TY - JOUR A1 - Steinfath, Matthias A1 - Strehmel, Nadine A1 - Peters, Rolf A1 - Schauer, Nicolas A1 - Groth, Detlef A1 - Hummel, Jan A1 - Steup, Martin A1 - Selbig, Joachim A1 - Kopka, Joachim A1 - Geigenberger, Peter A1 - Dongen, Joost T. van T1 - Discovering plant metabolic biomarkers for phenotype prediction using an untargeted approach N2 - Biomarkers are used to predict phenotypical properties before these features become apparent and, therefore, are valuable tools for both fundamental and applied research. Diagnostic biomarkers have been discovered in medicine many decades ago and are now commonly applied. While this is routine in the field of medicine, it is of surprise that in agriculture this approach has never been investigated. Up to now, the prediction of phenotypes in plants was based on growing plants and assaying the organs of interest in a time intensive process. For the first time, we demonstrate in this study the application of metabolomics to predict agronomic important phenotypes of a crop plant that was grown in different environments. Our procedure consists of established techniques to screen untargeted for a large amount of metabolites in parallel, in combination with machine learning methods. By using this combination of metabolomics and biomathematical tools metabolites were identified that can be used as biomarkers to improve the prediction of traits. The predictive metabolites can be selected and used subsequently to develop fast, targeted and low-cost diagnostic biomarker assays that can be implemented in breeding programs or quality assessment analysis. The identified metabolic biomarkers allow for the prediction of crop product quality. Furthermore, marker-assisted selection can benefit from the discovery of metabolic biomarkers when other molecular markers come to its limitation. The described marker selection method was developed for potato tubers, but is generally applicable to any crop and trait as it functions independently of genomic information. Y1 - 2010 UR - http://www3.interscience.wiley.com/cgi-bin/issn?DESCRIPTOR=PRINTISSN&VALUE=1467-7644 U6 - https://doi.org/10.1111/j.1467-7652.2010.00516.x SN - 1467-7644 ER - TY - JOUR A1 - Sprenger, Heike A1 - Rudack, Katharina A1 - Schudoma, Christian A1 - Neumann, Arne A1 - Seddig, Sylvia A1 - Peters, Rolf A1 - Zuther, Ellen A1 - Kopka, Joachim A1 - Hincha, Dirk K. A1 - Walther, Dirk A1 - Koehl, Karin T1 - Assessment of drought tolerance and its potential yield penalty in potato JF - Functional plant biology : an international journal of plant function N2 - Climate models predict an increased likelihood of seasonal droughts for many areas of the world. Breeding for drought tolerance could be accelerated by marker-assisted selection. As a basis for marker identification, we studied the genetic variance, predictability of field performance and potential costs of tolerance in potato (Solanum tuberosum L.). Potato produces high calories per unit of water invested, but is drought-sensitive. In 14 independent pot or field trials, 34 potato cultivars were grown under optimal and reduced water supply to determine starch yield. In an artificial dataset, we tested several stress indices for their power to distinguish tolerant and sensitive genotypes independent of their yield potential. We identified the deviation of relative starch yield from the experimental median (DRYM) as the most efficient index. DRYM corresponded qualitatively to the partial least square model-based metric of drought stress tolerance in a stress effect model. The DRYM identified significant tolerance variation in the European potato cultivar population to allow tolerance breeding and marker identification. Tolerance results from pot trials correlated with those from field trials but predicted field performance worse than field growth parameters. Drought tolerance correlated negatively with yield under optimal conditions in the field. The distribution of yield data versus DRYM indicated that tolerance can be combined with average yield potentials, thus circumventing potential yield penalties in tolerance breeding. KW - performance prediction KW - Solanum tuberosum KW - tolerance index KW - target environment Y1 - 2015 U6 - https://doi.org/10.1071/FP15013 SN - 1445-4408 SN - 1445-4416 VL - 42 IS - 7 SP - 655 EP - 667 PB - CSIRO CY - Clayton ER -