Unveiling the genetic basis of Fusarium wilt resistance in chickpea using GWAS analysis and characterization of candidate genes

A panomics-driven framework for the improvement of major food legume crops: advances, challenges, and future prospects

Food legume crops, including common bean, faba bean, mungbean, cowpea, chickpea, and pea, have long served as vital sources of energy, protein, and minerals worldwide, both as grains and vegetables. Advancements in high-throughput phenotyping, next-generation sequencing, transcriptomics, proteomics, and metabolomics have significantly expanded genomic resources for food legumes, ushering research into the panomics era. Despite their nutritional and agronomic importance, food legumes still face constraints in yield potential and genetic improvement due to limited genomic resources, complex inheritance patterns, and insufficient exploration of key traits, such as quality and stress resistance. This highlights the need for continued efforts to comprehensively dissect the phenome, genome, and regulome of these crops. This review summarizes recent advances in technological innovations and multi-omics applications in food legumes research and improvement. Given the critical role of germplasm resources and the challenges in applying phenomics to food legumes-such as complex trait architecture and limited standardized methodologies-we first address these foundational areas. We then discuss recent gene discoveries associated with yield stability, seed composition, and stress tolerance and their potential as breeding targets. Considering the growing role of genetic engineering, we provide an update on gene-editing applications in legumes, particularly CRISPR-based approaches for trait enhancement. We advocate for integrating chemical and biochemical signatures of cells ('molecular phenomics') with genetic mapping to accelerate gene discovery. We anticipate that combining panomics approaches with advanced breeding technologies will accelerate genetic gains in food legumes, enhancing their productivity, resilience, and contribution to sustainable global food security.

The transcriptional response to yellow and wilt disease, caused by race 6 of Fusarium oxysporum f. sp. Ciceris in two contrasting chickpea cultivars

BACKGROUND

Chickpea (Cicer arietinum L.) ranks as the third most crucial grain legume worldwide. Fusarium wilt (Fusarium oxysporum f. sp. ciceri (Foc)) is a devastating fungal disease that prevents the maximum potential for chickpea production.

RESULTS

To identify genes and pathways involved in resistance to race 6 of Foc, this study utilized transcriptome sequencing of two chickpea cultivars: resistant (Ana) and susceptible (Hashem) to Foc race 6. Illumina sequencing of the root samples yielded 133.5 million raw reads, with about 90% of the clean reads mapped to the chickpea reference genome. The analysis revealed that 548 genes (332 upregulated and 216 downregulated) in the resistant genotype (Ana) and 1115 genes (595 upregulated and 520 downregulated) in the susceptible genotype (Hashem) were differentially expressed under Fusarium wilt (FW) disease stress caused by Foc race 6. The expression patterns of some differentially expressed genes (DEGs) were validated using quantitative real-time PCR. A total of 131 genes were exclusively upregulated under FW stress in the resistant cultivar, including several genes involved in sensing (e.g., CaNLR-RPM1, CaLYK5-RLK, CaPR5-RLK, CaLRR-RLK, and CaRLP-EIX2), signaling (e.g., CaPP7, CaEPS1, CaSTY13, and CaPR-1), transcription regulation (e.g., CaMYBs, CaGLK, CaERFs, CaZAT11-like, and CaNAC6) and cell wall integrity (e.g., CaPGI2-like, CaEXLs, CaCSLD and CaCYP73A100-like).

CONCLUSIONS

The achieved results could provide insights into the molecular mechanism underlying resistance to FW and could be valuable for breeding programs aimed at developing FW-resistant chickpea varieties.

Modeling Chickpea Productivity with Artificial Image Objects and Convolutional Neural Network

The chickpea plays a significant role in global agriculture and occupies an increasing share in the human diet. The main aim of the research was to develop a model for the prediction of two chickpea productivity traits in the available dataset. Genomic data for accessions were encoded in Artificial Image Objects, and a model for the thousand-seed weight (TSW) and number of seeds per plant (SNpP) prediction was constructed using a Convolutional Neural Network, dictionary learning and sparse coding for feature extraction, and extreme gradient boosting for regression. The model was capable of predicting both traits with an acceptable accuracy of 84-85%. The most important factors for model solution were identified using the dense regression attention maps method. The SNPs important for the SNpP and TSW traits were found in 34 and 49 genes, respectively. Genomic prediction with a constructed model can help breeding programs harness genotypic and phenotypic diversity to more effectively produce varieties with a desired phenotype.

Insight into a region of chickpea (Cicer arietinum L.) Chromosome 2 revealed potential candidate genes linked to Foc4 Fusarium wilt resistance

Two markers on Chromosome 2 of chickpea (Cicer arietinum ) are reportedly associated with resistance to race 4 Fusarium wilt, and are frequently used in breeding. However, the genes in this region that actually confer wilt resistance are unknown. We aimed to characterise them using both in silico approaches and marker trait association (MTA) analysis. Of the 225 protein-encoding genes in this region, 51 showed significant differential expression in two contrasting chickpea genotypes under wilt, with potential involvement in stress response. From a diverse set of 244 chickpea genotypes, two sets of 40 resistant and 40 susceptible genotypes were selected based on disease incidence and amplification pattern of the TA59 marker. All cultivars were further genotyped with 1238 single nucleotide polymorphisms (SNPs) specific to the 51 genes; only seven SNPs were significantly correlated with disease. SNP Ca2_24099002, specific to the LOC101498008 (Transmembrane protein 87A) gene, accounted for the highest phenotypic variance for disease incidence at 16.30%, whereas SNPs Ca2_25166118 and Ca2_27029215, specific to the LOC101494644 (β-glucosidase BoGH3B-like) and LOC101505289 (Putative tRNA pseudouridine synthase) genes, explained 10.51% and 10.50% of the variation, respectively, in the sets with contrasting disease susceptibility. Together with the TA59 and TR19 markers, these SNPs can be used in a chickpea breeding scheme to develop wilt resistance.

Genome-wide association study reveals SNP markers controlling drought tolerance and related agronomic traits in chickpea across multiple environments

Chickpea, renowned for its exceptional nutritional value, stands as a crucial crop, serving as a dietary staple in various parts of the world. However, its productivity faces a significant challenge in the form of drought stress. This challenge highlights the urgent need to find genetic markers linked to drought tolerance for effective breeding programs. The primary objective of this study is to identify genetic markers associated with drought tolerance to facilitate effective breeding programs. To address this, we cultivated 185 chickpea accessions in two distinct locations in Lebanon over a two-year period, subjecting them to both irrigated and rain-fed environments. We assessed 11 drought-linked traits, including morphology, growth, yield, and tolerance score. SNP genotyping revealed 1344 variable SNP markers distributed across the chickpea genome. Genetic diversity across populations originating from diverse geographic locations was unveiled by the PCA, clustering, and structure analysis indicating that these genotypes have descend from five or four distinct ancestors. A genome-wide association study (GWAS) revealed several marker trait associations (MTAs) associated with the traits evaluated. Within the rainfed conditions, 11 significant markers were identified, each associated with distinct chickpea traits. Another set of 11 markers exhibited associations in both rainfed and irrigated environments, reflecting shared genetic determinants across these conditions for the same trait. The analysis of linkage disequilibrium (LD) highlighted two genomic regions with notably strong LD, suggesting significant interconnections among several investigated traits. This was further investigated by the correlation between major markers associated with these traits. Gene annotation of the identified markers has unveiled insights into 28 potential genes that play a role in influencing various chickpea drought-linked traits. These traits encompass crucial aspects such as blooming organ development, plant growth, seed weight, starch metabolism, drought regulation, and height index. Among the identified genes are CPN60-2, hsp70, GDSL(GELP), AHL16, NAT3, FAB1B, bZIP, and GL21. These genes collectively contribute to the multifaceted response of chickpea plants to drought stress. Our identified genetic factors exert their influence in both irrigated and rainfed environments, emphasizing their importance in shaping chickpea characteristics.