Grain carbon isotope composition is a marker for allocation and harvest index in wheat.

The natural 13 C abundance (δ13 C) in plant leaves has been used for decades with great success in agronomy to monitor water-use efficiency and select modern cultivars adapted to dry conditions. However, in wheat, it is also important to find genotypes with high carbon allocation to spikes and grains, and thus with a high harvest index (HI) and/or low carbon losses via respiration. Finding isotope-based markers of carbon partitioning to grains would be extremely useful since isotope analyses are inexpensive and can be performed routinely at high throughput. Here, we took the advantage of a set of field trials made of more than 600 plots with several wheat cultivars and measured agronomic parameters as well as δ13 C values in leaves and grains. We find a linear relationship between the apparent isotope discrimination between leaves and grain (denoted as Δδcorr ), and the respiration use efficiency-to-HI ratio. It means that overall, efficient carbon allocation to grains is associated with a small isotopic difference between leaves and grains. This effect is explained by postphotosynthetic isotope fractionations, and we show that this can be modelled by equations describing the carbon isotope composition in grains along the wheat growth cycle. Our results show that 13 C natural abundance in grains could be useful to find genotypes with better carbon allocation properties and assist current wheat breeding technologies.

A saturated SNP linkage map for the orange wheat blossom midge resistance gene Sm1.

KEY MESSAGE: SNP markers were developed for the OWBM resistance gene Sm1 that will be useful for MAS. The wheat Sm1 region is collinear with an inverted syntenic interval in B. distachyon. Orange wheat blossom midge (OWBM, Sitodiplosis mosellana Géhin) is an important insect pest of wheat (Triticum aestivum) in many growing regions. Sm1 is the only described OWBM resistance gene and is the foundation of managing OWBM through host genetics. Sm1 was previously mapped to wheat chromosome arm 2BS relative to simple sequence repeat (SSR) markers and the dominant, sequence characterized amplified region (SCAR) marker WM1. The objectives of this research were to saturate the Sm1 region with markers, develop improved markers for marker-assisted selection (MAS), and examine the synteny between wheat, Brachypodium distachyon, and rice (Oryza sativa) in the Sm1 region. The present study mapped Sm1 in four populations relative to single nucleotide polymorphisms (SNPs), SSRs, Diversity Array Technology (DArT) markers, single strand conformation polymorphisms (SSCPs), and the SCAR WM1. Numerous high quality SNP assays were designed that mapped near Sm1. BLAST delineated the syntenic intervals in B. distachyon and rice using gene-based SNPs as query sequences. The Sm1 region in wheat was inverted relative to B. distachyon and rice, which suggests a chromosomal rearrangement within the Triticeae lineage. Seven SNPs were tested on a collection of wheat lines known to carry Sm1 and not to carry Sm1. Sm1-flanking SNPs were identified that were useful for predicting the presence or absence of Sm1 based upon haplotype. These SNPs will be a major improvement for MAS of Sm1 in wheat breeding programs.

Genome-wide prediction of three important traits in bread wheat.

Five genomic prediction models were applied to three wheat agronomic traits-grain yield, heading date and grain test weight-in three breeding populations, each comprising about 350 doubled haploid or recombinant inbred lines evaluated in three locations during a 3-year period. The prediction accuracy, measured as the correlation between genomic estimated breeding value and observed trait, was in the range of previously published values for yield (r = 0.2-0.5), a trait with relatively low heritability. Accuracies for heading date and test weight, with relatively high heritabilities, were about 0.70. There was no improvement of prediction accuracy when two or three breeding populations were merged into one for a larger training set (e.g., for yield r ranged between 0.11 and 0.40 in the respective populations and between 0.18 and 0.35 in the merged populations). Cross-population prediction, when one population was used as the training population set and another population was used as the validation set, resulted in no prediction accuracy. This lack of cross-population prediction accuracy cannot be explained by a lower level of relatedness between populations, as measured by a shared SNP similarity, since it was only slightly lower between than within populations. Simulation studies confirm that cross-prediction accuracy decreases as the proportion of shared QTLs decreases, which can be expected from a higher level of QTL × environment interactions.