YIELD POTENTIAL IN ORGANIC AND CONVENTIONAL CEREAL SEED

Comparison between organic grown cereal seed and conventional gown seed shows minor differences in yield potential under low input conditions

A Comparative Transcriptome Analysis, Conserved Regulatory Elements and Associated Transcription Factors Related to Accumulation of Fusariotoxins in Grain of Rye (Secale cereale L.) Hybrids

Detoxification of fusariotoxin is a type V Fusarium head blight (FHB) resistance and is considered a component of type II resistance, which is related to the spread of infection within spikes. Understanding this type of resistance is vital for FHB resistance, but to date, nothing is known about candidate genes that confer this resistance in rye due to scarce genomic resources. In this study, we generated a transcriptomic resource. The molecular response was mined through a comprehensive transcriptomic analysis of two rye hybrids differing in the build-up of fusariotoxin contents in grain upon pathogen infection. Gene mining identified candidate genes and pathways contributing to the detoxification of fusariotoxins in rye. Moreover, we found cis regulatory elements in the promoters of identified genes and linked them to transcription factors. In the fusariotoxin analysis, we found that grain from the Nordic seed rye hybrid "Helltop" accumulated 4 times higher concentrations of deoxynivalenol (DON), 9 times higher nivalenol (NIV), and 28 times higher of zearalenone (ZEN) than that of the hybrid "DH372" after artificial inoculation under field conditions. In the transcriptome analysis, we identified 6675 and 5151 differentially expressed genes (DEGs) in DH372 and Helltop, respectively, compared to non-inoculated control plants. A Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that DEGs were associated with glycolysis and the mechanistic target of rapamycin (mTOR) signaling pathway in Helltop, whereas carbon fixation in photosynthesis organisms were represented in DH372. The gene ontology (GO) enrichment and gene set enrichment analysis (GSEA) of DEGs lead to identification of the metabolic and biosynthetic processes of peptides and amides in DH372, whereas photosynthesis, negative regulation of catalytic activity, and protein-chromophore linkage were the significant pathways in Helltop. In the process of gene mining, we found four genes that were known to be involved in FHB resistance in wheat and that were differentially expressed after infection only in DH372 but not in Helltop. Based on our results, we assume that DH372 employed a specific response to pathogen infection that led to detoxification of fusariotoxin and prevented their accumulation in grain. Our results indicate that DH372 might resist the accumulation of fusariotoxin through activation of the glycolysis and drug metabolism via cytochrome P450. The identified genes in DH372 might be regulated by the WRKY family transcription factors as associated cis regulatory elements found in the in silico analysis. The results of this study will help rye breeders to develop strategies against type V FHB

Integrating a growth degree-days based reaction norm methodology and multi-trait modeling for genomic prediction in wheat

Multi-trait and multi-environment analyses can improve genomic prediction by exploiting between-trait correlations and genotype-by-environment interactions. In the context of reaction norm models, genotype-by-environment interactions can be described as functions of high-dimensional sets of markers and environmental covariates. However, comprehensive multi-trait reaction norm models accounting for marker x environmental covariates interactions are lacking. In this article, we propose to extend a reaction norm model incorporating genotype-by-environment interactions through (co)variance structures of markers and environmental covariates to a multi-trait reaction norm case. To do that, we propose a novel methodology for characterizing the environment at different growth stages based on growth degree-days (GDD). The proposed models were evaluated by variance components estimation and predictive performance for winter wheat grain yield and protein content in a set of 2,015 F6-lines. Cross-validation analyses were performed using leave-one-year-location-out (CV1) and leave-one-breeding-cycle-out (CV2) strategies. The modeling of genomic [SNPs] x environmental covariates interactions significantly improved predictive ability and reduced the variance inflation of predicted genetic values for grain yield and protein content in both cross-validation schemes. Trait-assisted genomic prediction was carried out for multi-trait models, and it significantly enhanced predictive ability and reduced variance inflation in all scenarios. The genotype by environment interaction modeling via genomic [SNPs] x environmental covariates interactions, combined with trait-assisted genomic prediction, boosted the benefits in predictive performance. The proposed multi-trait reaction norm methodology is a comprehensive approach that allows capitalizing on the benefits of multi-trait models accounting for between-trait correlations and reaction norm models exploiting high-dimensional genomic and environmental information

Functional Validation of Candidate Genes Detected by Genomic Feature Models

Understanding the genetic underpinnings of complex traits requires knowledge of the genetic variants that contribute to phenotypic variability. Reliable statistical approaches are needed to obtain such knowledge. In genome-wide association studies, variants are tested for association with trait variability to pinpoint loci that contribute to the quantitative trait. Because stringent genome-wide significance thresholds are applied to control the false positive rate, many true causal variants can remain undetected. To ameliorate this problem, many alternative approaches have been developed, such as genomic feature models (GFM). The GFM approach tests for association of set of genomic markers, and predicts genomic values from genomic data utilizing prior biological knowledge. We investigated to what degree the findings from GFM have biological relevance. We used the Drosophila Genetic Reference Panel to investigate locomotor activity, and applied genomic feature prediction models to identify gene ontology (GO) categories predictive of this phenotype. Next, we applied the covariance association test to partition the genomic variance of the predictive GO terms to the genes within these terms. We then functionally assessed whether the identified candidate genes affected locomotor activity by reducing gene expression using RNA interference. In five of the seven candidate genes tested, reduced gene expression altered the phenotype. The ranking of genes within the predictive GO term was highly correlated with the magnitude of the phenotypic consequence of gene knockdown. This study provides evidence for five new candidate genes for locomotor activity, and provides support for the reliability of the GFM approach

Improvement of genomic prediction in advanced wheat breeding lines by including additive-by-additive epistasis

Key message Including additive and additive-by-additive epistasis in a NOIA parametrization did not yield orthogonal partitioning of genetic variances, nevertheless, it improved predictive ability in a leave-one-out cross-validation for wheat grain yield. Additive-by-additive epistasis is the principal non-additive genetic effect in inbred wheat lines and is potentially useful for developing cultivars based on total genetic merit; nevertheless, its practical benefits have been highly debated. In this article, we aimed to (i) evaluate the performance of models including additive and additive-by-additive epistatic effects for variance components (VC) estimation of grain yield in a wheat-breeding population, and (ii) to investigate whether including additive-by-additive epistasis in genomic prediction enhance wheat grain yield predictive ability (PA). In total, 2060 sixth-generation (F-6) lines from Nordic Seed A/S breeding company were phenotyped in 21 year-location combinations in Denmark, and genotyped using a 15 K-Illumina-BeadChip. Three models were used to estimate VC and heritability at plot level: (i) "I-model" (baseline), (ii) "I + G(A)-model", extending I-model with an additive genomic effect, and (iii) "I + G(A) + G(AA)-model", extending I + G(A)-model with an additive-by-additive genomic effects. The I + G(A)-model and I + G(A) + G(AA)-model were based on the Natural and Orthogonal Interactions Approach (NOIA) parametrization. The I + G(A) + G(AA)-model failed to achieve orthogonal partition of genetic variances, as revealed by a change in estimated additive variance of I + G(A)-model when epistasis was included in the I + G(A) + G(AA)-model. The PA was studied using leave-one-line-out and leave-one-breeding-cycle-out cross-validations. The I + G(A) + G(AA)-model increased PA significantly (16.5%) compared to the I + G(A)-model in leave-one-line-out cross-validation. However, the improvement due to including epistasis was not observed in leave-one-breeding-cycle-out cross-validation. We conclude that epistatic models can be useful to enhance predictions of total genetic merit. However, even though we used the NOIA parameterization, the variance partition into orthogonal genetic effects was not possible

Discovery of a Novel Leaf Rust (Puccinia recondita) Resistance Gene in Rye (Secale cereale L.) Using Association Genomics

Leaf rust constitutes one of the most important foliar diseases in rye (Secale cereale L.). To discover new sources of resistance, we phenotyped 180 lines belonging to a less well-characterized Gulzow germplasm at three field trial locations in Denmark and Northern Germany in 2018 and 2019. We observed lines with high leaf rust resistance efficacy at all locations in both years. A genome-wide association study using 261,406 informative single-nucleotide polymorphisms revealed two genomic regions associated with resistance on chromosome arms 1RS and 7RS, respectively. The most resistance-associated marker on chromosome arm 1RS physically co-localized with molecular markers delimiting Pr3. In the reference genomes Lo7 and Weining, the genomic region associated with resistance on chromosome arm 7RS contained a large number of nucleotide-binding leucine-rich repeat (NLR) genes. Residing in close proximity to the most resistance-associated marker, we identified a cluster of NLRs exhibiting close protein sequence similarity with the wheat leaf rust Lr1 gene situated on chromosome arm 5DL in wheat, which is syntenic to chromosome arm 7RS in rye. Due to the close proximity to the most resistance-associated marker, our findings suggest that the considered leaf rust R gene, provisionally denoted Pr6, could be a Lr1 ortholog in rye

Multi-Parental Populations Suitable for Identifying Sources of Resistance to Powdery Mildew in Winter Wheat

Wheat (Triticum aestivum L.) is one of the world's staple food crops and one of the most devastating foliar diseases attacking wheat is powdery mildew (PM). In Denmark only a few specific fungicides are available for controlling PM and the use of resistant cultivars is often recommended. In this study, two Chinese wheat landraces and two synthetic hexaploid wheat lines were used as donors for creating four multi-parental populations with a total of 717 individual lines to identify new PM resistance genetic variants. These lines and the nine parental lines (including the elite cultivars used to create the populations) were genotyped using a 20 K Illumina SNP chip, which resulted in 8,902 segregating single nucleotide polymorphisms for assessment of the population structure and whole genome association study. The largest genetic difference among the lines was between the donors and the elite cultivars, the second largest genetic difference was between the different donors; a difference that was also reflected in differences between the four multi-parental populations. The 726 lines were phenotyped for PM resistance in 2017 and 2018. A high PM disease pressure was observed in both seasons, with severities ranging from 0 to >50%. Whole genome association studies for genetic variation in PM resistance in the populations revealed significant markers mapped to either chromosome 2A, B, or D in each of the four populations. However, linkage disequilibrium between these putative quantitative trait loci (QTL) were all above 0.80, probably representing a single QTL. A combined analysis of all the populations confirmed this result and the most associated marker explained 42% of the variation in PM resistance. This study gives both knowledge about the resistance as well as molecular tools and plant material that can be utilised in marker-assisted selection. Additionally, the four populations produced in this study are highly suitable for association studies of other traits than PM resistance

Scald resistance in hybrid rye (Secale cereale): genomic prediction and GWAS

Rye (Secale cereale L.) is an important cereal crop used for food, beverages, and feed, especially in North-Eastern Europe. While rye is generally more tolerant to biotic and abiotic stresses than other cereals, it still can be infected by several diseases, including scald caused by Rhynchosporium secalis. The aims of this study were to investigate the genetic architecture of scald resistance, to identify genetic markers associated with scald resistance, which could be used in breeding of hybrid rye and to develop a model for genomic prediction for scald resistance. Four datasets with records of scald resistance on a population of 251 hybrid winter rye lines grown in 2 years and at 3 locations were used for this study. Four genomic models were used to obtain variance components and heritabilities of scald resistance. All genomic models included additive genetic effects of the parental components of the hybrids and three of the models included additive-by-additive epistasis and/or dominance effects. All models showed moderate to high broad sense heritabilities in the range of 0.31 (SE 0.05) to 0.76 (0.02). The model without non-additive genetic effects and the model with dominance effects had moderate narrow sense heritabilities ranging from 0.24 (0.06) to 0.55 (0.08). None of the models detected significant non-additive genomic variances, likely due to a limited data size. A genome wide association study was conducted to identify markers associated with scald resistance in hybrid winter rye. In three datasets, the study identified a total of twelve markers as being significantly associated with scald resistance. Only one marker was associated with a major quantitative trait locus (QTL) influencing scald resistance. This marker explained 11-12% of the phenotypic variance in two locations. Evidence of genotype-by-environment interactions was found for scald resistance between one location and the other two locations, which suggested that scald resistance was influenced by different QTLs in different environments. Based on the results of the genomic prediction models and GWAS, scald resistance seems to be a quantitative trait controlled by many minor QTL and one major QTL, and to be influenced by genotype-by-environment interactions

Candidate Genes Detected in Transcriptome Studies Are Strongly Dependent on Genetic Background

Whole genome transcriptomic studies can point to potential candidate genes for organismal traits. However, the importance of potential candidates is rarely followed up through functional studies and/or by comparing results across independent studies. We have analysed the overlap of candidate genes identified from studies of gene expression in Drosophila melanogaster using similar technical platforms. We found little overlap across studies between putative candidate genes for the same traits in the same sex. Instead there was a high degree of overlap between different traits and sexes within the same genetic backgrounds. Putative candidates found using transcriptomics therefore appear very sensitive to genetic background and this can mask or override effects of treatments. The functional importance of putative candidate genes emerging from transcriptome studies needs to be validated through additional experiments and in future studies we suggest a focus on the genes, networks and pathways affecting traits in a consistent manner across backgrounds

Metabolomic-genomic prediction can improve prediction accuracy of breeding values for malting quality traits in barley

BackgroundMetabolomics measures an intermediate stage between genotype and phenotype, and may therefore be useful for breeding. Our objectives were to investigate genetic parameters and accuracies of predicted breeding values for malting quality (MQ) traits when integrating both genomic and metabolomic information. In total, 2430 plots of 562 malting spring barley lines from three years and two locations were included. Five MQ traits were measured in wort produced from each plot. Metabolomic features used were 24,018 nuclear magnetic resonance intensities measured on each wort sample. Methods for statistical analyses were genomic best linear unbiased prediction (GBLUP) and metabolomic-genomic best linear unbiased prediction (MGBLUP). Accuracies of predicted breeding values were compared using two cross-validation strategies: leave-one-year-out (LOYO) and leave-one-line-out (LOLO), and the increase in accuracy from the successive inclusion of first, metabolomic data on the lines in the validation population (VP), and second, both metabolomic data and phenotypes on the lines in the VP, was investigated using the linear regression (LR) method.ResultsFor all traits, we saw that the metabolome-mediated heritability was substantial. Cross-validation results showed that, in general, prediction accuracies from MGBLUP and GBLUP were similar when phenotypes and metabolomic data were recorded on the same plots. Results from the LR method showed that for all traits, except one, accuracy of MGBLUP increased when including metabolomic data on the lines of the VP, and further increased when including also phenotypes. However, in general the increase in accuracy of MGBLUP when including both metabolomic data and phenotypes on lines of the VP was similar to the increase in accuracy of GBLUP when including phenotypes on the lines of the VP. Therefore, we found that, when metabolomic data were included on the lines of the VP, accuracies substantially increased for lines without phenotypic records, but they did not increase much when phenotypes were already known.ConclusionsMGBLUP is a useful approach to combine phenotypic, genomic and metabolomic data for predicting breeding values for MQ traits. We believe that our results have significant implications for practical breeding of barley and potentially many other species