Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 424663
Title Untargeted Metabolic Quantitative Trait Loci Analyses Reveal a Relationship between Primary Metabolism and Potato Tuber Quality
Author(s) Carreno Quintero, N.; Acharjee, A.; Maliepaard, C.A.; Bachem, C.W.B.; Mumm, R.; Bouwmeester, H.J.; Visser, R.G.F.; Keurentjes, J.J.B.
Source Plant Physiology 158 (2012)3. - ISSN 0032-0889 - p. 1306 - 1318.
Department(s) Laboratory of Plant Physiology
Laboratory of Plant Breeding
PRI BIOS Applied Metabolic Systems
Laboratory of Genetics
Publication type Refereed Article in a scientific journal
Publication year 2012
Keyword(s) chromatography-mass spectrometry - arabidopsis-thaliana - starch degradation - late blight - amino-acid - enzymatic discoloration - biochemical networks - introgression lines - genetic genomics - candidate genes
Abstract Recent advances in -omics technologies such as transcriptomics, metabolomics, and proteomics along with genotypic profiling have permitted dissection of the genetics of complex traits represented by molecular phenotypes in nonmodel species. To identify the genetic factors underlying variation in primary metabolism in potato (Solanum tuberosum), we have profiled primary metabolite content in a diploid potato mapping population, derived from crosses between S. tuberosum and wild relatives, using gas chromatography-time of flight-mass spectrometry. In total, 139 polar metabolites were detected, of which we identified metabolite quantitative trait loci for approximately 72% of the detected compounds. In order to obtain an insight into the relationships between metabolic traits and classical phenotypic traits, we also analyzed statistical associations between them. The combined analysis of genetic information through quantitative trait locus coincidence and the application of statistical learning methods provide information on putative indicators associated with the alterations in metabolic networks that affect complex phenotypic traits.
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