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  -