Literature DB >> 22983567

Quality of core collections for effective utilisation of genetic resources review, discussion and interpretation.

T L Odong1, J Jansen, F A van Eeuwijk, T J L van Hintum.   

Abstract

Definition of clear criteria for evaluation of the quality of core collections is a prerequisite for selecting high-quality cores. However, a critical examination of the different methods used in literature, for evaluating the quality of core collections, shows that there are no clear guidelines on the choices of quality evaluation criteria and as a result, inappropriate analyses are sometimes made leading to false conclusions being drawn regarding the quality of core collections and the methods to select such core collections. The choice of criteria for evaluating core collections appears to be based mainly on the fact that those criteria have been used in earlier publications rather than on the actual objectives of the core collection. In this study, we provide insight into different criteria used for evaluating core collections. We also discussed different types of core collections and related each type of core collection to their respective evaluation criteria. Two new criteria based on genetic distance are introduced. The consequences of the different evaluation criteria are illustrated using simulated and experimental data. We strongly recommend the use of the distance-based criteria since they not only allow the simultaneous evaluation of all variables describing the accessions, but they also provide intuitive and interpretable criteria, as compared with the univariate criteria generally used for the evaluation of core collections. Our findings will provide genebank curators and researchers with possibilities to make informed choices when creating, comparing and using core collections.

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Year:  2012        PMID: 22983567      PMCID: PMC3555244          DOI: 10.1007/s00122-012-1971-y

Source DB:  PubMed          Journal:  Theor Appl Genet        ISSN: 0040-5752            Impact factor:   5.699


  12 in total

1.  MSTRAT: an algorithm for building germ plasm core collections by maximizing allelic or phenotypic richness.

Authors:  B Gouesnard; T M Bataillon; G Decoux; C Rozale; D J Schoen; J L David
Journal:  J Hered       Date:  2001 Jan-Feb       Impact factor: 2.645

2.  Genetic distance sampling: a novel sampling method for obtaining core collections using genetic distances with an application to cultivated lettuce.

Authors:  J Jansen; Th van Hintum
Journal:  Theor Appl Genet       Date:  2006-12-16       Impact factor: 5.699

3.  PowerCore: a program applying the advanced M strategy with a heuristic search for establishing core sets.

Authors:  Kyu-Won Kim; Hun-Ki Chung; Gyu-Taek Cho; Kyung-Ho Ma; Dorothy Chandrabalan; Jae-Gyun Gwag; Tae-San Kim; Eun-Gi Cho; Yong-Jin Park
Journal:  Bioinformatics       Date:  2007-06-22       Impact factor: 6.937

4.  Evaluation of five strategies for obtaining a core subset from a large genetic resource collection of durum wheat.

Authors:  P L Zeuli; C O Qualset
Journal:  Theor Appl Genet       Date:  1993-11       Impact factor: 5.699

5.  Methods of developing a core collection of annual Medicago species.

Authors:  N Diwan; M S McIntosh; G R Bauchan
Journal:  Theor Appl Genet       Date:  1995-05       Impact factor: 5.699

6.  Probability plotting methods for the analysis of data.

Authors:  M B Wilk; R Gnanadesikan
Journal:  Biometrika       Date:  1968-03       Impact factor: 2.445

7.  Determination of genetic structure of germplasm collections: are traditional hierarchical clustering methods appropriate for molecular marker data?

Authors:  T L Odong; J van Heerwaarden; J Jansen; T J L van Hintum; F A van Eeuwijk
Journal:  Theor Appl Genet       Date:  2011-04-07       Impact factor: 5.699

8.  Nested core collections maximizing genetic diversity in Arabidopsis thaliana.

Authors:  Heather I McKhann; Christine Camilleri; Aurélie Bérard; Thomas Bataillon; Jacques L David; Xavier Reboud; Valérie Le Corre; Christophe Caloustian; Ivo G Gut; Dominique Brunel
Journal:  Plant J       Date:  2004-04       Impact factor: 6.417

9.  A worldwide bread wheat core collection arrayed in a 384-well plate.

Authors:  François Balfourier; Valérie Roussel; Pjotr Strelchenko; Florence Exbrayat-Vinson; Pierre Sourdille; Gilles Boutet; Jean Koenig; Catherine Ravel; Olga Mitrofanova; Michel Beckert; Gilles Charmet
Journal:  Theor Appl Genet       Date:  2007-02-21       Impact factor: 5.574

10.  A strategy on constructing core collections by least distance stepwise sampling.

Authors:  J C Wang; J Hu; H M Xu; S Zhang
Journal:  Theor Appl Genet       Date:  2007-04-03       Impact factor: 5.574

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  40 in total

Review 1.  Prospects of pan-genomics in barley.

Authors:  Cécile Monat; Mona Schreiber; Nils Stein; Martin Mascher
Journal:  Theor Appl Genet       Date:  2018-11-16       Impact factor: 5.699

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

Authors:  Thierry Leroy; Fabien De Bellis; Hyacinthe Legnate; Pascal Musoli; Adrien Kalonji; Rey Gastón Loor Solórzano; Philippe Cubry
Journal:  Genetica       Date:  2014-05-04       Impact factor: 1.082

3.  The Multisite PeachRefPop Collection: A True Cultural Heritage and International Scientific Tool for Fruit Trees.

Authors:  Marco Cirilli; Sabrina Micali; Maria José Aranzana; Pere Arús; Annarosa Babini; Teresa Barreneche; Marco Bink; Celia M Cantin; Angelo Ciacciulli; José Enrique Cos-Terrer; Pavlina Drogoudi; Iban Eduardo; Stefano Foschi; Daniela Giovannini; Walter Guerra; Alessandro Liverani; Igor Pacheco; Thierry Pascal; Benedicte Quilot-Turion; Ignazio Verde; Laura Rossini; Daniele Bassi
Journal:  Plant Physiol       Date:  2020-07-29       Impact factor: 8.340

4.  Genomics-informed prebreeding unlocks the diversity in genebanks for wheat improvement.

Authors:  Albert W Schulthess; Sandip M Kale; Fang Liu; Yusheng Zhao; Norman Philipp; Maximilian Rembe; Yong Jiang; Ulrike Beukert; Albrecht Serfling; Axel Himmelbach; Jörg Fuchs; Markus Oppermann; Stephan Weise; Philipp H G Boeven; Johannes Schacht; C Friedrich H Longin; Sonja Kollers; Nina Pfeiffer; Viktor Korzun; Matthias Lange; Uwe Scholz; Nils Stein; Martin Mascher; Jochen C Reif
Journal:  Nat Genet       Date:  2022-10-04       Impact factor: 41.307

5.  A multiple phenotype imputation method for genetic diversity and core collection in Taiwanese vegetable soybean.

Authors:  Yen-Hsiang Huang; Hsin-Mei Ku; Chong-An Wang; Ling-Yu Chen; Shan-Syue He; Shu Chen; Po-Chun Liao; Pin-Yuan Juan; Chung-Feng Kao
Journal:  Front Plant Sci       Date:  2022-09-02       Impact factor: 6.627

6.  Preparation and Curation of Omics Data for Genome-Wide Association Studies.

Authors:  Feng Zhu; Alisdair R Fernie; Federico Scossa
Journal:  Methods Mol Biol       Date:  2022

Review 7.  Lathyrus diversity: available resources with relevance to crop improvement--L. sativus and L. cicera as case studies.

Authors:  M C Vaz Patto; D Rubiales
Journal:  Ann Bot       Date:  2014-03-12       Impact factor: 4.357

8.  The AVRDC - The World Vegetable Center mungbean (Vigna radiata) core and mini core collections.

Authors:  Roland Schafleitner; Ramakrishnan Madhavan Nair; Abhishek Rathore; Yen-wei Wang; Chen-yu Lin; Shu-hui Chu; Pin-yun Lin; Jian-Cheng Chang; Andreas W Ebert
Journal:  BMC Genomics       Date:  2015-04-29       Impact factor: 3.969

9.  Exome sequence genotype imputation in globally diverse hexaploid wheat accessions.

Authors:  Fan Shi; Josquin Tibbits; Raj K Pasam; Pippa Kay; Debbie Wong; Joanna Petkowski; Kerrie L Forrest; Ben J Hayes; Alina Akhunova; John Davies; Steven Webb; German C Spangenberg; Eduard Akhunov; Matthew J Hayden; Hans D Daetwyler
Journal:  Theor Appl Genet       Date:  2017-04-04       Impact factor: 5.699

10.  The population structure and diversity of eggplant from Asia and the Mediterranean Basin.

Authors:  Fabio Cericola; Ezio Portis; Laura Toppino; Lorenzo Barchi; Nazareno Acciarri; Tommaso Ciriaci; Tea Sala; Giuseppe Leonardo Rotino; Sergio Lanteri
Journal:  PLoS One       Date:  2013-09-06       Impact factor: 3.240

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