Phenomic and genomic prediction of yield on multiple locations in winter wheat.

Genomic selection has recently become an established part of breeding strategies in cereals. However, a limitation of linear genomic prediction models for complex traits such as yield is that these are unable to accommodate Genotype by Environment effects, which are commonly observed over trials on multiple locations. In this study, we investigated how this environmental variation can be captured by the collection of a large number of phenomic markers using high-throughput field phenotyping and whether it can increase GS prediction accuracy. For this purpose, 44 winter wheat (Triticum aestivum L.) elite populations, comprising 2,994 lines, were grown on two sites over 2 years, to approximate the size of trials in a practical breeding programme. At various growth stages, remote sensing data from multi- and hyperspectral cameras, as well as traditional ground-based visual crop assessment scores, were collected with approximately 100 different data variables collected per plot. The predictive power for grain yield was tested for the various data types, with or without genome-wide marker data sets. Models using phenomic traits alone had a greater predictive value (R2 = 0.39-0.47) than genomic data (approximately R2 = 0.1). The average improvement in predictive power by combining trait and marker data was 6%-12% over the best phenomic-only model, and performed best when data from one full location was used to predict the yield on an entire second location. The results suggest that genetic gain in breeding programmes can be increased by utilisation of large numbers of phenotypic variables using remote sensing in field trials, although at what stage of the breeding cycle phenomic selection could be most profitably applied remains to be answered.

Limited haplotype diversity underlies polygenic trait architecture across 70 years of wheat breeding.

BACKGROUND: Selection has dramatically shaped genetic and phenotypic variation in bread wheat. We can assess the genomic basis of historical phenotypic changes, and the potential for future improvement, using experimental populations that attempt to undo selection through the randomizing effects of recombination. RESULTS: We bred the NIAB Diverse MAGIC multi-parent population comprising over 500 recombinant inbred lines, descended from sixteen historical UK bread wheat varieties released between 1935 and 2004. We sequence the founders' genes and promoters by capture, and the MAGIC population by low-coverage whole-genome sequencing. We impute 1.1 M high-quality SNPs that are over 99% concordant with array genotypes. Imputation accuracy only marginally improves when including the founders' genomes as a haplotype reference panel. Despite capturing 73% of global wheat genetic polymorphism, 83% of genes cluster into no more than three haplotypes. We phenotype 47 agronomic traits over 2 years and map 136 genome-wide significant associations, concentrated at 42 genetic loci with large and often pleiotropic effects. Around half of these overlap known quantitative trait loci. Most traits exhibit extensive polygenicity, as revealed by multi-locus shrinkage modelling. CONCLUSIONS: Our results are consistent with a gene pool of low haplotypic diversity, containing few novel loci of large effect. Most past, and projected future, phenotypic changes arising from existing variation involve fine-scale shuffling of a few haplotypes to recombine dozens of polygenic alleles of small effect. Moreover, extensive pleiotropy means selection on one trait will have unintended consequences, exemplified by the negative trade-off between yield and protein content, unless selection and recombination can break unfavorable trait-trait associations.

Understanding the classics: the unifying concepts of transgressive segregation, inbreeding depression and heterosis and their central relevance for crop breeding.

Transgressive segregation and heterosis are the reasons that plant breeding works. Molecular explanations for both phenomena have been suggested and play a contributing role. However, it is often overlooked by molecular genetic researchers that transgressive segregation and heterosis are most simply explained by dispersion of favorable alleles. Therefore, advances in molecular biology will deliver the most impact on plant breeding when integrated with sources of heritable trait variation - and this will be best achieved within a quantitative genetics framework. An example of the power of quantitative approaches is the implementation of genomic selection, which has recently revolutionized animal breeding. Genomic selection is now being applied to both hybrid and inbred crops and is likely to be the major source of improvement in plant breeding practice over the next decade. Breeders' ability to efficiently apply genomic selection methodologies is due to recent technology advances in genotyping and sequencing. Furthermore, targeted integration of additional molecular data (such as gene expression, gene copy number and methylation status) into genomic prediction models may increase their performance. In this review, we discuss and contextualize a suite of established quantitative genetics themes relating to hybrid vigour, transgressive segregation and their central relevance to plant breeding, with the aim of informing crop researchers outside of the quantitative genetics discipline of their relevance and importance to crop improvement. Better understanding between molecular and quantitative disciplines will increase the potential for further improvements in plant breeding methodologies and so help underpin future food security.

Agricultural Selection of Wheat Has Been Shaped by Plant-Microbe Interactions.

The influence of wheat (modern wheat, both bread and pasta, their wild ancestors and synthetic hybrids) on the microbiota of their roots and surrounding soil is characterized. We isolated lines of bread wheat by hybridizing diploid (Aegilops tauschii) with tetraploid Triticum durum and crossed it with a modern cultivar of Triticum aestivum. The newly created, synthetic hybrid wheat, which recapitulate the breeding history of wheat through artificial selection, is found to support a microbiome enriched in beneficial Glomeromycetes fungi, but also in, potentially detrimental, Nematoda. We hypothesize that during wheat domestication this plant-microbe interaction diminished, suggesting an evolutionary tradeoff; sacrificing advantageous nutrient acquisition through fungal interactions to minimize interaction with pathogenic fungi. Increased plant selection for Glomeromycetes and Nematoda is correlated with the D genome derived from A. tauschii. Despite differences in their soil microbiota communities, overall wheat plants consistently show a low ratio of eukaryotes to prokaryotes. We propose that this is a mechanism for protection against soil-borne fungal disease and appears to be deeply rooted in the wheat genome. We suggest that the influence of plants on the composition of their associated microbiota is an integral factor, hitherto overlooked, but intrinsic to selection during wheat domestication.

The effects of training population design on genomic prediction accuracy in wheat.

Genomic selection offers several routes for increasing the genetic gain or efficiency of plant breeding programmes. In various species of livestock, there is empirical evidence of increased rates of genetic gain from the use of genomic selection to target different aspects of the breeder's equation. Accurate predictions of genomic breeding value are central to this, and the design of training sets is in turn central to achieving sufficient levels of accuracy. In summary, small numbers of close relatives and very large numbers of distant relatives are expected to enable predictions with higher accuracy. To quantify the effect of some of the properties of training sets on the accuracy of genomic selection in crops, we performed an extensive field-based winter wheat trial. In summary, this trial involved the construction of 44 F2:4 bi- and tri-parental populations, from which 2992 lines were grown on four field locations and yield was measured. For each line, genotype data were generated for 25 K segregating SNP markers. The overall heritability of yield was estimated to 0.65, and estimates within individual families ranged between 0.10 and 0.85. Genomic prediction accuracies of yield BLUEs were 0.125-0.127 using two different cross-validation approaches and generally increased with training set size. Using related crosses in training and validation sets generally resulted in higher prediction accuracies than using unrelated crosses. The results of this study emphasise the importance of the training panel design in relation to the genetic material to which the resulting prediction model is to be applied.

A large-scale pedigree resource of wheat reveals evidence for adaptation and selection by breeders.

Information on crop pedigrees can be used to help maximise genetic gain in crop breeding and allow efficient management of genetic resources. We present a pedigree resource of 2,657 wheat (Triticum aestivum L.) genotypes originating from 38 countries, representing more than a century of breeding and variety development. Visualisation of the pedigree enables illustration of the key developments in United Kingdom wheat breeding, highlights the wide genetic background of the UK wheat gene pool, and facilitates tracing the origin of beneficial alleles. A relatively high correlation between pedigree- and marker-based kinship coefficients was found, which validated the pedigree and enabled identification of errors in the pedigree or marker data. Using simulations with a combination of pedigree and genotype data, we found evidence for significant effects of selection by breeders. Within crosses, genotypes are often more closely related than expected by simulations to one of the parents, which indicates selection for favourable alleles during the breeding process. Selection across the pedigree was demonstrated on a subset of the pedigree in which 110 genotyped varieties released before the year 2000 were used to simulate the distribution of marker alleles of 45 genotyped varieties released after the year 2000, in the absence of selection. Allelic diversity in the 45 varieties was found to deviate significantly from the simulated distributions at a number of loci, indicating regions under selection over this period. The identification of one of these regions as coinciding with a strong yield component quantitative trait locus (QTL) highlights both the potential of the remaining loci as wheat breeding targets for further investigation, as well as the utility of this pedigree-based methodology to identify important breeding targets in other crops. Further evidence for selection was found as greater linkage disequilibrium (LD) for observed versus simulated genotypes within all chromosomes. This difference was greater at shorter genetic distances, indicating that breeder selections have conserved beneficial linkage blocks. Collectively, this work highlights the benefits of generating detailed pedigree resources for crop species. The wheat pedigree database developed here represents a valuable community resource and will be updated as new varieties are released at https://www.niab.com/pages/id/501/UK_Wheat_varieties_Pedigree.

Applying association mapping and genomic selection to the dissection of key traits in elite European wheat.

KEY MESSAGE: We show the application of association mapping and genomic selection for key breeding targets using a large panel of elite winter wheat varieties and a large volume of agronomic data. The heightening urgency to increase wheat production in line with the needs of a growing population, and in the face of climatic uncertainty, mean new approaches, including association mapping (AM) and genomic selection (GS) need to be validated and applied in wheat breeding. Key adaptive responses are the cornerstone of regional breeding. There is evidence that new ideotypes for long-standing traits such as flowering time may be required. In order to detect targets for future marker-assisted improvement and validate the practical application of GS for wheat breeding we genotyped 376 elite wheat varieties with 3,046 DArT, single nucleotide polymorphism and gene markers and measured seven traits in replicated yield trials over 2 years in France, Germany and the UK. The scale of the phenotyping exceeds the breadth of previous AM and GS studies in these key economic wheat production regions of Northern Europe. Mixed-linear modelling (MLM) detected significant marker-trait associations across and within regions. Genomic prediction using elastic net gave low to high prediction accuracies depending on the trait, and could be experimentally increased by modifying the constituents of the training population (TP). We also tested the use of differentially penalised regression to integrate candidate gene and genome-wide markers to predict traits, demonstrating the validity and simplicity of this approach. Overall, our results suggest that whilst AM offers potential for application in both research and breeding, GS represents an exciting opportunity to select key traits, and that optimisation of the TP is crucial to its successful implementation.

Multiple quantitative trait analysis using bayesian networks.

Models for genome-wide prediction and association studies usually target a single phenotypic trait. However, in animal and plant genetics it is common to record information on multiple phenotypes for each individual that will be genotyped. Modeling traits individually disregards the fact that they are most likely associated due to pleiotropy and shared biological basis, thus providing only a partial, confounded view of genetic effects and phenotypic interactions. In this article we use data from a Multiparent Advanced Generation Inter-Cross (MAGIC) winter wheat population to explore Bayesian networks as a convenient and interpretable framework for the simultaneous modeling of multiple quantitative traits. We show that they are equivalent to multivariate genetic best linear unbiased prediction (GBLUP) and that they are competitive with single-trait elastic net and single-trait GBLUP in predictive performance. Finally, we discuss their relationship with other additive-effects models and their advantages in inference and interpretation. MAGIC populations provide an ideal setting for this kind of investigation because the very low population structure and large sample size result in predictive models with good power and limited confounding due to relatedness.

An eight-parent multiparent advanced generation inter-cross population for winter-sown wheat: creation, properties, and validation.

MAGIC populations represent one of a new generation of crop genetic mapping resources combining high genetic recombination and diversity. We describe the creation and validation of an eight-parent MAGIC population consisting of 1091 F7 lines of winter-sown wheat (Triticum aestivum L.). Analyses based on genotypes from a 90,000-single nucleotide polymorphism (SNP) array find the population to be well-suited as a platform for fine-mapping quantitative trait loci (QTL) and gene isolation. Patterns of linkage disequilibrium (LD) show the population to be highly recombined; genetic marker diversity among the founders was 74% of that captured in a larger set of 64 wheat varieties, and 54% of SNPs segregating among the 64 lines also segregated among the eight founder lines. In contrast, a commonly used reference bi-parental population had only 54% of the diversity of the 64 varieties with 27% of SNPs segregating. We demonstrate the potential of this MAGIC resource by identifying a highly diagnostic marker for the morphological character "awn presence/absence" and independently validate it in an association-mapping panel. These analyses show this large, diverse, and highly recombined MAGIC population to be a powerful resource for the genetic dissection of target traits in wheat, and it is well-placed to efficiently exploit ongoing advances in phenomics and genomics. Genetic marker and trait data, together with instructions for access to seed, are available at http://www.niab.com/MAGIC/.

Use of advanced recombinant lines to study the impact and potential of mutations affecting starch synthesis in barley.

The effects on barley starch and grain properties of four starch synthesis mutations were studied during the introgression of the mutations from diverse backgrounds into an elite variety. The lys5f (ADPglucose transporter), wax (granule-bound starch synthase), isa1 (debranching enzyme isoamylase 1) and sex6 (starch synthase IIa) mutations were introgressed into NFC Tipple to give mutant and wild-type BC2F4 families with different genomic contributions of the donor parent. Comparison of starch and grain properties between the donor parents, the BC2F4 families and NFC Tipple allowed the effects of the mutations to be distinguished from genetic background effects. The wax and sex6 mutations had marked effects on starch properties regardless of genetic background. The sex6 mutation conditioned low grain weight and starch content, but the wax mutation did not. The lys5 mutation conditioned low grain weight and starch content, but exceptionally high ß-glucan contents. The isa1 mutation promotes synthesis of soluble α-glucan (phytoglycogen). Its introgression into NFC Tipple increased grain weight and total α-glucan content relative to the donor parent, but reduced the ratio of phytoglycogen to starch. This study shows that introgression of mutations into a common, commercial background provides new insights that could not be gained from the donor parent.

Barley mutants with low rates of endosperm starch synthesis have low grain dormancy and high susceptibility to preharvest sprouting.

• Studies of embryo dormancy in relation to preharvest sprouting (PHS) in cereals have focused on ABA and other hormones. The relationship between these phenomena and the rate of grain filling has not been investigated. • A collection of barley mutants impaired in starch synthesis was assessed for preharvest sprouting in the field. In subsequent glasshouse experiments, developing grains were assayed for germination index, sugars, abscisic acid (ABA) and the effects of temperature and exogenous ABA on germination. • Mutant lines displayed greater preharvest sprouting in the field than parental lines. In the glasshouse, nondeep physiological dormancy was reduced in developing grains of five lines with mutations affecting proteins involved in endosperm starch synthesis. Inhibition of germination by exogenous ABA and elevated temperature was decreased in developing mutant grains. Sugar concentrations were high but embryo and endosperm ABA contents were unaltered. • We reveal a direct connection between grain filling and the extent of grain dormancy. Impaired endosperm starch synthesis directly influences the acquisition of embryo dormancy, perhaps because endosperm sugar concentrations modulate the ABA responsiveness of the embryo. Thus environmental or genetic factors that reduce grain filling are likely to reduce dormancy and enhance susceptibility to PHS.