Comparison of genomic-enabled cross selection criteria for the improvement of inbred line breeding populations.

A crucial step in inbred plant breeding is the choice of mating design to derive high-performing inbred varieties while also maintaining a competitive breeding population to secure sufficient genetic gain in future generations. In practice, the mating design usually relies on crosses involving the best parental inbred lines to ensure high mean progeny performance. This excludes crosses involving lower performing but more complementary parents in terms of favorable alleles. We predicted the ability of crosses to produce putative outstanding progenies (high mean and high variance progeny distribution) using genomic prediction models. This study compared the benefits and drawbacks of 7 genomic cross selection criteria (CSC) in terms of genetic gain for 1 trait and genetic diversity in the next generation. Six CSC were already published, and we propose an improved CSC that can estimate the proportion of progeny above a threshold defined for the whole mating plan. We simulated mating designs optimized using different CSC. The 835 elite parents came from a real breeding program and were evaluated between 2000 and 2016. We applied constraints on parental contributions and genetic similarities between selected parents according to usual breeder practices. Our results showed that CSC based on progeny variance estimation increased the genetic value of superior progenies by up to 5% in the next generation compared to CSC based on the progeny mean estimation (i.e. parental genetic values) alone. It also increased the genetic gain (up to 4%) and/or maintained more genetic diversity at QTLs (up to 4% more genic variance when the marker effects were perfectly estimated).

Breeding for Economically and Environmentally Sustainable Wheat Varieties: An Integrated Approach from Genomics to Selection.

There is currently a strong societal demand for sustainability, quality, and safety in bread wheat production. To address these challenges, new and innovative knowledge, resources, tools, and methods to facilitate breeding are needed. This starts with the development of high throughput genomic tools including single nucleotide polymorphism (SNP) arrays, high density molecular marker maps, and full genome sequences. Such powerful tools are essential to perform genome-wide association studies (GWAS), to implement genomic and phenomic selection, and to characterize the worldwide diversity. This is also useful to breeders to broaden the genetic basis of elite varieties through the introduction of novel sources of genetic diversity. Improvement in varieties particularly relies on the detection of genomic regions involved in agronomical traits including tolerance to biotic (diseases and pests) and abiotic (drought, nutrient deficiency, high temperature) stresses. When enough resolution is achieved, this can result in the identification of candidate genes that could further be characterized to identify relevant alleles. Breeding must also now be approached through in silico modeling to simulate plant development, investigate genotype × environment interactions, and introduce marker-trait linkage information in the models to better implement genomic selection. Breeders must be aware of new developments and the information must be made available to the world wheat community to develop new high-yielding varieties that can meet the challenge of higher wheat production in a sustainable and fluctuating agricultural context. In this review, we compiled all knowledge and tools produced during the BREEDWHEAT project to show how they may contribute to face this challenge in the coming years.

BWGS: A R package for genomic selection and its application to a wheat breeding programme.

We developed an integrated R library called BWGS to enable easy computation of Genomic Estimates of Breeding values (GEBV) for genomic selection. BWGS, for BreedWheat Genomic selection, was developed in the framework of a cooperative private-public partnership project called Breedwheat (https://breedwheat.fr) and relies on existing R-libraries, all freely available from CRAN servers. The two main functions enable to run 1) replicated random cross validations within a training set of genotyped and phenotyped lines and 2) GEBV prediction, for a set of genotyped-only lines. Options are available for 1) missing data imputation, 2) markers and training set selection and 3) genomic prediction with 15 different methods, either parametric or semi-parametric. The usefulness and efficiency of BWGS are illustrated using a population of wheat lines from a real breeding programme. Adjusted yield data from historical trials (highly unbalanced design) were used for testing the options of BWGS. On the whole, 760 candidate lines with adjusted phenotypes and genotypes for 47 839 robust SNP were used. With a simple desktop computer, we obtained results which compared with previously published results on wheat genomic selection. As predicted by the theory, factors that are most influencing predictive ability, for a given trait of moderate heritability, are the size of the training population and a minimum number of markers for capturing every QTL information. Missing data up to 40%, if randomly distributed, do not degrade predictive ability once imputed, and up to 80% randomly distributed missing data are still acceptable once imputed with Expectation-Maximization method of package rrBLUP. It is worth noticing that selecting markers that are most associated to the trait do improve predictive ability, compared with the whole set of markers, but only when marker selection is made on the whole population. When marker selection is made only on the sampled training set, this advantage nearly disappeared, since it was clearly due to overfitting. Few differences are observed between the 15 prediction models with this dataset. Although non-parametric methods that are supposed to capture non-additive effects have slightly better predictive accuracy, differences remain small. Finally, the GEBV from the 15 prediction models are all highly correlated to each other. These results are encouraging for an efficient use of genomic selection in applied breeding programmes and BWGS is a simple and powerful toolbox to apply in breeding programmes or training activities.

Identification of a major QTL and associated molecular marker for high arabinoxylan fibre in white wheat flour.

Dietary fibre (DF) has multiple health benefits and wheat grains are major sources of DF for human health. However, DF is depleted in white wheat flour which is more widely consumed than wholegrain. The major DF component in white flour is the cell wall polysaccharide arabinoxylan (AX). We have identified the Chinese wheat cultivar Yumai 34 as having unusually high contents of AX in both water-soluble and insoluble forms. We have therefore used populations generated from crosses between Yumai 34 and four other wheat cultivars, three with average contents of AX (Ukrainka, Altigo and Claire) and one also having unusually high AX (Valoris), in order to map QTLs for soluble AX (determined as relative viscosity of aqueous extracts of wholemeal flours) and total AX (determined by enzyme fingerprinting of white flour). A number of QTL were mapped, but most were only detected in one or two crosses. However, all four crosses showed strong QTLs for high RV/total AX on chromosome 1B, with Yumai 34 being the increasing parent, and a KASP marker for the Yumai 34 high AX allele was validated by analysis of high AX lines derived from Yumai 34 but selected by biochemical analysis. A QTL for RV was also mapped on chromosome 6B in Yumai 34 x Valoris, with Valoris being the increasing allele, which is consistent with the observation of transgressive segregation for this population. Association studies in an independent germplasm panel identified marker trait associations for relative viscosity in these same locations while direct selection for fibre content in breeding resulted in high levels of enrichment for the Yumai 34 1B allele. The data therefore indicate that marker-assisted breeding can be used to develop wheat with high AX fibre in white flour.

Tracing the ancestry of modern bread wheats.

For more than 10,000 years, the selection of plant and animal traits that are better tailored for human use has shaped the development of civilizations. During this period, bread wheat (Triticum aestivum) emerged as one of the world's most important crops. We use exome sequencing of a worldwide panel of almost 500 genotypes selected from across the geographical range of the wheat species complex to explore how 10,000 years of hybridization, selection, adaptation and plant breeding has shaped the genetic makeup of modern bread wheats. We observe considerable genetic variation at the genic, chromosomal and subgenomic levels, and use this information to decipher the likely origins of modern day wheats, the consequences of range expansion and the allelic variants selected since its domestication. Our data support a reconciled model of wheat evolution and provide novel avenues for future breeding improvement.

On the Accuracy of Genomic Selection.

Genomic selection is focused on prediction of breeding values of selection candidates by means of high density of markers. It relies on the assumption that all quantitative trait loci (QTLs) tend to be in strong linkage disequilibrium (LD) with at least one marker. In this context, we present theoretical results regarding the accuracy of genomic selection, i.e., the correlation between predicted and true breeding values. Typically, for individuals (so-called test individuals), breeding values are predicted by means of markers, using marker effects estimated by fitting a ridge regression model to a set of training individuals. We present a theoretical expression for the accuracy; this expression is suitable for any configurations of LD between QTLs and markers. We also introduce a new accuracy proxy that is free of the QTL parameters and easily computable; it outperforms the proxies suggested in the literature, in particular, those based on an estimated effective number of independent loci (Me). The theoretical formula, the new proxy, and existing proxies were compared for simulated data, and the results point to the validity of our approach. The calculations were also illustrated on a new perennial ryegrass set (367 individuals) genotyped for 24,957 single nucleotide polymorphisms (SNPs). In this case, most of the proxies studied yielded similar results because of the lack of markers for coverage of the entire genome (2.7 Gb).

(1)H-NMR screening for the high-throughput determination of genotype and environmental effects on the content of asparagine in wheat grain.

Free asparagine in cereals is known to be the precursor of acrylamide, a neurotoxic and carcinogenic product formed during cooking processes. Thus, the development of crops with lower asparagine is of considerable interest to growers and the food industry. In this study, we describe the development and application of a rapid (1)H-NMR-based analysis of cereal flour, that is, suitable for quantifying asparagine levels, and hence acrylamide-forming potential, across large numbers of samples. The screen was applied to flour samples from 150 bread wheats grown at a single site in 2005, providing the largest sample set to date. Additionally, screening of 26 selected cultivars grown for two further years in the same location and in three additional European locations in the third year (2007) provided six widely different environments to allow estimation of the environmental (E) and G x E effects on asparagine levels. Asparagine concentrations in the 150 genotypes ranged from 0.32 to 1.56 mg/g dry matter in wholemeal wheat flours. Asparagine levels were correlated with plant height and therefore, due to recent breeding activities to produce semi-dwarf varieties, a negative relationship with the year of registration of the cultivar was also observed. The multisite study indicated that only 13% of the observed variation in asparagine levels was heritable, whilst the environmental contribution was 36% and the GxE component was 43%. Thus, compared to some other phenotypic traits, breeding for low asparagine wheats presents a difficult challenge.

Breeding schemes for the implementation of genomic selection in wheat (Triticum spp.).

In the last decade the breeding technology referred to as 'genomic selection' (GS) has been implemented in a variety of species, with particular success in animal breeding. Recent research shows the potential of GS to reshape wheat breeding. Many authors have concluded that the estimated genetic gain per year applying GS is several times that of conventional breeding. GS is, however, a new technology for wheat breeding and many programs worldwide are still struggling to identify the best strategy for its implementation. This article provides practical guidelines on the key considerations when implementing GS. A review of the existing GS literature for a range of species is provided and used to prime breeder-oriented considerations on the practical applications of GS. Furthermore, this article discusses potential breeding schemes for GS, genotyping considerations, and methods for effective training population design. The components of selection intensity, progress toward inbreeding in half- or full-sibs recurrent schemes, and the generation of selection are also presented.

Genome-wide prediction of three important traits in bread wheat.

Five genomic prediction models were applied to three wheat agronomic traits-grain yield, heading date and grain test weight-in three breeding populations, each comprising about 350 doubled haploid or recombinant inbred lines evaluated in three locations during a 3-year period. The prediction accuracy, measured as the correlation between genomic estimated breeding value and observed trait, was in the range of previously published values for yield (r = 0.2-0.5), a trait with relatively low heritability. Accuracies for heading date and test weight, with relatively high heritabilities, were about 0.70. There was no improvement of prediction accuracy when two or three breeding populations were merged into one for a larger training set (e.g., for yield r ranged between 0.11 and 0.40 in the respective populations and between 0.18 and 0.35 in the merged populations). Cross-population prediction, when one population was used as the training population set and another population was used as the validation set, resulted in no prediction accuracy. This lack of cross-population prediction accuracy cannot be explained by a lower level of relatedness between populations, as measured by a shared SNP similarity, since it was only slightly lower between than within populations. Simulation studies confirm that cross-prediction accuracy decreases as the proportion of shared QTLs decreases, which can be expected from a higher level of QTL × environment interactions.

Effects of genotype and environment on the contents of betaine, choline, and trigonelline in cereal grains.

This study examined the environmental and genetic variation in methyl donor contents and compositions of 200 cereal genotypes. Glycine betaine, choline, and trigonelline contents were determined by (1)H NMR, and significant differences were observed between cereal types (G) and across harvesting years and growing locations (E). Glycine betaine was the most abundant methyl donor in all of the 200 lines grown on a single site, and concentrations ranged from 0.43 ± 0.09 mg/g dm in oats to 2.57 ± 0.25 mg/g dm in diploid Einkorn varieties. In bread wheat genotypes there was a 3-fold difference in glycine betaine content. Choline contents, in the same lines, were substantially lower, and mean concentrations ranged from 0.17 mg/g dm in oats to 0.27 mg/g dm in durum wheat. Trigonelline was by far the least abundant of the methyl donors studied. Despite this, however, there were large differences between cereal types. Twenty-six wheat genotypes were grown in additional years at four European locations. The average glycine betaine content was highest in grains grown in Hungary and lowest in those grown in the United Kingdom. Across the six environments, there was a 3.8-fold difference in glycine betaine content. Glycine betaine levels, although moderately heritable (0.36), were found to be the most susceptible to the environmental conditions. Free choline concentrations were less variable across genotypes, but heritability of this component was the lowest of all methyl donor components (0.25) and showed a high G × E interaction. Trigonelline showed the most variation due to genotype. Heritability of this metabolite was the highest (0.59), but given that it is at a very low concentration in wheat, it is probably not attractive to plant breeders.

Genotype and environment effects on the contents of vitamins B1, B2, B3, and B6 in wheat grain.

The total contents of thiamine (vitamin B1), riboflavin (B2), and pyridoxine (B6) and the bioavailable forms of niacin (B3) were determined on wholemeal flours of 24 winter wheat varieties grown on four sites (United Kingdom, Poland, France, and Hungary) in 2007 and of two spring varieties grown on the same sites with the exception of Poland. The contents of vitamins B1 (5.53-13.55 µg/g dw), B2 (0.77-1.40 µg/g dw), and B6 (1.27-2.97 µg/g dw) were within the ranges reported previously, while the content of bioavailable vitamin B3 (0.16-1.74 µg/g dw) was about 10-15% of the total contents of vitamin B3 reported in previous studies. Strong correlations were observed between the contents of vitamins B1, B3, and B6, and partitioning of the variance in the contents of these three B vitamins showed that between 48 and 70% was accounted for by the environment. By contrast, the content of vitamin B2 was not correlated with the contents of other B vitamins, and 73% of the variance was ascribed to the error term, which suggests that this trait may be influenced by genotype × environment interactions. Whereas the contents of vitamins B1, B3, and B6 were correlated positively with the mean temperature from heading to harvest (r > 0.8), the content of vitamin B2 was positively correlated with precipitation during the 3 months prior to heading. These results are discussed in relation to the development of new wheat varieties with enhanced health benefits.

Wheat domestication: lessons for the future.

Wheat was one of the first crops to be domesticated more than 10,000 years ago in the Middle East. Molecular genetics and archaeological data have allowed the reconstruction of plausible domestication scenarios leading to modern cultivars. For diploid einkorn and tetraploid durum wheat, a single domestication event has likely occurred in the Karacadag Mountains, Turkey. Following a cross between tetraploid durum and diploid T. tauschii, the resultant hexaploid bread wheat was domesticated and disseminated around the Caucasian region. These polyploidisation events facilitated wheat domestication and created genetic bottlenecks, which excluded potentially adaptive alleles. With the urgent need to accelerate genetic progress to confront the challenges of climate change and sustainable agriculture, wild ancestors and old landraces represent a reservoir of underexploited genetic diversity that may be utilized through modern breeding methods. Understanding domestication processes may thus help identifying new strategies.

Cross-genome map based dissection of a nitrogen use efficiency ortho-metaQTL in bread wheat unravels concerted cereal genome evolution.

Monitoring nitrogen use efficiency (NUE) in plants is becoming essential to maintain yield while reducing fertilizer usage. Optimized NUE application in major crops is essential for long-term sustainability of agriculture production. Here, we report the precise identification of 11 major chromosomal regions controlling NUE in wheat that co-localise with key developmental genes such as Ppd (photoperiod sensitivity), Vrn (vernalization requirement), Rht (reduced height) and can be considered as robust markers from a molecular breeding perspective. Physical mapping, sequencing, annotation and candidate gene validation of an NUE metaQTL on wheat chromosome 3B allowed us to propose that a glutamate synthase (GoGAT) gene that is conserved structurally and functionally at orthologous positions in rice, sorghum and maize genomes may contribute to NUE in wheat and other cereals. We propose an evolutionary model for the NUE locus in cereals from a common ancestral region, involving species specific shuffling events such as gene deletion, inversion, transposition and the invasion of repetitive elements.

Combined meta-genomics analyses unravel candidate genes for the grain dietary fiber content in bread wheat (Triticum aestivum L.).

Grain dietary fiber content in wheat not only affects its end use and technological properties including milling, baking and animal feed but is also of great importance for health benefits. In this study, integration of association genetics (seven detected loci on chromosomes 1B, 3A, 3D, 5B, 6B, 7A, 7B) and meta-QTL (three consensus QTL on chromosomes 1B, 3D and 6B) analyses allowed the identification of seven chromosomal regions underlying grain dietary fiber content in bread wheat. Based either on a diversity panel or on bi-parental populations, we clearly demonstrate that this trait is mainly driven by a major locus located on chromosome 1B associated with a log of p value >13 and a LOD score >8, respectively. In parallel, we identified 73 genes differentially expressed during the grain development and between genotypes with contrasting grain fiber contents. Integration of quantitative genetics and transcriptomic data allowed us to propose a short list of candidate genes that are conserved in the rice, sorghum and Brachypodium chromosome regions orthologous to the seven wheat grain fiber content QTL and that can be considered as major candidate genes for future improvement of the grain dietary fiber content in bread wheat breeding programs.

The HEALTHGRAIN wheat diversity screen: effects of genotype and environment on phytochemicals and dietary fiber components.

Analysis of the contents of bioactive components (tocols, sterols, alkylresorcinols, folates, phenolic acids, and fiber components) in 26 wheat cultivars grown in six site x year combinations showed that the extent of variation due to variety and environment differed significantly between components. The total contents of tocols, sterols, and arabinoxylan fiber were highly heritable and hence an appropriate target for plant breeding. However, significant correlations between the contents of bioactive components and environmental factors (precipitation and temperature) during grain development also occurred, with even highly heritable components differing in amount between grain samples grown in different years on different sites.

Effects of genotype and environment on the content and composition of phytochemicals and dietary fiber components in rye in the HEALTHGRAIN diversity screen.

The effects of genotype and environment on the content of bioactive components in rye were determined with four varieties being grown on one site for three years and on three additional sites in the third year and a fourth variety being included in all trials except year 1. Clear differences were observed in the extent to which the contents of dietary fiber components (arabinoxylan, beta-glucan, total dietary fiber) and phytochemicals (folates, alkylresorcinols, sterols, tocols, phenolic acids) varied between varieties and between the same varieties grown in different sites (United Kingdom, France, Hungary, Poland) and years (2005-2007 in Hungary), with sterols being the most stable and phenolic acids the least. However, no single variety could be selected as having the highest overall level of bioactive components or as being more stable in comparison across environments.

wDBTF: an integrated database resource for studying wheat transcription factor families.

BACKGROUND: Transcription factors (TFs) regulate gene expression by interacting with promoters of their target genes and are classified into families based on their DNA-binding domains. Genes coding for TFs have been identified in the sequences of model plant genomes. The rice (Oryza sativa spp. japonica) genome contains 2,384 TF gene models, which represent the mRNA transcript of a locus, classed into 63 families. RESULTS: We have created an extensive list of wheat (Triticum aestivum L) TF sequences based on sequence homology with rice TFs identified and classified in the Database of Rice Transcription Factors (DRTF). We have identified 7,112 wheat sequences (contigs and singletons) from a dataset of 1,033,960 expressed sequence tag and mRNA (ET) sequences available. This number is about three times the number of TFs in rice so proportionally is very similar if allowance is made for the hexaploidy of wheat. Of these sequences 3,820 encode gene products with a DNA-binding domain and thus were confirmed as potential regulators. These 3,820 sequences were classified into 40 families and 84 subfamilies and some members defined orphan families. The results were compiled in the Database of Wheat Transcription Factor (wDBTF), an inventory available on the web http://wwwappli.nantes.inra.fr:8180/wDBFT/. For each accession, a link to its library source and its Affymetrix identification number is provided. The positions of Pfam (protein family database) motifs were given when known. CONCLUSIONS: wDBTF collates 3,820 wheat TF sequences validated by the presence of a DNA-binding domain out of 7,112 potential TF sequences identified from publicly available gene expression data. We also incorporated in silico expression data on these TFs into the database. Thus this database provides a major resource for systematic studies of TF families and their expression in wheat as illustrated here in a study of DOF family members expressed during seed development.

Nucleotide polymorphism in the wheat transcriptional activator Spa influences its pattern of expression and has pleiotropic effects on grain protein composition, dough viscoelasticity, and grain hardness.

Storage protein activator (SPA) is a key regulator of the transcription of wheat (Triticum aestivum) grain storage protein genes and belongs to the Opaque2 transcription factor subfamily. We analyzed the sequence polymorphism of the three homoeologous Spa genes in hexaploid wheat. The level of polymorphism in these genes was high particularly in the promoter. The deduced protein sequences of each homoeolog and haplotype show greater than 93% identity. Two major haplotypes were studied for each Spa gene. The three Spa homoeologs have similar patterns of expression during grain development, with a peak in expression around 300 degree days after anthesis. On average, Spa-B is 10 and seven times more strongly expressed than Spa-A and Spa-D, respectively. The haplotypes are associated with significant quantitative differences in Spa expression, especially for Spa-A and Spa-D. Significant differences were found in the quantity of total grain nitrogen allocated to the gliadin protein fractions for the Spa-A haplotypes, whereas the synthesis of glutenins is not modified. Genetic association analysis between Spa and dough viscoelasticity revealed that Spa polymorphisms are associated with dough tenacity, extensibility, and strength. Except for Spa-A, these associations can be explained by differences in grain hardness. No association was found between Spa markers and the average single grain dry mass or grain protein concentration. These results demonstrate that in planta Spa is involved in the regulation of grain storage protein synthesis. The associations between Spa and dough viscoelasticity and grain hardness strongly suggest that Spa has complex pleiotropic functions during grain development.

Analysis of diversity and linkage disequilibrium along chromosome 3B of bread wheat (Triticum aestivum L.).

A highly polymorphic core collection of bread wheat and a more narrow-based breeding material, gathered from pedigrees of seven modern cultivars, was analysed in order to compare genetic diversity indices and linkage disequilibrium (LD) patterns along the chromosome 3B with microsatellite (SSR) and Diversity Arrays Technology markers. Five ancestral gene pools could be identified within the core collection, indicating a strong geographical structure (Northwest Europe, Southeast Europe, CIMMYT-ICARDA group, Asia, Nepal). The breeding material showed a temporal structure, corresponding to different periods of breeding programmes [old varieties (from old landraces to 1919), semi-modern varieties (1920-1959), modern varieties (1960-2006)]. Basic statistics showed a higher genetic diversity in the core collection than in the breeding material, indicating a stronger selection pressure in this latter material. More generally, the chromosome 3B had a lower diversity than the whole B-genome. LD was weak in all studied materials. Amongst geographical groups, the CIMMYT-ICARDA pool presented the longest ranged LD in contrast to Asian accessions. In the breeding material, LD increased from old cultivars to modern varieties. Genitors of seven modern cultivars were found to be different; most marker pairs in significant LD were observed amongst genitors of Alexandre and Koreli varieties, indicating an important inbreeding effect. At low genetic distances (0-5 cM), the breeding material had higher LD than the core collection, but globally the two materials had similar values in all classes. Marker pairs in significant LD are generally observed around the centromere in both arms and at distal position on the short arm of the chromosome 3B.

Genomics in cereals: from genome-wide conserved orthologous set (COS) sequences to candidate genes for trait dissection.

Recent updates in comparative genomics among cereals have provided the opportunity to identify conserved orthologous set (COS) DNA sequences for cross-genome map-based cloning of candidate genes underpinning quantitative traits. New tools are described that are applicable to any cereal genome of interest, namely, alignment criterion for orthologous couples identification, as well as the Intron Spanning Marker software to automatically select intron-spanning primer pairs. In order to test the software, it was applied to the bread wheat genome, and 695 COS markers were assigned to 1,535 wheat loci (on average one marker/2.6 cM) based on 827 robust rice-wheat orthologs. Furthermore, 31 of the 695 COS markers were selected to fine map a pentosan viscosity quantitative trait loci (QTL) on wheat chromosome 7A. Among the 31 COS markers, 14 (45%) were polymorphic between the parental lines and 12 were mapped within the QTL confidence interval with one marker every 0.6 cM defining candidate genes among the rice orthologous region.