Methods of developing a core collection of annual Medicago species

A core collection is a subset of a large germplasm collection that contains accessions chosen to represent the genetic variability of the germplasm collection. The purpose of the core collection is to improve management and use of a germplasm collection. Core collections are usually assembled by grouping accessions and selecting from within these groups. The objective of this study was to compare 11 methods of assembling a core collection of the U.S. National collection of annual Medicago species. These methods differed in their use of passport and evaluation data as well as their selection strategy. Another objective was to compare core collections with sample sizes of 5%, 10% and 17% of the germplasm collection. Core collections assembled with evaluation data and cluster analysis better represented the germplasm collection than core collections assembled based solely on passport data and random selection of accessions, The Relative Diversity and the logarithm methods generated better core collections than the proportional method. The 5% and 10% sample size core collection were judged insufficient to represent the germplasm collection.     

植物核心种质研究进展

丰富的植物遗传资源为作物育种和遗传研究提供广阔的遗传基础,然而资源庞大的数量也给植物遗传资源的收集、保存、研究和利用带来了困难。Frankel首先提出并与Brown等人完善了核心种质(core collection)的概念,即通过一定的方法从整个种质资源中选择一部分样本,以最小的资源数量和遗传重复,尽可能最大限度的代表整个资源的多样性。核心种质的提出,为遗传资源的研究和利用提供了崭新的解决途径。目前,已在多年生野生大豆、硬粒小麦、花生等20种植物上开展了核心种质研究。本围绕核心种质具有四个显特点即代表性、实用性、有效性、动态性,介绍在不同植物构建核心种质时,所利用的数据、分组原则及取样方法等,并介绍了核心种质的评价进展。通过比较发现,不同植物在构建核心种质各有区别,但是以根据地理来源分组,按聚类法取样为主。目前,对植物核心种质的研究,多处于取样策略的研究阶段。对建成的核心种质评价研究较少,主要集中在农艺性状遗传多样性方面的验证,而时分子水平的验证较少,构建的核心种质还有待于实践的检验。     

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。     

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     

Maximization of minority classes in core collections designed for association studies

Core collections are nowadays widely employed in diverse studies on plant genetics. The more extensively used method to build core collections (maximization strategy) is based on the selection, from a global collection, of those accessions which maximize the number of different alleles and phenotypic classes (classes’ richness). However, different core collections should be created for different types of studies, and though several years ago most of core collections were developed to make the characterization and use of germplasm collections easier with a smaller sample size, for either conservation or breeding purposes, today, they are widely employed for association studies that are broadly applied in plant genetic improvement. Following the M strategy, some alleles or phenotypic classes often appear in a very low frequency, which may reduce the power of the analysis, avoiding the detection of real associations (false negatives). In this work, we propose and evaluate a new way to build core collections using the maximization strategy in several sequential steps, to maximize the frequency of minority classes, thus increasing the statistical power of the association study.     

利用分子标记和数量性状基因型值构建作物核心种质库的研究

提出了一种基于分子标记数据及数量性状基因型值构建作物种质资源核心种质库的方法.采用包括基因型与环境互作的遗传模型及相应的混合线性模型统计分析方法,无偏预测各材料的基因型值,分别用基因型值和分子标记数据计算个体间的相似系数,加权得到最终的相似距离.采用不加权类平均法（UPGMA）进行系统聚类,用多次聚类随机取样法构建核心种质库.以水稻DH群体111个基因型8个农艺性状、175个分子标记位点的数据为实例,按四种抽样比率（25%,20%,15%,10%）构建了四个核心种质库,比较了核心种质库与整个群体的分子标记多样性及数量性状的遗传变异,评价了所用方法的有效性。     

The USDA Barley Core Collection: Genetic Diversity,Population Structure,and Potential for Genome-Wide Association Studies

New sources of genetic diversity must be incorporated into plant breeding programs if they are to continue increasing grain yield and quality, and tolerance to abiotic and biotic stresses. Germplasm collections provide a source of genetic and phenotypic diversity, but characterization of these resources is required to increase their utility for breeding programs. We used a barley SNP iSelect platform with 7,842 SNPs to genotype 2,417 barley accessions sampled from the USDA National Small Grains Collection of 33,176 accessions. Most of the accessions in this core collection are categorized as landraces or cultivars/breeding lines and were obtained from more than 100 countries. Both STRUCTURE and principal component analysis identified five major subpopulations within the core collection, mainly differentiated by geographical origin and spike row number (an inflorescence architecture trait). Different patterns of linkage disequilibrium (LD) were found across the barley genome and many regions of high LD contained traits involved in domestication and breeding selection. The genotype data were used to define ‘mini-core’ sets of accessions capturing the majority of the allelic diversity present in the core collection. These ‘mini-core’ sets can be used for evaluating traits that are difficult or expensive to score. Genome-wide association studies (GWAS) of ‘hull cover’, ‘spike row number’, and ‘heading date’ demonstrate the utility of the core collection for locating genetic factors determining important phenotypes. The GWAS results were referenced to a new barley consensus map containing 5,665 SNPs. Our results demonstrate that GWAS and high-density SNP genotyping are effective tools for plant breeders interested in accessing genetic diversity in large germplasm collections.     

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.     

Novel analytic criteria and effective plate designs for quality control in genome-scale RNAi screens

One of the most fundamental challenges in genome-wide RNA interference (RNAi) screens is to glean biological significance from mounds of data, which relies on the development and adoption of appropriate analytic methods and designs for quality control (QC) and hit selection. Currently, a Z-factor-based QC criterion is widely used to evaluate data quality. However, this criterion cannot take into account the fact that different positive controls may have different effect sizes and leads to inconsistent QC results in experiments with 2 or more positive controls with different effect sizes. In this study, based on a recently proposed parameter, strictly standardized mean difference (SSMD), novel QC criteria are constructed for evaluating data quality in genome-wide RNAi screens. Two good features of these novel criteria are: (1) SSMD has both clear original and probability meanings for evaluating the differentiation between positive and negative controls and hence the SSMD-based QC criteria have a solid probabilistic and statistical basis, and (2) these QC criteria obtain consistent QC results for multiple positive controls with different effect sizes. In addition, I propose multiple plate designs and the guidelines for using them in genome-wide RNAi screens. Finally, I provide strategies for using the SSMD-based QC criteria and effective plate design together to improve data quality. The novel SSMD-based QC criteria, effective plate designs, and related guidelines and strategies may greatly help to obtain high quality of data in genome-wide RNAi screens.     

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     

Comparison of different methods to construct a core germplasm collection in woody perennial species with simple sequence repeat markers. A case study in cherimoya (Annona cherimola, Annonaceae), an underutilised subtropical fruit tree species

Although molecular markers are becoming the tool of choice to develop core collections in plants, the examples of their use in woody perennial species are very scarce. In this work, we used simple sequence repeat (SSR) marker data to develop a core collection in an underutilised subtropical fruit tree species, cherimoya ( Annona cherimola , Annonaceae), from an initial collection of 279 genotypes from different countries. We compared six alternative allocation methods to construct the core collection, four not based upon the similarity dendrogram [random sampling, maximisation strategy (M strategy) and simulated annealing algorithm maximising both genetic diversity and number of SSR alleles] and two based on dendrogram data (logarithmic strategy and stepwise clustering). The diversity maintained in each subset was compared with that present in the entire collection. The results obtained indicate that the use of SSRs together with the M strategy is the most efficient method to develop a core collection in cherimoya. In the best subset, with 40 accessions, all the SSR alleles present in the whole collection were recovered and no significant differences in frequency distribution of alleles for any of the loci studied or in variability parameters ( H _O, H _E) were recorded between the core and the whole collection.     

Developing core collections to optimize the management and the exploitation of diversity of the coffee Coffea canephora

The management of diversity for conservation and breeding is of great importance for all plant species and is particularly true in perennial species, such as the coffee Coffea canephora. This species exhibits a large genetic and phenotypic diversity with six different diversity groups. Large field collections are available in the Ivory Coast, Uganda and other Asian, American and African countries but are very expensive and time consuming to establish and maintain in large areas. We propose to improve coffee germplasm management through the construction of genetic core collections derived from a set of 565 accessions that are characterized with 13 microsatellite markers. Core collections of 12, 24 and 48 accessions were defined using two methods aimed to maximize the allelic diversity (Maximization strategy) or genetic distance (Maximum-Length Sub-Tree method). A composite core collection of 77 accessions is proposed for both objectives of an optimal management of diversity and breeding. This core collection presents a gene diversity value of 0.8 and exhibits the totality of the major alleles (i.e., 184) that are present in the initial set. The seven proposed core collections constitute a valuable tool for diversity management and a foundation for breeding programs. The use of these collections for collection management in research centers and breeding perspectives for coffee improvement are discussed.     

A platform for soybean molecular breeding: the utilization of core collections for food security

Soybean is an important crop not only for human consumption but also for its addition of nitrogen to the soil during crop rotation. China has the largest collection of cultivated soybeans (Glycine max) and wild soybeans (Glycine soja) all over the world. The platform of soybean core, mini core and integrated applied core collections has been developed in the past decade based on systematic researches which included the sampling strategies, statistical methods, phenotypic data and SSR markers. Meanwhile, intergrated applied core collections including accessions with single or integrated favorite traits are being developed in order to meet the demand of soybean breeding. These kinds of core collections provide powerful materials for evaluation of germplasm, identification of trait-specific accessions, gene discovery, allele mining, genomic study, maker development, and molecular breeding. Some successful cases have proved the usefulness and efficiency of this platform. The platform is helpful for enhancing utilization of soybean genetic resources in sustainable crop improvement for food security. The efficient utilization of this platform in the future is relying on accurate phenotyping methods, abundant functional markers, high-throughput genotyping platforms, and effective breeding programs.     

APDB: a novel measure for benchmarking sequence alignment methods without reference alignments

MOTIVATION: We describe APDB, a novel measure for evaluating the quality of a protein sequence alignment, given two or more PDB structures. This evaluation does not require a reference alignment or a structure superposition. APDB is designed to efficiently and objectively benchmark multiple sequence alignment methods. RESULTS: Using existing collections of reference multiple sequence alignments and existing alignment methods, we show that APDB gives results that are consistent with those obtained using conventional evaluations. We also show that APDB is suitable for evaluating sequence alignments that are structurally equivalent. We conclude that APDB provides an alternative to more conventional methods used for benchmarking sequence alignment packages.     

Evaluating options in LCA: The emergence of conflicting paradigms for impact assessment and evaluation

LCA aims to help direct decisions in an environmentally sustainable direction. It indicates the environmental effects of choices and evaluates these against this background. Approaches to evaluation in LCA differ substantially, related to the way of modelling environmental effects and to the way these effects are combined into an overall judgement on alternative options. Several approaches are now operational, which are linked to different paradigms in decision making. It is shown that the choice of paradigm is quite decisive on the outcome of the analysis. Also within similar paradigms, different methods now operational may lead to different outcomes. These latter differences may be alleviated more easily than those related to paradigmatic choices, as they are partly a matter of refinement, and they partly result from legitimate differences in subjective priorities. The more basic paradigmatic differences can hardly be bridged. The practical relevancy of the subject is proven by applying different operational methods to one case, showing widely differing outcomes. The paradigm behind evaluating environmental effects is either values based, directly or through policy decisions, or economics based, as individual preferences measured in the monetary terms of willingness-to-pay. Accordingly, the different methods are “policy-oriented” or “monetary”. It may be doubted if the differences between these can be overcome in standardisation.     

Establishment of a Core Collection of Traditional Cuban <Emphasis Type="Italic">Theobroma cacao</Emphasis> Plants for Conservation and Utilization Purposes

Presently, Theobroma cacao L. (cacao) in Cuba is mainly cultivated in the eastern region where plantations comprise a mixture of clonal varieties, hybrids, progeny of Trinidad Selected Hybrids, and traditional—also known as ancient—cacao. The ancient genetic resources, probably the plants most closely related to the original introductions, are endangered by their progressive replacement by modern clones. To promote the conservation and utilization of these genetic resources, a representative sample of 537 traditional Cuban cacao plants was used to develop a core collection. Core collections based on 15 simple sequence repeat (SSR) markers were generated using five different sampling algorithms: random sampling, simulated annealing, stepwise clustering with random sampling, the M strategy, and maximum genetic diversity. The five core collections were designed to capture 95 % of the SSR alleles in the complete collection. The genetic, morphological, and geographical diversity of each core collection was compared with that of the entire collection. The entire collection contained 139 alleles, including 104 rare ones, with the 95 % allelic coverage threshold achieved with 133 alleles. The core collection generated by the maximum genetic diversity algorithm had the lowest number of accessions (185), the highest mean genetic distance (0.486), the lowest morphological character redundancy, and the highest diversity as assessed by the mean Shannon-Weaver diversity index (0.757). This core collection can thus serve as the basis of future improvement programs based on local genetic resources.     

A review of methodologies and success indicators for coastal wetland restoration

Coastal wetlands are considered to be amongst the most productive ecosystems and can provide invaluable ecological services. However, coastal wetlands are listed amongst the most threatened ecosystems suffering from anthropogenic activities. The loss or degradation of coastal wetlands has drawn a high level of attention to wetland restoration. Improvement of the structure and function of degraded, damaged and destroyed wetlands may be achieved through ecological restoration. Large numbers of restoration projects have been conducted worldwide based on different restoration goals and different methods. It is undoubtedly important to evaluate whether coastal wetland restoration is successful. However, coastal wetland restoration assessment has become challenging because of current disagreement on definitions and concepts of restoration evaluation. We reviewed the methodology of coastal wetland restoration and criteria for success evaluation, and then summarized the issues for current wetland restoration and success evaluation based on literature review. Moreover, we used an estuarine wetland affected by urbanization as a sample to demonstrate how to establish a success indicator system for guiding wetland restoration and evaluating the success of wetland restoration.     

Prediction Errors in Learning Drug Response from Gene Expression Data – Influence of Labeling,Sample Size,and Machine Learning Algorithm

Model-based prediction is dependent on many choices ranging from the sample collection and prediction endpoint to the choice of algorithm and its parameters. Here we studied the effects of such choices, exemplified by predicting sensitivity (as IC50) of cancer cell lines towards a variety of compounds. For this, we used three independent sample collections and applied several machine learning algorithms for predicting a variety of endpoints for drug response. We compared all possible models for combinations of sample collections, algorithm, drug, and labeling to an identically generated null model. The predictability of treatment effects varies among compounds, i.e. response could be predicted for some but not for all. The choice of sample collection plays a major role towards lowering the prediction error, as does sample size. However, we found that no algorithm was able to consistently outperform the other and there was no significant difference between regression and two- or three class predictors in this experimental setting. These results indicate that response-modeling projects should direct efforts mainly towards sample collection and data quality, rather than method adjustment.     

中国普通小麦初选核心种质的产生

对中国普通小麦种质资源构建了初选核心种质。地方品种和选育品种分别构建。按栽培区(地理生态区)分组。地方品种按亚区分为28组,选育品种按大区分为10组。各组内在21个表型性状聚类的基础上,按平方根法取样,并依遗传多样性指数与遗传丰富度加以调整。提出在生产上或育种中起过重要作用的品种为必选材料。初步选定的材料经种植核对,淘汰错杂后,产生初选核心种质。地方品种全部供试材料11694份,初选核心种质3283份,取样比例为28．18％。选育品种全部11441份,初选核心种质1684份,取样比例为14．9％。计划经分子标记分析,最后核心种质的比例占全部种质的10％左右。根据全部材料21个性状遗传多样性指数测验,初选核心种质,除芒和壳两性状外,与全部种质的遗传差异均未达到显水平。讨论了初选核心种质的构建方法。指出陕南部西山地和汾渭谷地是中国小麦地方品种遗传变异多样性的富集地。育成品种多样性程度以西南冬麦区和黄淮冬麦区为最高。     

An evaluation of the status of living collections for plant,environmental, and microbial research

While living collections are critical for biological research, support for these foundational infrastructure elements is inconsistent, which makes quality control, regulatory compliance, and reproducibility difficult. In recent years, the Ecological Society of America has hosted several National Science Foundation–sponsored workshops to explore and enhance the sustainability of biological research infrastructure. At the same time, the United States Culture Collection Network has brought together managers of living collections to foster collaboration and information exchange within a specific living collections community. To assess the sustainability of collections, a survey was distributed to collection scientists whose responses provide a benchmark for evaluating the resiliency of these collections. Among the key observations were that plant collections have larger staffing requirements and that living microbe collections were the most vulnerable to retirements or other disruptions. Many higher plant and vertebrate collections have institutional support and several have endowments. Other collections depend on competitive grant support in an era of intense competition for these resources. Opportunities for synergy among living collections depend upon complementing the natural strong engagement with the research communities that depend on these collections with enhanced information sharing, communication, and collective action to keep them sustainable for the future. External efforts by funding agencies and publishers could reinforce the advantages of having professional management of research resources across every discipline.