Positive effects of public breeding on US rice yields under future climate scenarios.

In this study, we model and predict rice yields by integrating molecular marker variation, varietal productivity, and climate, focusing on the Southern U.S. rice-growing region. This region spans the states of Arkansas, Louisiana, Texas, Mississippi, and Missouri and accounts for 85% of total U.S. rice production. By digitizing and combining four decades of county-level variety acreage data (1970 to 2015) with varietal information from genotyping-by-sequencing data, we estimate annual historical county-level allele frequencies. These allele frequencies are used together with county-level weather and yield data to develop ten machine learning models for yield prediction. A two-layer meta-learner ensemble model that combines all ten methods is externally evaluated against observations from historical Uniform Regional Rice Nursery trials (1980 to 2018) conducted in the same states. Finally, the ensemble model is used with forecasted weather from the Coupled Model Intercomparison Project across the 110 rice-growing counties to predict production in the coming decades for Composite Variety Groups assembled based on year of release, breeding program, and several breeding trends. Results indicate positive effects over time of public breeding on rice resilience to future climates, and potential reasons are discussed.

Genetic loci regulating the concentrations of anthocyanins and proanthocyanidins in the pericarps of purple and red rice.

The pigmented flavonoids, anthocyanins and proanthocyanidins, have health promoting properties. Previous work determined that the genes Pb and Rc turn on and off the biosynthesis of anthocyanins (purple) and proanthocyanidins (red), respectively. Not yet known is how the concentrations of these pigmented flavonoids are regulated in grain pericarps. Quantitative trait locus (QTL) analysis in a population of rice (Oryza sativa L.) F5 recombinant inbred lines from white pericarp "IR36ae" x red+purple pericarp "242" revealed three QTLs associated with grain concentrations of anthocyanins (TAC) or proanthocyanidins (PA). Both TAC and PA independently mapped to a 1.5 Mb QTL region on chromosome 3 between RM3400 (at 15.8 Mb) and RM15123 (17.3 Mb), named qPR3. Across 2 years, qPR3 explained 36.3% of variance in TAC and 35.8% in PA variance not attributable to Pb or Rc. The qPR3 region encompasses Kala3, a MYB transcription factor previously known to regulate purple grain characteristics. Study of PbPbRcrc progeny showed that TAC of RcRc near isogenic lines (NILs) was 2.1-4.5x that of rcrc. Similarly, study of PbPbRcRc NILs, which had 70% higher PA than pbpbRcRc NILs, revealed a mutual enhancement, not a trade-off between these compounds that share precursors. This suggests that Pb and Rc upregulate genes in a shared pathway as they activate TAC and PA synthesis, respectively. This study provides molecular markers for facilitating marker-assisted selection of qPR3, qPR5, and qPR7 to enhance grain concentrations of pigmented flavonoids and documented that stacking Rc and Pb genes further increases both flavonoid compounds.

Navigating rice seedling cold resilience: QTL mapping in two inbred line populations and the search for genes.

Due to global climate change resulting in extreme temperature fluctuations, it becomes increasingly necessary to explore the natural genetic variation in model crops such as rice to facilitate the breeding of climate-resilient cultivars. To uncover genomic regions in rice involved in managing cold stress tolerance responses and to identify associated cold tolerance genes, two inbred line populations developed from crosses between cold-tolerant and cold-sensitive parents were used for quantitative trait locus (QTL) mapping of two traits: degree of membrane damage after 1 week of cold exposure quantified as percent electrolyte leakage (EL) and percent low-temperature seedling survivability (LTSS) after 1 week of recovery growth. This revealed four EL QTL and 12 LTSS QTL, all overlapping with larger QTL regions previously uncovered by genome-wide association study (GWAS) mapping approaches. Within the QTL regions, 25 cold-tolerant candidate genes were identified based on genomic differences between the cold-tolerant and cold-sensitive parents. Of those genes, 20% coded for receptor-like kinases potentially involved in signal transduction of cold tolerance responses; 16% coded for transcription factors or factors potentially involved in regulating cold tolerance response effector genes; and 64% coded for protein chaperons or enzymes potentially serving as cold tolerance effector proteins. Most of the 25 genes were cold temperature regulated and had deleterious nucleotide variants in the cold-sensitive parent, which might contribute to its cold-sensitive phenotype.

Identification of quantitative trait loci for tillering, root, and shoot biomass at the maximum tillering stage in rice.

Tillering and plant biomass are key determinants of rice crop productivity. Tillering at the vegetative stage is associated with weed competition, nutrient uptake, and methane emissions. However, little information is available on quantitative trait loci (QTLs) associated with tiller number (qTN), root biomass (qRB), and shoot biomass (qSB) at the active tillering stage which occurs approximately 6 weeks after planting. Here, we mapped tiller and biomass QTLs with ~ 250 recombinant inbred lines derived from a 'Francis' by 'Rondo' cross using data collected at the maximum tillering stage from two years of greenhouse study, and further compared these QTLs with those mapped at the harvest stage from a field study. Across these three studies, we discovered six qTNs, two qRBs, and three qSBs. Multiple linear regression further indicated that qTN1-2, qTN3-3, qTN4-1, qRB3-1, and qRB5-1 were significant at the maximum tillering stage while qTN3-2 was detected only at the harvest stage. Moreover, qTN3-1 was consistently significant across different developmental stages and growing environments. The genes identified from the peak target qTN regions included a carotenoid metabolism enzyme, a MYB transcription factor, a CBS domain-containing protein, a SAC3/GANP family protein, a TIFY motif containing protein, and an ABC transporter protein. Two genes in the qRB peak target regions included an expressed protein and a WRKY gene. This knowledge of the QTLs, associated markers, candidate genes, and germplasm resources with high TN, RB and SB is of value to rice cultivar improvement programs.

Phenotypic Variation and the Impact of Admixture in the Oryza rufipogon Species Complex (ORSC).

Crop wild relatives represent valuable reservoirs of variation for breeding, but their populations are threatened in natural habitats, are sparsely represented in genebanks, and most are poorly characterized. The focus of this study is the Oryza rufipogon species complex (ORSC), wild progenitor of Asian rice (Oryza sativa L.). The ORSC comprises perennial, annual and intermediate forms which were historically designated as O. rufipogon, O. nivara, and O. sativa f. spontanea (or Oryza spp., an annual form of mixed O. rufipogon/O. nivara and O. sativa ancestry), respectively, based on non-standardized morphological, geographical, and/or ecologically-based species definitions and boundaries. Here, a collection of 240 diverse ORSC accessions, characterized by genotyping-by-sequencing (113,739 SNPs), was phenotyped for 44 traits associated with plant, panicle, and seed morphology in the screenhouse at the International Rice Research Institute, Philippines. These traits included heritable phenotypes often recorded as characterization data by genebanks. Over 100 of these ORSC accessions were also phenotyped in the greenhouse for 18 traits in Stuttgart, Arkansas, and 16 traits in Ithaca, New York, United States. We implemented a Bayesian Gaussian mixture model to infer accession groups from a subset of these phenotypic data and ascertained three phenotype-based group assignments. We used concordance between the genotypic subpopulations and these phenotype-based groups to identify a suite of phenotypic traits that could reliably differentiate the ORSC populations, whether measured in tropical or temperate regions. The traits provide insight into plant morphology, life history (perenniality versus annuality) and mating habit (self- versus cross-pollinated), and are largely consistent with genebank species designations. One phenotypic group contains predominantly O. rufipogon accessions characterized as perennial and largely out-crossing and one contains predominantly O. nivara accessions characterized as annual and largely inbreeding. From these groups, 42 "core" O. rufipogon and 25 "core" O. nivara accessions were identified for domestication studies. The third group, comprising 20% of our collection, has the most accessions identified as Oryza spp. (51.2%) and levels of O. sativa admixture accounting for more than 50% of the genome. This third group is potentially useful as a "pre-breeding" pool for breeders attempting to incorporate novel variation into elite breeding lines.

Assessment of Rice Sheath Blight Resistance Including Associations with Plant Architecture, as Revealed by Genome-Wide Association Studies.

BACKGROUND: Sheath blight (ShB) disease caused by Rhizoctonia solani Kühn, is one of the most economically damaging rice (Oryza sativa L.) diseases worldwide. There are no known major resistance genes, leaving only partial resistance from small-effect QTL to deploy for cultivar improvement. Many ShB-QTL are associated with plant architectural traits detrimental to yield, including tall plants, late maturity, or open canopy from few or procumbent tillers, which confound detection of physiological resistance. RESULTS: To identify QTL for ShB resistance, 417 accessions from the Rice Diversity Panel 1 (RDP1), developed for association mapping studies, were evaluated for ShB resistance, plant height and days to heading in inoculated field plots in Arkansas, USA (AR) and Nanning, China (NC). Inoculated greenhouse-grown plants were used to evaluate ShB using a seedling-stage method to eliminate effects from height or maturity, and tiller (TN) and panicle number (PN) per plant. Potted plants were used to evaluate the RDP1 for TN and PN. Genome-wide association (GWA) mapping with over 3.4 million SNPs identified 21 targeted SNP markers associated with ShB which tagged 18 ShB-QTL not associated with undesirable plant architecture traits. Ten SNPs were associated with ShB among accessions of the Indica subspecies, ten among Japonica subspecies accessions, and one among all RDP1 accessions. Across the 18 ShB QTL, only qShB4-1 was not previously reported in biparental mapping studies and qShB9 was not reported in the GWA ShB studies. All 14 PN QTL overlapped with TN QTL, with 15 total TN QTL identified. Allele effects at the five TN QTL co-located with ShB QTL indicated that increased TN does not inevitably increase disease development; in fact, for four ShB QTL that overlapped TN QTL, the alleles increasing resistance were associated with increased TN and PN, suggesting a desirable coupling of alleles at linked genes. CONCLUSIONS: Nineteen accessions identified as containing the most SNP alleles associated with ShB resistance for each subpopulation were resistant in both AR and NC field trials. Rice breeders can utilize these accessions and SNPs to develop cultivars with enhanced ShB resistance along with increased TN and PN for improved yield potential.

Genomic prediction and QTL mapping of root system architecture and above-ground agronomic traits in rice (Oryza sativa L.) with a multitrait index and Bayesian networks.

Root system architecture (RSA) is a crucial factor in resource acquisition and plant productivity. Roots are difficult to phenotype in the field, thus new tools for predicting phenotype from genotype are particularly valuable for plant breeders aiming to improve RSA. This study identifies quantitative trait loci (QTLs) for RSA and agronomic traits in a rice (Oryza sativa) recombinant inbred line (RIL) population derived from parents with contrasting RSA traits (PI312777 × Katy). The lines were phenotyped for agronomic traits in the field, and separately grown as seedlings on agar plates which were imaged to extract RSA trait measurements. QTLs were discovered from conventional linkage analysis and from a machine learning approach using a Bayesian network (BN) consisting of genome-wide SNP data and phenotypic data. The genomic prediction abilities (GPAs) of multi-QTL models and the BN analysis were compared with the several standard genomic prediction (GP) methods. We found GPAs were improved using multitrait (BN) compared to single trait GP in traits with low to moderate heritability. Two groups of individuals were selected based on GPs and a modified rank sum index (GSRI) indicating their divergence across multiple RSA traits. Selections made on GPs did result in differences between the group means for numerous RSA. The ranking accuracy across RSA traits among the individual selected RILs ranged from 0.14 for root volume to 0.59 for lateral root tips. We conclude that the multitrait GP model using BN can in some cases improve the GPA of RSA and agronomic traits, and the GSRI approach is useful to simultaneously select for a desired set of RSA traits in a segregating population.

Gene disruption by structural mutations drives selection in US rice breeding over the last century.

The genetic basis of general plant vigor is of major interest to food producers, yet the trait is recalcitrant to genetic mapping because of the number of loci involved, their small effects, and linkage. Observations of heterosis in many crops suggests that recessive, malfunctioning versions of genes are a major cause of poor performance, yet we have little information on the mutational spectrum underlying these disruptions. To address this question, we generated a long-read assembly of a tropical japonica rice (Oryza sativa) variety, Carolina Gold, which allowed us to identify structural mutations (>50 bp) and orient them with respect to their ancestral state using the outgroup, Oryza glaberrima. Supporting prior work, we find substantial genome expansion in the sativa branch. While transposable elements (TEs) account for the largest share of size variation, the majority of events are not directly TE-mediated. Tandem duplications are the most common source of insertions and are highly enriched among 50-200bp mutations. To explore the relative impact of various mutational classes on crop fitness, we then track these structural events over the last century of US rice improvement using 101 resequenced varieties. Within this material, a pattern of temporary hybridization between medium and long-grain varieties was followed by recent divergence. During this long-term selection, structural mutations that impact gene exons have been removed at a greater rate than intronic indels and single-nucleotide mutations. These results support the use of ab initio estimates of mutational burden, based on structural data, as an orthogonal predictor in genomic selection.

Relationships Among Arsenic-Related Traits, Including Rice Grain Arsenic Concentration and Straighthead Resistance, as Revealed by Genome-Wide Association.

There is global concern that rice grains and foods can contain harmful amounts of arsenic (As), motivating breeders to produce cultivars that restrict As accumulation in grains to protect human health. Arsenic is also toxic to plants, with straighthead disorder (StHD), causing panicle sterility, being observed in rice. The genetic variation in StHD resistance suggests that plants have evolved mechanisms that reduce As toxicity, possibly via regulation of As uptake, transport, or detoxification/sequestration. Because these mechanisms could also underlie the wide (3- to 100-fold) differences in grain As concentration (grain-As) observed among diverse rice genotypes, it was hypothesized that some genes reduce both grain-As content and StHD susceptibility and may be detectable as co-located StDH and As quantitative trait loci (QTL). We used a machine-learning Bayesian network approach plus high-resolution genome-wide association study (GWAS) to identify QTL for grain-As and StHD resistance within the USDA Rice Minicore Collection (RMC). Arsenic enters roots through phosphorus (P) and silica (Si) transporters, As detoxification involves sulfur (S), and cell signaling to activate stress tolerance mechanisms is impacted by Si, calcium (Ca), and copper (Cu). Therefore, concentrations of Si, P, S, Ca, and Cu were included in this study to elucidate physiological mechanisms underlying grain-As and StHD QTL. Multiple QTL (from 9 to 33) were identified for each of the investigated As-associated traits. Although the QTL for StHD, Si, and grain-As did not overlap as heavily as our hypothesis predicted (4/33 StHD and 4/15 As QTL co-located), they do provide useful guidance to future research. Furthermore, these are the first StHD and Si QTL to be identified using high-density mapping, resulting in their being mapped to shorter, more precise genomic regions than previously reported QTL. The candidate genes identified provide guidance for future research, such as gene editing or mutation studies to further investigate the role of antioxidants and ROS scavenging to StHD resistance, as indicated by candidate genes around the commonly reported qStHD8-2 QTL. Other genes indicated for future study for improving grain-As and StHD include several multidrug and toxic compound extrusion (MATE) genes, F-box genes, and NIPs not documented to date to transport As.

Vis/NIR hyperspectral imaging distinguishes sub-population, production environment, and physicochemical grain properties in rice.

Rice grain quality is a multifaceted quantitative trait that impacts crop value and is influenced by multiple genetic and environmental factors. Chemical, physical, and visual analyses are the standard methods for measuring grain quality. In this study, we evaluated high-throughput hyperspectral imaging for quantification of rice grain quality and classification of grain samples by genetic sub-population and production environment. Whole grain rice samples from the USDA mini-core collection grown in multiple locations were evaluated using hyperspectral imaging and compared with results from standard phenotyping. Loci associated with hyperspectral values were mapped in the mini-core with 3.2 million SNPs in a genome-wide association study (GWAS). Our results show that visible and near infra-red (Vis/NIR) spectroscopy can classify rice according to sub-population and production environment based on differences in physicochemical grain properties. The 702-900 nm range of the NIR spectrum was associated with the chalky grain trait. GWAS revealed that grain chalk and hyperspectral variation share genomic regions containing several plausible candidate genes for grain chalkiness. Hyperspectral quantification of grain chalk was validated using a segregating bi-parental mapping population. These results indicate that Vis/NIR can be used for non-destructive high throughput phenotyping of grain chalk and potentially other grain quality properties.

Phylogenetic origin and dispersal pattern of Taiwan weedy rice.

BACKGROUND: Knowledge of the genetic diversity and spatial structure of Taiwan weedy red rice (WRR) populations, which adapted in a transplanting system, will facilitate the design of effective methods to control this weed by tracing its origins and dispersal patterns in a given region. RESULTS: Taiwan WRR is genetically most similar to Taiwan indica cultivars and landraces according to genetic distance. The inbreeding coefficient of the Taiwan WRR population is greater than 0.8, which is similar to the inbred cultivars. The ancestry coefficients map suggests a dispersal pattern of long-distance and seed-mediated contamination across Taiwan, often from warmer, earlier-planted regions to cooler, later-planted regions. Parentage analysis of Taiwan WRR revealed that mostly early indica landraces and indica cultivars were present in the genetic pool; in rare cases temperate japonica was present. Based on the above results, the phylogenetic origin of most Taiwan weedy rice appears to be from hybrid progenies of old cultivated red rice accessions crossed with 'DGWG'. The inbreeding coefficient trend of the six TWR clusters suggests a temporal shift from 'old' indica landraces with red bran (high inbreeding coefficient) to modern indica varieties (low inbreeding coefficient). CONCLUSION: Although there were sustained efforts to remove these old red rice accessions from paddy fields before 1945, some farmers continued to use low purity seed. This practice, along with volunteer cultivation of these old varieties in the second cropping season, apparently has facilitated the long-distance, seed-mediated contamination of rice seed, and the increase in weedy rice seed in paddy soil. © 2019 Society of Chemical Industry.

Grain Inorganic Arsenic Content in Rice Managed Through Targeted Introgressions and Irrigation Management.

Arsenic (As) accumulation in rice grain is a significant public health concern. Inorganic As (iAs) is of particular concern because it has increased toxicity as compared to organic As. Irrigation management practices, such as alternate wetting and drying (AWD), as well as genotypic differences between cultivars, have been shown to influence As accumulation in rice grain. A 2 year field study using a Lemont × TeQing backcross introgression line (TIL) mapping population examined the impact of genotype and AWD severity on iAs grain concentrations. The "Safe"-AWD [35-40% soil volumetric water content (VWC)] treatment did not reduce grain iAs levels, whereas the more severe AWD30 (25-30% VWC) consistently reduced iAs concentrations across all genotypes. The TILs displayed a range of iAs concentrations by genotype, from less than 10 to up to 46 µg kg-1 under AWD30 and from 28 to 104 µg kg-1 under Safe-AWD. TIL grain iAs concentrations for flood treatments across both years ranged from 26 to 127 µg kg-1. Additionally, seven quantitative trait loci (QTLs) were identified in the mapping population associated with grain iAs. A subset of eight TILs and their parents were grown to confirm field-identified grain iAs QTLs in a controlled greenhouse environment. Greenhouse results confirmed the genotypic grain iAs patterns observed in the field; however, iAs concentrations were higher under greenhouse conditions as compared to the field. In the greenhouse, the number of days under AWD was negatively correlated with grain iAs concentrations. Thus, longer drying periods to meet the same soil VWC resulted in lower grain iAs levels. Both the number and combinations of iAs-affecting QTLs significantly impacted grain iAs concentrations. Therefore, identifying more grain iAs-affecting QTLs could be important to inform future breeding efforts for low iAs rice varieties. Our study suggests that coupling AWD practices targeting a soil VWC of less than or equal to 30% coupled with the use of cultivars developed to possess multiple QTLs that negatively regulate grain iAs concentrations will be helpful in mitigating exposure of iAs from rice consumption.

Identification of Superior Alleles for Seedling Stage Salt Tolerance in the USDA Rice Mini-Core Collection.

Salt stress is a major constraint to rice acreage and production worldwide. The purpose of this study was to evaluate the natural genetic variation available in the United States Department of Agriculture (USDA) rice mini-core collection (URMC) for early vigor traits under salt stress and identify quantitative trait loci (QTLs) for seedling-stage salt tolerance via a genome-wide association study (GWAS). Using a hydroponic system, the seedlings of 162 accessions were subjected to electrical conductivity (EC) 6.0 dS m-1 salt stress at the three-to-four leaf stage. After completion of the study, 59.4% of the accessions were identified as sensitive, 23.9% were identified as moderately tolerant, and 16.7% were identified as highly tolerant. Pokkali was the most tolerant variety, while Nerica-6 was the most sensitive. Adapting standard International Rice Research Institute (IRRI) protocols, eight variables associated with salt tolerance were determined. The GWAS of the URMC, using over three million single-nucleotide polymorphisms (SNPs), identified nine genomic regions associated with salt tolerance that were mapped to five different chromosomes. Of these, none were in the known Saltol QTL region, suggesting different probable genes and mechanisms responsible for salt tolerance in the URMC. The study uncovered genetic loci that explained a large portion of the variation in salt tolerance at the seedling stage. Fourteen highly salt-tolerant accessions, six novel loci, and 16 candidate genes in their vicinity were identified that may be useful in breeding for salt stress tolerance. Identified QTLs can be targeted for fine mapping, candidate gene verification, and marker-assisted breeding in future studies.

Association Analysis of Three Diverse Rice (Oryza sativa L.) Germplasm Collections for Loci Regulating Grain Quality Traits.

Rice ( L.) end-use cooking quality is vital for producers and billions of consumers worldwide. Grain quality is a complex trait with interacting genetic and environmental factors. Deciphering the complex genetic architecture associated with grain quality provides essential information for improved breeding strategies to enhance desirable traits that are stable across variable climatic and environmental conditions. In this study, genome-wide association (GWA) analysis of three rice diversity panels, the USDA rice core subset (1364 accessions), the minicore (MC) (173 accessions after removing non-), and the high density rice array-MC (HDMC) (383 accessions), with simple sequence repeats, single nucleotide polymorphic markers, or both, revealed large- and small-effect loci associated with known genes and previously uncharacterized genomic regions. Clustering of the significant regions in the GWA results suggests that multiple grain quality traits are inherited together. The 11 novel candidate loci for grain quality traits and the seven candidates for grain chalk identified are involved in the starch biosynthesis pathway. This study highlights the intricate pleiotropic relationships that exist in complex genotype-phenotypic associations and gives a greater insight into effective breeding strategies for grain quality improvement.

Validation of Yield Component Traits Identified by Genome-Wide Association Mapping in a tropical japonica × tropical japonica Rice Biparental Mapping Population.

The Rice Diversity Panel 1 (RDP1) was developed for genome-wide association (GWA) studies to explore five rice ( L.) subpopulations (, , , , and ). The RDP1 was evaluated for over 30 traits, including agronomic, panicle architecture, seed, and disease traits and genotyped with 700,000 single nucleotide polymorphisms (SNPs). Most rice grown in the southern United States is and thus the diversity in this subpopulation is interesting to U.S. breeders. Among the RDP1 accessions, 'Estrela' and 'NSFTV199' are both phenotypically and genotypically diverse, thus making them excellent parents for a biparental mapping population. The objectives were to (i) ascertain the GWA QTLs from the RDP1 GWA studies that overlapped with the QTLs uncovered in an Estrela × NSFTV199 recombinant inbred line (RIL) population evaluated for 15 yield traits, and (ii) identify known or novel genes potentially controlling specific yield component traits. The 256 RILs were genotyped with 132 simple sequence repeat markers and 70 QTLs were found. Perl scripts were developed for automatic identification of the underlying candidate genes in the GWA QTL regions. Approximately 100 GWA QTLs overlapped with 41 Estrela × NSFTV199 QTL (RIL QTL) regions and 47 known genes were identified. Two seed trait RIL QTLs with overlapping GWA QTLs were not associated with a known gene. Segregating SNPs in the overlapping GWA QTLs for RIL QTLs with high values will be evaluated as potential DNA markers useful to breeding programs for the associated yield trait.

BrAPI-an application programming interface for plant breeding applications.

MOTIVATION: Modern genomic breeding methods rely heavily on very large amounts of phenotyping and genotyping data, presenting new challenges in effective data management and integration. Recently, the size and complexity of datasets have increased significantly, with the result that data are often stored on multiple systems. As analyses of interest increasingly require aggregation of datasets from diverse sources, data exchange between disparate systems becomes a challenge. RESULTS: To facilitate interoperability among breeding applications, we present the public plant Breeding Application Programming Interface (BrAPI). BrAPI is a standardized web service API specification. The development of BrAPI is a collaborative, community-based initiative involving a growing global community of over a hundred participants representing several dozen institutions and companies. Development of such a standard is recognized as critical to a number of important large breeding system initiatives as a foundational technology. The focus of the first version of the API is on providing services for connecting systems and retrieving basic breeding data including germplasm, study, observation, and marker data. A number of BrAPI-enabled applications, termed BrAPPs, have been written, that take advantage of the emerging support of BrAPI by many databases. AVAILABILITY AND IMPLEMENTATION: More information on BrAPI, including links to the specification, test suites, BrAPPs, and sample implementations is available at https://brapi.org/. The BrAPI specification and the developer tools are provided as free and open source.

The rice blast resistance gene Ptr encodes an atypical protein required for broad-spectrum disease resistance.

Plant resistance genes typically encode proteins with nucleotide binding site-leucine rich repeat (NLR) domains. Here we show that Ptr is an atypical resistance gene encoding a protein with four Armadillo repeats. Ptr is required for broad-spectrum blast resistance mediated by the NLR R gene Pi-ta and by the associated R gene Pi-ta2. Ptr is expressed constitutively and encodes two isoforms that are mainly localized in the cytoplasm. A two base pair deletion within the Ptr coding region in the fast neutron-generated mutant line M2354 creates a truncated protein, resulting in susceptibility to M. oryzae. Targeted mutation of Ptr in a resistant cultivar using CRISPR/Cas9 leads to blast susceptibility, further confirming its resistance function. The cloning of Ptr may aid in the development of broad spectrum blast resistant rice.

Genetic architecture of cold tolerance in rice (Oryza sativa) determined through high resolution genome-wide analysis.

Cold temperature is an important abiotic stress which negatively affects morphological development and seed production in rice (Oryza sativa L.). At the seedling stage, cold stress causes poor germination, seedling injury and poor stand establishment; and at the reproductive stage cold decreases seed yield. The Rice Diversity Panel 1 (RDP1) is a global collection of over 400 O. sativa accessions representing the five major subpopulations from the INDICA and JAPONICA varietal groups, with a genotypic dataset consisting of 700,000 SNP markers. The objectives of this study were to evaluate the RDP1 accessions for the complex, quantitatively inherited cold tolerance traits at the germination and reproductive stages, and to conduct genome-wide association (GWA) mapping to identify SNPs and candidate genes associated with cold stress at these stages. GWA mapping of the germination index (calculated as percent germination in cold divided by warm treatment) revealed 42 quantitative trait loci (QTLs) associated with cold tolerance at the seedling stage, including 18 in the panel as a whole, seven in temperate japonica, six in tropical japonica, 14 in JAPONICA, and nine in INDICA, with five shared across all subpopulations. Twenty-two of these QTLs co-localized with 32 previously reported cold tolerance QTLs. GWA mapping of cold tolerance at the reproductive stage detected 29 QTLs, including seven associated with percent sterility, ten with seed weight per panicle, 14 with seed weight per plant and one region overlapping for two traits. Fifteen co-localized with previously reported QTLs for cold tolerance or yield components. Candidate gene ontology searches revealed these QTLs were associated with significant enrichment for genes related to with lipid metabolism, response to stimuli, response to biotic stimuli (suggesting cross-talk between biotic and abiotic stresses), and oxygen binding. Overall the JAPONICA accessions were more tolerant to cold stress than INDICA accessions.

The Sol Genomics Network (SGN)--from genotype to phenotype to breeding.

The Sol Genomics Network (SGN, http://solgenomics.net) is a web portal with genomic and phenotypic data, and analysis tools for the Solanaceae family and close relatives. SGN hosts whole genome data for an increasing number of Solanaceae family members including tomato, potato, pepper, eggplant, tobacco and Nicotiana benthamiana. The database also stores loci and phenotype data, which researchers can upload and edit with user-friendly web interfaces. Tools such as BLAST, GBrowse and JBrowse for browsing genomes, expression and map data viewers, a locus community annotation system and a QTL analysis tools are available. A new tool was recently implemented to improve Virus-Induced Gene Silencing (VIGS) constructs called the SGN VIGS tool. With the growing genomic and phenotypic data in the database, SGN is now advancing to develop new web-based breeding tools and implement the code and database structure for other species or clade-specific databases.

solGS: a web-based tool for genomic selection.

BACKGROUND: Genomic selection (GS) promises to improve accuracy in estimating breeding values and genetic gain for quantitative traits compared to traditional breeding methods. Its reliance on high-throughput genome-wide markers and statistical complexity, however, is a serious challenge in data management, analysis, and sharing. A bioinformatics infrastructure for data storage and access, and user-friendly web-based tool for analysis and sharing output is needed to make GS more practical for breeders. RESULTS: We have developed a web-based tool, called solGS, for predicting genomic estimated breeding values (GEBVs) of individuals, using a Ridge-Regression Best Linear Unbiased Predictor (RR-BLUP) model. It has an intuitive web-interface for selecting a training population for modeling and estimating genomic estimated breeding values of selection candidates. It estimates phenotypic correlation and heritability of traits and selection indices of individuals. Raw data is stored in a generic database schema, Chado Natural Diversity, co-developed by multiple database groups. Analysis output is graphically visualized and can be interactively explored online or downloaded in text format. An instance of its implementation can be accessed at the NEXTGEN Cassava breeding database, http://cassavabase.org/solgs. CONCLUSIONS: solGS enables breeders to store raw data and estimate GEBVs of individuals online, in an intuitive and interactive workflow. It can be adapted to any breeding program.