Search

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., Competing Interests: Competing interests statement:R.H. is currently employed by Bayer Crop Science but this is subsequent to her contributions to the project.

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., (Published 2023. This article is a U.S. Government work and is in the public domain in the USA. The Plant Genome published by Wiley Periodicals LLC on behalf of Crop Science Society of America.)

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., (© 2022. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

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., (Published by Oxford University Press 2021.)

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., Competing Interests: The authors have declared that no competing interests exist.

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.

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., (© 2019 Society of Chemical Industry.)

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., (Copyright © 2019 Crop Science Society of America.)

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., (Copyright © 2019 Crop Science Society of America.)

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.

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.

Ricebase (http://ricebase.org) is an integrative genomic database for rice (Oryza sativa) with an emphasis on combining datasets in a way that maintains the key links between past and current genetic studies. Ricebase includes DNA sequence data, gene annotations, nucleotide variation data and molecular marker fragment size data. Rice research has benefited from early adoption and extensive use of simple sequence repeat (SSR) markers; however, the majority of rice SSR markers were developed prior to the latest rice pseudomolecule assembly. Interpretation of new research using SNPs in the context of literature citing SSRs requires a common coordinate system. A new pipeline, using a stepwise relaxation of stringency, was used to map SSR primers onto the latest rice pseudomolecule assembly. The SSR markers and experimentally assayed amplicon sizes are presented in a relational database with a web-based front end, and are available as a track loaded in a genome browser with links connecting the browser and database. The combined capabilities of Ricebase link genetic markers, genome context, allele states across rice germplasm and potentially user curated phenotypic interpretations as a community resource for genetic discovery and breeding in rice., (Published by Oxford University Press 2016. This work is written by US Government employees and is in the public domain in the United States.)

A quantitative trait locus (QTL), dth1.1, was associated with transgressive variation for days to heading in an advanced backcross population derived from the Oryza sativa variety Jefferson and an accession of the wild rice relative Oryza rufipogon. A series of near-isogenic lines (NILs) containing different O. rufipogon introgressions across the target region were constructed to dissect dth1.1 using substitution mapping. In contrast to the late-flowering O. rufipogon parent, O. rufipogon alleles in the substitution lines caused early flowering under both short- and long-day lengths and provided evidence for at least two distinct sub-QTL: dth1.1a and dth1.1b. Potential candidate genes underlying these sub-QTL include genes with sequence similarity to Arabidopsis GI, FT, SOC1, and EMF1, and Pharbitis nil PNZIP. Evidence from families with nontarget O. rufipogon introgressions in combination with dth1.1 alleles also detected an early flowering QTL on chromosome 4 and a late-flowering QTL on chromosome 6 and provided evidence for additional sub-QTL in the dth1.1 region. The availability of a series of near-isogenic lines with alleles introgressed from a wild relative of rice provides an opportunity to better understand the molecular basis of transgressive variation in a quantitative trait.

The principal sources of genetic variation that can be assayed with restriction enzymes are base substitutions and insertions/deletions (indels). The likelihood of detecting indels as restriction fragment length polymorphisms (RFLPs) is determined by the size and frequency of the indels, and the ability to resolve small indels as RFLPs is limited by the distribution of restriction fragment sizes. In this study, we use aligned sequences from the indica and japonica subspecies of rice ( Oryza sativaL.) to quantify and compare the ability of restriction enzymes to detect indels. We look specifically at two abundant transposable element-derived indel sources: miniature inverted repeat transposable elements (MITEs) and long terminal repeat (LTR) retroelements. From this analysis we conclude that indels rather than base substitutions are the prevailing source of the polymorphism detected in rice. We show that, although MITE derived indels are more abundant than LTR-retroelement derived indels, LTR-retroelements have a greater capacity to generate visible restriction fragment length polymorphism because of their larger size. We find that the variation in the detectability of indels among restriction enzymes can be explained by differences in the frequency and dispersion of their restriction sites in the genome. The parameters that describe the fragment size distributions obtained with the restriction enzymes are highly correlated across the sequenced genomes of rice, Arabidopsis and human, with the exception of some extreme deviations in frequency for particular recognition sequences corresponding to variations in the levels and modes of DNA methylation in the three disparate organisms. Thus, we can predict the relative ability of a restriction enzyme to detect indels derived from a specific source based on the distribution of restriction fragment sizes, even when this is estimated for a distantly related genome.