Sampling Strategies for Conserving Maize Diversity When Forming Core Subsets Using Genetic Markers

Core subsets can be formed on the basis of molecular markers and different sampling strategies. This research used genetic markers on three maize data sets for studying 24 stratified sampling strategies to investigate which strategy conserved the most diversity in the core subset as compared with the original sample. The strategies were formed by combining three factors: (i) two clustering methods (UPGMA and Ward), based on (ii) two initial genetic distance measures, and using (iii) six allocation criteria [two based on the size of the cluster and four based on maximizing distances in the core (the D method) used with four diversity indices]. The objectives were (i) to study the influence of these factors and their interaction on the diversity of the core subsets and (ii) to compare the 24 stratified sampling strategies with the M strategy implemented in the MSTRAT algorithm. Success of each strategy was measured on the basis of maximizing genetic distances (Modified Roger and Cavalli-Sforza and Edwards distances) and genetic diversity indices (Shannon index, proportion of heterozygous loci, and number of effective alleles) in each core. Twenty independent stratified random samples were obtained for each strategy using a sampling intensity of 20% of the collection. For the three data sets, the UPGMA with D allocation methods produced core subsets with significantly more diversity than the other methods and were better than the M strategy for maximizing genetic distance. For most of the diversity indices, the M strategy outperformed the D method.

[1]  J. Berthaud,et al.  Patterns of population structure in maize landraces from the Central Valleys of Oaxaca in Mexico , 2004, Heredity.

[2]  José Crossa,et al.  User's manual for the LCDMV software (calculation software of molecular distances between varieties) : for fingerprinting and genetic diversity studies , 2003 .

[3]  J. Marita,et al.  Development of an algorithm identifying maximally diverse core collections , 2000, Genetic Resources and Crop Evolution.

[4]  J. H. Ward.  Hierarchical Grouping to Optimize an Objective Function , 1963 .

[5]  Robert R. Sokal,et al.  A statistical method for evaluating systematic relationships , 1958 .

[6]  C. Qualset,et al.  Evaluation of five strategies for obtaining a core subset from a large genetic resource collection of durum wheat , 1993, Theoretical and Applied Genetics.

[7]  J. Gower.  A General Coefficient of Similarity and Some of Its Properties , 1971 .

[8]  K. Basford,et al.  Three-Mode Analyses of Maize Using Morphological and Agronomic Attributes Measured in Multilocational Trials , 1995 .

[9]  J. Crossa,et al.  The Modified Location Model for Classifying Genetic Resources , 2002 .

[10]  T. Caliński,et al.  A dendrite method for cluster analysis , 1974 .

[11]  J. Peeters,et al.  Hierarchical cluster analysis as a tool to manage variation in germplasm collections , 1989, Theoretical and Applied Genetics.

[12]  A. Brown,et al.  Conservation of allelic richness in wild crop relatives is aided by assessment of genetic markers. , 1993, Proceedings of the National Academy of Sciences of the United States of America.

[13]  C. Spillane,et al.  Core collections of plant genetic resources. , 2000 .

[14]  G. W. Milligan,et al.  An examination of procedures for determining the number of clusters in a data set , 1985 .

[15]  J. Crossa,et al.  A Multivariate Method for Classifying Cultivars and Studying Group × Environment × Trait Interaction , 2003 .

[16]  Matthias Frisch,et al.  Genetical and Mathematical Properties of Similarity and Dissimilarity Coefficients Applied in Plant Breeding and Seed Bank Management , 2005 .

[17]  J. Crossa,et al.  A method for combining molecular markers and phenotypic attributes for classifying plant genotypes , 2001, Theoretical and Applied Genetics.

[18]  J. Crossa,et al.  Evaluation of Carribean Maize Accessions to Develop a Core Subset , 1998 .

[19]  J. Hamrick,et al.  Quantification of genetic diversity at allozyme loci , 1997 .

[20]  A. Brown,et al.  Core collections: a practical approach to genetic resources management , 1989 .

[21]  Peter J. Rousseeuw,et al.  Finding Groups in Data: An Introduction to Cluster Analysis , 1990 .

[22]  J. David,et al.  MSTRAT: an algorithm for building germ plasm core collections by maximizing allelic or phenotypic richness. , 2001, The Journal of heredity.

[23]  J. Crossa,et al.  Practical considerations for maintaining germplasm in maize , 1994, Theoretical and Applied Genetics.

[24]  S. Hari Krishna,et al.  Optimal sampling strategy and core collection size of Andean tetraploid potato based on isozyme data – a simulation study , 2002, Theoretical and Applied Genetics.

[25]  J. Crossa,et al.  A sampling strategy for conserving genetic diversity when forming core subsets , 2005 .

[26]  J. Crossa,et al.  Cluster analysis, an approach to sampling variability in maize accessions , 1996 .

[27]  T. Bataillon,et al.  Neutral genetic markers and conservation genetics: simulated germplasm collections. , 1996, Genetics.

[28]  I. Gut,et al.  Nested core collections maximizing genetic diversity in Arabidopsis thaliana. , 2004, The Plant journal : for cell and molecular biology.

[29]  C. Spillane,et al.  Implementing core collections - principles, procedures, progress, problems and promise. , 1999 .

[30]  J. Crossa,et al.  Classifying genetic resources by categorical and continuous variables , 1998 .

[31]  M. Warburton,et al.  More on the introduction of temperate maize into Europe: large-scale bulk SSR genotyping and new historical elements , 2006 .

[32]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[33]  G. Charmet,et al.  The use of geostatistics for sampling a core collection of perennial ryegrass populations , 1995, Genetic Resources and Crop Evolution.

[34]  J. Crossa,et al.  The modified location model for classifying genetic resources: I. Association between categorical and continuous variables , 2002 .