Key message Genomic prediction models for starch content and chipping quality show promising results, suggesting that genomic selection is a feasible breeding strategy in tetraploid potato. AbstractGenomic selection uses genome-wide molecular markers to predict performance of individuals and allows selections in the absence of direct phenotyping. It is regarded as a useful tool to accelerate genetic gain in breeding programs, and is becoming increasingly viable for crops as genotyping costs continue to fall. In this study, we have generated genomic prediction models for starch content and chipping quality in tetraploid potato to facilitate varietal development. Chipping quality was evaluated as the colour of a potato chip after frying following cold induced sweetening. We used genotyping-by-sequencing to genotype 762 offspring, derived from a population generated from biparental crosses of 18 tetraploid parents. Additionally, 74 breeding clones were genotyped, representing a test panel for model validation. We generated genomic prediction models from 171,859 single-nucleotide polymorphisms to calculate genomic estimated breeding values. Cross-validated prediction correlations of 0.56 and 0.73 were obtained within the training population for starch content and chipping quality, respectively, while correlations were lower when predicting performance in the test panel, at 0.30–0.31 and 0.42–0.43, respectively. Predictions in the test panel were slightly improved when including representatives from the test panel in the training population but worsened when preceded by marker selection. Our results suggest that genomic prediction is feasible, however, the extremely high allelic diversity of tetraploid potato necessitates large training populations to efficiently capture the genetic diversity of elite potato germplasm and enable accurate prediction across the entire spectrum of elite potatoes. Nonetheless, our results demonstrate that GS is a promising breeding strategy for tetraploid potato.Electronic supplementary materialThe online version of this article (doi:10.1007/s00122-017-2944-y) contains supplementary material, which is available to authorized users.

The relationship between population size, inbreeding, loss of genetic variation and evolutionary potential of fitness traits is still unresolved, and large-scale empirical studies testing theoretical expectations are surprisingly scarce. Here we present a highly replicated experimental evolution setup with 120 lines of Drosophila melanogaster having experienced inbreeding caused by low population size for a variable number of generations. Genetic variation in inbred lines and in outbred control lines was assessed by genotyping-by-sequencing (GBS) of pooled samples consisting of 15 males per line. All lines were reared on a novel stressful medium for 10 generations during which body mass, productivity, and extinctions were scored in each generation. In addition, we investigated egg-to-adult viability in the benign and the stressful environments before and after rearing at the stressful conditions for 10 generations. We found strong positive correlations between levels of genetic variation and evolutionary response in all investigated traits, and showed that genomic variation was more informative in predicting evolutionary responses than population history reflected by expected inbreeding levels. We also found that lines with lower genetic diversity were at greater risk of extinction. For viability, the results suggested a trade-off in the costs of adapting to the stressful environments when tested in a benign environment. This work presents convincing support for long-standing evolutionary theory, and it provides novel insights into the association between genetic variation and evolutionary capacity in a gradient of diversity rather than dichotomous inbred/outbred groups.

Genomic selection (GS) is becoming increasingly applicable to crops as the genotyping costs continue to decrease, which makes it an attractive alternative to traditional selective breeding based on observed phenotypes. With genome-wide molecular markers, selection based on predictions from genotypes can be made in the absence of direct phenotyping. The reliability of predictions depends strongly on the number of individuals used for training the predictive algorithms, particularly in a highly genetically diverse organism such as potatoes; however, the relationship between the individuals also has an enormous impact on prediction accuracy. Here we have studied genomic prediction in three different panels of potato cultivars, varying in size, design, and phenotypic profile. We have developed genomic prediction models for two important agronomic traits of potato, dry matter content and chipping quality. We used genotyping-by-sequencing to genotype 1,146 individuals and generated genomic prediction models from 167,637 markers to calculate genomic estimated breeding values with genomic best linear unbiased prediction. Cross-validated prediction correlations of 0.75–0.83 and 0.39–0.79 were obtained for dry matter content and chipping quality, respectively, when combining the three populations. These prediction accuracies were similar to those obtained when predicting performance within each panel. In contrast, but not unexpectedly, predictions across populations were generally lower, 0.37–0.71 and 0.28–0.48 for dry matter content and chipping quality, respectively. These predictions are not limited by the number of markers included, since similar prediction accuracies could be obtained when using merely 7,800 markers (<5%). Our results suggest that predictions across breeding populations in tetraploid potato are presently unreliable, but that individual prediction models within populations can be combined in an additive fashion to obtain high quality prediction models relevant for several breeding populations.

Predatory arthropods are increasingly used in biological control of insect pests. For this purpose, control agents are produced commercially in large quantities for release in crops. The production stocks, however, may have undergone numerous population bottlenecks and may have been exposed to artificial selection pressures in the production facilities. Accordingly, commercial populations may be experiencing loss of genetic variation through inbreeding and genetic drift, which may reduce fitness and biocontrol efficiency. In the present study we investigated whether populations of the pirate bug Orius majusculus (Reuter) purchased from three European companies differed in a range of performance traits including predation rate, starvation tolerance, body size, locomotor activity, and heat tolerance. Furthermore, we crossed all populations pairwise and tested whether outcrossed F2 hybrid offspring had increased performance compared to the parental populations, as would be expected if they were genetically distinct and depauperate. Transcriptome sequencing (RNA-seq) revealed similar overall levels of genetic variation among commercial populations, but also evidence for genetic differentiation. Generally, females performed better across phenotypic traits than males. F2 hybrid offspring differed from parental populations in a highly trait-and sex specific manner. Although F2 hybrids performed better than parental populations in some traits, the results of the present study do not provide conclusive evidence that crossing of different commercial stock populations of O. majusculus improve the genetic quality and performance of this species as a biological control agent.

Terrestrial arthropods in the Arctic are exposed to highly variable temperatures that frequently reach cold and warm extremes. Yet, ecophysiological studies on arctic insects typically focus on the ability of species to tolerate low temperatures, whereas studies investigating species’ physiological adaptations to periodically warm and variable temperatures are few. In this study, we investigate temporal changes in thermal tolerances and the transcriptome in the Greenlandic seed bug Nysius groenlandicus, collected in the field across different times and temperatures in Southern Greenland. We find that plastic changes in heat and cold tolerances occur rapidly (within hours) and at a daily scale in the field, and that these changes are correlated with diurnal temperature variation. Using RNA sequencing we provide molecular underpinnings of the rapid adjustments in thermal tolerance across ambient field temperatures and in the laboratory. We show that transcriptional responses are sensitive to daily temperature changes, and days characterized by high temperature variation induce markedly different expression patterns than thermally stable days. Further, genes associated with laboratory induced heat responses, including expression of heat shock proteins and vitellogenins, are shared across laboratory and the field, but induced at timepoints associated with lower temperatures in the field. Cold stress responses were not manifested at the transcriptomic level.