Genome-wide and gene-based association mapping for rice eating and cooking characteristics and protein content

Understanding natural genetic variation for nutritional quality in grain and identification of superior haplotypes in deepwater rice genotypes of Assam, India

Rice grown under deepwater ecosystem is considered to be natural farming and hence they are considered to be input efficient. Thus, to identify gene responsible for nutritional content under natural conditions, a genome-wide association study (GWAS)was performed. GWAS identified single nucleotide polymorphisms (SNPs) significantly associated with various nutritional quality traits such as Zn (mg/kg), Fe (mg/kg), Protein (%), Oil (%), Amylose (%), Starch (%), Phytic acid (%), Phenol (%) and TDF (%) in 184 deepwater rice accessions evaluated over 2 consecutive years. A total of 278 SNPs distributed across 12 chromosomes were found to be significantly associated with Zn, Oil and Phenol content. Among them, eight high confidence SNPs were significant and identified on chr1 (AX-95933712), chr7 (AX-95957036), and chr8 (AX-95965181) for Zn content. Similarly, on chr2 (AX-95945186), chr8 (AX-95964718), and chr11 (AX-95961099) have been found to be associated with Oil content and on chr3 (AX-95922121) and chr4 (AX-95963889) for Phenol content. Genomic regions of ± 220 kb flanking the three consistent lowest p value containing SNPs for each trait were considered for finding superior haplotypes. These SNPs showed significant phenotypic variations with different identified haplotype blocks. The allelic variations with phenotypes were considered to be superior haplotypes i.e., Block 1: Hap 1 (ACCC) for high Zn content, Block 2: Hap 1 (CT) for high Oil content, and Block 2: Hap 1(CGGG) for low Phenol content. The discovered superior haplotype with high nutritional content could be important for understanding the mechanisms involving nutrient use efficiency. Thus, the present study demonstrated that developing rice varieties with appropriate nutritional quality traits will be possible through the incorporation of such superior haplotypes in breeding programs.

Development of soft computing-based models for forecasting water quality index of Lorestan Province, Iran

The Water Quality Index (WQI) is widely used as a classification indicator and essential parameter for water resources management projects. WQI combines several physical and chemical parameters into a single metric to measure the status of Water Quality. This study explores the application of five soft computing techniques, including Gene Expression Programming, Gaussian Process, Reduced Error Pruning Tree (REPt), Artificial Neural Network with FireFly (ANN-FFA), and combinations of Reduced Error Pruning Tree with bagging. These models aim to predict the WQI of Khorramabad, Biranshahr, and Alashtar sub-watersheds in Lorestan province, Iran. The dataset consists of 124 observations, with input variables being sulfate (SO4), total dissolved solids (TDS), the potential of Hydrogen (pH), chloride (Cl), electrical conductivity (EC), Potassium (K), bicarbonate (HCO), magnesium (Mg), sodium (Na), and calcium (Ca), and WQI as the output variable. For model creation (train subset) and model validation (test subset), the data were split into two subsets (train and test) in a ratio of 70:30. The performance evaluation parameters values of training and testing stages of various models indicate that the ANN-FFA based data-driven model performs better than the other modeling techniques applied with the values of coefficient of correlation 0.9990 & 0.9989; coefficient of determination 0.9612 & 0.9980; root mean square error 0.3036 & 0.3340; Nash-Sutcliffe error 0.9980 & 0.9979; and Mean average percentage error 0.7259% & 0.7969% for the train and test subsets, respectively. Taylor diagram results also suggest that ANN-FFA is the best-performing model, followed by the GEP model. This study introduces a novel model for predicting WQI using advanced soft computing models that have not been previously applied in this study area, highlighting its novelty and relevance. The proposed model significantly enhances predictive accuracy and efficiency, offering real-time, cost-effective WQI predictions that outperform traditional methods in handling complex, nonlinear environmental data.

Identification of superior and rare haplotypes to optimize branch number in soybean

KEY MESSAGE

Using the integrated approach in the present study, we identified eleven significant SNPs, seven stable QTLs and 20 candidate genes associated with branch number in soybean. Branch number is a key yield-related quantitative trait that directly affects the number of pods and seeds per soybean plant. In this study, an integrated approach with a genome-wide association study (GWAS) and haplotype and candidate gene analyses was used to determine the detailed genetic basis of branch number across a diverse set of soybean accessions. The GWAS revealed a total of eleven SNPs significantly associated with branch number across three environments using the five GWAS models. Based on the consistency of the SNP detection in multiple GWAS models and environments, seven genomic regions within the physical distance of ± 202.4 kb were delineated as stable QTLs. Of these QTLs, six QTLs were novel, viz., qBN7, qBN13, qBN16, qBN18, qBN19 and qBN20, whereas the remaining one, viz., qBN12, has been previously reported. Moreover, 11 haplotype blocks, viz., Hap4, Hap7, Hap12, Hap13A, Hap13B, Hap16, Hap17, Hap18, Hap19A, Hap19B and Hap20, were identified on nine different chromosomes. Haplotype allele number across the identified haplotype blocks varies from two to five, and different branch number phenotype is regulated by these alleles ranging from the lowest to highest through intermediate branching. Furthermore, 20 genes were identified underlying the genomic region of ± 202.4 kb of the identified SNPs as putative candidates; and six of them showed significant differential expression patterns among the soybean cultivars possessing contrasting branch number, which might be the potential candidates regulating branch number in soybean. The findings of this study can assist the soybean breeding programs for developing cultivars with desirable branch numbers.

Superior haplotypes of key drought-responsive genes reveal opportunities for the development of climate-resilient rice varieties

Haplotype-based breeding is an emerging and innovative concept that enables the development of designer crop varieties by exploiting and exploring superior alleles/haplotypes among target genes to create new traits in breeding programs. In this regard, whole-genome re-sequencing of 399 genotypes (landraces and breeding lines) from the 3000 rice genomes panel (3K-RG) is mined to identify the superior haplotypes for 95 drought-responsive candidate genes. Candidate gene-based association analysis reveals 69 marker-trait associations (MTAs) in 16 genes for single plant yield (SPY) under drought stress. Haplo-pheno analysis of these 16 genes identifies superior haplotypes for seven genes associated with the higher SPY under drought stress. Our study reveals that the performance of lines possessing superior haplotypes is significantly higher (p ≤ 0.05) as measured by single plant yield (SPY), for the OsGSK1-H4, OsDSR2-H3, OsDIL1-H22, OsDREB1C-H3, ASR3-H88, DSM3-H4 and ZFP182-H4 genes as compared to lines without the superior haplotypes. The validation results indicate that a superior haplotype for the DREB transcription factor (OsDREB1C) is present in all the drought-tolerant rice varieties, while it was notably absent in all susceptible varieties. These lines carrying the superior haplotypes can be used as potential donors in haplotype-based breeding to develop high-yielding drought-tolerant rice varieties.

QTL detection for rice grain storage protein content and genetic effect verifications

Rice grain quality is a multifarious attribute mainly governed by multiple nutritional factors. Grain protein is the central component of rice grain nutrition dominantly affecting eating-cooking qualities. Grain protein content is quantitatively influenced by its protein fractions. Genetic quantification of five protein fractions-albumins, globulins, prolamins, glutelin, and grain protein content-were evaluated by exploiting two BC3F2 mapping populations, derived from Kongyu131/TKM9 (population-I) and Kongyu131/Bg94-1 (population-II), which were grown in a single environment. Correlation studies among protein fractions and grain protein content were thoroughly investigated. A genetic linkage map was developed by using 146 single sequence repeat (SSR) markers in population-I and 167 markers in population-II. In total, 40 QTLs were delineated for five traits in both populations. Approximately 22 QTLs were dissected in population-I, derived from Kongyu131/TKM9, seven QTLs for albumin content, four QTLs for globulin content, three QTLs for prolamin content, four QTLs for glutelin content, and four QTLs for grain protein content. In total, 18 QTLs were detected in population-II, derived from Kongyu131/Bg94-1, five QTLs for albumin content, three QTLs for globulin content, four QTLs for prolamin content, two QTLs for glutelin content, and four QTLs for grain protein content. Three QTLs, qAlb7.1, Alb7.2, and qGPC7.2, derived from population-II (Kongyu131/Bg94-1) for albumin and grain protein content were successfully validated in the near isogenic line (NIL) populations. The localized chromosomal locus of the validated QTLs could be helpful for fine mapping via map-based cloning to discover underlying candidate genes. The functional insights of the underlying candidate gene would furnish novel perceptivity for the foundation of rice grain protein content and trigger the development of nutritionally important rice cultivars by combining marker-assisted selection (MAS) breeding.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-023-01436-7.

Integration of genome-wide association studies reveal loci associated with salt tolerance score of rice at the seedling stage

Salt threatens rice cultivation in many countries. Hence, breeding new varieties with high salt tolerance is important.A panel of 2,391 rice accessions from the 3 K Rice Genome Project was selected to evaluate salt tolerance via the standard evaluation score (SES) in hydroponics under 60 mM NaCl at the seedling stage. Three sub-population panels including 1,332, 628, and 386 accessions from the original 2,391 ones were constructed based on low relatedness revealed by a phylogenetic tree generated by Archaeopteryx Tree. A genome-wide association study (GWAS) was conducted on the entire and sub-population panels using SES data and a selection of 5, 10, 20, and 40% of SNPs selected from the original 1,011,601 SNPs by filtering minor allele frequency > 5% and missing rate < 5%. To perform GWAS, three methods implemented in three different software packages were utilized.Using the integration of GWAS programs, a total of four QTLs associated with SES scores were identified in different panels. Some QTLs co-located with previously detected QTL-related traits. qSES1.1 was detected in three panels, qSES1.3 and qSES2.1 in two panels, and qSES3.1 in one panel through GWAS by all three methods used and selected SNPs. These four QTLs were selected to detect candidate genes. Combining gene-based association study plus haplotype analysis in the entire population and the three sub-populations let us shortlist three candidate genes, viz. LOC_Os01g23640 and LOC_Os01g23680 for qSES1.1, and LOC_Os01g71240 for qSES1.3 region affecting salt tolerance. The identified QTLs and candidate genes provided useful materials and genetic information for future functional characterization and genetic improvement of salt tolerance in rice.

Identification of Candidate Genes that Affect the Contents of 17 Amino Acids in the Rice Grain Using a Genome-Wide Haplotype Association Study

BACKGROUND

The amino acid content (AAC) of the rice grain is one of the most important determinants of nutritional quality in rice. Understanding the genetic basis of grain AAC and mining favorable alleles of target genes for AAC are important for developing new cultivars with improved nutritional quality.

RESULTS

Using a diverse panel of 164 accessions genotyped by 32 M SNPs derived from 3 K Rice Genome Project, we extracted 1,123,603 high quality SNPs in 44,248 genes and used them to construct haplotypes. We measured the contents of the 17 amino acids that included seven essential amino acids and 10 dispensable amino acids. Through a genome-wide haplotype association study, 261 gene-trait associations containing 174 genes for the 17 components of AAC were detected, and 34 of these genes were associated with at least two components. Furthermore, the associated SNPs in genes were also identified by a traditional genome-wide association study to identify the key natural variations in the specific genes.

CONCLUSIONS

The genome-wide haplotype association study allowed us to detected candidate genes directly and to identify key natural genetic variation as well. In the present study, twelve genes have been cloned, and 34 genes were associated with at least two components, suggesting that the genome-wide haplotype association study approach used in the current study is an efficient way to identify candidate genes for target traits. The identified candidate genes, favorable haplotypes, and key natural variations affecting AAC provide valuable resources for further functional characterization and genetic improvement of rice nutritional quality.

Identification of quantitative trait loci controlling nitrogen use efficiency-related traits in rice at the seedling stage under salt condition by genome-wide association study

Rice cultivation is facing both salt intrusion and overuse of nitrogen fertilizers. Hence, breeding new varieties aiming to improve nitrogen use efficiency (NUE), especially under salt conditions, is indispensable. We selected 2,391 rice accessions from the 3K Rice Genomes Project to evaluate the dry weight under two N concentrations [2.86 mM - standard N (SN), and 0.36 mM - low N (LN)] crossed with two NaCl concentrations [0 (0Na) and 60 mM (60Na)] at the seedling stage. Genome-wide association studies for shoot, root, and plant dry weight (DW) were carried out. A total of 55 QTLs - 32, 16, and 7 in the whole, indica, and japonica panel - associated with one of the tested traits were identified. Among these, 27 QTLs co-localized with previously identified QTLs for DW-related traits while the other 28 were newly detected; 24, 8, 11, and 4 QTLs were detected in SN-0Na, LN-0Na, SN-60Na, and LN-60Na, respectively, and the remaining 8 QTLs were for the relative plant DW between treatments. Three of the 11 QTLs in SN-60Na were close to the regions containing three QTLs detected in SN-0Na. Eleven candidate genes for eight important QTLs were identified. Only one of them was detected in both SN-0Na and SN-60Na, while 5, 0, 3, and 2 candidate genes were identified only once under SN-0Na, LN-0Na, SN-60Na, and LN-60Na, respectively. The identified QTLs and genes provide useful materials and genetic information for future functional characterization and genetic improvement of NUE in rice, especially under salt conditions.

Association mapping for protein, total soluble sugars, starch, amylose and chlorophyll content in rice

BACKGROUND

Protein, starch, amylose and total soluble sugars are basic metabolites of seed that influence the eating, cooking and nutritional qualities of rice. Chlorophyll is responsible for the absorption and utilization of the light energy influencing photosynthetic efficiency in rice plant. Mapping of these traits are very important for detection of more number of robust markers for improvement of these traits through molecular breeding approaches.

RESULTS

A representative panel population was developed by including 120 germplasm lines from the initial shortlisted 274 lines for mapping of the six biochemical traits using 136 microsatellite markers through association mapping. A wide genetic variation was detected for the traits, total protein, starch, amylose, total soluble sugars, chlorophyll a, and chlorophyll b content in the population. Specific allele frequency, gene diversity, informative markers and other diversity parameters obtained from the population indicated the effectiveness of utilization of the population and markers for mapping of these traits. The fixation indices values estimated from the population indicated the existence of linkage disequilibrium for the six traits. The population genetic structure at K = 3 showed correspondence with majority of the members in each group for the six traits. The reported QTL, qProt1, qPC6.2, and qPC8.2 for protein content; qTSS8.1 for total soluble sugar; qAC1.2 for amylose content; qCH2 and qSLCHH for chlorophyll a (Chl. a) while qChl5D for chlorophyll b (Chl. b) were validated in this population. The QTL controlling total protein content qPC1.2; qTSS7.1, qTSS8.2 and qTSS12.1 for total soluble sugars; qSC2.1, qSC2.2, qSC6.1 and qSC11.1 for starch content; qAC11.1, qAC11.2 and qAC11.3 for amylose content; qChla8.1 for Chl. a content and qChlb7.1 and qChlb8.1 for Chl. b identified by both Generalized Linear Model and Mixed Linear Model were detected as novel QTL. The chromosomal regions on chromosome 8 at 234 cM for grain protein content and total soluble sugars and at 363 cM for Chl. a and Chl. b along with the position at 48 cM on chromosome 11 for starch and amylose content are genetic hot spots for these traits.

CONCLUSION

The validated, co-localized and the novel QTL detected in this study will be useful for improvement of protein, starch, amylose, total soluble sugars and chlorophyll content in rice.

Pinpointing Genomic Regions and Candidate Genes Associated with Seed Oil and Protein Content in Soybean through an Integrative Transcriptomic and QTL Meta-Analysis

Soybean with enriched nutrients has emerged as a prominent source of edible oil and protein. In the present study, a meta-analysis was performed by integrating quantitative trait loci (QTLs) information, region-specific association and transcriptomic analysis. Analysis of about a thousand QTLs previously identified in soybean helped to pinpoint 14 meta-QTLs for oil and 16 meta-QTLs for protein content. Similarly, region-specific association analysis using whole genome re-sequenced data was performed for the most promising meta-QTL on chromosomes 6 and 20. Only 94 out of 468 genes related to fatty acid and protein metabolic pathways identified within the meta-QTL region were found to be expressed in seeds. Allele mining and haplotyping of these selected genes were performed using whole genome resequencing data. Interestingly, a significant haplotypic association of some genes with oil and protein content was observed, for instance, in the case of FAD2-1B gene, an average seed oil content of 20.22% for haplotype 1 compared to 15.52% for haplotype 5 was observed. In addition, the mutation S86F in the FAD2-1B gene produces a destabilizing effect of (ΔΔG Stability) -0.31 kcal/mol. Transcriptomic analysis revealed the tissue-specific expression of candidate genes. Based on their higher expression in seed developmental stages, genes such as sugar transporter, fatty acid desaturase (FAD), lipid transporter, major facilitator protein and amino acid transporter can be targeted for functional validation. The approach and information generated in the present study will be helpful in the map-based cloning of regulatory genes, as well as for marker-assisted breeding in soybean.

Germplasm variability-assisted near infrared reflectance spectroscopy chemometrics to develop multi-trait robust prediction models in rice

Rice is a major staple food across the world in which wide variations in nutrient composition are reported. Rice improvement programs need germplasm accessions with extreme values for any nutritional trait. Near infrared reflectance spectroscopy (NIRS) uses electromagnetic radiations in the NIR region to rapidly measure the biochemical composition of food and agricultural products. NIRS prediction models provide a rapid assessment tool but their applicability is limited by the sample diversity, used for developing them. NIRS spectral variability was used to select a diverse sample set of 180 accessions, and reference data were generated using association of analytical chemists and standard methods. Different spectral pre-processing (up to fourth-order derivatization), scatter corrections (SNV-DT, MSC), and regression methods (partial least square, modified partial least square, and principle component regression) were employed for each trait. Best-fit models for total protein, starch, amylose, dietary fiber, and oil content were selected based on high RSQ, RPD with low SEP(C) in external validation. All the prediction models had ratio of prediction to deviation (RPD) > 2 amongst which the best models were obtained for dietary fiber and protein with R² = 0.945 and 0.917, SEP(C) = 0.069 and 0.329, and RPD = 3.62 and 3.46. A paired sample t-test at a 95% confidence interval was performed to ensure that the difference in predicted and laboratory values was non-significant.

Identification of superior haplotypes in a diverse natural population for breeding desirable plant height in soybean

KEY MESSAGE

Plant height of soybean is associated with a haplotype block on chromosome 19, which classified 211 soybean accessions into five distinct groups showing significant differences for the target trait. Genetic variation is pivotal for crop improvement. Natural populations are precious genetic resources. However, efficient strategies for the targeted utilization of these resources for quantitative traits, such as plant height (PH), are scarce. Being an important agronomic trait associated with soybean yield and quality, it is imperative to unravel the genetic mechanisms underlying PH in soybean. Here, a genome-wide association study (GWAS) was performed to identify single nucleotide polymorphisms (SNPs) significantly associated with PH in a natural population of 211 cultivated soybeans, which was genotyped with NJAU 355 K Soy SNP Array and evaluated across six environments. A total of 128 SNPs distributed across 17 chromosomes were found to be significantly associated with PH across six environments and a combined environment. Three significant SNPs were consistently identified in at least three environments on Chr.02 (AX-93958260), Chr.17 (AX-94154834), and Chr.19 (AX-93897200). Genomic regions of ~ 130 kb flanking these three consistent SNPs were considered as stable QTLs, which included 169 genes. Of these, 22 genes (including Dt1) were prioritized and defined as putative candidates controlling PH. The genomic region flanking 12 most significant SNPs was in strong linkage disequilibrium (LD). These SNPs formed a single haplotype block containing five haplotypes for PH, namely Hap-A, Hap-B, Hap-C, Hap-D, and Hap-E. Deployment of such superior haplotypes in breeding programs will enable development of improved soybean varieties with desirable plant height.

Features and applications of haplotypes in crop breeding

Climate change with altered pest-disease dynamics and rising abiotic stresses threatens resource-constrained agricultural production systems worldwide. Genomics-assisted breeding (GAB) approaches have greatly contributed to enhancing crop breeding efficiency and delivering better varieties. Fast-growing capacity and affordability of DNA sequencing has motivated large-scale germplasm sequencing projects, thus opening exciting avenues for mining haplotypes for breeding applications. This review article highlights ways to mine haplotypes and apply them for complex trait dissection and in GAB approaches including haplotype-GWAS, haplotype-based breeding, haplotype-assisted genomic selection. Improvement strategies that efficiently deploy superior haplotypes to hasten breeding progress will be key to safeguarding global food security.

Genome-Wide Association Mapping of Mixed Linkage (1,3;1,4)-β-Glucan and Starch Contents in Rice Whole Grain

The glucan content of rice is a key factor defining its nutritional and economic value. Starch and its derivatives have many industrial applications such as in fuel and material production. Non-starch glucans such as (1,3;1,4)-β-D-glucan (mixed-linkage β-glucan, MLG) have many benefits in human health, including lowering cholesterol, boosting the immune system, and modulating the gut microbiome. In this study, the genetic variability of MLG and starch contents were analyzed in rice (Oryza sativa L.) whole grain, by performing a new quantitative analysis of the polysaccharide content of rice grains. The 197 rice accessions investigated had an average MLG content of 252 μg/mg, which was negatively correlated with the grain starch content. A new genome-wide association study revealed seven significant quantitative trait loci (QTLs) associated with the MLG content and two QTLs associated with the starch content in rice whole grain. Novel genes associated with the MLG content were a hexose transporter and anthocyanidin 5,3-O-glucosyltransferase. Also, the novel gene associated with the starch content was a nodulin-like domain. The data pave the way for a better understanding of the genes involved in determining both MLG and starch contents in rice grains and should facilitate future plant breeding programs.

Influence of starch fine structure and storage proteins on the eating quality of rice varieties with similar amylose contents

BACKGROUND

Rice eating quality largely dictates consumer preference, and the demand for new rice varieties with excellent eating quality from farmers is increasing. Identification of factors contributing to eating quality is helpful for developing high-quality rice varieties.

RESULTS

Two groups of rice with different apparent amylose content (AACs) were used in this study. One group contained four varieties with low AACs (8.8-9.4%), whereas the other contained four traditional varieties with medium AACs (17.2-17.5%). The physicochemical properties, starch fine structure and crystallinity and storage protein composition of the two groups were analyzed. We found that, in both groups, the rice varieties with high eating quality had more short-chain amylopectin, lower glutelin and prolamin content, and a higher albumin content. In addition, the low-AAC varieties produced opaque endosperms, which may result from an increased number of pores in the center of starch granules.

CONCLUSIONS

Both the fine structure of starch and the storage protein composition were closely related to rice eating quality. In both groups, short branch-chain amylopectin, short-chain amylopectin [degree of polymerization (DP) 6-12], and albumin had positive effects on eating quality. By contrast, long branch-chain amylopectin, long-chain amylopectin (DP 35-60), glutelin and prolamin had adverse effects on eating quality of rice. © 2020 Society of Chemical Industry.

Breeding of High Cooking and Eating Quality in Rice by Marker-Assisted Backcrossing (MABc) Using KASP Markers

The primary goals of rice breeding programs are grain quality and yield potential improvement. With the high demand for rice varieties of premium cooking and eating quality, we developed low-amylose content breeding lines crossed with Samgwang and Milkyqueen through the marker-assisted backcross (MABc) breeding program. Trait markers of the SSIIIa gene referring to low-amylose content were identified through an SNP mapping activity, and the markers were applied to select favorable lines for a foreground selection. To rapidly recover the genetic background of Samgwang (recurrent parent genome, RPG), 386 genome-wide markers were used to select BC₁F₁ and BC₂F₁ individuals. Seven BC₂F₁ lines with targeted traits were selected, and the genetic background recovery range varied within 97.4–99.1% of RPG. The amylose content of the selected BC₂F₂ grains ranged from 12.4–16.8%. We demonstrated the MABc using a trait and genome-wide markers, allowing us to efficiently select lines of a target trait and reduce the breeding cycle effectively. In addition, the BC₂F₂ lines confirmed by molecular markers in this study can be utilized as parental lines for subsequent breeding programs of high-quality rice for cooking and eating.

Development and characterization of chromosome segment substitution lines derived from backcross between japonica donor rice cultivar Yukihikari and japonica recipient cultivar Kirara397

Grain yield-related traits and grain quality-related traits are important for rice cultivars. The quantitative trait loci (QTLs) involved in controlling the natural variation in these traits among closely related cultivars are still unclear. The present study describes the development of a novel chromosome segment substitution line (CSSL) population derived from a cross between the temperate japonica cultivars Yukihikari and Kirara397, which are grown in Hokkaido, the northernmost limit for rice cultivation. Days to heading, culm length, panicle length, panicle number, brown grain weight per plant, thousand brown grain weight, brown grain length, brown grain width, brown grain thickness, apparent amylose content, and protein content were evaluated. Panicle length, brown grain length and amylose content differed significantly in the parental cultivars. Thirty-five significant changes in the evaluated traits were identified in the CSSLs. A total of 28 QTLs were located on chromosomes 1, 2, 3, 4, 5, 6, 8, 9, 10, 11 and 12. These findings could be useful for breeding rice cultivars in the northernmost limit for rice cultivation.

Benefiting others and self: Production of vitamins in plants

Vitamins maintain growth and development in humans, animals, and plants. Because plants serve as essential producers of vitamins, increasing the vitamin contents in plants has become a goal of crop breeding worldwide. Here, we begin with a summary of the functions of vitamins. We then review the achievements to date in elucidating the molecular mechanisms underlying how vitamins are synthesized, transported, and regulated in plants. We also stress the exploration of variation in vitamins by the use of forward genetic approaches, such as quantitative trait locus mapping and genome-wide association studies. Overall, we conclude that exploring the diversity of vitamins could provide new insights into plant metabolism and crop breeding.

Superior haplotypes for haplotype-based breeding for drought tolerance in pigeonpea (Cajanus cajan L.)

Haplotype-based breeding, a recent promising breeding approach to develop tailor-made crop varieties, deals with identification of superior haplotypes and their deployment in breeding programmes. In this context, whole genome re-sequencing data of 292 genotypes from pigeonpea reference set were mined to identify the superior haplotypes for 10 drought-responsive candidate genes. A total of 83, 132 and 60 haplotypes were identified in breeding lines, landraces and wild species, respectively. Candidate gene-based association analysis of these 10 genes on a subset of 137 accessions of the pigeonpea reference set revealed 23 strong marker-trait associations (MTAs) in five genes influencing seven drought-responsive component traits. Haplo-pheno analysis for the strongly associated genes resulted in the identification of most promising haplotypes for three genes regulating five component drought traits. The haplotype C. cajan_23080-H2 for plant weight (PW), fresh weight (FW) and turgid weight (TW), the haplotype C. cajan_30211-H6 for PW, FW, TW and dry weight (DW), the haplotype C. cajan_26230-H11 for FW and DW and the haplotype C. cajan_26230-H5 for relative water content (RWC) were identified as superior haplotypes under drought stress condition. Furthermore, 17 accessions containing superior haplotypes for three drought-responsive genes were identified. The identified superior haplotypes and the accessions carrying these superior haplotypes will be very useful for deploying haplotype-based breeding to develop next-generation tailor-made better drought-responsive pigeonpea cultivars.

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.

Identifying genes for resistant starch, slowly digestible starch, and rapidly digestible starch in rice using genome-wide association studies

BACKGROUND

The digestibility of starch is important for the nutritive value of staple food. Although several genes are responsible for resistant starch (RS) and slowly digestible starch (SDS), gaps persist concerning the molecular basis of RS and SDS formation due to the complex genetic mechanisms of starch digestibility.

OBJECTIVES

The objective of this study was to identify new genes for starch digestibility in rice and interprete the genetic mechanisms of RS and SDS by GWAS.

METHODS

Genome-wide association studies were conducted by associating the RS and SDS phenotypes of 104 re-sequenced rice lines to an SNP dataset of 2,288,867 sites using a compressed mixed linear model. Candidate genes were identified according to the position of the SNPs based on data from the MSU Rice Genome Annotation Project.

RESULTS

Seven quantitative trait loci (QTLs) were detected to be associated with the RS content, among which the SNP 6 m1765761 was located on Waxy. Starch branching enzymes IIa (BEIIa) close to QTL qRS-I4 was detected and further identified as a specific candidate gene for RS in INDICA. Two QTLs were associated with SDS, and the LOC_Os09g09360 encoding lipase was identified as a causal gene for SDS.

CONCLUSIONS

GWAS is a valid strategy to genetically dissect the formation of starch digestion properties in rice. RS formation in grains is dependent on the rice type; lipid might also contribute to starch digestibility and should be an alternative factor to improve rice starch digestibility.

Genome-wide association study identifies candidate genes related to oleic acid content in soybean seeds

BACKGROUND

Soybean oil is a complex mixture of five fatty acids (palmitic, stearic, oleic, linoleic, and linolenic). Soybean oil with a high oleic acid content is desirable because this monounsaturated fatty acid improves the oxidative stability of the oil. To investigate the genetic architecture of oleic acid in soybean seeds, 260 soybean germplasms from Northeast China were collected as natural populations. A genome-wide association study (GWAS) was conducted on a panel of 260 germplasm resources.

RESULTS

Phenotypic identification results showed that the oleic acid content varied from 8.2 to 35.0%. A total of 2,311,337 single-nucleotide polymorphism (SNP) markers were obtained. GWAS analysis showed that there were many genes related to oleic acid content with a contribution rate of 7%. The candidate genes Glyma.11G229600.1 on chromosome 11 and Glyma.04G102900.1 on chromosome 4 were detected in a 2-year-long GWAS. The candidate gene Glyma.11G229600.1 showed a positive correlation with the oleic acid content, and the correlation coefficient was 0.980, while Glyma.04G102900.1 showed a negative correlation, with a coefficient of - 0.964.

CONCLUSIONS

Glyma.04G102900.1 on chromosome 4 and Glyma.11G229600.1 on chromosome 11 were detected in both analyses (2018 and 2019). Glyma.04G102900.1 and Glyma.11G229600.1 are new key candidate genes related to oleic acid in soybean seeds. These results will be useful for high-oleic soybean breeding.

Mapping QTLs underpin nutrition components in aromatic rice germplasm

As rice is an important staple food globally, research for development and enhancement of its nutritional value it is an imperative task. Identification of nutrient enriched rice germplasm and exploiting them for breeding programme is the easiest way to develop better quality rice. In this study, we analyzed 113 aromatic rice germplasm in order to identify quantitative trait loci (QTL) underpinning nutrition components and determined by measuring the normal frequency distribution for Fe, Zn, amylose, and protein content in those rice germplasm. Comparatively, the germplasm Radhuni pagal, Kalobakri, Thakurbhog (26.6 ppm) and Hatisail exhibited the highest mean values for Fe (16.9 ppm), Zn (34.1 ppm), amylose (26.6 ppm) and protein content (11.0 ppm), respectively. Moreover, a significant linear relationship (R2 = 0.693) was observed between Fe and Zn contents. Cluster analysis based on Mahalanobis D2 distances revealed four major clusters of 113 rice germplasm, with cluster III containing a maximum 37 germplasm and a maximum inter-cluster distance between clusters III and IV. The 45 polymorphic SSRs and four trait associations exhibited eight significant quantitative trait loci (QTL) located on eight different chromosomes using composite interval mapping (CIM). The highly significant QTL (variance 7.89%, LOD 2.02) for protein content (QTL.pro.1) was observed on chromosome 1 at 94.9cM position. Also, four QTLs for amylose content were observed with the highly significant QTL.amy.8 located on chromosome 8 exhibiting 7.2% variance with LOD 1.83. Only one QTL (QTL.Fe.9) for Fe content was located on chromosome 9 (LOD 1.24), and two (QTL.Zn.4 and QTL.Zn.5) for Zn on chromosome 4 (LOD 1.71) and 5 (LOD 1.18), respectively. Overall, germplasm from clusters III and IV might offer higher heterotic response with the identified QTLs playing a significant role in any rice biofortification breeding program and released with development of new varieties.

Whole-Genome Sequencing of the NARO World Rice Core Collection (WRC) as the Basis for Diversity and Association Studies

Genebanks provide access to diverse materials for crop improvement. To utilize and evaluate them effectively, core collections, such as the World Rice Core Collection (WRC) in the Genebank at the National Agriculture and Food Research Organization, have been developed. Because the WRC consists of 69 accessions with a high degree of genetic diversity, it has been used for >300 projects. To allow deeper investigation of existing WRC data and to further promote research using Genebank rice accessions, we performed whole-genome resequencing of these 69 accessions, examining their sequence variation by mapping against the Oryza sativa ssp. japonica Nipponbare genome. We obtained a total of 2,805,329 single nucleotide polymorphisms (SNPs) and 357,639 insertion-deletions. Based on the principal component analysis and population structure analysis of these data, the WRC can be classified into three major groups. We applied TASUKE, a multiple genome browser to visualize the different WRC genome sequences, and classified haplotype groups of genes affecting seed characteristics and heading date. TASUKE thus provides access to WRC genotypes as a tool for reverse genetics. We examined the suitability of the compact WRC population for genome-wide association studies (GWASs). Heading date, affected by a large number of quantitative trait loci (QTLs), was not associated with known genes, but several seed-related phenotypes were associated with known genes. Thus, for QTLs of strong effect, the compact WRC performed well in GWAS. This information enables us to understand genetic diversity in 37,000 rice accessions maintained in the Genebank and to find genes associated with different phenotypes. The sequence data have been deposited in DNA Data Bank of Japan Sequence Read Archive (DRA) (Supplementary Table S1).

Plant-microbe Interactions for Sustainable Agriculture in the Post-genomic Era

Plant-microbe interactions are both symbiotic and antagonistic, and the knowledge of both these interactions is equally important for the progress of agricultural practice and produce. This review gives an insight into the recent advances that have been made in the plant-microbe interaction study in the post-genomic era and the application of those for enhancing agricultural production. Adoption of next-generation sequencing (NGS) and marker assisted selection of resistant genes in plants, equipped with cloning and recombination techniques, has progressed the techniques for the development of resistant plant varieties by leaps and bounds. Genome-wide association studies (GWAS) of both plants and microbes have made the selection of desirable traits in plants and manipulation of the genomes of both plants and microbes effortless and less time-consuming. Stress tolerance in plants has been shown to be accentuated by association of certain microorganisms with the plant, the study and application of the same have helped develop stress-resistant varieties of crops. Beneficial microbes associated with plants are being extensively used for the development of microbial consortia that can be applied directly to the plants or the soil. Next-generation sequencing approaches have made it possible to identify the function of microbes associated in the plant microbiome that are both culturable and non-culturable, thus opening up new doors and possibilities for the use of these huge resources of microbes that can have a potential impact on agriculture.

Pleiotropic Effects of Rice Florigen Gene RFT1 on the Amino Acid Content of Unmilled Rice

In rice, the contents of protein and amino acids are the major parameters of nutritional quality. Co-localization of quantitative trait loci (QTLs) for heading date and protein content were reported, but pleiotropism of heading-date genes on protein contents has not been investigated. Here, we reported that rice florigen gene RFT1 plays an important role in controlling amino acid contents of rice grain. Firstly, 73 QTLs for the contents of 17 amino acids in unmilled rice were detected using recombinant inbred lines (RILs) of the indica rice cross Zhenshan 97 (ZS97)/Milyang 46 (MY46). Then, the effect of the largest cluster consisting of 14 QTLs, located in proximity to the rice florigen genes RFT1 and Hd3a, was validated using three populations consisting of near isogenic lines (NILs) that only segregated a region covering the target QTL. The first and second NIL populations were derived from a residual heterozygote identified from the ZS97/MY46 RIL population, consisting of homozygous lines that were only segregated in a 29.9-kb region covering the two florigen genes and a 1.7-kb region for RFT1, respectively. The third NIL population was segregated for the RFT1^ZS97 transgene in the background of japonica rice cultivar Zhonghua 11. In all the three NIL populations, RFT1 was shown to have a strong effect on the contents of most amino acids, with the ZS97 allele always having the reducing effects. By comparing QTLs for amino acid contents detected in the ZS97/MY46 RIL population and genes/QTLs previously identified for heading date difference between ZS97 and MY46, possible pleiotropism on amino acid contents was also shown for other key heading-date genes including Hd1, Ghd7, and OsGI.

Genome-Wide Association Analysis Dissects the Genetic Basis of the Grain Carbon and Nitrogen Contents in Milled Rice

BACKGROUND

Carbon (C) and nitrogen (N) are two fundamental components of starch and protein, which are important determinants of grain yield and quality. The food preferences of consumers and the expected end-use of grains in different rice-growing regions require diverse varieties that differ in terms of the grain N content (GNC) and grain C content (GCC) of milled rice. Thus, it is important that quantitative trait loci (QTLs)/genes with large effects on the variation of GNC and GCC are identified in breeding programs.

RESULTS

To dissect the genetic basis of the variation of GNC and GCC in rice, the Dumas combustion method was used to analyze 751 diverse accessions regarding the GNC, GCC, and C/N ratio of the milled grains. The GCC and GNC differed significantly among the rice subgroups, especially between Xian/Indica (XI) and Geng/Japonica (GJ). Interestingly, in the GJ subgroup, the GNC was significantly lower in modern varieties (MV) than in landraces (LAN). In the XI subgroup, the GCC was significantly higher in MV than in LAN. One, six, and nine QTLs, with 55 suggestively associated single nucleotide polymorphisms, were detected for the GNC, GCC, and C/N ratio in three panels during a single-locus genome-wide association study (GWAS). Three of these QTLs were also identified in a multi-locus GWAS. We screened 113 candidate genes in the 16 QTLs in gene-based haplotype analyses. Among these candidate genes, LOC_Os01g06240 at qNC-1.1, LOC_Os05g33300 at qCC-5.1, LOC_Os01g04360 at qCN-1.1, and LOC_Os05g43880 at qCN-5.2 may partially explain the significant differences between the LAN and MV. These candidate genes should be cloned and may be useful for molecular breeding to rapidly improve the GNC, GCC, and C/N ratio of rice.

CONCLUSIONS

Our findings represent valuable information regarding the genetic basis of the GNC and GCC and may be relevant for enhancing the application of favorable haplotypes of candidate genes for the molecular breeding of new rice varieties with specific grain N and C contents.

GWAS for Starch-Related Parameters in Japonica Rice (Oryza sativa L.)

Rice quality is mainly related to the following two starch components, apparent amylose content (AAC) and resistant starch (RS). The former affects grain cooking properties, while RS acts as a prebiotic. In the present study, a Genome Wide Association Scan (GWAS) was performed using 115 rice japonica accessions, including tropical and temperate genotypes, with the purpose of expanding the knowledge of the genetic bases affecting RS and AAC. High phenotypic variation was recorded for the two traits, which positively correlated. Moreover, both the parameters correlated with seed length (positive correlation) and seed width (negative correlation). A correlational selection according to human preferences has been hypothesized for the two starch traits and grain size. In addition, human selection has been proposed as the causal agent even for the different phenotypes related to starch and grain size showed by the tropical and temperate japonica accessions utilized in this study. The present GWAS led to the identification of 11 associations for RS on seven chromosomes and five associations for AAC on chromosome 6. Candidate genes and co-positional relationships with quantitative trait loci (QTLs) previously identified as affecting RS and AAC were identified for 6 associations. The candidate genes and the new RS- and/or AAC-associated regions detected provide valuable sources for future functional characterizations and for breeding programs aimed at improving rice grain quality.

Detection of stable QTLs for grain protein content in rice (Oryza sativa L.) employing high throughput phenotyping and genotyping platforms

Lack of appropriate donors, non-utilization of high throughput phenotyping and genotyping platforms with high genotype × environment interaction restrained identification of robust QTLs for grain protein content (GPC) in rice. In the present investigation a  BC₃F₄ mapping population was developed using grain protein donor, ARC10075 and high-yielding cultivar Naveen and 190 lines were genotyped using 40 K Affimetrix custom SNP array with the objective to identify stable QTLs for protein content. Three of the identified QTLs, one for GPC (qGPC1.1) and the other two for single grain protein content (qSGPC2.1, qSGPC7.1) were stable over the environments explaining  13%, 14% and 7.8% of the phenotypic variances, respectively. Stability and repeatability of these additive QTLs were supported by the synergistic additive effects of multi-environmental-QTLs. One epistatic-QTL, independent of  the  main effect QTL was detected over the environment for SGPC. A few functional genes governing seed storage protein were hypothesised inside these identified QTLs. The qGPC1.1 was validated by NIR Spectroscopy-based high throughput phenotyping in BC₃F₅ population. Higher glutelin content was estimated in high-protein lines with the introgression of qGPC1.1 in telomeric region of short arm of chromosome 1. This was supported by the postulation of probable candidate gene inside this QTL region encoding glutelin family proteins.

Toward the sequence-based breeding in legumes in the post-genome sequencing era

Efficiency of breeding programs of legume crops such as chickpea, pigeonpea and groundnut has been considerably improved over the past decade through deployment of modern genomic tools and technologies. For instance, next-generation sequencing technologies have facilitated availability of genome sequence assemblies, re-sequencing of several hundred lines, development of HapMaps, high-density genetic maps, a range of marker genotyping platforms and identification of markers associated with a number of agronomic traits in these legume crops. Although marker-assisted backcrossing and marker-assisted selection approaches have been used to develop superior lines in several cases, it is the need of the hour for continuous population improvement after every breeding cycle to accelerate genetic gain in the breeding programs. In this context, we propose a sequence-based breeding approach which includes use of independent or combination of parental selection, enhancing genetic diversity of breeding programs, forward breeding for early generation selection, and genomic selection using sequencing/genotyping technologies. Also, adoption of speed breeding technology by generating 4-6 generations per year will be contributing to accelerate genetic gain. While we see a huge potential of the sequence-based breeding to revolutionize crop improvement programs in these legumes, we anticipate several challenges especially associated with high-quality and precise phenotyping at affordable costs, data analysis and management related to improving breeding operation efficiency. Finally, integration of improved seed systems and better agronomic packages with the development of improved varieties by using sequence-based breeding will ensure higher genetic gains in farmers' fields.

Tolerance of Iron-Deficient and -Toxic Soil Conditions in Rice

Iron (Fe) deficiency and toxicity are the most widely prevalent soil-related micronutrient disorders in rice (Oryza sativa L.). Progress in rice cultivars with improved tolerance has been hampered by a poor understanding of Fe availability in the soil, the transportation mechanism, and associated genetic factors for the tolerance of Fe toxicity soil (FTS) or Fe deficiency soil (FDS) conditions. In the past, through conventional breeding approaches, rice varieties were developed especially suitable for low- and high-pH soils, which indirectly helped the varieties to tolerate FTS and FDS conditions. Rice-Fe interactions in the external environment of soil, internal homeostasis, and transportation have been studied extensively in the past few decades. However, the molecular and physiological mechanisms of Fe uptake and transport need to be characterized in response to the tolerance of morpho-physiological traits under Fe-toxic and -deficient soil conditions, and these traits need to be well integrated into breeding programs. A deeper understanding of the several factors that influence Fe absorption, uptake, and transport from soil to root and above-ground organs under FDS and FTS is needed to develop tolerant rice cultivars with improved grain yield. Therefore, the objective of this review paper is to congregate the different phenotypic screening methodologies for prospecting tolerant rice varieties and their responsible genetic traits, and Fe homeostasis related to all the known quantitative trait loci (QTLs), genes, and transporters, which could offer enormous information to rice breeders and biotechnologists to develop rice cultivars tolerant of Fe toxicity or deficiency. The mechanism of Fe regulation and transport from soil to grain needs to be understood in a systematic manner along with the cascade of metabolomics steps that are involved in the development of rice varieties tolerant of FTS and FDS. Therefore, the integration of breeding with advanced genome sequencing and omics technologies allows for the fine-tuning of tolerant genotypes on the basis of molecular genetics, and the further identification of novel genes and transporters that are related to Fe regulation from FTS and FDS conditions is incredibly important to achieve further success in this aspect.

Evolutionary and ecological functional genomics, from lab to the wild

Plant phenotypes are the result of both genetic and environmental forces that act to modulate trait expression. Over the last few years, numerous approaches in functional genomics and systems biology have led to a greater understanding of plant phenotypic variation and plant responses to the environment. These approaches, and the questions that they can address, have been loosely termed evolutionary and ecological functional genomics (EEFG), and have been providing key insights on how plants adapt and evolve. In particular, by bringing these studies from the laboratory to the field, EEFG studies allow us to gain greater knowledge of how plants function in their natural contexts.

Endosperm enzyme activity is responsible for texture and eating quality of cooked rice grains in Japanese cultivars

Eating quality of cooked rice grains is an important determinant of its market price and consumer acceptance. To comprehensively assess the variation of eating-quality traits in 152 Japanese rice cultivars, we evaluated activities of eight endosperm enzymes related to degradation of starch and cell-wall polysaccharides. Endosperm enzyme activities showed a wide range of variations and were lower in recently developed cultivars than in landraces and old improved cultivars. Activities of most endosperm enzymes correlated significantly with the eating-quality score and surface texture of cooked rice grains. Principal component analysis revealed that rice cultivars with high eating-quality scores had high stickiness of the grain surface and low levels of endosperm enzyme activities. These results suggest that endosperm enzyme activities control texture and eating quality of cooked rice grains in Japanese rice cultivars.

Deciphering the Genetic Architecture of Cooked Rice Texture

The textural attributes of cooked rice determine palatability and consumer acceptance. Henceforth, understanding the underlying genetic basis is pivotal for the genetic improvement of preferred textural attributes in breeding programs. We characterized diverse set of 236 Indica accessions from 37 countries for textural attributes, which includes adhesiveness (ADH), hardness (HRD), springiness (SPR), and cohesiveness (COH) as well as amylose content (AC). A set of 147,692 high quality SNPs resulting from genotyping data of 700K high Density Rice Array (HDRA) derived from the Indica diversity panels of 218 lines were retained for marker-trait associations of textural attributes using single-locus (SL) genome wide association studies (GWAS) which resulted in identifying hotspot on chromosome 6 for AC and ADH attributes. Four independent multi-locus approaches (ML-GWAS) including FASTmrEMMA, pLARmEB, mrMLM, and ISIS_EM-BLASSO were implemented to dissect additional loci of major/minor effects influencing the rice texture and to overcome limitations of SL-based GWAS approach. In total 224 significant quantitative trait nucleotide (QTNs) were identified using ML-GWAS, of which 97 were validated with at least two out of the four multi-locus methods. The GWAS results were in accordance with the very significant negative correlation (r = -0.83) observed between AC and ADH, and the significant correlation exhibited by AC (r < 0.4) with HRD, SPR, and COH. The novel haplotypes and putative candidate genes influencing textural properties beyond AC will be a useful resource for deployment into the marker assisted program to capture consumer preferences influencing rice texture and palatability.

Colocalization of QTLs for hull-cracked rice and grain size in elite rice varieties in Japan

The control of insects that consume cereal grains is important for the production and storage of grains. Hull-cracked rice, which has splits in the hull, becomes more susceptible to insects both in the paddy field and during storage. The development of varieties with a low frequency of hull-cracked rice is the most economical and effective strategy to avoid insect damage and the environmental risks from agricultural chemical entering rice grains. In this study, we identified that QTLs for the frequency of hull-cracked rice and for grain width are located on the same chromosome using recombinant inbred lines derived from a cross between the elite rice varieties in Hokkaido, Japan, which are from the same pedigree and are genetically closely related. These QTLs were detected close to different molecular markers, which were separated by 1,101,675 bp, on chromosome 5 in the reference Nipponbare genome. In addition, low coefficient values of the phenotype were found between hull-cracked rice and grain size. These results suggested that the ratio of hull-cracked rice is independent of grain size. Using these QTLs, new varieties with low hull-cracked rice could be developed regardless of grain size.

Identification of candidate genes for gelatinization temperature, gel consistency and pericarp color by GWAS in rice based on SLAF-sequencing

Rice is an important cereal in the world. The study of the genetic basis of important agronomic traits in rice landraces and identification of genes will facilitate the breed improvement. Gelatinization temperature (GT), gel consistency (GC) and pericarp color (PC) are important indices of rice cooking and eating quality evaluation and potential nutritional importance, which attract wide attentions in the application of genetic and breeding. To dissect the genetic basis of GT, GC and PC, a total of 419 rice landraces core germplasm collections consisting of 330 indica lines, 78 japonica lines and 11 uncertain varieties were planted, collected, then GT, GC, PC were measured for two years, and sequenced using specific-locus amplified fragment sequencing (SLAF-seq) technology. In this study, 261,385,070 clean reads and 56,768 polymorphic SLAF tags were obtained, which a total of 211,818 single nucleotide polymorphisms (SNPs) were discovered. With 208,993 SNPs meeting the criterion of minor allele frequency (MAF) > 0.05 and integrity> 0.5, the phylogenetic tree and population structure analysis were performed for all 419 rice landraces, and the whole panel mainly separated into six subpopulations based on population structure analysis. Genome-wide association study (GWAS) was carried out for the whole panel, indica subpanel and japonica subpanel with subset SNPs respectively. One quantitative trait locus (QTL) on chromosome 6 for GT was detected in the whole panel and indica subpanel, and one QTL associated with GC was located on chromosome 6 in the whole panel and indica subpanel. For the PC trait, 8 QTLs were detected in the whole panel on chromosome 1, 3, 4, 7, 8, 10 and 11, and 7 QTLs in the indica subpanel on chromosome 3, 4, 7, 8, 10 and 11. For the three traits, no QTL was detected in japonica subpanel, probably because of the polymorphism repartition between the subpanel, or small population size of japonica subpanel. This paper provides new gene resources and insights into the molecular mechanisms of important agricultural trait of rice phenotypic variation and genetic improvement of rice quality variety breeding.