No abstract

The objective of this study was to map quantitative trait loci (QTL) for the vernalization response in perennial ryegrass (Lolium perenne L.). The mapping population consisted of 184 F2 genotypes produced from a cross between one genotype of a synthetic perennial ryegrass variety "Veyo" and one genotype from the perennial ryegrass ecotype "Falster". Veyo and Falster were chosen among four different populations because of their contrasting vernalization requirements. In total, five QTL for the vernalization response, measured as days to heading, were identified and mapped to linkage groups (LG) LG2, LG4, LG6 and LG7. Individually, these QTL explained between 5.4 and 28.0% of the total phenotypic variation. The overall contribution of these five QTL was 80% of the total phenotypic variation. A putative orthologue of Triticum monococcum VRN1 was amplified from genomic DNA from perennial ryegrass. PCR fragments covering the proximal part of the promoter and the 5' end of the orthologue were subsequently PCR-amplified from both parents of the mapping population and shown to possess 95% DNA sequence identity to VRN1. Several polymorphisms were identified between Veyo and Falster in this fragment of the putative VRN1 orthologue. A CAPS marker, vrn-1, was developed and found to co-segregate with a major QTL on LG4 for the vernalization response. This indicates that the CAPS marker vrn-1 could be located in an orthologous gene of the wheat VRN1.

Wheat breeding programs generate a large amount of variation which cannot be completely explored because of limited phenotyping throughput. Genomic prediction (GP) has been proposed as a new tool which provides breeding values estimations without the need of phenotyping all the material produced but only a subset of it named training population (TP). However, genotyping of all the accessions under analysis is needed and, therefore, optimizing TP dimension and genotyping strategy is pivotal to implement GP in commercial breeding schemes. Here, we explored the optimum TP size and we integrated pedigree records and genome wide association studies (GWAS) results to optimize the genotyping strategy. A total of 988 advanced wheat breeding lines were genotyped with the Illumina 15K SNPs wheat chip and phenotyped across several years and locations for yield, lodging, and starch content. Cross-validation using the largest possible TP size and all the SNPs available after editing (~11k), yielded predictive abilities (rGP) ranging between 0.5–0.6. In order to explore the Training population size, rGP were computed using progressively smaller TP. These exercises showed that TP of around 700 lines were enough to yield the highest observed rGP. Moreover, rGP were calculated by randomly reducing the SNPs number. This showed that around 1K markers were enough to reach the highest observed rGP. GWAS was used to identify markers associated with the traits analyzed. A GWAS-based selection of SNPs resulted in increased rGP when compared with random selection and few hundreds SNPs were sufficient to obtain the highest observed rGP. For each of these scenarios, advantages of adding the pedigree information were shown. Our results indicate that moderate TP sizes were enough to yield high rGP and that pedigree information and GWAS results can be used to greatly optimize the genotyping strategy.

The timing of transition from vegetative growth to flowering is important in nature as well as in agriculture. One of several pathways influencing this transition in plants is the gibberellin (GA) pathway. In maize (Zea mays L.), the Dwarf8 (D8) gene has been identified as an orthologue of the gibberellic acid-insensitive (GAI) gene, a negative regulator of GA response in Arabidopsis. Nine intragenic polymorphisms in D8 have been linked with variation in flowering time of maize. We tested the general applicability of these polymorphisms as functional markers in an independent set of inbred lines. Single nucleotide primer extension (SNuPe) and gel-based indel markers were developed, and a set of 71 elite European inbred lines were phenotyped for flowering time and plant height across four environments. To control for population structure, we genotyped the plant material with 55 simple sequence repeat markers evenly distributed across the genome. When population structure was ignored, six of the nine D8 polymorphisms were significantly associated with flowering time and none with plant height. However, when population structure was taken into consideration, an association with flowering time was only detected in a single environment, whereas an association across environments was identified between a 2-bp indel in the promoter region and plant height. As the number of lines with different haplotypes within subpopulations was a limiting factor in the analysis, D8 alleles would need to be compared in isogenic backgrounds for a reliable estimation of allelic effects.

The aim of the this study was to identify SNP markers associated with five important wheat quality traits (grain protein content, Zeleny sedimentation, test weight, thousand-kernel weight, and falling number), and to investigate the predictive abilities of GBLUP and Bayesian Power Lasso models for genomic prediction of these traits. In total, 635 winter wheat lines from two breeding cycles in the Danish plant breeding company Nordic Seed A/S were phenotyped for the quality traits and genotyped for 10,802 SNPs. GWAS were performed using single marker regression and Bayesian Power Lasso models. SNPs with large effects on Zeleny sedimentation were found on chromosome 1B, 1D, and 5D. However, GWAS failed to identify single SNPs with significant effects on the other traits, indicating that these traits were controlled by many QTL with small effects. The predictive abilities of the models for genomic prediction were studied using different cross-validation strategies. Leave-One-Out cross-validations resulted in correlations between observed phenotypes corrected for fixed effects and genomic estimated breeding values of 0.50 for grain protein content, 0.66 for thousand-kernel weight, 0.70 for falling number, 0.71 for test weight, and 0.79 for Zeleny sedimentation. Alternative cross-validations showed that the genetic relationship between lines in training and validation sets had a bigger impact on predictive abilities than the number of lines included in the training set. Using Bayesian Power Lasso instead of GBLUP models, gave similar or slightly higher predictive abilities. Genomic prediction based on all SNPs was more effective than prediction based on few associated SNPs.