KEY MESSAGE : This study identified 333 genomic regions associated to 28 traits related to nitrogen use efficiency in European winter wheat using genome-wide association in a 214-varieties panel ...experimented in eight environments. Improving nitrogen use efficiency is a key factor to sustainably ensure global production increase. However, while high-throughput screening methods remain at a developmental stage, genetic progress may be mainly driven by marker-assisted selection. The objective of this study was to identify chromosomal regions associated with nitrogen use efficiency-related traits in bread wheat (Triticum aestivum L.) using a genome-wide association approach. Two hundred and fourteen European elite varieties were characterised for 28 traits related to nitrogen use efficiency in eight environments in which two different nitrogen fertilisation levels were tested. The genome-wide association study was carried out using 23,603 SNP with a mixed model for taking into account parentage relationships among varieties. We identified 1,010 significantly associated SNP which defined 333 chromosomal regions associated with at least one trait and found colocalisations for 39 % of these chromosomal regions. A method based on linkage disequilibrium to define the associated region was suggested and discussed with reference to false positive rate. Through a network approach, colocalisations were analysed and highlighted the impact of genomic regions controlling nitrogen status at flowering, precocity, and nitrogen utilisation on global agronomic performance. We were able to explain 40 ± 10 % of the total genetic variation. Numerous colocalisations with previously published genomic regions were observed with such candidate genes as Ppd-D1, Rht-D1, NADH-Gogat, and GSe. We highlighted selection pressure on yield and nitrogen utilisation discussing allele frequencies in associated regions.
•We quantified how N deficiency increases ear N partitioning in 16 wheat cultivars.•We quantified genetic variation in leaf and stem N remobilization under high and low N.•We showed leaf lamina N ...remobilization was associated with senescence duration under high N.•We showed leaf lamina N remobilization was associated with grain N% under low N in one out of two sites (Clermont Ferrand).
Our objective was to investigate the determinants of genetic variation in N accumulation, N partitioning and N remobilization to the grain post-flowering and associations with flag-leaf senescence, grain yield and grain N% in 16 wheat cultivars grown under high N (HN) and low N (LN) conditions in the UK and France. Overall, cultivars ranged in leaf lamina N accumulation at anthesis from 5.32 to 8.03gNm−2 at HN and from 2.69 to 3.62gNm−2 at LN, and for the stem-and leaf-sheath from 5.45 to 7.25gNm−2 at HN and from 2.55 to 3.41gNm−2 at LN (P<0.001). Cultivars ranged in N partitioning index (proportion of above-ground N in the crop component) at anthesis for the leaf lamina from 0.37 to 0.42 at HN and 0.34 to 0.40 at LN; and for the stem-and leaf-sheath from 0.39 to 0.43 at HN and from 0.35 to 0.41 at LN (P<0.001). The amount of leaf lamina N remobilized post-anthesis was negatively associated with the duration of post-anthesis flag-leaf senescence amongst cultivars in all experiments under HN. In general, it was difficult to separate genetic differences in lamina N remobilization from those in lamina N accumulation at anthesis. Genetic variation in grain yield and grain N% (through N dilution effects) appeared to be mainly influenced by pre-anthesis N accumulation rather than post-anthesis N remobilization under high N conditions and under milder N stress (Sutton Bonington LN). Where N stress was increased (Clermont Ferrand LN), there was some evidence that lamina N remobilization was a determinant of genetic variation in grain N% although not of grain yield. Our results suggested that selection for lamina N accumulation at anthesis and lamina N remobilization post-anthesis may have value in breeding programmes aimed at optimizing senescence duration and improving grain yield, N-use efficiency and grain N% of wheat.
The strong negative correlation between grain protein concentration (GPC) and grain yield (GY) in bread wheat complicates the simultaneous improvement of these traits. However, earlier studies have ...concluded that the deviation from this relationship (grain protein deviation or GPD) has strong genetic basis. Genotypes with positive GPD have an increased ability to uptake nitrogen (N) during the post-flowering period independently of the amount of N taken up before flowering, suggesting that genetic variability for N satiety could enable the breakage of the negative relationship. This study is based on two genotypes markedly contrasted for GPD grown under semi-hydroponic conditions differentiated for nitrate availability both before and after flowering. This allows exploration of the genetic determinants of post-flowering N uptake (PANU) by combining whole plant sampling and targeted gene expression approaches. The results highlights the correlation (r² = 0.81) with GPC of PANU occurring early during grain development (flowering-flowering + 250 degree-days) independently of GY. Early PANU was in turn correlated (r² = 0.80) to the stem-biomass increment after flowering through its effect on N sink activity. Differences in early PANU between genotypes, despite comparable N statuses at flowering, suggest that genetic differences in N satiety could be involved in the establishment of the GPC. Through its strong negative correlation with genes implied in N assimilation, root nitrate concentration appears to be a good marker for evaluating instantaneous plant N demand, and may provide valuable information on the genotypic N satiety level. This trait may help breeders to identify genotypes having high GPC independently of their GY.
In Australian wheat (Triticum aestivum L.) production, optimizing wheat phenology is essential for yield potential and to avoid stress, especially around flowering. Breeding could be accelerated by ...identifying key loci and developing models to predict genotype flowering times under different pedoclimatic scenarios. Here, association genetics for heading date, earliness components (photoperiod sensitivity PS; vernalization requirement VR; earliness per se EPS) and simulation model (APSIM) phenology parameters from a panel of Australian cultivars and breeding lines identified loci with stable, repeatable effects. Major chromosomal regions with stable effects included the Ppd‐D1 region on chromosome 2D for PS and EPS, one region on 5B for PS, one region on 6B for EPS, and the Vrn‐A1 region on 5A for VR. Regions with stable, smaller effects were detected on 1A and 2D for PS, on 5A and 6B for EPS, and on 1A and 5D for VR. Other regions with stable effects on heading date and earliness components were located on 1A, 2B, 4B, 5B, 6B and 7B (PS and EPS), 2A, 3A and 7A (EPS and VR). Quantitative trait loci (QTL)–based model parameters were used to simulate heading dates across the Australian wheat belt for set of independent genotypes. Comparisons of average observed and predicted heading dates for four main regions of the Australian wheat belt showed good performance in prediction of independent lines from QTL information alone (r2 = .61–.83). The model allows testing of putative genotypes under various pedoclimatic scenarios including for adaptation to anticipated climate changes.
Key message
Phenomic selection is a promising alternative or complement to genomic selection in wheat breeding. Models combining spectra from different environments maximise the predictive ability of ...grain yield and heading date of wheat breeding lines.
Phenomic selection (PS) is a recent breeding approach similar to genomic selection (GS) except that genotyping is replaced by near-infrared (NIR) spectroscopy. PS can potentially account for non-additive effects and has the major advantage of being low cost and high throughput. Factors influencing GS predictive abilities have been intensively studied, but little is known about PS. We tested and compared the abilities of PS and GS to predict grain yield and heading date from several datasets of bread wheat lines corresponding to the first or second years of trial evaluation from two breeding companies and one research institute in France. We evaluated several factors affecting PS predictive abilities including the possibility of combining spectra collected in different environments. A simple H-BLUP model predicted both traits with prediction ability from 0.26 to 0.62 and with an efficient computation time. Our results showed that the environments in which lines are grown had a crucial impact on predictive ability based on the spectra acquired and was specific to the trait considered. Models combining NIR spectra from different environments were the best PS models and were at least as accurate as GS in most of the datasets. Furthermore, a GH-BLUP model combining genotyping and NIR spectra was the best model of all (prediction ability from 0.31 to 0.73). We demonstrated also that as for GS, the size and the composition of the training set have a crucial impact on predictive ability. PS could therefore replace or complement GS for efficient wheat breeding programs.
Key message
The response of a large panel of European elite wheat varieties to post-anthesis heat stress is influenced by 17 QTL linked to grain weight or the stay-green phenotype.
Heat stress is a ...critical abiotic stress for winter bread wheat (
Triticum aestivum
L.) especially at the flowering and grain filling stages, limiting its growth and productivity in Europe and elsewhere. The breeding of new high-yield and stress-tolerant wheat varieties requires improved understanding of the physiological and genetic bases of heat tolerance. To identify genomic areas associated with plant and grain characteristics under heat stress, a panel of elite European wheat varieties (
N
= 199) was evaluated under controlled conditions in 2016 and 2017. A split-plot design was used to test the effects of high temperature for ten days after flowering. Flowering time, leaf chlorophyll content, the number of productive spikes, grain number, grain weight and grain size were measured, and the senescence process was modeled. Using genotyping data from a 280 K SNP chip, a genome-wide association study was carried out to test the main effect of each SNP and the effect of SNP × treatment interaction. Genotype × treatment interactions were mainly observed for grain traits measured on the main shoots and tillers. We identified 10 QTLs associated with the main effect of at least one trait and seven QTLs associated with the response to post-anthesis heat stress. Of these, two main QTLs associated with the heat tolerance of thousand-kernel weight were identified on chromosomes 4B and 6B. These QTLs will be useful for breeders to improve grain yield in environments where terminal heat stress is likely to occur.
In this review, recent developments and future prospects of obtaining a better understanding of the regulation of nitrogen use efficiency in the main crop species cultivated in the world are ...presented. In these crops, an increased knowledge of the regulatory mechanisms controlling plant nitrogen economy is vital for improving nitrogen use efficiency and for reducing excessive input of fertilizers, while maintaining an acceptable yield. Using plants grown under agronomic conditions at low and high nitrogen fertilization regimes, it is now possible to develop whole-plant physiological studies combined with gene, protein, and metabolite profiling to build up a comprehensive picture depicting the different steps of nitrogen uptake, assimilation, and recycling to the final deposition in the seed. A critical overview is provided on how understanding of the physiological and molecular controls of N assimilation under varying environmental conditions in crops has been improved through the use of combined approaches, mainly based on whole-plant physiology, quantitative genetics, and forward and reverse genetics approaches. Current knowledge and prospects for future agronomic development and application for breeding crops adapted to lower fertilizer input are explored, taking into account the world economic and environmental constraints in the next century.
Plant interactions with plant growth‐promoting rhizobacteria (PGPR) are highly dependent on plant genotype. Modern plant breeding has largely sought to improve crop performance but with little focus ...on the optimization of plant × PGPR interactions. The interactions of the model PGPR strain Pseudomonas kilonensis F113 were therefore compared in 199 ancient and modern wheat genotypes. A reporter system, in which F113 colonization and expression of 2,4‐diacetylphloroglucinol biosynthetic genes (phl) were measured on roots was used to quantify F113 × wheat interactions under gnotobiotic conditions. Thereafter, eight wheat accessions that differed in their ability to interact with F113 were inoculated with F113 and grown in greenhouse in the absence or presence of stress. F113 colonization was linked to improved stress tolerance. Moreover, F113 colonization and phl expression were higher overall on ancient genotypes than modern genotypes. F113 colonization improved wheat performance in the four genotypes that showed the highest level of phl expression compared with the four genotypes in which phl expression was lowest. Taken together, these data suggest that recent wheat breeding strategies have had a negative impact on the ability of the plants to interact with PGPR.
The exponential development of molecular markers enables a more effective study of the genetic architecture of traits of economic importance, like test weight in wheat (Triticum aestivum L.), for ...which a high value is desired by most end-users. The association mapping (AM) method now allows more precise exploration of the entire genome. AM requires populations with substantial genetic variability of the traits of interest. The breeding lines at the end of a selection cycle, characterized for numerous traits, represent a potentially useful population for AM studies. Using three elite line populations, selected by several breeders and genotyped with about 2,500 Diversity Arrays Technology markers, several associations were identified between these markers and test weight, grain yield and heading date. To minimize spurious associations, we compared the general linear model and mixed linear model (MLM), which adjust for population structure and kinship differently. The MLM model with the kinship matrix was the most efficient. Finally, elite lines from several breeding programs had sufficient genetic variability to allow for the mapping of several chromosomal regions involved in the variation of three important traits.
Genomic selection - the prediction of breeding values using DNA polymorphisms - is a disruptive method that has widely been adopted by animal and plant breeders to increase productivity. It was ...recently shown that other sources of molecular variations such as those resulting from transcripts or metabolites could be used to accurately predict complex traits. These endophenotypes have the advantage of capturing the expressed genotypes and consequently the complex regulatory networks that occur in the different layers between the genome and the phenotype. However, obtaining such omics data at very large scales, such as those typically experienced in breeding, remains challenging. As an alternative, we proposed using near-infrared spectroscopy (NIRS) as a high-throughput, low cost and non-destructive tool to indirectly capture endophenotypic variants and compute relationship matrices for predicting complex traits, and coined this new approach "phenomic selection" (PS). We tested PS on two species of economic interest (
L. and
L.) using NIRS on various tissues (grains, leaves, wood). We showed that one could reach predictions as accurate as with molecular markers, for developmental, tolerance and productivity traits, even in environments radically different from the one in which NIRS were collected. Our work constitutes a proof of concept and provides new perspectives for the breeding community, as PS is theoretically applicable to any organism at low cost and does not require any molecular information.
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