BackgroundApparent Amylose Content (AAC), regulated by the Waxy gene, represents the key determinant of rice cooking properties. In occidental countries high AAC rice represents the most requested market class but the availability of molecular markers allowing specific selection of high AAC varieties is limited.ResultsIn this study, the effectiveness of available molecular markers in predicting AAC was evaluated in a collection of 127 rice accessions (125 japonica ssp. and 2 indica ssp.) characterized by AAC values from glutinous to 26%. The analyses highlighted the presence of several different allelic patterns identifiable by a few molecular markers, and two of them, i.e., the SNPs at intron1 and exon 6, were able to explain a maximum of 79.5% of AAC variation. However, the available molecular markers haplotypes did not provide tools for predicting accessions with AAC higher than 24.5%. To identify additional polymorphisms, the re-sequencing of the Waxy gene and 1kbp of the putative upstream regulatory region was performed in 21 genotypes representing all the AAC classes identified. Several previously un-characterized SNPs were identified and four of them were used to develop dCAPS markers.ConclusionsThe addition of the SNPs newly identified slightly increased the AAC explained variation and allowed the identification of a haplotype almost unequivocally associated to AAC higher than 24.5%. Haplotypes at the waxy locus were also associated to grain length and length/width (L/W) ratio. In particular, the SNP at the first intron, which identifies the Wx a and Wx b alleles, was associated with differences in the width of the grain, the L/W ratio and the length of the kernel, most likely as a result of human selection.

BackgroundIn this study we carried out a genome-wide association analysis for plant and grain morphology and root architecture in a unique panel of temperate rice accessions adapted to European pedo-climatic conditions. This is the first study to assess the association of selected phenotypic traits to specific genomic regions in the narrow genetic pool of temperate japonica. A set of 391 rice accessions were GBS-genotyped yielding—after data editing—57000 polymorphic and informative SNPS, among which 54% were in genic regions.ResultsIn total, 42 significant genotype-phenotype associations were detected: 21 for plant morphology traits, 11 for grain quality traits, 10 for root architecture traits. The FDR of detected associations ranged from 3 · 10−7 to 0.92 (median: 0.25). In most cases, the significant detected associations co-localised with QTLs and candidate genes controlling the phenotypic variation of single or multiple traits. The most significant associations were those for flag leaf width on chromosome 4 (FDR = 3 · 10−7) and for plant height on chromosome 6 (FDR = 0.011).ConclusionsWe demonstrate the effectiveness and resolution of the developed platform for high-throughput phenotyping, genotyping and GWAS in detecting major QTLs for relevant traits in rice. We identified strong associations that may be used for selection in temperate irrigated rice breeding: e.g. associations for flag leaf width, plant height, root volume and length, grain length, grain width and their ratio. Our findings pave the way to successfully exploit the narrow genetic pool of European temperate rice and to pinpoint the most relevant genetic components contributing to the adaptability and high yield of this germplasm. The generated data could be of direct use in genomic-assisted breeding strategies.

A rice GWAS panel of 281 accessions of japonica rice was phenotypically characterized for 26 traits related to phenology, plant and seed morphology, physiology and yield for 2 years in field conditions under permanent flooding (PF) and limited water (LW). A genome-wide analysis uncovered a total of 160 significant marker-trait associations (MTAs), of which 32 were LW-specific, 59 were PF-specific, and 69 were in common between the two water management systems. LW-specific associations were identified for several agronomic traits including days to maturation, days from flowering to maturation, leaf traits, plant height, panicle and seed traits, hundred grain weight, yield and tillering. Significant MTAs were detected across all the 12 rice chromosomes, while clusters of effects influencing different traits under LW or in both watering conditions were, respectively, observed on chromosomes 4, 8, and 12 and on chromosomes 1, 3, 4, 5, and 8. The analysis of genes annotated in the Nipponbare reference sequence and included in the regions associated to traits related to plant morphology, grain yield, and physiological parameters allowed the identification of genes that were demonstrated to affect the respective traits. Among these, three (OsOFP2, Dlf1, OsMADS56) and seven (SUI1, Sd1, OsCOL4, Nal1, OsphyB, GW5, Ehd1) candidate genes were, respectively, identified to co-localize with LW-specific associations and associations in common between the two water treatments. For several LW-specific MTAs, or in common among the two treatments, positional co-localizations with previously identified QTLs for rice adaptation to water shortages were observed, a result that further supports the role of the loci identified in this work in conferring adaptation to LW. The most robust associations identified here could represent suitable targets for genomic selection approaches to improve yield-related traits under LW.

Key messageRice breeding programs based on pedigree schemes can use a genomic model trained with data from their working collection to predict performances of progenies produced through rapid generation advancement.AbstractSo far, most potential applications of genomic prediction in plant improvement have been explored using cross validation approaches. This is the first empirical study to evaluate the accuracy of genomic prediction of the performances of progenies in a typical rice breeding program. Using a cross validation approach, we first analyzed the effects of marker selection and statistical methods on the accuracy of prediction of three different heritability traits in a reference population (RP) of 284 inbred accessions. Next, we investigated the size and the degree of relatedness with the progeny population (PP) of sub-sets of the RP that maximize the accuracy of prediction of phenotype across generations, i.e., for 97 F5–F7 lines derived from biparental crosses between 31 accessions of the RP. The extent of linkage disequilibrium was high (r 2 = 0.2 at 0.80 Mb in RP and at 1.1 Mb in PP). Consequently, average marker density above one per 22 kb did not improve the accuracy of predictions in the RP. The accuracy of progeny prediction varied greatly depending on the composition of the training set, the trait, LD and minor allele frequency. The highest accuracy achieved for each trait exceeded 0.50 and was only slightly below the accuracy achieved by cross validation in the RP. Our results thus show that relatively high accuracy (0.41–0.54) can be achieved using only a rather small share of the RP, most related to the PP, as the training set. The practical implications of these results for rice breeding programs are discussed.Electronic supplementary materialThe online version of this article (10.1007/s00122-017-3011-4) contains supplementary material, which is available to authorized users.

BackgroundRice represents one the most important foods all over the world. In Europe, Italy is the first rice producer and Italian production is driven by tradition and quality. All main rice grain quality traits, like cooking properties, texture, gelatinization temperature, chalkiness and yield, are related to the content and composition of starch and seed-storage proteins in the endosperm and to grain shape. In addition, a number of nutraceutical compounds and allergens are known to have a significant effect on grain quality determination. To investigate the genetic bases underlying the qualitative differences that characterize traditional Italian rice cultivars, a comparative RNA-Seq-based transcriptomic analysis of developing caryopsis was conducted at 14 days after flowering on six popular Italian varieties (Carnaroli, Arborio, Balilla, Vialone Nano, Gigante Vercelli and Volano) phenotypically differing for qualitative grain-related traits.ResultsCo-regulation analyses of differentially expressed genes showing the same expression patterns in the six genotypes highlighted clusters of loci up or down-regulated in specific varieties, with respect to the others. Among them, we detected loci involved in cell wall biosynthesis, protein metabolism and redox homeostasis, classes of genes affecting in chalkiness determination. Moreover, loci encoding for seed-storage proteins, allergens or involved in the biosynthesis of specific nutraceutical compounds were also present and specifically regulated in the different clusters. A wider investigation of all the DEGs detected in pair-wise comparisons revealed transcriptional variation, among the six genotypes, for quality-related loci involved in starch biosynthesis (e.g. GBSSI, starch synthases and AGPase), genes encoding for transcription factors, additional seed storage proteins, allergens or belonging to additional nutraceutical compounds biosynthetic pathways and loci affecting grain size. Putative functional SNPs associated to amylose content in starch, gelatinization temperature and grain size were also identified.ConclusionsThe present work represents a more extended phenotypic characterization of a set of rice accessions that present a wider genetic variability than described nowadays in literature. The results provide the first transcriptional picture for several of the grain quality differences observed among the Italian rice varieties analyzed and reveal that each variety is characterized by the over-expression of a peculiar set of loci affecting grain appearance and quality. A list of candidates and SNPs affecting specific grain properties has been identified offering a starting point for further works aimed to characterize genes and molecular markers for breeding programs.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-2321-7) contains supplementary material, which is available to authorized users.