Genetic dissection of green pod yield in dolichos bean, an orphan vegetable legume, using new molecular markers.

In the era of genomic-assisted breeding for crop improvement, developing new molecular markers and validating them for use in breeding programs are the prelude. Dolichos bean is one of the most important vegetable legume crops owing to its nutrient-rich green pods used as vegetables. Limitations in genomic resources, including molecular markers, restrict the accelerated improvement of the crop. In the present investigation, a set of 430 new simple sequence repeat markers was developed from sequence information of a reference variety. These markers included di- and tri-nucleotide repeats. The markers were assayed on an association panel, which was evaluated for green pod yield over 5 years. A multi-locus model, FarmCPU, was used to assess the marker-trait association analysis. A total of 106 marker-trait associations were identified using an efficient mixed-model approach. Tri-nucleotide repeats were more informative and predominantly associated with trait. Among these markers, 17 were associated with a high level of significance. Markers LP-D-68 and LP-D-14 were identified with a high level of significance in 5-year pooled data and explained 12.70% and 12% of the phenotypic variance, respectively. These markers associated with a high level of confidence have significant scope for use in marker-assisted selection programmes. Other associated markers may be utilized for improving parents through marker-assisted recurrent selection or genomic selection programs.

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.

Genome-wide association studies for a comprehensive understanding of the genetic architecture of culm strength and yield traits in rice.

Lodging resistance in rice is a complex trait determined by culm morphological and culm physical strength traits, and these traits are a major determinant of yield. We made a detailed analysis of various component traits with the aim of deriving optimized parameters for measuring culm strength. Genotyping by sequencing (GBS)-based genome-wide association study (GWAS) was employed among 181 genotypes for dissecting the genetic control of culm strength traits. The VanRaden kinship algorithm using 6,822 filtered single-nucleotide polymorphisms (SNPs) revealed the presence of two sub-groups within the association panel with kinship values concentrated at<0.5 level, indicating greater diversity among the genotypes. A wide range of phenotypic variation and high heritability for culm strength and yield traits were observed over two seasons, as reflected in best linear unbiased prediction (BLUP) estimates. The multi-locus model for GWAS resulted in the identification of 15 highly significant associations (p< 0.0001) for culm strength traits. Two novel major effect marker-trait associations (MTAs) for section modulus and bending stress were identified on chromosomes 2 and 12 with a phenotypic variance of 21.87% and 10.14%, respectively. Other MTAs were also noted in the vicinity of previously reported putative candidate genes for lodging resistance, providing an opportunity for further research on the biochemical basis of culm strength. The quantitative trait locus (QTL) hotspot identified on chromosome 12 with the synergistic association for culm strength trait (section modulus, bending stress, and internode breaking weight) and grain number can be considered a novel genomic region that can serve a dual purpose of enhancing culm strength and grain yield. Elite donors in the indica background with beneficial alleles of the identified major QTLs could be a valuable resource with greater significance in practical plant breeding programs focusing on improving lodging resistance in rice.

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.

Understanding the Dynamics of Blast Resistance in Rice-Magnaporthe oryzae Interactions.

Rice is a global food grain crop for more than one-third of the human population and a source for food and nutritional security. Rice production is subjected to various stresses; blast disease caused by Magnaporthe oryzae is one of the major biotic stresses that has the potential to destroy total crop under severe conditions. In the present review, we discuss the importance of rice and blast disease in the present and future global context, genomics and molecular biology of blast pathogen and rice, and the molecular interplay between rice-M. oryzae interaction governed by different gene interaction models. We also elaborated in detail on M. oryzae effector and Avr genes, and the role of noncoding RNAs in disease development. Further, rice blast resistance QTLs; resistance (R) genes; and alleles identified, cloned, and characterized are discussed. We also discuss the utilization of QTLs and R genes for blast resistance through conventional breeding and transgenic approaches. Finally, we review the demonstrated examples and potential applications of the latest genome-editing tools in understanding and managing blast disease in rice.

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.