WP 1: Field trials of three bi-parental mapping populations developed from well-adapted wheat with contrasting PHS phenotypesThe field trials will form the basis for phenotyping and QTL identification in WP 2 and WP 3, respectively. The trials will include the bi-parental mapping populations with two replications. As incidence of PHS is not compelling under natural field weathering, field trials with artificial raining at two environments are planned in each year.
WP 2: Phenotypic assessment of PHS toleranceThe success of the QTL identification studies in WP 3 heavily depends on the accurate evaluation of the phenotype of interest. In this work package it is intended to evaluate three bi-parental mapping populations for PHS, a trait showing different heritability in multiple environments across years.
WP 3: QTL analysis of pre-harvest sprouting tolerance and marker saturation of QTL regionsWP 3 aims to provide molecular markers for efficient breeding through identifying QTL for PHS tolerance and assessing their effects in three bi-parental mapping populations of common wheat. All populations will be mapped to a great extent with a common set of markers that targets particularly gene-containing regions. For genetic map-based QTL analysis, genotyping data for the segregating populations from DArT, SSR and candidate gene analyses will be collected and combined with PHS tolerance phenotyping data from WP 2 allowing to reveal all possible marker loci where allelic variation correlates with the phenotype. In addition, saturation mapping of QTL regions using additional SSR and single nucleotide polymorphism (SNP) markers will enhance efficiency of marker selection appropriate for marker-assisted breeding strategies. For populations with an already existing molecular map the generation of molecular data will be restricted to genomic regions of interest.
WP 4: QTL meta-analysisWP 4 aims to establish an integrated map for PHS tolerance QTL using results from this study and already published mapping studies. Statistic tools such as meta-analysis can be used to determine whether QTL linked to the same traits detected in independent experiments and located in the same region represent a single locus or not. Genetic maps must share a sufficient number of common loci allowing the projection of the remaining loci, including QTL, into an integrated map. This will result in more precise QTL localisation by reducing confidence interval and more efficient target selection for MAS. Comparability of our QTL maps will be achieved with common SSR and DArT markers. Regarding our populations, confidence interval lengths of QTL positions will be compared. Further, PHS tolerance QTL from published wheat mapping populations, whose most likely position overlap with QTL confidence intervals in our study, will be included. Map compilation and QTL meta-analysis computation will be carried out using BioMercator software.