Genomic prediction for root and yield traits of barley under a water availability gradient: a case study comparing different spatial adjustments.

BACKGROUND: In drought periods, water use efficiency depends on the capacity of roots to extract water from deep soil. A semi-field phenotyping facility (RadiMax) was used to investigate above-ground and root traits in spring barley when grown under a water availability gradient. Above-ground traits included grain yield, grain protein concentration, grain nitrogen removal, and thousand kernel weight. Root traits were obtained through digital images measuring the root length at different depths. Two nearest-neighbor adjustments (M1 and M2) to model spatial variation were used for genetic parameter estimation and genomic prediction (GP). M1 and M2 used (co)variance structures and differed in the distance function to calculate between-neighbor correlations. M2 was the most developed adjustment, as accounted by the Euclidean distance between neighbors. RESULTS: The estimated heritabilities ([Formula: see text]) ranged from low to medium for root and above-ground traits. The genetic coefficient of variation ([Formula: see text]) ranged from 3.2 to 7.0% for above-ground and 4.7 to 10.4% for root traits, indicating good breeding potential for the measured traits. The highest [Formula: see text] observed for root traits revealed that significant genetic change in root development can be achieved through selection. We studied the genotype-by-water availability interaction, but no relevant interaction effects were detected. GP was assessed using leave-one-line-out (LOO) cross-validation. The predictive ability (PA) estimated as the correlation between phenotypes corrected by fixed effects and genomic estimated breeding values ranged from 0.33 to 0.49 for above-ground and 0.15 to 0.27 for root traits, and no substantial variance inflation in predicted genetic effects was observed. Significant differences in PA were observed in favor of M2. CONCLUSIONS: The significant [Formula: see text] and the accurate prediction of breeding values for above-ground and root traits revealed that developing genetically superior barley lines with improved root systems is possible. In addition, we found significant spatial variation in the experiment, highlighting the relevance of correctly accounting for spatial effects in statistical models. In this sense, the proposed nearest-neighbor adjustments are flexible approaches in terms of assumptions that can be useful for semi-field or field experiments.

Integration of DNA Methylation and Transcriptome Data Improves Complex Trait Prediction in Hordeum vulgare.

Whole-genome multi-omics profiles contain valuable information for the characterization and prediction of complex traits in plants. In this study, we evaluate multi-omics models to predict four complex traits in barley (Hordeum vulgare); grain yield, thousand kernel weight, protein content, and nitrogen uptake. Genomic, transcriptomic, and DNA methylation data were obtained from 75 spring barley lines tested in the RadiMax semi-field phenomics facility under control and water-scarce treatment. By integrating multi-omics data at genomic, transcriptomic, and DNA methylation regulatory levels, a higher proportion of phenotypic variance was explained (0.72-0.91) than with genomic models alone (0.55-0.86). The correlation between predictions and phenotypes varied from 0.17-0.28 for control plants and 0.23-0.37 for water-scarce plants, and the increase in accuracy was significant for nitrogen uptake and protein content compared to models using genomic information alone. Adding transcriptomic and DNA methylation information to the prediction models explained more of the phenotypic variance attributed to the environment in grain yield and nitrogen uptake. It furthermore explained more of the non-additive genetic effects for thousand kernel weight and protein content. Our results show the feasibility of multi-omics prediction for complex traits in barley.

Relative importance of genotype, gene expression, and DNA methylation on complex traits in perennial ryegrass.

The growing demand for food and feed crops in the world because of growing population and more extreme weather events requires high-yielding and resilient crops. Many agriculturally important traits are polygenic, controlled by multiple regulatory layers, and with a strong interaction with the environment. In this study, 120 F2 families of perennial ryegrass (Lolium perenne L.) were grown across a water gradient in a semifield facility with subsoil irrigation. Genomic (single-nucleotide polymorphism [SNP]), transcriptomic (gene expression [GE]), and DNA methylomic (MET) data were integrated with feed quality trait data collected from control and drought sections in the semifield facility, providing a treatment effect. Deep root length (DRL) below 110 cm was assessed with convolutional neural network image analysis. Bayesian prediction models were used to partition phenotypic variance into its components and evaluated the proportion of phenotypic variance in all traits captured by different regulatory layers (SNP, GE, and MET). The spatial effects and effects of SNP, GE, MET, the interaction between GE and MET (GE × MET) and GE × treatment (GEControl and GEDrought ) interaction were investigated. Gene expression explained a substantial part of the genetic and spatial variance for all the investigated phenotypes, whereas MET explained residual variance not accounted for by SNPs or GE. For DRL, MET also contributed to explaining spatial variance. The study provides a statistically elegant analytical paradigm that integrates genomic, transcriptomic, and MET information to understand the regulatory mechanisms of polygenic effects for complex traits.

Genome-Wide Association Mapping of Crown and Brown Rust Resistance in Perennial Ryegrass.

A population of 239 perennial ryegrass (Lolium perenne L.) genotypes was analyzed to identify marker-trait associations for crown rust (Puccinia coronata f. sp. lolii) and brown rust (Puccinia graminis f. sp. loliina) resistance. Phenotypic data from field trials showed a low correlation (r = 0.17) between the two traits. Genotypes were resequenced, and a total of 14,538,978 SNPs were used to analyze population structure, linkage disequilibrium (LD), and for genome-wide association study. The SNP heritability (h2SNP) was 0.4 and 0.8 for crown and brown rust resistance, respectively. The high-density SNP dataset allowed us to estimate LD decay with the highest possible precision to date for perennial ryegrass. Results showed a low LD extension with a rapid decay of r2 value below 0.2 after 520 bp on average. Additionally, QTL regions for both traits were detected, as well as candidate genes by applying Genome Complex Trait Analysis and Multi-marker Analysis of GenoMic Annotation. Moreover, two significant genes, LpPc6 and LpPl6, were identified for crown and brown rust resistance, respectively, when SNPs were aggregated to the gene level. The two candidate genes encode proteins with phosphatase activity, which putatively can be induced by the host to perceive, amplify and transfer signals to downstream components, thus activating a plant defense response.

Genome-Wide Association Mapping of Resistance to Septoria Nodorum Leaf Blotch in a Nordic Spring Wheat Collection.

CORE IDEAS: First genome-wide association mapping of adult plant Septoria nodorum blotch resistance. Some adult plant resistance loci were shared with seedling resistance loci. Other adult plant resistance loci were significant across environments. Resistant haplotypes were identified, which can be used for breeding. Parastagonospora nodorum is the causal agent of Septoria nodorum leaf blotch (SNB) in wheat (Triticum aestivum L.). It is the most important leaf blotch pathogen in Norwegian spring wheat. Several quantitative trait loci (QTL) for SNB susceptibility have been identified. Some of these QTL are the result of underlying gene-for-gene interactions involving necrotrophic effectors (NEs) and corresponding sensitivity (Snn) genes. A collection of diverse spring wheat lines was evaluated for SNB resistance and susceptibility over seven growing seasons in the field. In addition, wheat seedlings were inoculated and infiltrated with culture filtrates (CFs) from four single spore isolates and infiltrated with semipurified NEs (SnToxA, SnTox1, and SnTox3) under greenhouse conditions. In adult plants, the most stable SNB resistance QTL were located on chromosomes 2B, 2D, 4A, 4B, 5A, 6B, 7A, and 7B. The QTL on chromosome 2D was effective most years in the field. At the seedling stage, the most significant QTL after inoculation were located on chromosomes 1A, 1B, 3A, 4B, 5B, 6B, 7A, and 7B. The QTL on chromosomes 3A and 6B were significant both after inoculation and CF infiltration, indicating the presence of novel NE-Snn interactions. The QTL on chromosomes 4B and 7A were significant in both seedlings and adult plants. Correlations between SnToxA sensitivity and disease severity in the field were significant. To our knowledge, this is the first genome-wide association mapping study (GWAS) to investigate SNB resistance at the adult plant stage under field conditions.

Mapping of SnTox3-Snn3 as a major determinant of field susceptibility to Septoria nodorum leaf blotch in the SHA3/CBRD × Naxos population.

KEY MESSAGE: The effect of the SnTox3-Snn3 interaction was documented for the first time under natural infection at the adult plant stage in the field. Co-segregating SNP markers were identified. Parastagonospora nodorum is a necrotrophic pathogen of wheat, causing Septoria nodorum blotch (SNB) affecting both the leaf and glume. P. nodorum is the major leaf blotch pathogen on spring wheat in Norway. Resistance to the disease is quantitative, but several host-specific interactions between necrotrophic effectors (NEs) and host sensitivity (Snn) genes have been identified, playing a major role at the seedling stage. However, the effect of these interactions in the field under natural infection has not been investigated. In the present study, we saturated the genetic map of the recombinant inbred (RI) population SHA3/CBRD × Naxos using the Illumina 90 K SNP chip. The population had previously been evaluated for segregation of SNB susceptibility in field trials. Here, we infiltrated the population with the purified NEs SnToxA, SnTox1 and SnTox3, and mapped the Snn3 locus on 5BS based on sensitivity segregation and SNP marker data. We also conducted inoculation and culture filtrate (CF) infiltration experiments on the population with four selected P. nodorum isolates from Norway and North America. Remapping of quantitative trait loci (QTL) for field resistance showed that the SnTox3-Snn3 interaction could explain 24% of the phenotypic variation in the field, and more than 51% of the variation in seedling inoculations. To our knowledge, this is the first time the effect of this interaction has been documented at the adult plant stage under natural infection in the field.