No abstract

Steroidal glycoalkaloids (SGAs) are produced following the general steroid biosynthesis pathway, starting from acetyl-coenzyme A and followed by the intermediates mevalonic acid, squalene, cycloartenol, and cholesterol. a-Chaconine and a-solanine are the main SGAs of the cultivated potato (Solanurn tuberosum), whereas many other SGAs are known in the wild potato species. Low concentrations of SGAs improve the taste of potato, but concentrations greater than 200 mgkg can have toxic effects on animals and humans. SGAs have antimicrobial activity and confer resistance to some insects, but many such pests of potato are not greatly affected. Certain environmental conditions and wounding enhance SGA accumulation in tubers in the field and storage. Low production of SGAs is a dominant character inherited in a relatively simple manner and can be selected for in potato-breeding programs, whereas the use of wild potato germplasm tends to increase the SGA accumulation in the breeding lines. Further efforts are likely to be directed toward the reduction of the SGA content in the edible potato products through breeding and biotechnological methodologies, whereas potato genotypes with high SGA production may be developed for use in the pharmaceutical industry.

Fusarium head blight (FHB) and the mycotoxins produced by its causal agents in oats (Avena sativa L.) have become a growing problem in northern countries over the last decades. The development of resistant cultivars would offer a highly needed and economical solution to the problem. To tackle the high genotype×environment interaction of FHB, a combined analysis was carried out on eight greenhouse and 13 field experiments inoculated with DON-producing Fusarium species. Our data included 406 oat genotypes consisting of Nordic cultivars, breeding lines and potentially resistant gene bank accessions. High variation in the DON accumulation estimates in the material shows that the selection of genotypes with better resistance would be valuable. The greenhouse and field studies resulted in significantly different oat genotype susceptibility rankings for both DON and Fusarium infected kernels. The results obtained from the field experiments have more practical relevance for farmers and breeders for the identification of DON resistant cultivars than greenhouse screenings. Days to maturity and the plant height of the genotypes both significantly affected the Fusarium infections and DON in the field. The relationship between Fusarium infected kernels, DONand germination capacity provide an insight into the composition of genotypes with resistance. The core set of 30 oat genotypes, which were phenotyped in several experiments, provides valuable examples of both highly susceptible and moderately resistant oat genotypes.

Fusarium head blight (FHB) and the accumulation of deoxynivalenol (DON) mycotoxin induced by Fusarium graminearum and other Fusarium fungi cause serious problems for oat production in the Nordic region (Scandinavia, Fennoscandia). Besides toxin accumulation, FHB causes reduction in grain yield and in germination capacity. Here, genomic approaches for accelerating breeding efforts against FHB and DON accumulation were studied. Resistance-related traits included DON content, F. graminearum DNA (relative to oat DNA) content (qFUSG) measured with real-time quantitative polymerase chain reaction (PCR), Fusarium-infected kernels (FIKs) and germination capacity (GC). Plant germplasm used in the study consisted of mostly breeding lines, and additionally, a few cultivars and exotic accessions. Genome-wide association study (GWAS) and genomic prediction, enabling genomic selection (GS) on the resistance-related and collected agronomic traits, were performed. Considerable genetic correlations between resistance-related traits were observed: DON content had a positive correlation (0.60) with qFUSG and a negative correlation (−0.63) with germination capacity. With the material in hand, we were not able to find any significant associations between markers and resistance-related traits. On the other hand, in genomic prediction, some resistance-related traits showed favorable accuracy in fivefold cross-validation (GC = 0.57). Genomic prediction is a promising method and genomic estimated breeding values (GEBVs) generated for germination capacity are applicable in oat breeding programs.

Genomic selection has been adopted in many plant breeding programmes. In this paper, we cover some aspects of information necessary before starting genomic selection. Spring oat and barley breeding data sets from commercial breeding programmes were studied using single, multitrait and trait‐assisted models for predicting grain yield. Heritabilities were higher when estimated using multitrait models compared to single‐trait models. However, no corresponding increase in prediction accuracy was observed in a cross‐validation scenario. On the other hand, forward prediction showed a slight, but not significant, increase in accuracy of genomic estimated breeding values for breeding cohorts when a multitrait model was applied. When a correlated trait was used in a trait‐assisted model, on average the accuracies increased by 9%–14% for oat and by 11%–28% for barley compared with a single‐trait model. Overall, accuracies in forward validation varied between breeding cohorts and years for grain yield. Forward prediction accuracies for multiple cohorts and multiple years’ data are reported for oat for the first time.