Genome-Wide Approach to Identify Quantitative Trait Loci for Drought Tolerance in Tetraploid Potato (Solanum tuberosum L.)

Drought represents a major abiotic stress factor negatively affecting growth, yield and tuber quality of potatoes. Quantitative trait locus (QTL) analyses were performed in cultivated potatoes for drought tolerance index DRYM (deviation of relative starch yield from the experimental median), tuber starch content, tuber starch yield, tuber fresh weight, selected transcripts and metabolites under control and drought stress conditions. Eight genomic regions of major interest for drought tolerance were identified, three representing standalone DRYM QTL. Candidate genes, e.g., from signaling pathways for ethylene, abscisic acid and brassinosteroids, and genes encoding cell wall remodeling enzymes were identified within DRYM QTL. Co-localizations of DRYM QTL and QTL for tuber starch content, tuber starch yield and tuber fresh weight with underlying genes of the carbohydrate metabolism were observed. Overlaps of DRYM QTL with metabolite QTL for ribitol or galactinol may indicate trade-offs between starch and compatible solute biosynthesis. Expression QTL confirmed the drought stress relevance of selected transcripts by overlaps with DRYM QTL. Bulked segregant analyses combined with next-generation sequencing (BSAseq) were used to identify mutations in genes under the DRYM QTL on linkage group 3. Future analyses of identified genes for drought tolerance will give a better insight into drought tolerance in potatoes.

Metabolite and transcript markers for the prediction of potato drought tolerance

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.