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.

Understanding natural genetic variation for grain phytic acid content and functional marker development for phytic acid-related genes in rice.

BACKGROUND: The nutritional value of rice can be improved by developing varieties with optimum levels of grain phytic acid (PA). Artificial low-PA mutants with impaired PA biosynthesis have been developed in rice through induced mutagenesis. However, low-PA mutant stocks with drastically reduced grain PA content have poor breeding potential, and their use in rice breeding is restricted due to their detrimental pleiotropic effects, which include decreased seed viability, low grain weight, and low seed yield. Therefore, it is necessary to take advantage of the natural variation in grain PA content in order to reduce the PA content to an ideal level without compromising the crop's agronomic performance. Natural genetic diversity in grain PA content has not been thoroughly examined among elite genetic stocks. Additionally, given grain PA content as a quantitative trait driven by polygenes, DNA marker-assisted selection may be required for manipulation of such a trait; however, informative DNA markers for PA content have not yet been identified in rice. Here we investigated and dissected natural genetic variation and genetic variability components for grain PA content in rice varieties cultivated in Eastern and North-Eastern India during the last 50 years. We developed novel gene-based markers for the low-PA-related candidate genes in rice germplasm, and their allelic diversity and association with natural variation in grain PA content were studied. RESULTS: A wide (0.3-2.8%), significant variation for grain PA content, with decade-wise and ecology-wise differences, was observed among rice varieties. Significant genotype x environment interaction suggested polygenic inheritance. The novel candidate gene-based markers detected 43 alleles in the rice varieties. The new markers were found highly informative as indicated by PIC values (0.11-0.65; average: 0.34) and coverage of total diversity. Marker alleles developed from two putative transporter genes viz., SPDT and OsPT8 were significantly associated with grain PA variation assayed on the panel. A 201 bp allele at the 3' UTR of SPDT gene was negatively associated with grain PA content and explained 7.84% of the phenotypic variation. A rare allele in the coding sequence of OsPT8 gene was positively associated with grain PA content which explained phenotypic variation of 18.49%. CONCLUSION: Natural variation in grain PA content is substantial and is mostly controlled by genetic factors. The unique DNA markers linked with PA content have significant potential as genomic resources for the development of low-PA rice varieties through genomics-assisted breeding procedures.

Unravelling genetic architecture and development of core set from elite rice lines using yield-related candidate gene markers.

Assessing genetic diversity and development of a core set of elite breeding lines is a prerequisite for selective hybridization programes intended to improve the yield potential in rice. In the present study, the genetic diversity of newly developed elite lines derived from indicax tropical japonica and indicax indica crosses were estimated by 38 reported molecular markers. The markers used in the study consist of 24 gene-based and 14 random markers related to grain yield-related QTLs distributed across the rice genome. Genotypic characterization was carried out to determine the genetic similarities between the elite lines. In total, 75 alleles were found using 38 polymorphic markers, with polymorphism information content ranging from 0.10 to 0.51 with an average of 0.35. The genotypes were divided into three groups based on cluster analysis, structure analysis and also dispersed throughout the quadrangle of PCA, but nitrogen responsive lines clustered in one quadrangle. Seven markers (GS3_RGS1, GS3_RGS2, GS5_Indel1, Ghd 7_05SNP, RM 12289, RM 23065 and RM 25457) exhibited PIC values ≥ 0.50 indicating that they were effective in detecting genetic relationships among elite rice. Additionally, a core set of 11 elite lines was made from 96 lines in order to downsize the diversity of the original population into a small set for parental selection. In general, the genetic information collected in this work will aid in the study of grain yield traits at molecular level for other sets of rice genotypes and for selecting diverse elite lines to develop a strong crossing programme in rice. Supplementary Information: The online version contains supplementary material available at 10.1007/s12298-022-01190-8.

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.

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.

Novel screening protocol for precise phenotyping of salt-tolerance at reproductive stage in rice.

The present study reports an unequivocal and improved protocol for efficient screening of salt tolerance at flowering stage in rice, which can aid phenotyping of population for subsequent identification of QTLs associated with salinity stress, particularly at reproductive stage. To validate the new method, the selection criteria, level and time of imposition of stress; plant growth medium were standardized using three rice genotypes. The setup was established with a piezometer placed in a perforated pot for continuous monitoring of soil EC and pH throughout the period of study. Further, fertilizer enriched soil was partially substituted by gravels for stabilization and maintaining the uniformity of soil EC in pots without hindering its buffering capacity. The protocol including modified medium (Soil:Stone, 4:1) at 8 dS m-1 salinity level was validated using seven different genotypes possessing differential salt sensitivity. Based on the important selection traits such as high stability index for plant yield, harvest index and number of grains/panicle and also high K+ concentration and low Na+- K+ ratio in flag leaf at grain filling stage were validated and employed in the evaluation of a mapping population in the modified screening medium. The method was found significantly efficient for easy maintenance of desired level of soil salinity and identification of genotypes tolerant to salinity at reproductive stage.

Morphological and molecular dissection of wild rices from eastern India suggests distinct speciation between O. rufipogon and O. nivara populations.

The inter relationships between the two progenitors is interesting as both wild relatives are known to be the great untapped gene reservoirs. The debate continues on granting a separate species status to Oryza nivara. The present study was conducted on populations of Oryza rufipogon and Oryza nivara from Eastern India employing morphological and molecular characteristics. The cluster analysis of the data on morphological traits could clearly classify the two wild forms into two separate discrete groups without any overlaps i.e. lack of intermediate forms, suggesting the non-sympatric existence of the wild forms. Amplification of hyper variable regions of the genome could reveal 144 alleles suggesting high genetic diversity values (average He = 0.566). Moreover, with 42.37% of uncommon alleles between the two wild relatives, the molecular variance analysis (AMOVA) could detect only 21% of total variation (p < 0.001) among them and rest 59% was within them. The population structure analysis clearly classified these two wild populations into two distinct sub-populations (K = 2) without any overlaps i.e. lack of intermediate forms, suggesting the non-sympatric existence of the wild forms. Clear differentiation into two distinct groups indicates that O. rufipogon and O. nivara could be treated as two different species.