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Key messageIncluding 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 (F6) 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 + GA-model”, extending I-model with an additive genomic effect, and (iii) “I + GA + GAA-model”, extending I + GA-model with an additive-by-additive genomic effects. The I + GA-model and I + GA + GAA-model were based on the Natural and Orthogonal Interactions Approach (NOIA) parametrization. The I + GA + GAA-model failed to achieve orthogonal partition of genetic variances, as revealed by a change in estimated additive variance of I + GA-model when epistasis was included in the I + GA + GAA-model. The PA was studied using leave-one-line-out and leave-one-breeding-cycle-out cross-validations. The I + GA + GAA-model increased PA significantly (16.5%) compared to the I + GA-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.

We investigated prediction of malting quality (MQ) phenotypes in different locations using metabolomic spectra, and compared the prediction ability of different models, and training population (TP) sizes. Data of five MQ traits was measured on 2667 individual plots of 564 malting spring barley lines from three years and two locations. A total of 24,018 metabolomic features (MFs) were measured on each wort sample. Two statistical models were used, a metabolomic best linear unbiased prediction (MBLUP) and a partial least squares regression (PLSR). Predictive ability within location and across locations were compared using cross-validation methods. For all traits, more than 90% of the total variance in MQ traits could be explained by MFs. The prediction accuracy increased with increasing TP size and stabilized when the TP size reached 1000. The optimal number of components considered in the PLSR models was 20. The accuracy using leave-one-line-out cross-validation ranged from 0.722 to 0.865 and using leave-one-location-out cross-validation from 0.517 to 0.817. In conclusion, the prediction accuracy of metabolomic prediction of MQ traits using MFs was high and MBLUP is better than PLSR if the training population is larger than 100. The results have significant implications for practical barley breeding for malting quality.

Rye is used as food, feed, and for bioenergy production and remain an essential grain crop for cool temperate zones in marginal soils. Ergot is known to cause severe problems in cross-pollinated rye by contamination of harvested grains. The molecular response of the underlying mechanisms of this disease is still poorly understood due to the complex infection pattern. RNA sequencing can provide astonishing details about the transcriptional landscape, hence we employed a transcriptomic approach to identify genes in the underlying mechanism of ergot infection in rye. In this study, we generated de novo assemblies from twelve biological samples of two rye hybrids with identified contrasting phenotypic responses to ergot infection. The final transcriptome of ergot susceptible (DH372) and moderately ergot resistant (Helltop) hybrids contain 208,690 and 192,116 contigs, respectively. By applying the BUSCO pipeline, we confirmed that these transcriptome assemblies contain more than 90% of gene representation of the available orthologue groups at Virdiplantae odb10 . We employed a de novo assembled and the draft reference genome of rye to count the differentially expressed genes (DEGs) between the two hybrids with and without inoculation. The gene expression comparisons revealed that 228 genes were linked to ergot infection in both hybrids. The genome ontology enrichment analysis of DEGs associated them with metabolic processes, hydrolase activity, pectinesterase activity, cell wall modification, pollen development and pollen wall assembly. In addition, gene set enrichment analysis of DEGs linked them to cell wall modification and pectinesterase activity. These results suggest that a combination of different pathways, particularly cell wall modification and pectinesterase activity contribute to the underlying mechanism that might lead to resistance against ergot in rye. Our results may pave the way to select genetic material to improve resistance against ergot through better understanding of the mechanism of ergot infection at molecular level. Furthermore, the sequence data and de novo assemblies are valuable as scientific resources for future studies in rye.

Background Metabolomics 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. Results For 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. Conclusions MGBLUP 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.

Rye (Secale cereale L.) responds strongly to changes in heterozygosity with hybrids portraying strong heterosis effect on all developmental and yielding characteristics. In order to achieve the highest potential heterosis effect parental lines must originate from genetically distinct gene pools. Here we report the first comprehensive SNP-based population study of an elite germplasm using fertilization control system for hybrid breeding in rye that is genetically different to the predominating P-type. In total 376 inbred lines from Nordic Seed Germany GmbH were genotyped for 4419 polymorphic SNPs. The aim of this study was to confirm and quantify the genetic separation of parental populations, unveil their genetic characteristics and investigate underlying population structures. Through a palette of complimenting analysis, we confirmed a strong genetic differentiation (FST = 0.332) of parental populations validating the germplasms suitability for hybrid breeding. These were, furthermore, found to diverge considerably in several features with the maternal population portraying a strong population structure characterized by a narrow genetic profile, small effective population size and high genome-wise linkage disequilibrium. We propose that the employed male-sterility system putatively constitutes a population determining parameter by influencing the rate of introducing novel genetic variation to the parental populations. Functional analysis of linkage blocks led to identification of a conserved segment on the distal 4RL chromosomal region annotated to the Rfp3 male-fertility restoration 'Pampa' type gene. Findings of our study emphasized the immediate value of comprehensive population studies on elite breeding germplasms as a pre-requisite for application of genomic-based breeding techniques, introgression of novel material and to support breeder decision-making.

Understanding the genotype–phenotype map and how variation at different levels of biological organization is associated are central topics in modern biology. Fast developments in sequencing technologies and other molecular omic tools enable researchers to obtain detailed information on variation at DNA level and on intermediate endophenotypes, such as RNA, proteins and metabolites. This can facilitate our understanding of the link between genotypes and molecular and functional organismal phenotypes. Here, we use the Drosophila melanogaster Genetic Reference Panel and nuclear magnetic resonance (NMR) metabolomics to investigate the ability of the metabolome to predict organismal phenotypes. We performed NMR metabolomics on four replicate pools of male flies from each of 170 different isogenic lines. Our results show that metabolite profiles are variable among the investigated lines and that this variation is highly heritable. Second, we identify genes associated with metabolome variation. Third, using the metabolome gave better prediction accuracies than genomic information for four of five quantitative traits analyzed. Our comprehensive characterization of population-scale diversity of metabolomes and its genetic basis illustrates that metabolites have large potential as predictors of organismal phenotypes. This finding is of great importance, e.g., in human medicine, evolutionary biology and animal and plant breeding.

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 × 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] × 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] × 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.

Background Metabolomic profiling of blood samples can be done on selection candidates and could be a valuable information source for genetic evaluation of pigs. We hypothesized that integrating metabolomic data from pigs without individual phenotypes into the metabolomic-genomic best linear unbiased prediction (MGBLUP) model would generate estimated breeding values (EBVs) with a higher accuracy compared to what would be obtained without metabolomic data. We tested this hypothesis by predicting breeding values for average daily gain (ADG) using phenotypic, genomic, and metabolomic data. MGBLUP models were fitted to average daily gain of 8174 Duroc pigs that were genotyped and profiled for metabolomic features. Approximately half the pigs were males from a test station and the other half were females from breeding herds. Variance components were estimated, and we employed two validation schemes: test station to breeding herd validation and fivefold cross-validation. Accuracies of EBVs in the validation population were computed by combining results on predictive abilities with results on increases in accuracies from the linear regression method. Results Parameter estimates from MGBLUP showed a direct heritability of ADG of 0.15, a proportion of variance explained by metabolomic features of 0.18, and a heritability of metabolomic intensities of 0.14, together resulting in a total heritability of 0.17. Thus, the majority of the heritability was not mediated by the metabolome. For the test station to breeding herd validation, the accuracies of EBVs were 0.60 for genomic best linear unbiased prediction (GBLUP) with genotypes in validation population, 0.61 for MGBLUP with genotypes in validation population, 0.62 for MGBLUP with genotypes and metabolomic features in validation population, 0.72 for GBLUP with genotypes and phenotypes in validation population, and 0.74 for MGBLUP with genotypes, phenotypes and metabolomic features in validation population, whereas the corresponding numbers were 0.87, 0.87, 0.87, 0.91 and 0.92 for the fivefold cross-validation. Therefore, small increases in accuracies were observed when including metabolomic features. Conclusions The inclusion of metabolomics data provided small improvements in the accuracy of genetic evaluations for average daily gain in pigs. Further work will be needed to investigate, e.g., alternative time points for blood sampling, metabolomics on samples of other tissues, and other traits.

Background Evolutionary theory predicts that antagonistically selected alleles, such as those with divergent pleiotropic effects in early and late life, may often reach intermediate population frequencies due to balancing selection, an elusive process when sought out empirically. Alternatively, genetic diversity may increase as a result of positive frequency-dependent selection and genetic purging in bottlenecked populations. Results While experimental evolution systems with directional phenotypic selection typically result in at least local heterozygosity loss, we report that selection for increased lifespan in Drosophila melanogaster leads to an extensive genome-wide increase of nucleotide diversity in the selected lines compared to replicate control lines, pronounced in regions with no or low recombination, such as chromosome 4 and centromere neighborhoods. These changes, particularly in coding sequences, are most consistent with the operation of balancing selection and the antagonistic pleiotropy theory of aging and life history traits that tend to be intercorrelated. Genes involved in antioxidant defenses, along with multiple lncRNAs, were among those most affected by balancing selection. Despite the overwhelming genetic diversification and the paucity of selective sweep regions, two genes with functions important for central nervous system and memory, Ptp10D and Ank2 , evolved under positive selection in the longevity lines. Conclusions Overall, the ‘evolve-and-resequence’ experimental approach proves successful in providing unique insights into the complex evolutionary dynamics of genomic regions responsible for longevity.

Genomic models for prediction of additive and non-additive effects within and across different heterotic groups are lacking for breeding of hybrid crops. In this study, genomic prediction models accounting for incomplete inbreeding in parental lines from two different heterotic groups were developed and evaluated. The models can be used for prediction of general combining ability (GCA) of parental lines from each heterotic group as well as specific combining ability (SCA) of all realized and potential crosses. Here, GCA was estimated as the sum of additive genetic effects and within-group epistasis due to high degree of inbreeding in parental lines. SCA was estimated as the sum of across-group epistasis and dominance effects. Three models were compared. In model 1, it was assumed that each hybrid was produced from two completely inbred parental lines. Model 1 was extended to include three-way hybrids from parental lines with arbitrary levels of inbreeding: In model 2, parents of the three-way hybrids could have any levels of inbreeding, while the grandparents of the maternal parent were assumed completely inbred. In model 3, all parental components could have any levels of inbreeding. Data from commercial breeding programs for hybrid rye and sugar beet was used to evaluate the models. The traits grain yield and root yield were analyzed for rye and sugar beet, respectively. Additive genetic variances were larger than epistatic and dominance variances. The models’ predictive abilities for total genetic value, for GCA of each parental line and for SCA were evaluated based on different cross-validation strategies. Predictive abilities were highest for total genetic values and lowest for SCA. Predictive abilities for SCA and for GCA of maternal lines were higher for model 2 and model 3 than for model 1. The implementation of the genomic prediction models in hybrid breeding programs can potentially lead to increased genetic gain in two different ways: I) by facilitating the selection of crossing parents with high GCA within heterotic groups and II) by prediction of SCA of all realized and potential combinations of parental lines to produce hybrids with high total genetic values.