Genetic Dissection and Simultaneous Improvement of Drought and Low Nitrogen Tolerances by Designed QTL Pyramiding in Rice

KASP: a high-throughput genotyping system and its applications in major crop plants for biotic and abiotic stress tolerance

Advances in plant molecular breeding have resulted in the development of new varieties with superior traits, thus improving the crop germplasm. Breeders can screen a large number of accessions without rigorous and time-consuming phenotyping by marker-assisted selection (MAS). Molecular markers are one of the most imperative tools in plant breeding programmes for MAS to develop new cultivars possessing multiple superior traits. Single nucleotide polymorphisms (SNPs) are ideal for MAS due to their low cost, low genotyping error rates, and reproducibility. Kompetitive Allele Specific PCR (KASP) is a globally recognized technology for SNP genotyping. KASP is an allele-specific oligo extension-based PCR assay that uses fluorescence resonance energy transfer (FRET) to detect genetic variations such as SNPs and insertions/deletions (InDels) at a specific locus. Additionally, KASP allows greater flexibility in assay design, which leads to a higher success rate and the capability to genotype a large population. Its versatility and ease of use make it a valuable tool in various fields, including genetics, agriculture, and medical research. KASP has been extensively used in various plant-breeding applications, such as the identification of germplasm resources, quality control (QC) analysis, allele mining, linkage mapping, quantitative trait locus (QTL) mapping, genetic map construction, trait-specific marker development, and MAS. This review provides an overview of the KASP assay and emphasizes its validation in crop improvement related to various biotic and abiotic stress tolerance and quality traits.

Gene based markers improve precision of genome-wide association studies and accuracy of genomic predictions in rice breeding

It is hypothesized that the genome-wide genic markers may increase the prediction accuracy of genomic selection for quantitative traits. To test this hypothesis, a set of candidate gene-based markers for yield and grain traits-related genes cloned across the rice genome were custom-designed. A multi-model, multi-locus genome-wide association study (GWAS) was performed using new genic markers developed to test their effectiveness for gene discovery. Two multi-locus models, FarmCPU and mrMLM, along with a single-locus mixed linear model (MLM), identified 28 significant marker-trait associations. These associations revealed novel causative alleles for grain weight and pleiotropic associations with other traits. For instance, the marker YD91 derived from the gene OsAAP3 on chromosome 1 was consistently associated with grain weight, while the gene has a significant effect on grain yield. Furthermore, nine genomic selection methods, including regression-based and machine learning-based models, were used to predict grain weight using a leave-one-out five-fold cross-validation approach to optimize the genomic selection model with genic markers. Among nine prediction models, Kernel Hilbert Space Regression (RKHS) is the best among regression-based models, and Random Forest Regression (RFR) is the best among machine learning-based models. Genomic prediction accuracies with and without GWAS significant markers were compared to assess the effectiveness of markers. The rapid decreases in prediction accuracy upon dropping GWAS significant markers indicate the effectiveness of new genic markers in genomic selection. Apart from that, the candidate gene-based markers were found to be more effective in genomic selection programs for better accuracy.

Introgression Lines: Valuable Resources for Functional Genomics Research and Breeding in Rice (Oryza sativa L.)

The narrow base of genetic diversity of modern rice varieties is mainly attributed to the overuse of the common backbone parents that leads to the lack of varied favorable alleles in the process of breeding new varieties. Introgression lines (ILs) developed by a backcross strategy combined with marker-assisted selection (MAS) are powerful prebreeding tools for broadening the genetic base of existing cultivars. They have high power for mapping quantitative trait loci (QTLs) either with major or minor effects, and are used for precisely evaluating the genetic effects of QTLs and detecting the gene-by-gene or gene-by-environment interactions due to their low genetic background noise. ILs developed from multiple donors in a fixed background can be used as an IL platform to identify the best alleles or allele combinations for breeding by design. In the present paper, we reviewed the recent achievements from ILs in rice functional genomics research and breeding, including the genetic dissection of complex traits, identification of elite alleles and background-independent and epistatic QTLs, analysis of genetic interaction, and genetic improvement of single and multiple target traits. We also discussed how to develop ILs for further identification of new elite alleles, and how to utilize IL platforms for rice genetic improvement.

Development of a Temperate Climate-Adapted indica Multi-stress Tolerant Rice Variety by Pyramiding Quantitative Trait Loci

Successful cultivation of rice (Oryza sativa L.) in many Asian countries requires submergence stress tolerance at the germination and early establishment stages. Two quantitative trait loci, Sub1 (conferring submergence tolerance) and AG1 (conferring anaerobic germination), were recently pyramided into a single genetic background, without compromising any desirable agronomic traits, leading to the development of Ciherang-Sub1 + AG1 (CSA). However, little research has been conducted to enhance plant tolerance to abiotic stress (submergence) and biotic stress (rice blast), which occur in a damp climate following flooding. The BC₂F₅ breeding line was phenotypically characterized using the AvrPi9 isolate. The biotic and abiotic stress tolerance of selected lines was tested under submergence stress and anaerobic germination conditions, and lines tolerant to each stress condition were identified through phenotypic and gene expression analyses. The Ciherang-Sub1 + AG1 + Pi9 (CSA-Pi9) line showed similar agronomic performance to its recurrent parent, CSA, but had significantly reduced chalkiness in field trials conducted in temperate regions. Unexpectedly, the CSA-Pi9 line also showed salinity tolerance. Thus, the breeding line newly developed in this study, CSA-Pi9, functioned under stress conditions, in which Sub1, AG1, and Pi9 play a role and had superior grain quality traits compared to its recurrent parent in temperate regions. We speculate that CSA-Pi9 will enable the establishment of climate-resilient rice cropping systems, particularly in East Asia.

Mapping of QTLs for Yield Traits Using F2:3:4 Populations Derived From Two Alien Introgression Lines Reveals qTGW8.1 as a Consistent QTL for Grain Weight From Oryza nivara

Wild introgressions play a crucial role in crop improvement by transferring important novel alleles and broadening allelic diversity of cultivated germplasm. In this study, two stable backcross alien introgression lines 166s and 14s derived from Swarn/Oryza nivara IRGC81848 were used as parents to generate populations to map quantitative trait loci (QTLs) for yield-related traits. Field evaluation of yield-related traits in F₂, F₃, and F₄ population was carried out in normal irrigated conditions during the wet season of 2015 and dry seasons of 2016 and 2018, respectively. Plant height, tiller number, productive tiller number, total dry matter, and harvest index showed a highly significant association to single plant yield in F₂, F₃, and F₄. In all, 21, 30, and 17 QTLs were identified in F₂, F_2:3, and F_2:4, respectively, for yield-related traits. QTLs qPH6.1 with 12.54% phenotypic variance (PV) in F₂, qPH1.1 with 13.01% PV, qTN6.1 with 10.08% PV in F_2:3, and qTGW6.1 with 15.19% PV in F_2:4 were identified as major effect QTLs. QTLs qSPY4.1 and qSPY6.1 were detected for grain yield in F₂ and F_2:3 with PV 8.5 and 6.7%, respectively. The trait enhancing alleles of QTLs qSPY4.1, qSPY6.1, qPH1.1, qTGW6.1, qTGW8.1, qGN4.1, and qTDM5.1 were from O. nivara. QTLs of the yield contributing traits were found clustered in the same chromosomal region. qTGW8.1 was identified in a 2.6 Mb region between RM3480 and RM3452 in all three generations with PV 6.1 to 9.8%. This stable and consistent qTGW8.1 allele from O. nivara can be fine mapped for identification of causal genes. From this population, lines C₂12, C₂124, C₂128, and C₂143 were identified with significantly higher SPY and C₂103, C₂116, and C₂117 had consistently higher thousand-grain weight values than both the parents and Swarna across the generations and are useful in gene discovery for target traits and further crop improvement.

From Green Super Rice to green agriculture: Reaping the promise of functional genomics research

Producing sufficient food with finite resources to feed the growing global population while having a smaller impact on the environment has always been a great challenge. Here, we review the concept and practices of Green Super Rice (GSR) that have led to a paradigm shift in goals for crop genetic improvement and models of food production for promoting sustainable agriculture. The momentous achievements and global deliveries of GSR have been fueled by the integration of abundant genetic resources, functional gene discoveries, and innovative breeding techniques with precise gene and whole-genome selection and efficient agronomic management to promote resource-saving, environmentally friendly crop production systems. We also provide perspectives on new horizons in genomic breeding technologies geared toward delivering green and nutritious crop varieties to further enhance the development of green agriculture and better nourish the world population.

Physiological and molecular implications of multiple abiotic stresses on yield and quality of rice

Abiotic stresses adversely affect rice yield and productivity, especially under the changing climatic scenario. Exposure to multiple abiotic stresses acting together aggravates these effects. The projected increase in global temperatures, rainfall variability, and salinity will increase the frequency and intensity of multiple abiotic stresses. These abiotic stresses affect paddy physiology and deteriorate grain quality, especially milling quality and cooking characteristics. Understanding the molecular and physiological mechanisms behind grain quality reduction under multiple abiotic stresses is needed to breed cultivars that can tolerate multiple abiotic stresses. This review summarizes the combined effect of various stresses on rice physiology, focusing on grain quality parameters and yield traits, and discusses strategies for improving grain quality parameters using high-throughput phenotyping with omics approaches.

Meta-QTL Analysis in Rice and Cross-Genome Talk of the Genomic Regions Controlling Nitrogen Use Efficiency in Cereal Crops Revealing Phylogenetic Relationship

The phenomenal increase in the use of nitrogenous fertilizers coupled with poor nitrogen use efficiency is among the most important threats to the environment, economic, and social health. During the last 2 decades, a number of genomic regions associated with nitrogen use efficiency (NUE) and related traits have been reported by different research groups, but none of the stable and major effect QTL have been utilized in the marker-assisted introgression/pyramiding program. Compiling the data available in the literature could be very useful in identifying stable and major effect genomic regions associated with the root and NUE-related trait improving the rice grain yield. In the present study, we performed meta-QTL analysis on 1,330 QTL from 29 studies published in the past 2 decades. A total of 76 MQTL with a stable effect over different genetic backgrounds and environments were identified. The significant reduction in the confidence interval of the MQTL compared to the initial QTL resulted in the identification of annotated and putative candidate genes related to the traits considered in the present study. A hot spot region associated with correlated traits on chr 1, 4, and 8 and candidate genes associated with nitrate transporters, nitrogen content, and ammonium uptake on chromosomes 2, 4, 6, and 8 have been identified. The identified MQTL, putative candidate genes, and their orthologues were validated on our previous studies conducted on rice and wheat. The research-based interventions such as improving nitrogen use efficiency via identification of major genomic regions and candidate genes can be a plausible, simple, and low-cost solution to address the challenges of the crop improvement program.

Comparative Genomic Analysis of Quantitative Trait Loci Associated With Micronutrient Contents, Grain Quality, and Agronomic Traits in Wheat (Triticum aestivum L.)

Comparative genomics and meta-quantitative trait loci (MQTLs) analysis are important tools for the identification of reliable and stable QTLs and functional genes controlling quantitative traits. We conducted a meta-analysis to identify the most stable QTLs for grain yield (GY), grain quality traits, and micronutrient contents in wheat. A total of 735 QTLs retrieved from 27 independent mapping populations reported in the last 13 years were used for the meta-analysis. The results showed that 449 QTLs were successfully projected onto the genetic consensus map which condensed to 100 MQTLs distributed on wheat chromosomes. This consolidation of MQTLs resulted in a three-fold reduction in the confidence interval (CI) compared with the CI for the initial QTLs. Projection of QTLs revealed that the majority of QTLs and MQTLs were in the non-telomeric regions of chromosomes. The majority of micronutrient MQTLs were located on the A and D genomes. The QTLs of thousand kernel weight (TKW) were frequently associated with QTLs for GY and grain protein content (GPC) with co-localization occurring at 55 and 63%, respectively. The co- localization of QTLs for GY and grain Fe was found to be 52% and for QTLs of grain Fe and Zn, it was found to be 66%. The genomic collinearity within Poaceae allowed us to identify 16 orthologous MQTLs (OrMQTLs) in wheat, rice, and maize. Annotation of promising candidate genes (CGs) located in the genomic intervals of the stable MQTLs indicated that several CGs (e.g., TraesCS2A02G141400, TraesCS3B02G040900, TraesCS4D02G323700, TraesCS3B02G077100, and TraesCS4D02G290900) had effects on micronutrients contents, yield, and yield-related traits. The mapping refinements leading to the identification of these CGs provide an opportunity to understand the genetic mechanisms driving quantitative variation for these traits and apply this information for crop improvement programs.

Integration of Dual Stress Transcriptomes and Major QTLs from a Pair of Genotypes Contrasting for Drought and Chronic Nitrogen Starvation Identifies Key Stress Responsive Genes in Rice

We report here the genome-wide changes resulting from low N (N-W+), low water (N+W-)) and dual stresses (N-W-) in root and shoot tissues of two rice genotypes, namely, IR 64 (IR64) and Nagina 22 (N22), and their association with the QTLs for nitrogen use efficiency. For all the root parameters, except for root length under N-W+, N22 performed better than IR64. Chlorophyll a, b and carotenoid content were higher in IR64 under N+W+ treatment and N-W+ and N+W- stresses; however, under dual stress, N22 had higher chlorophyll b content. While nitrite reductase, glutamate synthase (GS) and citrate synthase assays showed better specific activity in IR64, glutamate dehydrogenase showed better specific activity in N22 under dual stress (N-W-); the other N and C assimilating enzymes showed similar but low specific activities in both the genotypes. A total of 8926 differentially expressed genes (DEGs) were identified compared to optimal (N+W+) condition from across all treatments. While 1174, 698 and 903 DEGs in IR64 roots and 1197, 187 and 781 in N22 roots were identified, nearly double the number of DEGs were found in the shoot tissues; 3357, 1006 and 4005 in IR64 and 4004, 990 and 2143 in N22, under N-W+, N+W- and N-W- treatments, respectively. IR64 and N22 showed differential expression in 15 and 11 N-transporter genes respectively, under one or more stress treatments, out of which four showed differential expression also in N+W- condition. The negative regulators of N- stress, e.g., NIGT1, OsACTPK1 and OsBT were downregulated in IR64 while in N22, OsBT was not downregulated. Overall, N22 performed better under dual stress conditions owing to its better root architecture, chlorophyll and porphyrin synthesis and oxidative stress management. We identified 12 QTLs for seed and straw N content using 253 recombinant inbred lines derived from IR64 and N22 and a 5K SNP array. The QTL hotspot region on chromosome 6 comprised of 61 genes, of which, five were DEGs encoding for UDP-glucuronosyltransferase, serine threonine kinase, anthocyanidin 3-O-glucosyltransferase, and nitrate induced proteins. The DEGs, QTLs and candidate genes reported in this study can serve as a major resource for both rice improvement and functional biology.

QTL Mapping and Favorable Allele Mining of Nitrogen Deficiency Tolerance Using an Interconnected Breeding Population in Rice

Nitrogen is one of the most important nutrients for rice growth and development. Breeding of nitrogen deficiency tolerance (NDT) variety is considered to be the most economic measure to solve the constrain of low nitrogen stress on grain yield in rice. An interconnected breeding (IB) population of 497 lines developed using Huanghuazhan (HHZ) as the recurrent parent and eight elite lines as the donor parents were tested for five traits including grain yield, biomass, harvest index, thousand grain weight, and spikelet fertility under two nitrogen treatments in three growing seasons. Association analysis using 7,388 bins generated by sequencing identified a total of 14, 14, and 12 QTLs for the five traits under low nitrogen (LN), normal nitrogen (NN), and LN/NN conditions, respectively, across three seasons. Favorable alleles were dissected for the 40 QTLs at the 10 NDT regions, and OM1723 was considered as the most important parent with the highest frequency of favorable alleles contributing to NDT-related traits. Six superior lines all showed significantly higher GY in LN environments and similar GY under NN environments except for H10. Substitution mapping using near-isogenic introgression lines delimited the qTGW2-1, which was identified on chromosome 2 under LN, NN, and LN/NN conditions into two QTLs, which were located in the two regions of about 200 and 350 kb with different favorable alleles. The bins 16, 1301, 1465, 1486, 3464, and 6249 harbored the QTLs for NDT detected in this study, and the QTLs/genes previously identified for NDT or nitrogen use efficiency (NUE) could be used for enhancing NDT and NUE by marker-assisted selection (MAS).

Leaf mass area determines water use efficiency through its influence on carbon gain in rice mutants

Saving water and enhancing rice productivity are consensually the most important research goals globally. While increasing canopy cover would enhance growth rates by higher photosynthetic carbon gain, an accompanied increase in transpiration would have a negative impact on saving water as well as for sustainability under water-limited conditions. Increased water use efficiency (WUE) by virtue of higher carbon assimilatory capacity can significantly circumvent this trade-off. Here, we report leaf mass area (LMA) has an important canopy architecture trait which when combined with superior carboxylation efficiency (CE) would achieve higher water productivity in rice. A set of 130 ethyl methanesulfonate induced mutants of an upland cultivar Nagina-22 (N22), was screened for leaf morphological traits leading to the identification of mutants differing in LMA. The wild-type, N22, along with a selected low-LMA (380-4-3) and two high-LMA mutants (392-9-1 and 457-1-3), all with comparable total leaf area, were raised under well-watered (100% Field Capacity (FC)) and water-limited (60% FC) conditions. Low Δ¹³ C and a higher RuBisCO content in high-LMA mutants indicated higher carboxylation efficiency, leading to increased carbon gain. Single parent backcross populations developed by crossing high and the low-LMA mutants with N22, separately, were screened for LMA, Δ¹³ C and growth traits. Comparison of dry matter accumulation per unit leaf area among the progenies differing in LMA and Δ¹³ C reiterated the association of LMA with CE. Results illustrated that high-LMA when combined with higher CE (low Δ¹³ C) lead to increased WUE and growth rates.

Genomic Breeding of Green Super Rice Varieties and Their Deployment in Asia and Africa

KEY MESSAGE

The "Green Super Rice" (GSR) project aims to fundamentally transform crop production techniques and promote the development of green agriculture based on functional genomics and breeding of GSR varieties by whole-genome breeding platforms. Rice (Oryza sativa L.) is one of the leading food crops of the world, and the safe production of rice plays a central role in ensuring food security. However, the conflicts between rice production and environmental resources are becoming increasingly acute. For this reason, scientists in China have proposed the concept of Green Super Rice for promoting resource-saving and environment-friendly rice production, while still achieving a yield increase and quality improvement. GSR is becoming one of the major goals for agricultural research and crop improvement worldwide, which aims to mine and use vital genes associated with superior agronomic traits such as high yield, good quality, nutrient efficiency, and resistance against insects and stresses; establish genomic breeding platforms to breed and apply GSR; and set up resource-saving and environment-friendly cultivation management systems. GSR has been introduced into eight African and eight Asian countries and has contributed significantly to rice cultivation and food security in these countries. This article mainly describes the GSR concept and recent research progress, as well as the significant achievements in GSR breeding and its application.

Development of nutritious rice with high zinc/selenium and low cadmium in grains through QTL pyramiding

Enriching zinc (Zn) and selenium (Se) levels, while reducing cadmium (Cd) concentration in rice grains is of great benefit for human diet and health. Large natural variations in grain Zn, Se, and Cd concentrations in different rice accessions enable Zn/Se-biofortification and Cd-minimization through molecular breeding. Here, we report the development of new elite varieties by pyramiding major quantitative trait loci (QTLs) that significantly contribute to high Zn/Se and low Cd accumulation in grains. A chromosome segment substitution line CSSL^GCC7 with the PA64s-derived GCC7 allele in the 93-11 background, exhibited steadily higher Mn and lower Cd concentrations in grains than those of 93-11. This elite chromosome segment substitution line (CSSL) was used as the core breeding material to cross with CSSLs harboring other major QTLs for essential mineral elements, especially CSSL^GZC6 for grain Zn concentration and CSSL^GSC5 for grain Se concentration. The CSSL^GCC7+GZC6 and CSSL^GCC7+GSC5 exhibited lower Cd concentration with higher Zn and Se concentrations in grains, respectively. Our study thus provides elite materials for rice breeding targeting high Zn/Se and low Cd concentrations in grains.

Identification of main-effect quantitative trait loci (QTLs) for low-temperature stress tolerance germination- and early seedling vigor-related traits in rice (Oryza sativa L.)

An attempt was made in the current study to identify the main-effect and co-localized quantitative trait loci (QTLs) for germination and early seedling growth traits under low-temperature stress (LTS) conditions in rice. The plant material used in this study was an early backcross population of 230 introgression lines (ILs) in BCIF7 generation derived from the Weed Tolerant Rice-1 (WTR-1) (as the recipient) and Haoannong (HNG) (as the donor). Genetic analyses of LTS tolerance revealed a total of 27 main-effect quantitative trait loci (M-QTLs) mapped on 12 chromosomes. These QTLs explained more than 10% of phenotypic variance (PV), and average PV of 12.71% while employing 704 high-quality SNP markers. Of these 27 QTLs distributed on 12 chromosomes, 11 were associated with low-temperature germination (LTG), nine with low-temperature germination stress index (LTGS), five with root length stress index (RLSI), and two with biomass stress index (BMSI) QTLs, shoot length stress index (SLSI) and root length stress index (RLSI), seven with seed vigor index (SVI), and single QTL with root length (RL). Among them, five significant major QTLs (qLTG(I) 1 , qLTGS(I) 1-2 , qLTG(I) 5 , qLTGS(I) 5 , and qLTG(I) 7 ) mapped on chromosomes 1, 5, and 7 were associated with LTG and LTGS traits and the PV explained ranged from 16 to 23.3%. The genomic regions of these QTLs were co-localized with two to six QTLs. Most of the QTLs were growth stage-specific and found to harbor QTLs governing multiple traits. Eight chromosomes had more than four QTLs and were clustered together and designated as promising LTS tolerance QTLs (qLTTs), as qLTT 1 , qLTT 2 , qLTT 3 , qLTT 5 , qLTT 6 , qLTT 8 , qLTT 9 , and qLTT 11 . A total of 16 putative candidate genes were identified in the major M-QTLs and co-localized QTL regions distributed on different chromosomes. Overall, these significant genomic regions of M-QTLs are responsible for multiple traits and this suggested that these could serve as the best predictors of LTS tolerance at germination and early seedling growth stages. Furthermore, it is necessary to fine-map these regions and to find functional markers for marker-assisted selection in rice breeding programs for cold tolerance.

Genetic dissection of developmental responses of agro-morphological traits under different doses of nutrient fertilizers using high-density SNP markers

The production and productivity of rice (Oryza sativa L.) are primarily influenced by the application of the critical nutrients nitrogen (N), phosphorus (P), and potassium (K). However, excessive application of these fertilizers is detrimental to the environment and increases the cost of production. Hence, there is a need to develop varieties that simultaneously increase yields under both optimal and suboptimal rates of fertilizer application by maximizing nutrient use efficiency (NuUE). To unravel the hidden genetic variation and understand the molecular and physiological mechanisms of NuUE, three different mapping populations (MPs; BC1F5) derived from three donors (Haoannong, Cheng-Hui 448, and Zhong 413) and recipient Weed Tolerant Rice 1 were developed. A total of three favorable agronomic traits (FATs) were considered as the measure of NuUE. Analysis of variance and descriptive statistics indicated the existence of genetic variation for NuUE and quantitative inheritance of FATs. The genotypic data from single-nucleotide polymorphism (SNP) markers from Tunable Genotyping-By-Sequencing (tGBS) and phenotypic values were used for locating the genomic regions conferring NuUE. A total of 19 quantitative trait loci (QTLs) were detected, out of which 11 QTLs were putative on eight chromosomes, which individually explained 17.02% to 34.85% of the phenotypic variation. Notably, qLC-II_1 and qLC-II_11 detected at zero fertilizer application showed higher performance for LC under zero percentage of NPK fertilizer. The remarkable findings of the present study are that the detected QTLs were associated in building tolerance to low/no nutrient application and six candidate genes on chromosomes 2 and 5 within these putative QTLs were found associated with low nutrient tolerance and related to several physiological and metabolic pathways involved in abiotic stress tolerance. The identified superior introgressed lines (ILs) and trait-associated genetic regions can be effectively used in marker-assisted selection (MAS) for NuUE breeding programs.

QTL and QTL networks for cold tolerance at the reproductive stage detected using selective introgression in rice

Low temperature stress is one of the major abiotic stresses limiting the productivity of Geng (japonica) rice grown the temperate regions as well as in tropical high lands worldwide. To develop rice varieties with improved cold tolerance (CT) at the reproductive stage, 84 BC2 CT introgression lines (ILs) were developed from five populations through backcross breeding. These CT ILs plus 310 random ILs from the same BC populations were used for dissecting genetic networks underlying CT in rice by detecting QTLs and functional genetic units (FGUs) contributing to CT. Seventeen major QTLs for CT were identified using five selective introgression populations and the method of segregation distortion. Of them, three QTLs were confirmed using the random populations and seven others locate in the regions with previously reported CT QTLs/genes. Using multi-locus probability tests and linkage disequilibrium (LD) analyses, 46 functional genetic units (FGUs) (37 single loci and 9 association groups or AGs) distributed in 37 bins (~20%) across the rice genome for CT were detected. Together, each of the CT loci (bins) was detected in 1.7 populations, including 18 loci detected in two or more populations. Putative genetic networks (multi-locus structures) underlying CT were constructed based on strong non-random associations between or among donor alleles at the unlinked CT loci/FGUs identified in the CT ILs, suggesting the presence of strong epistasis among the detected CT loci. Our results demonstrated the power and usefulness of using selective introgression for simultaneous improvement and genetic dissection of complex traits such as CT in rice.

Molecular Genetics and Breeding for Nutrient Use Efficiency in Rice

In the coming decades, rice production needs to be carried out sustainably to keep the balance between profitability margins and essential resource input costs. Many fertilizers, such as N, depend primarily on fossil fuels, whereas P comes from rock phosphates. How long these reserves will last and sustain agriculture remains to be seen. Therefore, current agricultural food production under such conditions remains an enormous and colossal challenge. Researchers have been trying to identify nutrient use-efficient varieties over the past few decades with limited success. The concept of nutrient use efficiency is being revisited to understand the molecular genetic basis, while much of it is not entirely understood yet. However, significant achievements have recently been observed at the molecular level in nitrogen and phosphorus use efficiency. Breeding teams are trying to incorporate these valuable QTLs and genes into their rice breeding programs. In this review, we seek to identify the achievements and the progress made so far in the fields of genetics, molecular breeding and biotechnology, especially for nutrient use efficiency in rice.

Genome-wide association mapping of aluminum toxicity tolerance and fine mapping of a candidate gene for Nrat1 in rice

Aluminum (Al) stress is becoming the major limiting factor in crop production in acidic soils. Rice has been reported as the most Al-tolerant crop and the capacity of Al toxicity tolerance is generally evaluated by comparing root growth under Al stress. Here, we performed an association mapping of Al toxicity tolerance using a core collection of 211 indica rice accessions with 700 K high quality SNP data. A total of 21 putative QTL affecting shoot height (SH), root length (RL), shoot fresh weight (SFW), shoot dry weight (SDW), root dry weight (RDW) and shoot water content (SWC) were identified at seedling stage, including three QTL detected only under control condition, eight detected only under Al stress condition, ten simultaneously detected in both control and Al stress conditions, and seven were identified by stress tolerance index of their corresponding traits. Total of 21 candidate genes for 7 important QTL regions associated with Al toxicity tolerance were identified based on combined haplotype analysis and functional annotation, and the most likely candidate gene(s) for each important QTL were also discussed. Also a candidate gene Nrat1 on chromosome 2 was further fine-mapped using BSA-seq and linkage analysis in the F2 population derived from the cross of Al tolerant accession CC105 and super susceptible accession CC180. A new non-synonymous SNP variation was observed at Nrat1 between CC105 and CC180, which resulted in an amino-acid substitution from Ala (A) in CC105 to Asp (D) in CC180. Haplotype analysis of Nrat1 using 327 3K RGP accessions indicated that minor allele variations in aus and indica subpopulations decreased Al toxicity tolerance in rice. The candidate genes identified in this study provide valuable information for improvement of Al toxicity tolerance in rice. Our research indicated that minor alleles are important for QTL mapping and its application in rice breeding when natural gene resources are used.