the field can be used for predicting genotypic values by an adjusted unbiased prediction (AUP) method. Mahalanobis distance, calculated based on the genotypic values, is then applied to measure the genetic distance among accessions. The unweighted pair-group average, Ward’s and the complete linkage methods of hierarchical clustering combined with three sampling strategies are proposed to construct core collections in a procedure of stepwise clustering. A homogeneous test and t-tests are suggested for use in testing variances and means, respectively. The coincidence rate (CR%) for range and the variable rate (VR%) for the coefficient of variation are designed to evaluate the property of core collections. A worked example of constructing core collections in cotton with 21 traits was conducted. Random sampling can represent the genetic diversity structure of the initial collection. Preferred sampling can keep the accessions with special or valuable characteristics in the initial collection. Deviation sampling can retain the larger genetic variability of the initial collection. For better representation of the core collection, cluster methods should be combined with different sampling strategies. The core collections based on genotypic values retained larger genetic variability and had superior representatives than those based on phenotypic values. Received: 15 October 1999 / Accepted: 24 November 1999     

A strategy on constructing core collections by least distance stepwise sampling

A strategy was proposed for constructing core collections by least distance stepwise sampling (LDSS) based on genotypic values. In each procedure of cluster, the sampling is performed in the subgroup with the least distance in the dendrogram during constructing a core collection. Mean difference percentage (MD), variance difference percentage (VD), coincidence rate of range (CR) and variable rate of coefficient of variation (VR) were used to evaluate the representativeness of core collections constructed by this strategy. A cotton germplasm collection of 1,547 accessions with 18 quantitative traits was used to construct core collections. Genotypic values of all quantitative traits of the cotton collection were unbiasedly predicted based on mixed linear model approach. By three sampling percentages (10, 20 and 30%), four genetic distances (city block distance, Euclidean distance, standardized Euclidean distance and Mahalanobis distance) combining four hierarchical cluster methods (nearest distance method, furthest distance method, unweighted pair-group average method and Ward’s method) were adopted to evaluate the property of this strategy. Simulations were conducted in order to draw consistent, stable and reproducible results. The principal components analysis was performed to validate this strategy. The results showed that core collections constructed by LDSS strategy had a good representativeness of the initial collection. As compared to the control strategy (stepwise clusters with random sampling strategy), LDSS strategy could construct more representative core collections. For LDSS strategy, cluster methods did not need to be considered because all hierarchical cluster methods could give same results completely. The results also suggested that standardized Euclidean distance was an appropriate genetic distance for constructing core collections in this strategy.     

Methods of developing core collections based on the predicted genotypic value of rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.)

The selection of an appropriate sampling strategy and a clustering method is important in the construction of core collections based on predicted genotypic values in order to retain the greatest degree of genetic diversity of the initial collection. In this study, methods of developing rice core collections were evaluated based on the predicted genotypic values for 992 rice varieties with 13 quantitative traits. The genotypic values of the traits were predicted by the adjusted unbiased prediction (AUP) method. Based on the predicted genotypic values, Mahalanobis distances were calculated and employed to measure the genetic similarities among the rice varieties. Six hierarchical clustering methods, including the single linkage, median linkage, centroid, unweighted pair-group average, weighted pair-group average and flexible-beta methods, were combined with random, preferred and deviation sampling to develop 18 core collections of rice germplasm. The results show that the deviation sampling strategy in combination with the unweighted pair-group average method of hierarchical clustering retains the greatest degree of genetic diversities of the initial collection. The core collections sampled using predicted genotypic values had more genetic diversity than those based on phenotypic values.Communicated by D.J. Mackill     

SSR-based genetic diversity and structure of garlic accessions from Brazil

Garlic is a spice and a medicinal plant; hence, there is an increasing interest in ‘developing’ new varieties with different culinary properties or with high content of nutraceutical compounds. Phenotypic traits and dominant molecular markers are predominantly used to evaluate the genetic diversity of garlic clones. However, 24 SSR markers (codominant) specific for garlic are available in the literature, fostering germplasm researches. In this study, we genotyped 130 garlic accessions from Brazil and abroad using 17 polymorphic SSR markers to assess the genetic diversity and structure. This is the first attempt to evaluate a large set of accessions maintained by Brazilian institutions. A high level of redundancy was detected in the collection (50 % of the accessions represented eight haplotypes). However, non-redundant accessions presented high genetic diversity. We detected on average five alleles per locus, Shannon index of 1.2, H_O of 0.5, and H_E of 0.6. A core collection was set with 17 accessions, covering 100 % of the alleles with minimum redundancy. Overall F_ST and D values indicate a strong genetic structure within accessions. Two major groups identified by both model-based (Bayesian approach) and hierarchical clustering (UPGMA dendrogram) techniques were coherent with the classification of accessions according to maturity time (growth cycle): early-late and midseason accessions. Assessing genetic diversity and structure of garlic collections is the first step towards an efficient management and conservation of accessions in genebanks, as well as to advance future genetic studies and improvement of garlic worldwide.     

A core collection and mini core collection of Oryza sativa L. in China

The extent of and accessibility to genetic variation in a large germplasm collection are of interest to biologists and breeders. Construction of core collections (CC) is a favored approach to efficient exploration and conservation of novel variation in genetic resources. Using 4,310 Chinese accessions of Oryza sativa L. and 36 SSR markers, we investigated the genetic variation in different sized sub-populations, the factors that affect CC size and different sampling strategies in establishing CC. Our results indicated that a mathematical model could reliably simulate the relationship between genetic variation and population size and thus predict the variation in large germplasm collections using randomly sampled populations of 700?C1,500 accessions. We recommend two principles in determining the CC size: (1) compromising between genetic variation and genetic redundancy and (2) retaining the main types of alleles. Based on the most effective scheme selected from 229 sampling schemes, we finally developed a hierarchical CC system, in which different population scales and genetic diversities allow a flexible use of genetic resources. The CC, comprising 1.7% (932) of the accessions in the basic collection, retained more than 85% of both the SSR and phenotypic variations. A mini core collection, comprising 0.3% (189) of the accessions in the basic collection, retained 70.65% of the SSR variation and 76.97% of the phenotypic variation, thus providing a rational framework for intensive surveys of natural variation in complex traits in rice genetic resources and hence utilization of variation in rice breeding.     

Genetic diversity and population structure of pea (Pisum sativum L.) varieties derived from combined retrotransposon, microsatellite and morphological marker analysis

One hundred and sixty-four accessions representing Czech and Slovak pea (Pisum sativum L.) varieties bred over the last 50 years were evaluated for genetic diversity using morphological, simple sequence repeat (SSR) and retrotransposon-based insertion polymorphism (RBIP) markers. Polymorphic information content (PIC) values of 10 SSR loci and 31 RBIP markers were on average high at 0.89 and 0.73, respectively. The silhouette method after the Ward clustering produced the most probable cluster estimate, identifying nine clusters from molecular data and five to seven clusters from morphological characters. Principal component analysis of nine qualitative and eight quantitative morphological parameters explain over 90 and 93% of total variability, respectively, in the first three axes. Multidimensional scaling of molecular data revealed a continuous structure for the set. To enable integration and evaluation of all data types, a Bayesian method for clustering was applied. Three clusters identified using morphology data, with clear separation of fodder, dry seed and afila types, were resolved by DNA data into 17, 12 and five sub-clusters, respectively. A core collection of 34 samples was derived from the complete collection by BAPS Bayesian analysis. Values for average gene diversity and allelic richness for molecular marker loci and diversity indexes of phenotypic data were found to be similar between the two collections, showing that this is a useful approach for representative core selection.