Association study between the gibberellic acid insensitive gene and leaf length in a Lolium perenne L. synthetic variety

<p>Abstract</p> <p>Background</p> <p>Association studies are of great interest to identify genes explaining trait variation since they deal with more than just a few alleles like classical QTL analyses. They are usually performed using collections representing a wide range of variability but which could present a genetic substructure. The aim of this paper is to demonstrate that association studies can be performed using synthetic varieties obtained after several panmictic generations. This demonstration is based on an example of association between the gibberellic acid insensitive gene (GAI) polymorphism and leaf length polymorphism in 'Herbie', a synthetic variety of perennial ryegrass.</p> <p>Methods</p> <p>Leaf growth parameters, consisted of leaf length, maximum leaf elongation rate (LERmax) and leaf elongation duration (LED), were evaluated in spring and autumn on 216 plants of Herbie with three replicates. For each plant, a sequence of 370 bp in GAI was analysed for polymorphism.</p> <p>Results</p> <p>Genetic effect was highly significant for all traits. Broad sense heritabilities were higher for leaf length and LERmax with about 0.7 in each period and 0.5 considering both periods than for LED with about 0.4 in each period and 0.3 considering both periods. GAI was highly polymorphic with an average of 12 bp between two consecutive SNPs and 39 haplotypes in which 9 were more frequent. Linkage disequilibrium declined rapidly with distance with r ²values lower than 0.2 beyond 150 bp. Sequence polymorphism of GAI explained 8-14% of leaf growth parameter variation. A single SNP explained 4% of the phenotypic variance of leaf length in both periods which represents a difference of 33 mm on an average of 300 mm.</p> <p>Conclusions</p> <p>Synthetic varieties in which linkage disequilibrium declines rapidly with distance are suitable for association studies using the "candidate gene" approach. GAI polymorphism was found to be associated with leaf length polymorphism which was more correlated to LERmax than to LED in Herbie. It is a good candidate to explain leaf length variation in other plant material.</p

Etudes d association dans des variétés synthétiques (Cas du gène GAI et de la croissance foliaire chez le ray-grass anglais)

L identification des gènes ou des régions du génome responsables de la variabilité des caractères quantitatifs est généralement réalisées à partir de populations de cartographie dérivées d hybrides F1. Cependant, ces populations sont peu variables et parfois difficiles à obtenir. Une autre approche possible est la conduite d études d association à l aide de collections d individus, construites pour représenter une grande variabilité. Cependant, ces collections sont souvent structurées, ce qui peut induire la détection de fausses associations entre marqueurs et caractères. Ainsi, un matériel végétal idéal pour des études d association serait des populations multi alléliques et non structurées. C est le cas des variétés fourragères qui sont des variétés synthétiques, obtenues après plusieurs générations successives de panmixie, à partir d un n ombre plus ou moins grand d individus. L objectif de cette thèse est de tester si les variétés synthétiques peuvent être utilisées pour identifier les gènes ou les régions du génome responsables de la variabilité des caractères quantitatifs dar des études d association.RENNES-Agrocampus-CRD (352382323) / SudocSudocFranceF

Breeding value estimation for Yield and Quality traits using BWGS pipeline

Post-050 (#166)Breeding value estimation for Yield and Quality traits using BWGS pipeline. JOBIM : Journées Ouvertes Biologie Informatique Mathématique

Breeding value estimation for yield and quality traits in wheat using BWGS pipeline

Breeding value estimation for yield and quality traits in wheat using BWGS pipeline. 9. Intern. Wheat conferenc

Integration of genomic selection into winter-type bread wheat breeding schemes: a simulation pipeline including economic constraints

International audienceRelatively little effort has been made yet to optimize the allocation of resources when using genomic predictions to maximize the return to investment in terms of genetic gain per unit of time and cost.Methods: We built a simulation pipeline in the R environment designed to become a decision tool to help breeders adjusting breeding schemes, according to their either short or long-term objectives. We used it to explore different scenarios in order to investigate under which conditions (at what step of the breeding program) genomic predictions could improve genetic gain. For a given budget per cycle, we compared 36 scenarios, varying strategies (phenotypic selection PS or genomic selection + phenotypic selection: GPS), trait heritability, relative selection rate at two key steps and genotyping cost. With GPS strategy, we also optimized mating using genomic predictions. The reference population is a 20 years historical data set from the INRAE-Agri-Obtentions bread wheat breeding program. We simulated 3 cycles of 5 years selection.Results: We showed that GPS selection using mating optimization significantly improved genetic gain for all scenarios while GPS without mating optimization and PS had similar efficiency in terms of genetic gain. Our results also highlighted that the loss of genetic diversity over successive cycles was faster using GPS strategies. Those were more efficient to increase favourable allele frequency, rare alleles in particular

Training population optimization for genomic selection improves the predictive ability of a costly measure in bread wheat, the gliadin to glutenin ratio

International audienceEnd-use value of wheat four depends strongly on the concentration and composition of storage proteins, namely the gliadins and glutenins. As protein concentration in wheat grain is negatively correlated with grain yield, monitoring the gliadin to glutenin ratio is a mean to maintain end-use quality in modern varieties. However, the measurement of this ratio is expensive and time consuming. As genomic selection (GS) has proved very successful for traits controlled by many Quantitative Trait Loci and is already used for breeding, we decided to apply it to the gliadin to glutenin ratio. Therefore, we phenotyped for this trait and genotyped with a 420,000 SNP (Single Nucleotide Polymorphism) array a set of 88 modern varieties and 325 core-collection varieties. A GS model taking into account the genotypic, environmental and genotype x environment interaction efects was tested. Its predictive ability depends on the composition of the training population (TP). Adding signifcant SNPs as fxed efects did not improve the predictive ability. However, we observed improvements by optimizing the TP with fve methods based on relatedness between genotypes and obtained a maximum predictive ability of 0.62 and a minimum Root Mean Square Error of 0.056 for the gliadin to glutenin ratio. To conclude, our results are promising and strongly suggested that GS can be efciently applied to the gliadin to glutenin ratio. In addition, genotypes phenotyped and genotyped in previous breeding generations could be useful to train the model

Identification of factors influencing predictive ability of phenomic selection and comparison to genomic selection in wheat breeding programs

International audienceIn plant breeding, the selection of the best individuals is mainly based on phenotyping records. Because phenotyping is costly and time consuming, predictive tools such as Genomic selection (GS) have been developed in order to select among unphenotyped candidates. GS allows predicting the target traits for the selection candidates using the phenotypes of a training set and genotypic information collected on the training set and the selection candidates. Despite a good potential of the method to assist breeders in their selection choices, the cost of the genotyping still remains expensive, as GS requires to genotype each year the new selection candidates. In 2018, Rincent et al. developed a new, low cost, and high throughput method to predict the target trait of unobserved selection candidates. This method called phenomic selection (PS) is similar to GS, but genotyping is replaced by near infrared spectroscopy (NIRS). NIRS has the main advantage of being affordable, and already routinely applied on the selection candidates for many species such as wheat. GS has been well studied for twenty years, and many factors influencing its predictive ability are well understood. In PS, little is known about the factors influencing the predictive abilities, and about its performance relative to GS. We conducted the analyses on several datasets, corresponding to breeding lines drawn from 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 or at different steps of the breeding program. Contrary to genotypic data, near infrared spectra are indeed influenced by both the genotype and the environment. Thus, a selection candidate can be characterised by a multitude of spectra measured in different environments. The statistical model used was a simple H-BLUP model, reaching prediction ability from 0.26 to 0.62.Our results showed that the environment in which the NIR spectra was collected had an impor-tant impact on predictive ability and this impact was specific to the trait considered. Among all the models tested, combining NIR spectra from different environments were the best PS models and were at least as accurate as GS in most of the datasets. We finally tested a model which gathered NIRS and molecular marker effects. This model, GH-BLUP, was the best model of all, regardless of the trait or dataset, with prediction abilities reaching 0.31 to 0.73. In this study we showed that PS could be a great support tool for breeders to improve wheat breeding programs and could efficiently replace or complement GS.

Phenomic selection in wheat breeding: identification and optimisation of factors influencing prediction accuracy and comparison to genomic selection

International audiencePhenomic 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

Phenomic selection in wheat breeding: prediction of the genotype-by-environment interaction in multi-environment breeding trials

International audienceKey message Phenomic prediction of wheat grain yield and heading date in different multi-environmental trial scenarios is accurate. Modelling the genotype-by-environment interaction effect using phenomic data is a potentially low-cost complement to genomic prediction. The performance of wheat cultivars in multi-environmental trials (MET) is difficult to predict because of the genotype-by-environment interactions (G x E). Phenomic selection is supposed to be efficient for modelling the G x E effect because it accounts for non-additive effects. Here, phenomic data are near-infrared (NIR) spectra obtained from plant material. While phenomic selection has recently been shown to accurately predict wheat grain yield in single environments, its accuracy needs to be investigated for MET. We used four datasets from two winter wheat breeding programs to test and compare the predictive abilities of phenomic and genomic models for grain yield and heading date in different MET scenarios. We also compared different methods to model the G x E using different covariance matrices based on spectra. On average, phenomic and genomic prediction abilities are similar in all different MET scenarios. Better predictive abilities were obtained when G x E effects were modelled with NIR spectra than without them, and it was better to use all the spectra of all genotypes in all environments for modelling the G x E. To facilitate the implementation of phenomic prediction, we tested MET designs where the NIR spectra were measured only on the genotype-environment combinations phenotyped for the target trait. Missing spectra were predicted with a weighted multivariate ridge regression. Intermediate predictive abilities for grain yield were obtained in a sparse testing scenario and for new genotypes, which shows that phenomic selection is an efficient and practicable prediction method for dealing with G x E