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 () regulates inflorescence architecture in bread wheat () 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 alters inflorescence architecture and growth rate in a process that includes reduced expression of meristem identity genes, with allelic diversity for found to associate genetically with paired spikelet development in modern cultivars. We propose coordinates formation of axillary spikelets during the vegetative to floral transition and that alleles known to modify dosage or function of could help increase wheat yields.

Diverse forms of nanoscale architecture generate structural colour and perform signalling functions within and between species. Structural colour is the result of the interference of light from approximately regular periodic structures; some structural disorder is, however, inevitable in biological organisms. Is this disorder functional and subject to evolutionary selection, or is it simply an unavoidable outcome of biological developmental processes? Here we show that disordered nanostructures enable flowers to produce visual signals that are salient to bees. These disordered nanostructures (identified in most major lineages of angiosperms) have distinct anatomies but convergent optical properties; they all produce angle-dependent scattered light, predominantly at short wavelengths (ultraviolet and blue). We manufactured artificial flowers with nanoscale structures that possessed tailored levels of disorder in order to investigate how foraging bumblebees respond to this optical effect. We conclude that floral nanostructures have evolved, on multiple independent occasions, an effective degree of relative spatial disorder that generates a photonic signature that is highly salient to insect pollinators.

Disentangling species boundaries and phylogenetic relationships within recent evolutionary radiations is a challenge due to the poor morphological differentiation and low genetic divergence between species, frequently accompanied by phenotypic convergence, interspecific gene flow and incomplete lineage sorting. Here we employed a genotyping-by-sequencing (GBS) approach, in combination with morphometric analyses, to investigate a small western Mediterranean clade in the flowering plant genus Linaria that radiated in the Quaternary. After confirming the morphological and genetic distinctness of eight species, we evaluated the relative performances of concatenation and coalescent methods to resolve phylogenetic relationships. Specifically, we focused on assessing the robustness of both approaches to variations in the parameter used to estimate sequence homology (clustering threshold). Concatenation analyses suffered from strong systematic bias, as revealed by the high statistical support for multiple alternative topologies depending on clustering threshold values. By contrast, topologies produced by two coalescent-based methods (NJ$_{\mathrm{st}}$, SVDquartets) were robust to variations in the clustering threshold. Reticulate evolution may partly explain incongruences between NJ$_{\mathrm{st}}$, SVDquartets and concatenated trees. Integration of morphometric and coalescent-based phylogenetic results revealed (i) extensive morphological divergence associated with recent splits between geographically close or sympatric sister species and (ii) morphological convergence in geographically disjunct species. These patterns are particularly true for floral traits related to pollinator specialization, including nectar spur length, tube width and corolla color, suggesting pollinator-driven diversification. Given its relatively simple and inexpensive implementation, GBS is a promising technique for the phylogenetic and systematic study of recent radiations, but care must be taken to evaluate the robustness of results to variation of data assembly parameters.

Parastagonospora nodorum is a necrotrophic fungal pathogen of wheat (Triticum aestivum L.), one of the world’s most important crops. P. nodorum mediates host cell death using proteinaceous necrotrophic effectors, presumably liberating nutrients that allow the infection process to continue. The identification of pathogen effectors has allowed host genetic resistance mechanisms to be separated into their constituent parts. In P. nodorum, three proteinaceous effectors have been cloned: SnToxA, SnTox1, and SnTox3. Here, we survey sensitivity to all three effectors in a panel of 480 European wheat varieties, and fine-map the wheat SnTox3 sensitivity locus Snn3-B1 using genome-wide association scans (GWAS) and an eight-founder wheat multi-parent advanced generation inter-cross (MAGIC) population. Using a Bonferroni corrected P ≤ 0.05 significance threshold, GWAS identified 10 significant markers defining a single locus, Snn3-B1, located on the short arm of chromosome 5B explaining 32% of the phenotypic variation [peak single nucleotide polymorphisms (SNPs), Excalibur_c47452_183 and GENE-3324_338, -log10P = 20.44]. Single marker analysis of SnTox3 sensitivity in the MAGIC population located Snn3-B1 via five significant SNPs, defining a 6.2-kb region that included the two peak SNPs identified in the association mapping panel. Accordingly, SNP Excalibur_c47452_183 was converted to the KASP genotyping system, and validated by screening a subset of 95 wheat varieties, providing a valuable resource for marker assisted breeding and for further genetic investigation. In addition, composite interval mapping in the MAGIC population identified six minor SnTox3 sensitivity quantitative trait loci, on chromosomes 2A (QTox3.niab-2A.1, P-value = 9.17-7), 2B (QTox3.niab-2B.1, P = 0.018), 3B (QTox3.niab-3B.1, P = 48.51-4), 4D (QTox3.niab-4D.1, P = 0.028), 6A (QTox3.niab-6A.1, P = 8.51-4), and 7B (QTox3.niab-7B.1, P = 0.020), each accounting for between 3.1 and 6.0 % of the phenotypic variance. Collectively, the outcomes of this study provides breeders with knowledge and resources regarding the sensitivity of European wheat germplasm to P. nodorum effectors, as well as simple diagnostic markers for determining allelic state at Snn3-B1.

Phenotypic integration, the coordinated covariance of suites of morphological traits, is critical for proper functioning of organisms. Angiosperm flowers are complex structures comprising suites of traits that function together to achieve effective pollen transfer. Floral integration could reflect shared genetic and developmental control of these traits, or could arise through pollinator-imposed stabilizing correlational selection on traits. We sought to expose mechanisms underlying floral trait integration in the sexually deceptive daisy, Gorteria diffusa , by testing the hypothesis that stabilizing selection imposed by male pollinators on floral traits involved in mimicry has resulted in tighter integration. To do this, we quantified patterns of floral trait variance and covariance in morphologically divergent G. diffusa floral forms representing a continuum in the levels of sexual deception. We show that integration of traits functioning in visual attraction of male pollinators increases with pollinator deception, and is stronger than integration of non-mimicry trait modules. Consistent patterns of within-population trait variance and covariance across floral forms suggest that integration has not been built by stabilizing correlational selection on genetically independent traits. Instead pollinator specialization has selected for tightened integration within modules of linked traits. Despite potentially strong constraint on morphological evolution imposed by developmental genetic linkages between traits, we demonstrate substantial divergence in traits across G. diffusa floral forms and show that divergence has often occurred without altering within-population patterns of trait correlations.

Editor: Patrick S. HerendeenEvolutionary developmental biology has come to prominence in the past two decades, in both the plant kingdom and the animal kingdom, particularly following the description of homeotic genes linked to key morphological transitions. A primary goal of evolutionary developmental biology ("evo-devo") is to define how developmental programs are modified to generate novel or labile morphologies. This requires an understanding of the molecular genetic basis of these programs and of the evolutionary changes they have undergone. The past decade has seen the establishment of a common language and common standards, and these changes have greatly improved the integration of evo-devo. Recently, a more comparative approach has been added to mechanistic developmental biology. In this review we attempt to show how, by using this "next-generation evo-devo" approach, insights into both developmental biology and evolutionary biology can be gained. Although the concepts we discuss are more broadly applicable, we have focused our examples on traits of the angiosperm flower, a structure that has undergone enormous morphological and developmental evolution since its relatively recent appearance in the fossil record.

Bread wheat (Triticum aestivum L.) is one of the world’s most important crops. Maintaining wheat yield gains across all of its major production areas is a key target toward underpinning global food security. Brazil is a major wheat producer in South America, generating grain yields of around 6.8 million tons per year. Here, we establish and genotype a wheat association mapping resource relevant to contemporary Brazilian wheat breeding programs. The panel of 558 wheat accessions was genotyped using an Illumina iSelect 90,000 single nucleotide polymorphism array. Following quality control, the final data matrix consisted of 470 accessions and 22,475 polymorphic genetic markers (minor allele frequency ≥5%, missing data <5%). Principal component analysis identified distinct differences between materials bred predominantly for the northern Cerrado region, compared to those bred for southern Brazilian agricultural areas. We augmented the genotypic data with 26 functional Kompetitive Allele-Specific PCR (KASP) markers to identify the allelic combinations at genes with previously known effects on agronomically important traits in the panel. This highlighted breeding targets for immediate consideration – notably, increased Fusarium head blight resistance via the Fhb1 locus. To demonstrate the panel’s likely future utility, genome-wide association scans for several phenotypic traits were undertaken. Significant (Bonferroni corrected P < 0.05) marker-trait associations were detected for Fusarium kernel damage (a proxy for type 2 Fusarium resistance), identifying previously known quantitative trait loci in the panel. This association mapping panel represents an important resource for Brazilian wheat breeding, allowing future genetic studies to analyze multiple agronomic traits within a single genetically diverse population.

A barrier to the adoption of genomic prediction in small breeding programs is the initial cost of genotyping material. Although decreasing, marker costs are usually higher than field trial costs. In this study we demonstrate the utility of stratifying a narrowbase biparental oat population genotyped with a modest number of markers to employ genomic prediction at early and later generations. We also show that early generation genotyping data can reduce the number of lines for later phenotyping based on selections of siblings to progress. Using sets of small families selected at an early generation could enable the use of genomic prediction for adaptation to multiple target environments at an early stage in the breeding program. In addition, we demonstrate that mixed marker data can be effectively integrated to combine cheap dominant marker data (including legacy data) with more expensive but higher density codominant marker data in order to make within generation and between lineage predictions based on genotypic information. Taken together, our results indicate that small programs can test and initiate genomic predictions using sets of stratified, narrow-base populations and incorporating low density legacy genotyping data. This can then be scaled to include higher density markers and a broadened population base.