@article{SprengerRudackSchudomaetal.2015,
  author    = {Sprenger, Heike and Rudack, Katharina and Schudoma, Christian and Neumann, Arne and Seddig, Sylvia and Peters, Rolf and Zuther, Ellen and Kopka, Joachim and Hincha, Dirk K. and Walther, Dirk and Koehl, Karin},
  title     = {Assessment of drought tolerance and its potential yield penalty in potato},
  series = {Functional plant biology : an international journal of plant function},
  volume    = {42},
  journal   = {Functional plant biology : an international journal of plant function},
  number    = {7},
  publisher = {CSIRO},
  address   = {Clayton},
  issn      = {1445-4408},
  doi       = {10.1071/FP15013},
  pages     = {655 -- 667},
  year      = {2015},
  abstract  = {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.},
  language  = {en}
}
@article{SteinfathStrehmelPetersetal.2010,
  author    = {Steinfath, Matthias and Strehmel, Nadine and Peters, Rolf and Schauer, Nicolas and Groth, Detlef and Hummel, Jan and Steup, Martin and Selbig, Joachim and Kopka, Joachim and Geigenberger, Peter and Dongen, Joost T. van},
  title     = {Discovering plant metabolic biomarkers for phenotype prediction using an untargeted approach},
  issn      = {1467-7644},
  doi       = {10.1111/j.1467-7652.2010.00516.x},
  year      = {2010},
  abstract  = {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.},
  language  = {en}
}