TY - JOUR A1 - Sprenger, Heike A1 - Erban, Alexander A1 - Seddig, Sylvia A1 - Rudack, Katharina A1 - Thalhammer, Anja A1 - Le, Mai Q. A1 - Walther, Dirk A1 - Zuther, Ellen A1 - Koehl, Karin I. A1 - Kopka, Joachim A1 - Hincha, Dirk K. T1 - Metabolite and transcript markers for the prediction of potato drought tolerance JF - Plant Biotechnology Journal N2 - Potato (Solanum tuberosum L.) is one of the most important food crops worldwide. Current potato varieties are highly susceptible to drought stress. In view of global climate change, selection of cultivars with improved drought tolerance and high yield potential is of paramount importance. Drought tolerance breeding of potato is currently based on direct selection according to yield and phenotypic traits and requires multiple trials under drought conditions. Marker-assisted selection (MAS) is cheaper, faster and reduces classification errors caused by noncontrolled environmental effects. We analysed 31 potato cultivars grown under optimal and reduced water supply in six independent field trials. Drought tolerance was determined as tuber starch yield. Leaf samples from young plants were screened for preselected transcript and nontargeted metabolite abundance using qRT-PCR and GC-MS profiling, respectively. Transcript marker candidates were selected from a published RNA-Seq data set. A Random Forest machine learning approach extracted metabolite and transcript markers for drought tolerance prediction with low error rates of 6% and 9%, respectively. Moreover, by combining transcript and metabolite markers, the prediction error was reduced to 4.3%. Feature selection from Random Forest models allowed model minimization, yielding a minimal combination of only 20 metabolite and transcript markers that were successfully tested for their reproducibility in 16 independent agronomic field trials. We demonstrate that a minimum combination of transcript and metabolite markers sampled at early cultivation stages predicts potato yield stability under drought largely independent of seasonal and regional agronomic conditions. KW - drought tolerance KW - machine learning KW - metabolite markers KW - potato (Solanum tuberosum) KW - prediction models KW - transcript markers Y1 - 2017 U6 - https://doi.org/10.1111/pbi.12840 SN - 1467-7644 SN - 1467-7652 VL - 16 IS - 4 SP - 939 EP - 950 PB - Wiley CY - Hoboken ER - TY - GEN A1 - Sprenger, Heike A1 - Erban, Alexander A1 - Seddig, Sylvia A1 - Rudack, Katharina A1 - Thalhammer, Anja A1 - Le, Mai Q. A1 - Walther, Dirk A1 - Zuther, Ellen A1 - Köhl, Karin I. A1 - Kopka, Joachim A1 - Hincha, Dirk K. T1 - Metabolite and transcript markers for the prediction of potato drought tolerance T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - Potato (Solanum tuberosum L.) is one of the most important food crops worldwide. Current potato varieties are highly susceptible to drought stress. In view of global climate change, selection of cultivars with improved drought tolerance and high yield potential is of paramount importance. Drought tolerance breeding of potato is currently based on direct selection according to yield and phenotypic traits and requires multiple trials under drought conditions. Marker‐assisted selection (MAS) is cheaper, faster and reduces classification errors caused by noncontrolled environmental effects. We analysed 31 potato cultivars grown under optimal and reduced water supply in six independent field trials. Drought tolerance was determined as tuber starch yield. Leaf samples from young plants were screened for preselected transcript and nontargeted metabolite abundance using qRT‐PCR and GC‐MS profiling, respectively. Transcript marker candidates were selected from a published RNA‐Seq data set. A Random Forest machine learning approach extracted metabolite and transcript markers for drought tolerance prediction with low error rates of 6% and 9%, respectively. Moreover, by combining transcript and metabolite markers, the prediction error was reduced to 4.3%. Feature selection from Random Forest models allowed model minimization, yielding a minimal combination of only 20 metabolite and transcript markers that were successfully tested for their reproducibility in 16 independent agronomic field trials. We demonstrate that a minimum combination of transcript and metabolite markers sampled at early cultivation stages predicts potato yield stability under drought largely independent of seasonal and regional agronomic conditions. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 673 KW - drought tolerance KW - machine learning KW - metabolite markers KW - potato (Solanum tuberosum) KW - prediction models KW - transcript markers Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-424630 SN - 1866-8372 IS - 673 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 - TY - JOUR A1 - Hilker, Monika A1 - Schwachtje, Jens A1 - Baier, Margarete A1 - Balazadeh, Salma A1 - Bäurle, Isabel A1 - Geiselhardt, Sven A1 - Hincha, Dirk K. A1 - Kunze, Reinhard A1 - Mueller-Roeber, Bernd A1 - Rillig, Matthias G. A1 - Rolff, Jens A1 - Schmülling, Thomas A1 - Steppuhn, Anke A1 - van Dongen, Joost A1 - Whitcomb, Sarah J. A1 - Wurst, Susanne A1 - Zuther, Ellen A1 - Kopka, Joachim T1 - Priming and memory of stress responses in organisms lacking a nervous system JF - Biological reviews KW - priming KW - stress signalling KW - epigenetics KW - memory KW - fitness KW - stress tolerance KW - defence KW - bet hedging Y1 - 2016 U6 - https://doi.org/10.1111/brv.12215 SN - 1464-7931 SN - 1469-185X VL - 91 SP - 1118 EP - 1133 PB - Wiley-Blackwell CY - Hoboken ER -