Quantitative genetic analyses of the response of potato (Solanum tuberosum) to abiotic stress

Potato (Solanum tuberosum) belongs to the most important staple crops, contributing significantly to global food security. Originating from the Andes and adapted to their cooler climate, the potato suffers from the ongoing climate crisis. The breeding of potato cultivars with enhanced heat tolerance as well as a progressing understanding of the molecular mechanisms underlying the cultivar-dependent differences in reaction to elevated temperatures are paramount. This study was concerned with both of these objectives by investigating the genetic causes of the heat response in a panel containing over 200 potato cultivars. The first and central analysis of this thesis was a genome-wide association study (GWAS), which assessed the statistical association between genetic markers on a genome-wide scale and the change of agronomically important traits, such as yield and starch content, in response to elevated growth temperatures. Using genotyping-by-sequencing (GBS) data as well as phenotype data obtained from greenhouse experiments, the GWAS allowed for the detection of 117 unique quantitative trait loci (QTLs) for the investigated traits distributed over all 12 chromosomes. Also, the non-random correlation between markers, or linkage disequilibrium (LD), was used to estimate haploblocks, which are regions exhibiting reduced recombination. Moreover, population genetics analyses were conducted on all 261 genotyped cultivars, revealing differences in genetic diversity between distinct subpopulations. Starting from the results obtained during the GWAS, functional analysis of the QTLs obtained for the reduction of starch content under heat stress led to the identification of multiple candidate genes. Additionally, a mixed model analysis and random forest (RF) regression were used to predict the heritability of the traits as well as the phenotypic values as a function of the identified QTLs, respectively. The RF regression yielded promising results, showing that the allele combinations of QTLs may be used for predicting heat-dependent starch reduction. This was measured by a Pearson correlation coefficient of r = 0.61 between observed and predicted values. Heritability estimates were low and of limited reliability, likely due to technical constraints. Finally, whole-genome-sequencing (WGS) was conducted on a subset of 25 cultivars to compensate for the low marker density inherent to the previously used GBS data. The primary goal was the identification of causal genetic variants that are linked to the previously identified variants within a QTL of interest. LD was calculated between those markers and WGS-derived variants, corroborating the previously determined haploblock boundaries. Moreover, additional candidate genes were determined and physiological hypothesis were formed regarding the effect of allelic difference on the phenotypic level. In summary, the genetic architecture of the heat-induced change of agronomically valuable traits was characterized and important genomic regions isolated. Predictive models for these traits based on genomic data were implemented. Functional genomic analyses were used to identify candidate genes which may be subjected to further molecular studies, eventually leading to an increased understanding of the molecular processes underlying heat responses in potato.