Gibberellin and miRNA156-targeted SlSBP genes synergistically regulate tomato floral meristem determinacy and ovary patterning.

Many developmental processes associated with fruit development occur at the floral meristem (FM). Age-regulated microRNA156 (miR156) and gibberellins (GAs) interact to control flowering time, but their interplay in subsequent stages of reproductive development is poorly understood. Here, in tomato (Solanum lycopersicum), we show that GA and miR156-targeted SQUAMOSA PROMOTER-BINDING PROTEIN-LIKE (SPL or SBP) genes interact in the tomato FM and ovary patterning. High GA responses or overexpression of miR156 (156OE), which leads to low expression levels of miR156-silenced SBP genes, resulted in enlarged FMs, ovary indeterminacy and fruits with increased locule number. Conversely, low GA responses reduced indeterminacy and locule number, and overexpression of a S. lycopersicum (Sl)SBP15 allele that is miR156 resistant (rSBP15) reduced FM size and locule number. GA responses were partially required for the defects observed in 156OE and rSBP15 fruits. Transcriptome analysis and genetic interactions revealed shared and divergent functions of miR156-targeted SlSBP genes, PROCERA/DELLA and the classical WUSCHEL/CLAVATA pathway, which has been previously associated with meristem size and determinacy. Our findings reveal that the miR156/SlSBP/GA regulatory module is deployed differently depending on developmental stage and create novel opportunities to fine-tune aspects of fruit development that have been important for tomato domestication.

Genomic prediction with allele dosage information in highly polyploid species.

KEY MESSAGE: Including allele, dosage can improve genomic selection in highly polyploid species under higher frequency of different heterozygous genotypic classes and high dominance degree levels. Several studies have shown how to leverage allele dosage information to improve the accuracy of genomic selection models in autotetraploid. In this study, we expanded the methodology used for genomic selection in autotetraploid to higher (and mixed) ploidy levels. We adapted the models to build covariance matrices of both additive and digenic dominance effects that are subsequently used in genomic selection models. We applied these models using estimates of ploidy and allele dosage to sugarcane and sweet potato datasets and validated our results by also applying the models in simulated data. For the simulated datasets, including allele dosage information led up to 140% higher mean predictive abilities in comparison to using diploidized markers. Including dominance effects were highly advantageous when using diploidized markers, leading to mean predictive abilities which were up to 115% higher in comparison to only including additive effects. When the frequency of heterozygous genotypes in the population was low, such as in the sugarcane and sweet potato datasets, there was little advantage in including allele dosage information in the models. Overall, we show that including allele dosage can improve genomic selection in highly polyploid species under higher frequency of different heterozygous genotypic classes and high dominance degree levels.

Transcriptome changes in the developing sugarcane culm associated with high yield and early-season high sugar content.

Sugarcane, with its exceptional carbon dioxide assimilation, biomass and sugar yield, has a high potential for the production of bio-energy, bio-plastics and high-value products in the food and pharmaceutical industries. A crucial challenge for long-term economic viability and environmental sustainability is also to optimize the production of biomass composition and carbon sequestration. Sugarcane varieties such as KQ228 and Q253 are highly utilized in the industry. These varieties are characterized by a high early-season sugar content associated with high yield. In order to investigate these correlations, 1,440 internodes were collected and combined to generate a set of 120 samples in triplicate across 24 sugarcane cultivars at five different development stages. Weighted gene co-expression network analysis (WGCNA) was used and revealed for the first time two sets of co-expressed genes with a distinct and opposite correlation between fibre and sugar content. Gene identification and metabolism pathways analysis was used to define these two sets of genes. Correlation analysis identified a large number of interconnected metabolic pathways linked to sugar content and fibre content. Unsupervised hierarchical clustering of gene expression revealed a stronger level of segregation associated with the genotypes than the stage of development, suggesting a dominant genetic influence on biomass composition and facilitating breeding selection. Characterization of these two groups of co-expressed key genes can help to improve breeding program for high fibre, high sugar species or plant synthetic biology.

A fully automated pipeline for quantitative genotype calling from next generation sequencing data in autopolyploids.

BACKGROUND: Genotyping-by-sequencing (GBS) has been used broadly in genetic studies for several species, especially those with agricultural importance. However, its use is still limited in autopolyploid species because genotype calling software generally fails to properly distinguish heterozygous classes based on allele dosage. RESULTS: VCF2SM is a Python script that integrates sequencing depth information of polymorphisms in variant call format (VCF) files and SUPERMASSA software for quantitative genotype calling. VCFs can be obtained from any variant discovery software that outputs exact allele sequencing depth, such as a modified version of the TASSEL-GBS pipeline provided here. VCF2SM was successfully applied in analyzing GBS data from diverse panels (alfalfa and potato) and full-sib mapping populations (alfalfa and switchgrass) of polyploid species. CONCLUSIONS: We demonstrate that our approach can help plant geneticists working with autopolyploid species to advance their studies by distinguishing allele dosage from GBS data.

ConPADE: genome assembly ploidy estimation from next-generation sequencing data.

As a result of improvements in genome assembly algorithms and the ever decreasing costs of high-throughput sequencing technologies, new high quality draft genome sequences are published at a striking pace. With well-established methodologies, larger and more complex genomes are being tackled, including polyploid plant genomes. Given the similarity between multiple copies of a basic genome in polyploid individuals, assembly of such data usually results in collapsed contigs that represent a variable number of homoeologous genomic regions. Unfortunately, such collapse is often not ideal, as keeping contigs separate can lead both to improved assembly and also insights about how haplotypes influence phenotype. Here, we describe a first step in avoiding inappropriate collapse during assembly. In particular, we describe ConPADE (Contig Ploidy and Allele Dosage Estimation), a probabilistic method that estimates the ploidy of any given contig/scaffold based on its allele proportions. In the process, we report findings regarding errors in sequencing. The method can be used for whole genome shotgun (WGS) sequencing data. We also show applicability of the method for variant calling and allele dosage estimation. Results for simulated and real datasets are discussed and provide evidence that ConPADE performs well as long as enough sequencing coverage is available, or the true contig ploidy is low. We show that ConPADE may also be used for related applications, such as the identification of duplicated genes in fragmented assemblies, although refinements are needed.

Long-term comparison between index selection and optimal independent culling in plant breeding programs with genomic prediction.

In the context of genomic selection, we evaluated and compared breeding programs using either index selection or independent culling for recurrent selection of parents. We simulated a clonally propagated crop breeding program for 20 cycles using either independent culling or an economic index with two unfavourably correlated traits under selection. Cycle time from crossing to selection of parents was kept the same for both strategies. Both methods led to increasingly unfavourable genetic correlations between traits and, compared to independent culling, index selection led to larger changes in the genetic correlation between the two traits. When linkage disequilibrium was not considered, the two methods had similar losses of genetic diversity. Two independent culling approaches were evaluated, one using optimal culling levels and one using the same selection intensity for both traits. Optimal culling levels outperformed the same selection intensity even when traits had the same economic importance. Therefore, accurately estimating optimal culling levels is essential for maximizing gains when independent culling is performed. Once optimal culling levels are achieved, independent culling and index selection lead to comparable genetic gains.

Genomic resources for energy cane breeding in the post genomics era.

Sugarcane is one of the most sustainable energy crops among cultivated crops presenting the highest tonnage of cultivated plants. Its high productivity of sugar, bioethanol and bioelectricity make it a promising green alternative to petroleum. Furthermore, the myriad of products that can be derived from sugarcane biomass has been driving breeding programs towards varieties with a higher yield of fiber and a more vigorous and sustainable performance: the energy cane. Here we provide an overview of the energy cane including plant description, breeding efforts, types, and end-uses. In addition, we describe recently published genomic resources for the development of this crop, discuss current knowledge of cell wall metabolism, bioinformatic tools and databases available for the community.

Evidence for Strong Kinship Influence on the Extent of Linkage Disequilibrium in Cultivated Common Beans.

Phaseolus vulgaris is an important grain legume for human consumption. Recently, association mapping studies have been performed for the species aiming to identify loci underlying quantitative variation of traits. It is now imperative to know whether the linkage disequilibrium (LD) reflects the true association between a marker and causative loci. The aim of this study was to estimate and analyze LD on a diversity panel of common beans using ordinary r² and r2 extensions which correct bias due to population structure (rS²), kinship (rV²), and both (rVS²). A total of 10,362 single nucleotide polymorphisms (SNPs) were identified by genotyping by sequencing (GBS), and polymorphisms were found to be widely distributed along the 11 chromosomes. In terms of r2, high values of LD (over 0.8) were identified between SNPs located at opposite chromosomal ends. Estimates for rV² were lower than those for rS². Results for rV² and rVS² were similar, suggesting that kinship may also include information on population structure. Over genetic distance, LD decayed to 0.1 at a distance of 1 Mb for rVS². Inter-chromosomal LD was also evidenced. This study showed that LD estimates decay dramatically according to the population structure, and especially the degree of kinship. Importantly, the LD estimates reported herein may influence our ability to perform association mapping studies on P. vulgaris.

Linkage Disequilibrium and Population Structure in Wild and Cultivated Populations of Rubber Tree (Hevea brasiliensis).

Among rubber tree species, which belong to the Hevea genus of the Euphorbiaceae family, Hevea brasiliensis (Willd. ex Adr.de Juss.) Muell. Arg. is the main commercial source of natural rubber production worldwide. Knowledge of the population structure and linkage disequilibrium (LD) of this species is essential for the efficient organization and exploitation of genetic resources. Here, we obtained single-nucleotide polymorphisms (SNPs) using a genotyping-by-sequencing (GBS) approach and then employed the SNPs for the following objectives: (i) to identify the positions of SNPs on a genetic map of a segregating mapping population, (ii) to evaluate the population structure of a germplasm collection, and (iii) to detect patterns of LD decay among chromosomes for future genetic association studies in rubber tree. A total of 626 genotypes, including both germplasm accessions (368) and individuals from a genetic mapping population (254), were genotyped. A total of 77,660 and 21,283 SNPs were detected by GBS in the germplasm and mapping populations, respectively. The mapping population, which was previously mapped, was constructed with 1,062 markers, among which only 576 SNPs came from GBS, reducing the average interval between two adjacent markers to 4.4 cM. SNPs from GBS genotyping were used for the analysis of genetic structure and LD estimation in the germplasm accessions. Two groups, which largely corresponded to the cultivated and wild populations, were detected using STRUCTURE and via principal coordinate analysis. LD analysis, also using the mapped SNPs, revealed that non-random associations varied along chromosomes, with regions of high LD interspersed with regions of low LD. Considering the length of the genetic map (4,693 cM) and the mean LD (0.49 for cultivated and 0.02 for wild populations), a large number of evenly spaced SNPs would be needed to perform genome-wide association studies in rubber tree, and the wilder the genotypes used, the more difficult the mapping saturation.