Training Population Optimization for Genomic Selection.

Training populations can be optimized for specific testing populations. Optimized training populations are smaller, more related, and more predictive. Stratified sampling with a relationship matrix weighted by marker effect is optimal.

Resource allocation optimization with multi-trait genomic prediction for bread wheat (Triticum aestivum L.) baking quality.

Multi-trait genomic prediction models are useful to allocate available resources in breeding programs by targeted phenotyping of correlated traits when predicting expensive and labor-intensive quality parameters. Multi-trait genomic prediction models can be used to predict labor-intensive or expensive correlated traits where phenotyping depth of correlated traits could be larger than phenotyping depth of targeted traits, reducing resources and improving prediction accuracy. This is particularly important in the context of allocating phenotyping resource in plant breeding programs.

Strategies for Selecting Crosses Using Genomic Prediction in Two Wheat Breeding Programs.

The single most important decision in plant breeding programs is the selection of appropriate crosses. The ideal cross would provide superior predicted progeny performance and enough diversity to maintain genetic gain. The aim of this study was to compare the best crosses predicted using combinations of mid-parent value and variance prediction accounting for linkage disequilibrium (V) or assuming linkage equilibrium (V).

Ascertainment bias from imputation methods evaluation in wheat.

Background: Whole-genome genotyping techniques like Genotyping-by-sequencing (GBS) are being used for genetic studies such as Genome-Wide Association (GWAS) and Genomewide Selection (GS), where different strategies for imputation have been developed. Nevertheless, imputation error may lead to poor performance (i.e.

Increased genomic prediction accuracy in wheat breeding through spatial adjustment of field trial data.

In crop breeding, the interest of predicting the performance of candidate cultivars in the field has increased due to recent advances in molecular breeding technologies. However, the complexity of the wheat genome presents some challenges for applying new technologies in molecular marker identification with next-generation sequencing. We applied genotyping-by-sequencing, a recently developed method to identify single-nucleotide polymorphisms, in the genomes of 384 wheat (Triticum aestivum) genotypes that were field tested under three different water regimes in Mediterranean climatic conditions: rain-fed only, mild water stress, and fully irrigated.

Quantitative trait loci analysis for resistance to Cephalosporium stripe, a vascular wilt disease of wheat.

Cephalosporium stripe, caused by Cephalosporium gramineum, can cause severe loss of wheat (Triticum aestivum L.) yield and grain quality and can be an important factor limiting adoption of conservation tillage practices. Selecting for resistance to Cephalosporium stripe is problematic; however, as optimum conditions for disease do not occur annually under natural conditions, inoculum levels can be spatially heterogeneous, and little is known about the inheritance of resistance.