Since the inception of the theory and conceptual framework of genomic selection (GS), extensive research has been done on evaluating its efficiency for utilization in crop improvement. Though, the marker-assisted selection has proven its potential for improvement of qualitative traits controlled by one to few genes with large effects. Its role in improving quantitative traits controlled by several genes with small effects is limited. In this regard, GS that utilizes genomic-estimated breeding values of individuals obtained from genome-wide markers to choose candidates for the next breeding cycle is a powerful approach to improve quantitative traits. In the last two decades, GS has been widely adopted in animal breeding programs globally because of its potential to improve selection accuracy, minimize phenotyping, reduce cycle time, and increase genetic gains. In addition, given the promising initial evaluation outcomes of GS for the improvement of yield, biotic and abiotic stress tolerance, and quality in cereal crops like wheat, maize, and rice, prospects of integrating it in breeding crops are also being explored. Improved statistical models that leverage the genomic information to increase the prediction accuracies are critical for the effectiveness of GS-enabled breeding programs. Study on genetic architecture under drought and heat stress helps in developing production markers that can significantly accelerate the development of stress-resilient crop varieties through GS. This review focuses on the transition from traditional selection methods to GS, underlying statistical methods and tools used for this purpose, current status of GS studies in crop plants, and perspectives for its successful implementation in the development of climate-resilient crops.

Wheat is a staple food worldwide and provides 40% of the calories in the diet. Climate change and global warming pose a threat to wheat production, however, and demand a deeper understanding of how heat stress might impact wheat production and wheat biology. However, it is difficult to identify novel heat stress associated genes when the genomic information is not available. Wheat has a very large and complex genome that is about 37 times the size of the rice genome. The present study sequenced the whole transcriptome of the wheat cv. HD2329 at the flowering stage, under control (22°-3°C) and heat stress (42°C, 2 h) conditions using Illumina HiSeq and Roche GS-FLX 454 platforms. We assembled more than 26.3 and 25.6 million high-quality reads from the control and HS-treated tissues transcriptome sequences respectively. About 76,556 (control) and 54,033 (HS-treated) contigs were assembled and annotated de novo using different assemblers and a total of 21,529 unigenes were obtained. Gene expression profile showed significant differential expression of 1525 transcripts under heat stress, of which 27 transcripts showed very high (>10) fold upregulation. Cellular processes such as metabolic processes, protein phosphorylation, oxidations-reductions, among others were highly influenced by heat stress. In summary, these observations significantly enrich the transcript dataset of wheat available on public domain and show a de novo approach to discover the heat-responsive transcripts of wheat, which can accelerate the progress of wheat stress-genomics as well as the course of wheat breeding programs in the era of climate change.

Abiotic stress is one of the major factors responsible for huge yield loss in crop plants. MicroRNAs play a key role in adaptive responses of plants under abiotic stress conditions through post-transcriptional gene regulations. In present study, 95 potential miRNAs were predicted in Brassica juncea using comparative genomics approach. It was noted that these miRNAs, target several transcription factors (TFs), transporter family proteins, signaling related genes, and protease encoding genes. Nineteen distinct miRNA-target regulatory networks were observed with significant involvement in regulation of transcription, response to stimulus, hormone and auxin mediated signaling pathway related gene ontology (GO) term. The sucrose-starch metabolism and pentose-gluconate interconversion pathways were found significantly enriched for these target genes. Molecular markers such as Simple Sequence Repeats (SSR) and Single Nucleotide Polymorphism (SNPs) were identified on miRNAs (miR-SSRs and miR-SNPs) and their target genes in B. juncea. Notably, one of the miR-SNP (C/T) was found at the 5th position on mature region of miR2926. This C/T transition led to the distorted and unstable hairpin structure of miR2926, consequently complete loss of target function. Hence, findings from this study will lay a foundation for marker assisted breeding for abiotic stress tolerant varieties of B. juncea.

The analysis of gene sets is usually carried out based on gene ontology terms and known biological pathways. These approaches may not establish any formal relation between genotype and trait specific phenotype. In plant biology and breeding, analysis of gene sets with trait specific Quantitative Trait Loci (QTL) data are considered as great source for biological knowledge discovery. Therefore, we proposed an innovative statistical approach called Gene Set Analysis with QTLs (GSAQ) for interpreting gene expression data in context of gene sets with traits. The utility of GSAQ was studied on five different complex abiotic and biotic stress scenarios in rice, which yields specific trait/stress enriched gene sets. Further, the GSAQ approach was more innovative and effective in performing gene set analysis with underlying QTLs and identifying QTL candidate genes than the existing approach. The GSAQ approach also provided two potential biological relevant criteria for performance analysis of gene selection methods. Based on this proposed approach, an R package, i.e., GSAQ (https://cran.r-project.org/web/packages/GSAQ) has been developed. The GSAQ approach provides a valuable platform for integrating the gene expression data with genetically rich QTL data.

The ongoing COVID-19 pandemic, caused by SARS-CoV-2, has now spread across the nations with high mortality rates and multifaceted impact on human life. The proper treatment methods to overcome this contagious disease are still limited. The main protease enzyme (Mpro, also called 3CLpro) is essential for viral replication and has been considered as one of the potent drug targets for treating COVID-19. In this study, virtual screening was performed to find out the molecular interactions between 36 natural compounds derived from sesame and the Mpro of COVID-19. Four natural metabolites, namely, sesamin, sesaminol, sesamolin, and sesamolinol have been ranked as the top interacting molecules to Mpro based on the affinity of molecular docking. Moreover, stability of these four sesame-specific natural compounds has also been evaluated using molecular dynamics (MD) simulations for 200 nanoseconds. The molecular dynamics simulations and free energy calculations revealed that these compounds have stable and favorable energies, causing strong binding with Mpro. These screened natural metabolites also meet the essential conditions for drug likeness such as absorption, distribution, metabolism, and excretion (ADME) properties as well as Lipinski’s rule of five. Our finding suggests that these screened natural compounds may be evolved as promising therapeutics against COVID-19.