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.

Mutation breeding provides a valuable tool for generating variability and enhancing agronomic traits in crop plants that are vital for food security. However, it demands precise implementation and thorough screening to promote higher mutation frequencies without excessive lethality. The present study was carried out to compare two sources of radiations, viz., electron beams and gamma rays and to estimate optimum doses of GR₅₀ and LD₅₀ in four soybean genotypes. The study showed dose dependent negative correlation with radiation doses for both type of radiations. Germination was the least sensitive to radiation, exhibiting minor or cultivar-specific effects, making it a relatively stable trait under radiation stress compared to other seedling traits. Epicotyl length and survival percentage were the most sensitive traits to irradiation doses with drastic reductions in epicotyl length and complete seedling mortality occurring at higher doses (600-700 Gy). For electron beams, the GR₅₀ dose based on reduction in epicotyl length ranged from 338.8 to 360.2 Gy; while the LD₅₀ dose varied from 332.7 to 352.5 Gy across the genotypes. Similarly, gamma rays also showed GR₅₀ dose ranging from 284.3 to 318.2 Gy and LD₅₀ dose from 310.4 and 337.0 Gy among soybean genotypes. These dose differences among genotypes can be attributed to genotype-specific radiosensitivity. The study demonstrated comparable effects on various seedling traits between electron beam and gamma ray treatments at equivalent doses, indicating that electron beam could serve as a potential alternative radiation source in soybean mutation breeding programs.