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.

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.

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.

Univariate and Multivariate QTL Analyses Reveal Covariance Among Mineral Elements in the Rice Ionome.

Rice provides more than one fifth of daily calories for half of the world's human population, and is a major dietary source of both essential mineral nutrients and toxic elements. Rice grains are generally poor in some essential nutrients but may contain unsafe levels of some toxic elements under certain conditions. Identification of quantitative trait loci (QTLs) controlling the concentrations of mineral nutrients and toxic trace metals (the ionome) in rice will facilitate development of nutritionally improved rice varieties. However, QTL analyses have traditionally considered each element separately without considering their interrelatedness. In this study, we performed principal component analysis (PCA) and multivariate QTL analyses to identify the genetic loci controlling the covariance among mineral elements in the rice ionome. We resequenced the whole genomes of a rice recombinant inbred line (RIL) population, and performed univariate and multivariate QTL analyses for the concentrations of 16 elements in grains, shoots and roots of the RIL population grown in different conditions. We identified a total of 167 unique elemental QTLs based on analyses of individual elemental concentrations as separate traits, 53 QTLs controlling covariance among elemental concentrations within a single environment/tissue (PC-QTLs), and 152 QTLs which determined covariation among elements across environments/tissues (aPC-QTLs). The candidate genes underlying the QTL clusters with elemental QTLs, PC-QTLs and aPC-QTLs co-localized were identified, including OsHMA4 and OsNRAMP5. The identification of both elemental QTLs and PC QTLs will facilitate the cloning of underlying causal genes and the dissection of the complex regulation of the ionome in rice.

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.

Genome-wide association mapping for grain manganese in rice (Oryza sativa L.) using a multi-experiment approach.

Manganese (Mn) is an essential trace element for plants and commonly contributes to human health; however, the understanding of the genes controlling natural variation in Mn in crop plants is limited. Here, the integration of two of genome-wide association study approaches was used to increase the identification of valuable quantitative trait loci (QTL) and candidate genes responsible for the concentration of grain Mn across 389 diverse rice cultivars grown in Arkansas and Texas, USA, in multiple years. Single-trait analysis was initially performed using three different SNP datasets. As a result, significant loci could be detected using the high-density SNP dataset. Based on the 5.2 M SNP dataset, major QTLs were located on chromosomes 3 and 7 for Mn containing six candidate genes. In addition, the phenotypic data of grain Mn concentration were combined from three flooded-field experiments from the two sites and 3 years using multi-experiment analysis based on the 5.2 M SNP dataset. Two previous QTLs on chromosome 3 were identified across experiments, whereas new Mn QTLs were identified that were not found in individual experiments, on chromosomes 3, 4, 9 and 11. OsMTP8.1 was identified in both approaches and is a good candidate gene that could be controlling grain Mn concentration. This work demonstrates the utilisation of multi-experiment analysis to identify constitutive QTLs and candidate genes associated with the grain Mn concentration. Hence, the approach should be advantageous to facilitate genomic breeding programmes in rice and other crops considering QTLs and genes associated with complex traits in natural populations.

Genome-wide association studies of ionomic and agronomic traits in USDA mini core collection of rice and comparative analyses of different mapping methods.

BACKGROUND: Rice is an important human staple food vulnerable to heavy metal contamination leading to serious concerns. High yield with low heavy metal contamination is a common but highly challenging goal for rice breeders worldwide due to lack of genetic knowledge and markers. RESULTS: To identify candidate QTLs and develop molecular markers for rice yield and heavy metal content, a total of 191 accessions from the USDA Rice mini-core collection with over 3.2 million SNPs were employed to investigate the QTLs. Sixteen ionomic and thirteen agronomic traits were analyzed utilizing two univariate (GLM and MLM) and two multivariate (MLMM and FarmCPU) GWAS methods. 106, 47, and 97 QTLs were identified for ionomics flooded, ionomics unflooded, and agronomic traits, respectively, with the criterium of p-value < 1.53 × 10- 8, which was determined by the Bonferroni correction for p-value of 0.05. While 49 (~ 20%) of the 250 QTLs were coinciding with previously reported QTLs/genes, about 201 (~ 80%) were new. In addition, several new candidate genes involved in ionomic and agronomic traits control were identified by analyzing the DNA sequence, gene expression, and the homologs of the QTL regions. Our results further showed that each of the four GWAS methods can identify unique as well as common QTLs, suggesting that using multiple GWAS methods can complement each other in QTL identification, especially by combining univariate and multivariate methods. CONCLUSIONS: While 49 previously reported QTLs/genes were rediscovered, over 200 new QTLs for ionomic and agronomic traits were found in the rice genome. Moreover, multiple new candidate genes for agronomic and ionomic traits were identified. This research provides novel insights into the genetic basis of both ionomic and agronomic variations in rice, establishing the foundation for marker development in breeding and further investigation on reducing heavy-metal contamination and improving crop yields. Finally, the comparative analysis of the GWAS methods showed that each method has unique features and different methods can complement each other.

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.

Natural variation in a molybdate transporter controls grain molybdenum concentration in rice.

Molybdenum (Mo) is an essential micronutrient for most living organisms, including humans. Cereals such as rice (Oryza sativa) are the major dietary source of Mo. However, little is known about the genetic basis of the variation in Mo content in rice grain. We mapped a quantitative trait locus (QTL) qGMo8 that controls Mo accumulation in rice grain by using a recombinant inbred line population and a backcross introgression line population. We identified a molybdate transporter, OsMOT1;1, as the causal gene for this QTL. OsMOT1;1 exhibits transport activity for molybdate, but not sulfate, when heterogeneously expressed in yeast cells. OsMOT1;1 is mainly expressed in roots and is involved in the uptake and translocation of molybdate under molybdate-limited condition. Knockdown of OsMOT1;1 results in less Mo being translocated to shoots, lower Mo concentration in grains and higher sensitivity to Mo deficiency. We reveal that the natural variation of Mo concentration in rice grains is attributed to the variable expression of OsMOT1;1 due to sequence variation in its promoter. Identification of natural allelic variation in OsMOT1;1 may facilitate the development of rice varieties with Mo-enriched grain for dietary needs and improve Mo nutrition of rice on Mo-deficient soils.

Functional Interactions Between Major Rice Blast Resistance Genes, Pi-ta and Pi-b, and Minor Blast Resistance Quantitative Trait Loci.

Major blast resistance (R) genes confer resistance in a gene-for-gene manner. However, little information is available on interactions between R genes. In this study, interactions between two rice blast R genes, Pi-ta and Pi-b, and other minor blast resistance quantitative trait loci (QTLs) were investigated in a recombinant inbred line (RIL) population comprising 243 RILs from a Cybonnet (CYBT) × Saber (SB) cross. CYBT has the R gene Pi-ta and SB has Pi-b. Ten differential isolates of four Magnaporthe oryzae races (IB-1, IB-17, IB-49, and IE-1K) were used to evaluate disease reactions of the 243 RILs under greenhouse conditions. Five resistance QTLs were mapped on chromosomes 2, 3, 8, 9, and 12 with a linkage map of 179 single nucleotide polymorphism markers. Among them, qBR12 (Q1), was mapped at the Pi-ta locus and accounted for 45.41% of phenotypic variation while qBR2 (Q2) was located at the Pi-b locus and accounted for 24.81% of disease reactions. The additive-by-additive epistatic interaction between Q1 (Pi-ta) and Q2 (Pi-b) was detected; they can enhance the disease resistance by an additive 0.93 using the 0 to 9 standard phenotyping method. These results suggest that Pi-ta interacts synergistically with Pi-b.

A heavy metal P-type ATPase OsHMA4 prevents copper accumulation in rice grain.

Rice is a major source of calories and mineral nutrients for over half the world's human population. However, little is known in rice about the genetic basis of variation in accumulation of copper (Cu), an essential but potentially toxic nutrient. Here we identify OsHMA4 as the likely causal gene of a quantitative trait locus controlling Cu accumulation in rice grain. We provide evidence that OsHMA4 functions to sequester Cu into root vacuoles, limiting Cu accumulation in the grain. The difference in grain Cu accumulation is most likely attributed to a single amino acid substitution that leads to different OsHMA4 transport activity. The allele associated with low grain Cu was found in 67 of the 1,367 rice accessions investigated. Identification of natural allelic variation in OsHMA4 may facilitate the development of rice varieties with grain Cu concentrations tuned to both the concentration of Cu in the soil and dietary needs.

Genome wide association mapping of grain arsenic, copper, molybdenum and zinc in rice (Oryza sativa L.) grown at four international field sites.

The mineral concentrations in cereals are important for human health, especially for individuals who consume a cereal subsistence diet. A number of elements, such as zinc, are required within the diet, while some elements are toxic to humans, for example arsenic. In this study we carry out genome-wide association (GWA) mapping of grain concentrations of arsenic, copper, molybdenum and zinc in brown rice using an established rice diversity panel of ∼ 300 accessions and 36.9 k single nucleotide polymorphisms (SNPs). The study was performed across five environments: one field site in Bangladesh, one in China and two in the US, with one of the US sites repeated over two years. GWA mapping on the whole dataset and on separate subpopulations of rice revealed a large number of loci significantly associated with variation in grain arsenic, copper, molybdenum and zinc. Seventeen of these loci were detected in data obtained from grain cultivated in more than one field location, and six co-localise with previously identified quantitative trait loci. Additionally, a number of candidate genes for the uptake or transport of these elements were located near significantly associated SNPs (within 200 kb, the estimated global linkage disequilibrium previously employed in this rice panel). This analysis highlights a number of genomic regions and candidate genes for further analysis as well as the challenges faced when mapping environmentally-variable traits in a highly genetically structured diversity panel.

Mapping and validation of quantitative trait loci associated with concentrations of 16 elements in unmilled rice grain.

KEY MESSAGE: QTLs controlling the concentrations elements in rice grain were identified in two mapping populations. The QTLs were clustered such that most genomic regions were associated with more than one element. In this study, quantitative trait loci (QTLs) affecting the concentrations of 16 elements in whole, unmilled rice (Oryza sativa L.) grain were identified. Two rice mapping populations, the 'Lemont' × 'TeQing' recombinant inbred lines (LT-RILs), and the TeQing-into-Lemont backcross introgression lines (TILs) were used. To increase opportunity to detect and characterize QTLs, the TILs were grown under two contrasting field conditions, flooded and irrigated-but-unflooded. Correlations between the individual elements and between each element with grain shape, plant height, and time of heading were also studied. Transgressive segregation was observed among the LT-RILs for all elements. The 134 QTLs identified as associated with the grain concentrations of individual elements were found clustered into 39 genomic regions, 34 of which were found associated with grain element concentration in more than one population and/or flooding treatment. More QTLs were found significant among flooded TILs (92) than among unflooded TILs (47) or among flooded LT-RILs (40). Twenty-seven of the 40 QTLs identified among the LT-RILs were associated with the same element among the TILs. At least one QTL per element was validated in two or more population/environments. Nearly all of the grain element loci were linked to QTLs affecting additional elements, supporting the concept of element networks within plants. Several of the grain element QTLs co-located with QTLs for grain shape, plant height, and days to heading; but did not always differ for grain elemental concentration as predicted by those traits alone. A number of interesting patterns were found, including a strong Mg­P­K complex.

Variation in grain arsenic assessed in a diverse panel of rice (Oryza sativa) grown in multiple sites.

• Inorganic arsenic (As(i) ) in rice (Oryza sativa) grains is a possible threat to human health, with risk being strongly linked to total dietary rice consumption and consumed rice As(i) content. This study aimed to identify the range and stability of genetic variation in grain arsenic (As) in rice. • Six field trials were conducted (one each in Bangladesh and China, two in Arkansas, USA over 2 yr, and two in Texas, USA comparing flooded and nonflood treatments) on a large number of common rice cultivars (c. 300) representing genetic diversity among international rice cultivars. • Within each field there was a 3-34 fold range in grain As concentration which varied between rice subpopulations. Importantly, As(i) correlated strongly with total As among a subset of 40 cultivars harvested in Bangladesh and China. • Genetic variation at all field sites was a large determining factor for grain As concentration, indicating that cultivars low in grain As could be developed through breeding. The temperate japonicas exhibited lower grain As compared with other subpopulations. Effects for year, location and flooding management were also statistically significant, suggesting that breeding strategies must take into account environmental factors.

Proteomic and genetic approaches to identifying defence-related proteins in rice challenged with the fungal pathogen Rhizoctonia solani.

SUMMARY Sheath blight, caused by the fungus Rhizoctonia solani, is a major disease of rice world-wide, but little is known about the host response to infection. The objective of this study was to identify proteins and DNA markers in resistant and susceptible rice associated with response to infection by R. solani. Replicated two-dimensional polyacrylamide gel electrophoresis experiments were conducted to detect proteins differentially expressed under inoculated and non-inoculated conditions. Tandem mass spectra analysis using electrospray ionization quadrupole-time of flight mass spectrometry (ESI Q-TOF MS) was carried out for protein identification with the NCBI non-redundant protein database. Seven proteins were increased after inoculation in both susceptible and resistant plants. Six of the seven proteins were identified with presumed antifungal, photosynthetic and proteolytic activities. An additional 14 proteins were detected in the response of the resistant line. Eleven of the 14 proteins were identified with presumed functions relating to antifungal activity, signal transduction, energy metabolism, photosynthesis, molecular chaperone, proteolysis and antioxidation. The induction of 3-beta-hydroxysteroid dehydrogenase/isomerase was detected for the first time in resistant rice plants after pathogen challenge, suggesting a defensive role of this enzyme in rice against attack by R. solani. The chromosomal locations of four induced proteins were found to be in close physical proximity to genetic markers for sheath blight resistance in two genetic mapping populations. The proteomic and genetic results from this study indicate a complex response of rice to challenge by R. solani that involves simultaneous induction of proteins from multiple defence pathways.