The recent advancements in forward genetics have expanded the applications of mutation techniques in advanced genetics and genomics, ahead of direct use in breeding programs. The advent of next-generation sequencing (NGS) has enabled easy identification and mapping of causal mutations within a short period and at relatively low cost. Identifying the genetic mutations and genes that underlie phenotypic changes is essential for understanding a wide variety of biological functions. To accelerate the mutation mapping for crop improvement, several high-throughput and novel NGS based forward genetic approaches have been developed and applied in various crops. These techniques are highly efficient in crop plants, as it is relatively easy to grow and screen thousands of individuals. These approaches have improved the resolution in quantitative trait loci (QTL) position/point mutations and assisted in determining the functional causative variations in genes. To be successful in the interpretation of NGS data, bioinformatics computational methods are critical elements in delivering accurate assembly, alignment, and variant detection. Numerous bioinformatics tools/pipelines have been developed for such analysis. This article intends to review the recent advances in NGS based forward genetic approaches to identify and map the causal mutations in the crop genomes. The article also highlights the available bioinformatics tools/pipelines for reducing the complexity of NGS data and delivering the concluding outcomes.

Salt stress is one of the most severe environmental stresses limiting the productivity of crops, including rice. However, there is a lack of information on how salt-stress sensitivity varies across different developmental stages in rice. In view of this, a comparative evaluation of contrasting rice varieties CSR36 (salt tolerant) and Jaya (salt sensitive) was conducted, wherein NaCl stress (50 mM) was independently given either at seedling (S-stage), tillering (T-stage), flowering (F-stage), seed-setting (SS-stage) or throughout plant growth, from seedling till maturity. Except for S-stage, CSR36 exhibited improved NaCl stress tolerance than Jaya, at all other tested stages. Principal component analysis (PCA) revealed that the improved NaCl stress tolerance in CSR36 coincided with enhanced activities/levels of enzymatic/non-enzymatic antioxidants (root ascorbate peroxidase for T- (2.74-fold) and S+T- (2.12-fold) stages and root catalase for F- (5.22-fold), S+T- (2.10-fold) and S+T+F- (2.61-fold) stages) and higher accumulation of osmolytes (shoot proline for F-stage (5.82-fold) and S+T+F- (2.31-fold) stage), indicating better antioxidant capacitance and osmotic adjustment, respectively. In contrast, higher shoot accumulation of Na⁺ (14.25-fold) and consequent increase in Na⁺/K⁺ (14.56-fold), Na⁺/Mg⁺² (13.09-fold) and Na⁺/Ca⁺² (8.38-fold) ratio in shoot, were identified as major variables associated with S-stage salinity in Jaya. Higher root Na⁺ and their associated ratio were major deriving force for other stage specific and combined stage salinity in Jaya. In addition, CSR36 exhibited higher levels of Fe³⁺, Mn²⁺ and Co³⁺ and lower Cl⁻ and SO₄²⁻, suggesting its potential to discriminate essential and non-essential nutrients, which might contribute to NaCl stress tolerance. Taken together, the findings provided the framework for stage-specific salinity responses in rice, which will facilitate crop-improvement programs for specific ecological niches, including coastal regions.

Rice has been cultivating and utilizing by humans for thousands of years under diverse environmental conditions. Therefore, tremendous genetic differentiation and diversity has occurred at various agro-ecosystems. The significant indica–japonica differentiation in rice provides great opportunities for its genetic improvement. In the present investigation, a total of 42 polymorphic InDel markers were used for differentiating 188 rice landraces and two local varieties of Chhattisgarh, India into indica and japonica related genotypes based on ‘InDel molecular index’. Frequency of japonica alleles varied from 0.11 to 0.89 among landraces. Results revealed that 104 rice landraces have indica type genetic architecture along with three tested indica cultivars Swarna, Mahamaya and Rajeshwari. Another 60 landraces were placed under ‘close to indica’ type. It was found that three rice landraces i.e. Kalajeera, Kapri, Tulsimala were ‘close to japonica’ type and 21 landraces were ‘intermediate’ type. The result from the calculation of ‘InDel molecular index’ was further verified with STRUCTURE, AMOVA, PCA and cluster analysis. Population structure analysis revealed two genetically distinct populations within the 190 rice landraces/genotypes. Based on AMOVA, ‘intermediate’ type, ‘close to japonica’ type and Dongjinbyeo (a japonica cultivar from Republic of Korea) displayed significant genetic differentiation (ɸ_PT = 0.642, P = 0.000) from ‘indica’ and ‘close to indica’ groups. The PCA scatter plot and dendrogram demonstrated a clear pattern of two major group differentiations. ‘Close to japonica’ type and ‘intermediate’ type landraces/genotypes were grouped with Dongjinbyeo and formed a separate cluster at 30% Jaccard’s similarity level from rest of the landraces/genotypes which were ‘close to indica’ or ‘indica’ type. Such a significant genetic differentiation among the locally adapted landraces could be exploited for the development of rice varieties introgressing higher yield potential and better plant types of japonica type as per the need of consumers and rice traders.