The patterns of population differentiation in a Brassica rapa core collection View Full Text


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Article Info

DATE

2010-12-31

AUTHORS

Dunia Pino Del Carpio, Ram Kumar Basnet, Ric C. H. De Vos, Chris Maliepaard, Richard Visser, Guusje Bonnema

ABSTRACT

With the recent advances in high throughput profiling techniques the amount of genetic and phenotypic data available has increased dramatically. Although many genetic diversity studies combine morphological and genetic data, metabolite profiling has yet to be integrated into these studies. For our study we selected 168 accessions representing the different morphotypes and geographic origins of Brassica rapa. Metabolite profiling was performed on all plants of this collection in the youngest expanded leaves, 5 weeks after transplanting and the same material was used for molecular marker profiling. During the same season a year later, 26 morphological characteristics were measured on plants that had been vernalized in the seedling stage. The number of groups and composition following a hierarchical clustering with molecular markers was highly correlated to the groups based on morphological traits (r = 0.420) and metabolic profiles (r = 0.476). To reveal the admixture levels in B. rapa, comparison with the results of the programme STRUCTURE was needed to obtain information on population substructure. To analyze 5546 metabolite (LC–MS) signals the groups identified with STRUCTURE were used for random forests classification. When comparing the random forests and STRUCTURE membership probabilities 86% of the accessions were allocated into the same subgroup. Our findings indicate that if extensive phenotypic data (metabolites) are available, classification based on this type of data is very comparable to genetic classification. These multivariate types of data and methodological approaches are valuable for the selection of accessions to study the genetics of selected traits and for genetic improvement programs, and additionally provide information on the evolution of the different morphotypes in B. rapa. More... »

PAGES

1105-1118

References to SciGraph publications

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  • 2006-06-04. The genetics of plant metabolism in NATURE GENETICS
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  • 2001-10. Random Forests in MACHINE LEARNING
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  • 2007-09-03. GeneSrF and varSelRF: a web-based tool and R package for gene selection and classification using random forest in BMC BIOINFORMATICS
  • 2005-04-02. Genetic relationships within Brassica rapa as inferred from AFLP fingerprints in THEORETICAL AND APPLIED GENETICS
  • 2005-12-25. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness in NATURE GENETICS
  • 2006-10-10. TreeDyn: towards dynamic graphics and annotations for analyses of trees in BMC BIOINFORMATICS
  • 2007-04-05. Untargeted large-scale plant metabolomics using liquid chromatography coupled to mass spectrometry in NATURE PROTOCOLS
  • 2006-12-21. Genetic diversity of wheat gene pool of recurrent selection assessed by microsatellite markers and morphological traits in EUPHYTICA
  • 2002-04-05. Isolation and characterization of microsatellites in Brassica rapa L. in THEORETICAL AND APPLIED GENETICS
  • 2006-01-06. Gene selection and classification of microarray data using random forest in BMC BIOINFORMATICS
  • 2004-04-01. Efficient targeting of plant disease resistance loci using NBS profiling in THEORETICAL AND APPLIED GENETICS
  • Identifiers

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    http://scigraph.springernature.com/pub.10.1007/s00122-010-1516-1

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    32 schema:description With the recent advances in high throughput profiling techniques the amount of genetic and phenotypic data available has increased dramatically. Although many genetic diversity studies combine morphological and genetic data, metabolite profiling has yet to be integrated into these studies. For our study we selected 168 accessions representing the different morphotypes and geographic origins of Brassica rapa. Metabolite profiling was performed on all plants of this collection in the youngest expanded leaves, 5 weeks after transplanting and the same material was used for molecular marker profiling. During the same season a year later, 26 morphological characteristics were measured on plants that had been vernalized in the seedling stage. The number of groups and composition following a hierarchical clustering with molecular markers was highly correlated to the groups based on morphological traits (r = 0.420) and metabolic profiles (r = 0.476). To reveal the admixture levels in B. rapa, comparison with the results of the programme STRUCTURE was needed to obtain information on population substructure. To analyze 5546 metabolite (LC–MS) signals the groups identified with STRUCTURE were used for random forests classification. When comparing the random forests and STRUCTURE membership probabilities 86% of the accessions were allocated into the same subgroup. Our findings indicate that if extensive phenotypic data (metabolites) are available, classification based on this type of data is very comparable to genetic classification. These multivariate types of data and methodological approaches are valuable for the selection of accessions to study the genetics of selected traits and for genetic improvement programs, and additionally provide information on the evolution of the different morphotypes in B. rapa.
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    41 accessions
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    43 advances
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    48 clustering
    49 collection
    50 comparison
    51 composition
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    54 different morphotypes
    55 differentiation
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    57 evolution
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    59 findings
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    63 genetic data
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    65 genetic improvement programs
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    97 profile
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