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Background: The frequency and severity of abiotic stress events, especially drought, are increasing due to climate change. The plant root is the most important organ for water uptake and the first to be affected by water limitation. It is therefore becoming increasingly important to include root traits in studies on drought stress tolerance. However, phenotyping under field conditions remains a challenging task. In this study, plants were grown in a hydroponic system with polyethylene glycol as an osmotic stressor and in sand pots to examine the root system of eleven spring barley genotypes. The root anatomy of two genotypes with different response to drought was investigated microscopically. Results: Root diameter increased significantly (p < 0.05) under polyethylene glycol treatment by 54% but decreased significantly (p < 0.05) by 12% under drought stress in sand pots. Polyethylene glycol treatment increased root tip diameter (51%) and reduced diameter of the elongation zone (14%) compared to the control. Under drought stress, shoot mass of plants grown in sand pots showed a higher correlation (r = 0.30) with the shoot mass under field condition than polyethylene glycol treated plants (r = -0.22). Conclusion: These results indicate that barley roots take up polyethylene glycol by the root tip and polyethylene glycol prevents further water uptake. Polyethylene glycol-triggered osmotic stress is therefore unsuitable for investigating root morphology traits in barley. Root architecture of roots grown in sand pots is more comparable to roots grown under field conditions. [ABSTRACT FROM AUTHOR]

Advances in sequencing technology allow whole plant genomes to be sequenced with high quality. Combining genotypic and phenotypic data in genomic prediction helps breeders to select crossing partners in partially phenotyped populations. In plant breeding programs, the cost of sequencing entire breeding populations still exceeds available genotyping budgets. Hence, the method for genotyping is still mainly single nucleotide polymorphism (SNP) arrays; however, arrays are unable to assess the entire genome- and population-wide diversity. A compromise involves genotyping the entire population using an SNP array and a subset of the population with whole-genome sequencing. Both datasets can then be used to impute markers from whole-genome sequencing onto the entire population. Here, we evaluate whether imputation of whole-genome sequencing data enhances genomic predictions, using data from a nested association mapping population of rapeseed (Brassica napus). Employing two cross-validation schemes that mimic scenarios for the prediction of close and distant relatives, we show that imputed marker data do not significantly improve prediction accuracy, likely due to redundancy in relationship estimates and imputation errors. In simulation studies, only small improvements were observed, further corroborating the findings. We conclude that SNP arrays are already equipped with the information that is added by imputation through relationship and linkage disequilibrium. [ABSTRACT FROM AUTHOR]

Rapeseed (Brassica napus L.) is an oil‐containing crop of great economic value but with considerable nitrogen requirement. Breeding root systems that efficiently absorb nitrogen from the soil could be a driver to ensure genetic gains for more sustainable rapeseed production. The aim of this study is to identify genomic regions that regulate root morphology in response to nitrate availability. The natural variability offered by 300 inbred lines was screened at two experimental locations. Seedlings grew hydroponically with low or elevated nitrate levels. Fifteen traits related to biomass production and root morphology were measured. On average across the panel, a low nitrate level increased the root‐to‐shoot biomass ratio and the lateral root length. A large phenotypic variation was observed, along with important heritability values and genotypic effects, but low genotype‐by‐nitrogen interactions. Genome‐wide association study and bulk segregant analysis were used to identify loci regulating phenotypic traits. The first approach nominated 319 SNPs that were combined into 80 QTLs. Three QTLs identified on the A07 and C07 chromosomes were stable across nitrate levels and/or experimental locations. The second approach involved genotyping two groups of individuals from an experimental F2 population created by crossing two accessions with contrasting lateral root lengths. These individuals were found in the tails of the phenotypic distribution. Co‐localized QTLs found in both mapping approaches covered a chromosomal region on the A06 chromosome. The QTL regions contained some genes putatively involved in root organogenesis and represent selection targets for redesigning the root morphology of rapeseed. [ABSTRACT FROM AUTHOR]

SUMMARY: Genome‐wide association studies (GWAS) identified thousands of genetic loci associated with complex plant traits, including many traits of agronomical importance. However, functional interpretation of GWAS results remains challenging because of large candidate regions due to linkage disequilibrium. High‐throughput omics technologies, such as genomics, transcriptomics, proteomics and metabolomics open new avenues for integrative systems biological analyses and help to nominate systems information supported (prime) candidate genes. In the present study, we capitalise on a diverse canola population with 477 spring‐type lines which was previously analysed by high‐throughput phenotyping of growth‐related traits and by RNA sequencing and metabolite profiling for multi‐omics‐based hybrid performance prediction. We deepened the phenotypic data analysis, now providing 123 time‐resolved image‐based traits, to gain insight into the complex relations during early vegetative growth and reanalysed the transcriptome data based on the latest Darmor‐bzh v10 genome assembly. Genome‐wide association testing revealed 61 298 robust quantitative trait loci (QTL) including 187 metabolite QTL, 56814 expression QTL and 4297 phenotypic QTL, many clustered in pronounced hotspots. Combining information about QTL colocalisation across omics layers and correlations between omics features allowed us to discover prime candidate genes for metabolic and vegetative growth variation. Prioritised candidate genes for early biomass accumulation include A06p05760.1_BnaDAR (PIAL1), A10p16280.1_BnaDAR, C07p48260.1_BnaDAR (PRL1) and C07p48510.1_BnaDAR (CLPR4). Moreover, we observed unequal effects of the Brassica A and C subgenomes on early biomass production. Significance Statement: In crop plants, deciphering the genetic basis of complex traits, and the identification of causal genes based on GWAS results is a major challenge. Here, we demonstrate the power of combining extensive multi‐omics data, genome‐wide associations, QTL co‐localisations and system‐genetic analyses to pinpoint well supported candidate genes for metabolic variation, early vegetative growth and biomass accumulation despite extended regions of linkage disequilibrium typically present in an elite canola breeding population. [ABSTRACT FROM AUTHOR]