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The flowers of major cereals are arranged on reproductive branches known as spikelets, which group together to form an inflorescence. Diversity for inflorescence architecture has been exploited during domestication to increase crop yields, and genetic variation for this trait has potential to further boost grain production. Multiple genes that regulate inflorescence architecture have been identified by studying alleles that modify gene activity or dosage; however, little is known in wheat. Here, we show TEOSINTE BRANCHED1 (TB1) regulates inflorescence architecture in bread wheat (Triticum aestivum) by investigating lines that display a form of inflorescence branching known as "paired spikelets." We show that TB1 interacts with FLOWERING LOCUS T1 and that increased dosage of TB1 alters inflorescence architecture and growth rate in a process that includes reduced expression of meristem identity genes, with allelic diversity for TB1 found to associate genetically with paired spikelet development in modern cultivars. We propose TB1 coordinates formation of axillary spikelets during the vegetative to floral transition and that alleles known to modify dosage or function of TB1 could help increase wheat yields.

Highlight Rhizosheath size of wheat was used as a surrogate for root hair length to map QTL in lines derived from multiparent advanced generation intercrosses., Rhizosheaths comprise soil that adheres to plant roots and, in some species, are indicative of root hair length. In this study, the genetics of rhizosheath size in wheat was investigated by screening the progeny of multiparent advanced generation intercrosses (MAGIC). Two MAGIC populations were screened for rhizosheath size using a high throughput method. One MAGIC population was developed from intercrosses between four parents (4-way) and the other from intercrosses between eight parents (8-way). Transgressive segregation for rhizosheath size was observed in both the 4-way and 8-way MAGIC populations. A quantitative trait loci (QTL) analysis of the 4-way population identified six major loci located on chromosomes 2B, 4D, 5A, 5B, 6A, and 7A together accounting for 42% of the variation in rhizosheath size. Rhizosheath size was strongly correlated with root hair length and was robust across different soil types in the absence of chemical constraints. Rhizosheath size in the MAGIC populations was a reliable surrogate for root hair length and, therefore, the QTL identified probably control root hair elongation. Members of the basic helix-loop-helix family of transcription factors have previously been identified to regulate root hair length in Arabidopsis and rice. Since several wheat members of the basic helix-loop-helix family of genes are located within or near the QTL, these genes are candidates for controlling the long root hair trait. The QTL for rhizosheath size identified in this study provides the opportunity to implement marker-assisted selection to increase root hair length for improved phosphate acquisition in wheat.

Multiparent Advanced Generation Inter-Cross (MAGIC) populations are now being utilized to more accurately identify the underlying genetic basis of quantitative traits through quantitative trait loci (QTL) analyses and subsequent gene discovery. The expanded genetic diversity present in such populations and the amplified number of recombination events mean that QTL can be identified at a higher resolution. Most QTL analyses are conducted separately for each trait within a single environment. Separate analysis does not take advantage of the underlying correlation structure found in multienvironment or multitrait data. By using this information in a joint analysis—be it multienvironment or multitrait — it is possible to gain a greater understanding of genotype- or QTL-by-environment interactions or of pleiotropic effects across traits. Furthermore, this can result in improvements in accuracy for a range of traits or in a specific target environment and can influence selection decisions. Data derived from MAGIC populations allow for founder probabilities of all founder alleles to be calculated for each individual within the population. This presents an additional layer of complexity and information that can be utilized to identify QTL. A whole-genome approach is proposed for multienvironment and multitrait QTL analysis in MAGIC. The whole-genome approach simultaneously incorporates all founder probabilities at each marker for all individuals in the analysis, rather than using a genome scan. A dimension reduction technique is implemented, which allows for high-dimensional genetic data. For each QTL identified, sizes of effects for each founder allele, the percentage of genetic variance explained, and a score to reflect the strength of the QTL are found. The approach was demonstrated to perform well in a small simulation study and for two experiments, using a wheat MAGIC population.