Le Couviour F.,Biogemma |
Faure S.,Biogemma |
Poupard B.,Limagrain Europe |
Flodrops Y.,ARVALIS Institute du vegetal |
And 2 more authors.
Theoretical and Applied Genetics | Year: 2011
During the last decades, with the intensification of selection and breeding using crosses between varieties, a very complex genetic structure was shaped in the elite wheat germplasm. However, precise description of this structure with panels and collections is becoming more and more crucial with the development of resource management and new statistical tools for mapping genetic determinants (e.g. association studies). In this study, we investigated the genetic structure of 195 Western European elite wheat varieties using the recent development of high throughput screening methods for molecular markers. After observing that both microsatellites and Diversity Array Technology markers are efficient to estimate the structure of the panel, we used different complementary approaches (Genetic distances, principal component analysis) that showed that the varieties are separated by geographical origin (France, Germany and UK) and also by breeding history, confirming the impact of plant breeding on the wheat germplasm structure. Moreover, by analysing three phenotypic traits presenting significant average differences across groups (plant height, heading date and awnedness), and by using markers linked to major genes for these traits (Ppd-D1, Rht-B1, Rht-D1 and B1), we showed that for each trait, there is a specific optimal Q matrix to use as a covariate in association tests. © Springer-Verlag 2011. Source
Van Inghelandt D.,University of Hohenheim |
Van Inghelandt D.,Limagrain GmbH |
Melchinger A.E.,University of Hohenheim |
Martinant J.-P.,Limagrain Europe |
And 2 more authors.
BMC Plant Biology | Year: 2012
Background: Setosphaeria turcica is a fungal pathogen that causes northern corn leaf blight (NCLB) which is a serious foliar disease in maize. In order to unravel the genetic architecture of the resistance against this disease, a vast association mapping panel comprising 1487 European maize inbred lines was used to (i) identify chromosomal regions affecting flowering time (FT) and northern corn leaf blight (NCLB) resistance, (ii) examine the epistatic interactions of the identified chromosomal regions with the genetic background on an individual molecular marker basis, and (iii) dissect the correlation between NCLB resistance and FT.Results: The single marker analyses performed for 8 244 single nucleotide polymorphism (SNP) markers revealed seven, four, and four SNP markers significantly (α=0.05, amplicon wise Bonferroni correction) associated with FT, NCLB, and NCLB resistance corrected for FT, respectively. These markers explained individually between 0.36 and 14.29% of the genetic variance of the corresponding trait.Conclusions: The very well interpretable pattern of SNP associations observed for FT suggested that data from applied plant breeding programs can be used to dissect polygenic traits. This in turn indicates that the associations identified for NCLB resistance might be successfully used in marker-assisted selection programs. Furthermore, the associated genes are also of interest for further research concerning the mechanism of resistance to NCLB and plant diseases in general, because some of the associated genes have not been mentioned in this context so far. © 2012 Van Inghelandt et al.; licensee BioMed Central Ltd. Source
Schulz-Streeck T.,University of Hohenheim |
Schulz-Streeck T.,KWS SAAT AG |
Ogutu J.O.,University of Hohenheim |
Karaman Z.,Limagrain Europe |
And 2 more authors.
Crop Science | Year: 2012
Using different populations in genomic selection raises the possibility of marker effects varying across populations. However, common models for genomic selection only account for the main marker effects, assuming that they are consistent across populations. We present an approach in which the main plus population-specific marker effects are simultaneously estimated in a single mixed model. Cross-validation is used to compare the predictive ability of this model to that of the ridge regression best linear unbiased prediction (RR-BLUP) method involving only either the main marker effects or the population-specific marker effects. We used a maize (Zea mays L.) data set with 312 genotypes derived from five biparental populations, which were genotyped with 39,339 markers. A combined analysis incorporating genotypes for all the populations and hence using a larger training set was better than separate analyses for each population. Modeling the main plus the population-specific marker effects simultaneously improved predictive ability only slightly compared with modeling only the main marker effects. The performance of the RR-BLUP method was comparable to that of two regularization methods, namely the ridge regression and the elastic net, and was more accurate than that of the least absolute shrinkage and selection operator (LASSO). Overall, combining information from related populations and increasing the number of genotypes improved predictive ability, but further allowing for population-specific marker effects made minor improvement. © Crop Science Society of America. Source
Limagrain Europe | Date: 2010-11-05
The present invention relates to isolated nucleotide sequences useful for the production of plants with a modified embryo sac, embryo and or endosperm development, and to transgenic cells and plants transformed with the nucleotide sequences.
Lehermeier C.,TU Munich |
Kramer N.,TU Munich |
Bauer E.,TU Munich |
Bauland C.,French National Institute for Agricultural Research |
And 16 more authors.
Genetics | Year: 2014
The efficiency of marker-assisted prediction of phenotypes has been studied intensively for different types of plant breeding populations. However, one remaining question is how to incorporate and counterbalance information from biparental and multiparental populations into model training for genome-wide prediction. To address this question, we evaluated testcross performance of 1652 doubled-haploid maize (Zea mays L.) lines that were genotyped with 56,110 single nucleotide polymorphism markers and phenotyped for five agronomic traits in four to six European environments. The lines are arranged in two diverse half-sib panels representing two major European heterotic germplasm pools. The data set contains 10 related biparental dent families and 11 related biparental flint families generated from crosses of maize lines important for European maize breeding. With this new data set we analyzed genome-based best linear unbiased prediction in different validation schemes and compositions of estimation and test sets. Further, we theoretically and empirically investigated marker linkage phases across multiparental populations. In general, predictive abilities similar to or higher than those within biparental families could be achieved by combining several half-sib families in the estimation set. For the majority of families, 375 half-sib lines in the estimation set were sufficient to reach the same predictive performance of biomass yield as an estimation set of 50 full-sib lines. In contrast, prediction across heterotic pools was not possible for most cases. Our findings are important for experimental design in genome-based prediction as they provide guidelines for the genetic structure and required sample size of data sets used for model training. © 2014 by the Genetics Society of America. Source