Spike architecture influences grain yield in wheat. We report the map-based cloning of a gene determining the number of spikelet nodes per spike in common wheat. The cloned gene is named TaCOL-B5 and encodes a CONSTANS-like protein that is orthologous to COL5 in plant species. Constitutive overexpression of the dominant TaCol-B5 allele but without the region encoding B-boxes in a common wheat cultivar increases the number of spikelet nodes per spike and produces more tillers and spikes, thereby enhancing grain yield in transgenic plants under field conditions. Allelic variation in TaCOL-B5 results in amino acid substitutions leading to differential protein phosphorylation by the protein kinase Ta K4. The TaCol-B5 allele is present in emmer wheat but is rare in a global collection of modern wheat cultivars.

SummaryWheat (Triticum aestivum) has low nitrogen use efficiency (NUE). The genetic mechanisms controlling NUE are unknown. Positional cloning of a major quantitative trait locus for N‐related agronomic traits showed that the vernalization gene TaVRN‐A1 was tightly linked with TaNUE1, the gene shown to influence NUE in wheat. Because of an Ala180/Val180 substitution, Ta VRN‐A1a and Ta VRN‐A1b proteins interact differentially with Ta ANR1, a protein encoded by a wheat orthologue of Arabidopsis nitrate regulated 1 (ANR1). The transcripts of both TaVRN‐A1 and TaANR1 were down‐regulated by nitrogen. TaANR1 was functionally characterized in TaANR1::RNAi transgenic wheat, and in a natural mutant with a 23‐bp deletion including 10‐bp at the 5′ end of intron 5 and 13‐bp of exon 6 in gDNA sequence in its gDNA sequence, which produced transcript that lacked the full 84‐bp exon 6. Both Ta ANR1 and Ta HOX1 bound to the Ala180/Val180 position of Ta VRN‐A1. Genetically incorporating favourable alleles from TaVRN‐A1, TaANR1 and TaHOX1 increased grain yield from 9.84% to 11.58% in the field. Molecular markers for allelic variation of the genes that regulate nitrogen can be used in breeding programmes aimed at improving NUE and yield in novel wheat cultivars.

Crop improvement is a long-term, expensive institutional endeavor. Genomic selection (GS), which uses single nucleotide polymorphism (SNP) information to estimate genomic breeding values, has proven efficient to increasing genetic gain by accelerating the breeding process in animal breeding programs. As for crop improvement, with few exceptions, GS applicability remains in the evaluation of algorithm performance. In this study, we examined factors related to GS applicability in line development stage for grain yield using a hard red winter wheat (Triticum aestivum L.) doubled-haploid population. The performance of GS was evaluated in two consecutive years to predict grain yield. In general, the semi-parametric reproducing kernel Hilbert space prediction algorithm outperformed parametric genomic best linear unbiased prediction. For both parametric and semi-parametric algorithms, an upward bias in predictability was apparent in within-year cross-validation, suggesting the prerequisite of cross-year validation for a more reliable prediction. Adjusting the training population’s phenotype for genotype by environment effect had a positive impact on GS model’s predictive ability. Possibly due to marker redundancy, a selected subset of SNPs at an absolute pairwise correlation coefficient threshold value of 0.4 produced comparable results and reduced the computational burden of considering the full SNP set. Finally, in the context of an ongoing breeding and selection effort, the present study has provided a measure of confidence based on the deviation of line selection from GS results, supporting the implementation of GS in wheat variety development.Electronic supplementary materialThe online version of this article (doi:10.1007/s11032-017-0715-8) contains supplementary material, which is available to authorized users.

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The Plant Genome W heat breeding has progressed dramatically in the last century thanks to the combination of various technologies (Poland et al., 2012); taken together these advancements have driven the yearly genetic gain through selective breeding to nearly a linear increase of 1% in the potential grain yield (Bassi et al., 2016). Faced against human population growth and uncertain climates, global wheat production, however, still falls short (Curtis and Halford, 2014), as the global demand for wheat is projected to increase 60% when the population reaches 9.8 billion by 2050 (Alexandratos and Bruinsma, 2012). The emphasis now is increasingly not only meeting the food