Using crop growth model stress covariates and AMMI decomposition to better predict genotype-by-environment interactions.

KEY MESSAGE: We propose new methods to predict genotype × environment interaction by selecting relevant environmental covariates and using an AMMI decomposition of the interaction. Farmers are asked to produce more efficiently and to reduce their inputs in the context of climate change. They have to face more and more limiting factors that can combine in numerous stress scenarios. One solution to this challenge is to develop varieties adapted to specific environmental stress scenarios. For this, plant breeders can use genomic predictions coupled with environmental characterization to identify promising combinations of genes in relation to stress covariates. One way to do it is to take into account the genetic similarity between varieties and the similarity between environments within a mixed model framework. Molecular markers and environmental covariates (EC) can be used to estimate relevant covariance matrices. In the present study, based on a multi-environment trial of 220 European elite winter bread wheat (Triticum aestivum L.) varieties phenotyped in 42 environments, we compared reference regression models potentially including ECs, and proposed alternative models to increase prediction accuracy. We showed that selecting a subset of ECs, and estimating covariance matrices using an AMMI decomposition to benefit from the information brought by the phenotypic records of the training set are promising approaches to better predict genotype-by-environment interactions (G × E). We found that using a different kinship for the main genetic effect and the G × E effect increased prediction accuracy. Our study also demonstrates that integrative stress indexes simulated by crop growth models are more efficient to capture G × E than climatic covariates.

Optimization of multi-environment trials for genomic selection based on crop models.

KEY MESSAGE: We propose a statistical criterion to optimize multi-environment trials to predict genotype × environment interactions more efficiently, by combining crop growth models and genomic selection models. Genotype × environment interactions (GEI) are common in plant multi-environment trials (METs). In this context, models developed for genomic selection (GS) that refers to the use of genome-wide information for predicting breeding values of selection candidates need to be adapted. One promising way to increase prediction accuracy in various environments is to combine ecophysiological and genetic modelling thanks to crop growth models (CGM) incorporating genetic parameters. The efficiency of this approach relies on the quality of the parameter estimates, which depends on the environments composing this MET used for calibration. The objective of this study was to determine a method to optimize the set of environments composing the MET for estimating genetic parameters in this context. A criterion called OptiMET was defined to this aim, and was evaluated on simulated and real data, with the example of wheat phenology. The MET defined with OptiMET allowed estimating the genetic parameters with lower error, leading to higher QTL detection power and higher prediction accuracies. MET defined with OptiMET was on average more efficient than random MET composed of twice as many environments, in terms of quality of the parameter estimates. OptiMET is thus a valuable tool to determine optimal experimental conditions to best exploit MET and the phenotyping tools that are currently developed.

Genomic regions associated with the nitrogen limitation response revealed in a global wheat core collection.

Modern wheat (Triticum aestivum L.) varieties in Western Europe have mainly been bred, and selected in conditions where high levels of nitrogen-rich fertilizer are applied. However, high input crop management has greatly increased the risk of nitrates leaching into groundwater with negative impacts on the environment. To investigate wheat nitrogen tolerance characteristics that could be adapted to low input crop management, we supplied 196 accessions of a wheat core collection of old and modern cultivars with high or moderate amounts of nitrogen fertilizer in an experimental network consisting of three sites and 2 years. The main breeding traits were assessed including grain yield and grain protein content. The response to nitrogen level was estimated for grain yield and grain number per m(2) using both the difference and the ratio between performance at the two input levels and the slope of joint regression. A large variability was observed for all the traits studied and the response to nitrogen level. Whole genome association mapping was carried out using 899 molecular markers taking into account the five ancestral group structure of the collection. We identified 54 main regions involving almost all chromosomes that influence yield and its components, plant height, heading date and grain protein concentration. Twenty-three regions, including several genes, spread over 16 chromosomes were involved in the response to nitrogen level. These chromosomal regions may be good candidates to be used in breeding programs to improve the performance of wheat varieties at moderate nitrogen input levels.

Genome-wide association analysis to identify chromosomal regions determining components of earliness in wheat.

The modification of flowering date is considered an important way to escape the current or future climatic constraints that affect wheat crops. A better understanding of its genetic bases would enable a more efficient and rapid modification through breeding. The objective of this study was to identify chromosomal regions associated with earliness in wheat. A 227-wheat core collection chosen to be highly contrasted for earliness was characterized for heading date. Experiments were conducted in controlled conditions and in the field for 3 years to break down earliness in the component traits: photoperiod sensitivity, vernalization requirement and narrow-sense earliness. Whole-genome association mapping was carried out using 760 molecular markers and taking into account the five ancestral group structure. We identified 62 markers individually associated to earliness components corresponding to 33 chromosomal regions. In addition, we identified 15 other significant markers and seven more regions by testing marker pair interactions. Co-localizations were observed with the Ppd-1, Vrn-1 and Rht-1 candidate genes. Using an independent set of lines to validate the model built for heading date, we were able to explain 34% of the variation using the structure and the significant markers. Results were compared with already published data using bi-parental populations giving an insight into the genetic architecture of flowering time in wheat.

Most significant genome regions involved in the control of earliness traits in bread wheat, as revealed by QTL meta-analysis.

Earliness is one of the most important adaptation traits in plant breeding. Our purpose was to identify the genome regions of bread wheat involved in the control of earliness and its three components: photoperiod sensitivity (PS), vernalization requirement (VR) and intrinsic earliness (IE). A QTL meta-analysis was carried out to examine the replicability of QTL across 13 independent studies and to propose meta-QTL (MQTL). Initial QTL were projected on a recent consensus map (2004). Quality criteria were proposed to assess the reliability of this projection. These criteria were based on the distances between markers in the QTL regions. Chromosomes of groups 2 and 5 had a greater incidence on earliness control as they carry the known, major genes Ppd and Vrn. Other chromosome regions played an intermediate role in earliness control: 4A [heading date (HD) Meta-QTL], 4B (HD MQTL), 2B (VR MQTL) and 5B (IE MQTL). Markers at this four MQTL should prove helpful in marker-assisted selection, to better control earliness.

A simplified conceptual model of carbon/nitrogen functioning for QTL analysis of winter wheat adaptation to nitrogen deficiency.

Breeding new varieties adapted to low-input agricultural practices is of particular interest in light of current economical and environmental concerns. Improving nitrogen (N) uptake and N utilization efficiency (NUE) are two ways of producing varieties tolerant to low N input. To offer new possibilities to breeders, it is necessary to acquire more knowledge about these two processes. Knowing C and N metabolisms are linked and knowing N uptake is partly explained by root characteristics, we carried out a QTL analysis for traits associated with N uptake and NUE by using both a conceptual model of C/N plant functioning and a root architecture description. A total of 120 lines were selected according to their genotype among 241 doubled haploids derived from two varieties, one N stress tolerant and the other N stress sensitive. They were grown in hydroponic rhizotrons under N-limited nutritional conditions. Initial conditions varied among genotypes; therefore, total root length on day 1 was used to correct traits. Heritabilities ranged from 13 to 84%. Thirty-two QTL were located: 6 associated with root architecture (on chromosomes 4B, 5A, 5D and 7B), 6 associated with model efficiencies (1B, 2B, 6A, 6B, 7A, 7B and 7D) and 20 associated with state variables (1A, 1B, 2B, 4B, 5A, 5B and 6B). The effects of the dwarfing gene Rht-B1 on root traits are discussed, as well as the features of a conceptual plant functioning model, as a useful tool to assess pertinent traits for QTL detection. It is suggested that further studies that couple QTL with a functioning model and a root architecture description could serve in the search for ideotypes.

Partial sequences of nitrogen metabolism genes in hexaploid wheat.

Our objective was to partially sequence genes controlling nitrogen metabolism in wheat species in order to find sequence polymorphism that would enable their mapping. Primers were designed for nitrate reductase, nitrite reductase, glutamate dehydrogenase and glutamate synthase (GOGAT), and gene fragments were amplified on Triticum aestivum, T. durum, T. monococcum, T. speltoides and T. tauschii. We obtained more than 8 kb of gene sequences, mainly as coding regions (60%). Polymorphism was quantified by comparing two-by-two the three genomes of the hexaploid cultivar Arche and genomes of diploid wheat species. On average, the polymorphism rate was higher for non-coding regions, where it ranged from 1/60 to 1/23, than for coding regions (range: 1/110-1/40) except when the hexaploid D genome was compared to that of T. tauschii (1/800 and 1/816, respectively). Genome-specific primers were devised for the ferredoxin-dependent (Fd)-GOGAT gene, and they enabled the mapping of this gene on homoeologous chromosomes of group 2 using Chinese Spring deletion lines. A single nucleotide polymorphism (SNP) detected between the two hexaploid wheat cultivars Arche and Recital was used to genetically map Fd-GOGAT on chromosome 2D using a population of dihaploid lines. Fd-GOGAT-specific primers were used to estimate the SNP rate on a set of 11 hexaploid and nine Durum wheat genotypes leading to the estimate of 1 SNP/515 bp. We demonstrate that polymorphism detection enables heterologous, homeologous and even paralogous copies to be assigned, even if the elaboration of specific primer pairs is time-consuming and expensive because of the sequencing.

Detection and mapping of QTL for earliness components in a bread wheat recombinant inbred lines population.

Earliness, an adaptative trait and factor of variation for agronomic characters, is a major trait in plant breeding. Its constituent traits, photoperiod sensitivity (PS), vernalization requirement (VR) and intrinsic earliness (IE), are largely under independent genetic controls. Mapping of major genes and quantitative trait loci (QTL) controlling these components is in progress. Most of the studies focusing on earliness considered it as a whole or through one (or two) of its components. The purpose of this study was to detect and map QTL for the three traits together through an experimental design combining field trials and controlled growth conditions. QTL were mapped in a population of F(7) recombinant inbred lines derived by single-seed descent from a cross between two French varieties, 'Renan' and 'Recital'. A map was previously constructed, based on 194 lines and 254 markers, covering about 77% of the genome. Globally, 13 QTL with a LOD>2.5 were detected, of which four control PS, five control VR and four control IE. Two major photoperiod sensitive QTL, together explaining more than 31% of the phenotypic variation, were mapped on chromosomes 2B and 2D, at the same position as the two major genes Ppd-B1 and Ppd-D1. One major VR QTL explaining (depending on the year) 21.8-39.6% of the phenotypic variation was mapped on 5A. Among the other QTL, two QTL of PS and VR not referenced so far were detected on 5A and 6D, respectively. A VR QTL already detected on 2B in a connected population was confirmed.

rym15 from the Japanese cultivar Chikurin Ibaraki 1 is a new barley mild mosaic virus (BaMMV) resistance gene mapped on chromosome 6H.

Breeding for resistant cultivars is the only way to prevent high yield loss in barley caused by the soil-borne barley mild mosaic virus (BaMMV) complex. We have characterized the BaMMV resistance of barley cv. Chikurin Ibaraki 1. Doubled haploid lines were obtained from the F(1) between the susceptible six-rowed winter barley cultivar, Plaisant, and Chikurin Ibaraki 1. Each line was tested for reaction to BaMMV by mechanical inoculation followed by DAS-ELISA. Of 44 microsatellites that covered the genome, 22 polymorphic markers were tested on one susceptible and one resistant bulk, each comprising 30 lines. Differential markers and additional microsatellite markers in the same region were then tested on the whole population. A bootstrap analysis was used to compute confidence intervals of distances and to test the orders of the resistance gene and the closest markers. A segregation of 84 resistant/98 susceptible lines fitted a 1:1 ratio (chi(2)=1.08, P=0.30), which corresponds to a single gene in this DH lines population. The resistance gene was flanked by two markers near the centromeric region of chromosome 6HS-Bmag0173, at 0.6+/-1.2 cM, and EBmac0874, at 5.8 +/- 3.4 cM. We propose to name this new resistance gene rym15. This resistance gene and associated markers will increase the possibilities to breed efficiently for new cultivars resistant to the barley mosaic disease.

Genetic diversity of old french six-rowed winter barley varieties assessed with molecular, biochemical and morphological markers and its relation to BaMMV resistance

Twenty-six old French six-rowed winter barley (Hordeum vulgare L.) varieties were characterized for their reaction against barley mild mosaic virus (BaMMV). The genetic diversity of these varieties and two recent barley varieties was assessed using molecular, biochemical and morphological data. Seven old varieties were fully resistant to BaMMV. A higher differentiation level between varieties was observed by using DNA molecular markers compared to biochemical and morphological ones. Correspondence analysis using all markers showed that DNA molecular data could fully discriminate between all varieties, whereas biochemical and morphological markers were not able to achieve a complete discrimination. The dendrogram clustering computed with the DNA marker dissimilarity index showed two main groups. The first group included the seven varieties resistant to the BaMMV, whereas the second contained susceptible varieties. The relationships between these varieties, their diversity level, and their characterization are discussed. We infer that the seven BaMMV-resistant varieties have a common origin.