Prediction of hybrid biomass in Arabidopsis thaliana by selected parental SNP and metabolic markers.
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ABSTRACT:
SUBMITTER: Steinfath M
PROVIDER: S-EPMC2793375 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:A major goal of today's biology is to understand the genetic basis of quantitative traits. This can be achieved by statistical methods that evaluate the association between molecular marker variation and phenotypic variation in different types of mapping populations. The objective of this work was to evaluate the statistical power of quantitative trait loci (QTL) detection of various multi-parental mating designs, as well as to assess the reasons for the observed differences. Our study was based on an empirical data of 20 Arabidopsis thaliana accessions, which have been selected to capture the maximum genetic diversity. The examined mating designs differed strongly with respect to the statistical power to detect QTL. We observed the highest power to detect QTL for the diallel cross with random mating design. The results of our study suggested that performing sibling mating within subpopulations of joint-linkage mapping populations has the potential to considerably increase the power for QTL detection. Our results, however, revealed that using designs in which more than two parental alleles segregate in each subpopulation increases the power even more.
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Project description:Metabolic phenotyping at cellular resolution may be considered one of the challenges in current plant physiology. A method is described which enables the cell type-specific metabolic analysis of epidermal cell types in Arabidopsis thaliana pavement, basal, and trichome cells. To achieve the required high spatial resolution, single cell sampling using microcapillaries was combined with routine gas chromatography-time of flight-mass spectrometry (GC-TOF-MS) based metabolite profiling. The identification and relative quantification of 117 mostly primary metabolites has been demonstrated. The majority, namely 90 compounds, were accessible without analytical background correction. Analyses were performed using cell type-specific pools of 200 microsampled individual cells. Moreover, among these identified metabolites, 38 exhibited differential pool sizes in trichomes, basal or pavement cells. The application of an independent component analysis confirmed the cell type-specific metabolic phenotypes. Significant pool size changes between individual cells were detectable within several classes of metabolites, namely amino acids, fatty acids and alcohols, alkanes, lipids, N-compounds, organic acids and polyhydroxy acids, polyols, sugars, sugar conjugates and phenylpropanoids. It is demonstrated here that the combination of microsampling and GC-MS based metabolite profiling provides a method to investigate the cellular metabolism of fully differentiated plant cell types in vivo.
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