Rajeev K Varshney FRS

Genomic prediction (GP) in mango breeding faces challenges due to the species’ complex biology, long cycles, and limited reference populations. To accelerate genetic improvement, this study integrated data from diverse global populations to increase the reference population size. It included three mango collections reserved in Australia (225), USA (161), and China (224), totalling 610 individuals. Fruit weight (FW) and total soluble solids (TSS) were measured in multiple datasets, while several other traits were measured in specific datasets. We evaluated genetic diversity, performed genome-wide association studies (GWAS), and assessed GP accuracy using standard, genotype-by-environment (GxE), and multi-trait models, both within and across collections. Findings revealed a highly admixed genetic structure, with faster linkage disequilibrium (LD) decay in the Chinese collection, indicating higher genetic diversity. Data integration significantly enhanced GWAS power, identifying 19 quantitative trait loci (QTL) for FW and 9 for TSS. GxE models consistently achieved higher or comparable prediction accuracies for FW and TSS compared to the non-GxE models, especially when combining Australian and USA collections. This was not the case when predicting into or from the Chinese collection, mostly due to differences in the phenotyping protocol. While single-trait models performed comparably to multi-trait models in predicting new individuals (Coss-Validation: CV1), multi-trait models significantly improved prediction accuracy in scenarios with incomplete phenotypic records (CV2). This study demonstrates that strategic global data integration significantly enhances GWAS power and GP accuracy in mango. This collaborative approach is crucial for developing more efficient and accelerated breeding programs for mango and other perennial trees.

Wheat production is increasingly threatened by biotic and abiotic stresses, with stripe rust, caused by Puccinia striiformis f. sp. tritici being among the most devastating diseases. To dissect stripe rust resistance mechanisms, 329 diverse wheat genotypes were evaluated across six distinct environments in India (three locations over two years). The panel exhibited wide variation for stripe rust resistance and was genotyped using a 35K SNP-array. Genome-wide association study (GWAS) revealed 49 significant marker-trait associations (MTAs), explaining 1.58% to 29.7% of phenotypic variation, with notable quantitative-trait locus (QTL) hotspots on chromosomes 2A, 3B and 4B. Several MTAs co-localized with known resistance loci, while AX-92621629 appeared novel, suggesting new genomic region contributing to adult plant resistance. Candidate genes near significant single-nucleotide polymorphisms (SNPs) were enriched for defense-related functions, including nucleotide-binding site leucine-rich repeat (NBS-LRR) proteins, receptor-like kinases and transcription factors involved in defense signaling. To further investigate resistance mechanisms, metabolomic profiling, phytohormone and flavonoid dynamics were conducted on two contrasting wheat genotypes (resistant SKUA_415; susceptible SKUA_246) using untargeted Gas Chromatography-Mass Spectrometry (GC‒MS) and Liquid Chromatography-Mass Spectrometry (LC‒MS) approaches. Key defense-related metabolites, including myo-inositol, ketoglutaric acid, rutin and schaftoside and kaempferol derivatives were identified. These metabolites were downregulated in SKUA_246 following infection, while SKUA_415 showed up-regulation of defense phytohormones, anthocyanins and flavonoids. The two contrasting genotypes also exhibited clear allelic differentiation at key resistance-linked SNP loci, consistent with their divergent metabolomic responses. This study highlights identification of promising genes/QTLs/MTAs and metabolic markers for breeding next-generation stripe rust resistant wheat cultivars.

Accumulating evidences have shown that the mid-oleic fatty acid phenotype in peanuts cannot be explained by the traditional two-gene model involving AhFAD2A and AhFAD2B, which are genes encoding fatty-acid desaturase 2. But the underlying genetic mechanism remains unclear. Here, we present a population-specific pangenome using the eight founder genomes of the PeanutMAGIC population. This graph-based pangenome serves as a comprehensive reference, capturing all segregating haplotypes within the population. We conduct whole genome sequencing for the MAGIC Core, a subset of 310 RILs, for genotyping. Using pangenome-based genotypes, we trace recombination for detailed genomic analysis and phenotypic association. This investigation identifies a unique third gene, named AhFAD2C, near AhFAD2B. When recombination occurs, AhFAD2C segregates from AhFAD2B. We reveal the genotype determining mid-oleic fatty acid phenotype. Our findings underscore the limitations of a single-reference genome, which leads to false association and marker discovery. In contrast, a population-specific pangenome provides a more reliable framework for genomic studies. This study reveals insights into the genetic mechanism of peanut oil quality and demonstrates the advantages of population-specific pangenomes.

Charcoal rot is a soil- and seed-borne disease caused by a necrotrophic fungal pathogen—Macrophomina phaseolina. To understand the genetic architecture of resistance against it, a genome-wide association study (GWAS) was conducted based on a glasshouse experiment and a 3-year field experiment using 214 diverse soybean accessions. In a glasshouse experiment at the seedling stage, eight single-nucleotide polymorphisms (SNPs) were identified: one SNP each on chromosome (chr) 8 (S8_16817767), chr 10 (S10_52066337), chr 14 (S14_50857981), chr 15 (S15_32620059), chr 17 (S17_1689021), and chr 18 (S18_9413708), while two SNPs (S16_34569104 and S16_37878937) were located on chr 16. In the case of the field experiment at the reproductive stage, 10 SNPs were identified: 1 SNP each on chr 12 (S12_14977708), chr 14 (S14_51754926), and chr 16 (S16_33491560), 2 SNPs each were identified on chr 6 (S6_41109641 and S6_41863847) and chr 10 (S10_40644409 and S10_44768495), while 3 SNPs (S18_25004105, S18_55655188, and S18_56366541) were located on chr 18. The SNP S14_50857981 associated with seedling resistance and S14_51754926 associated with adult plant resistance are present within the 1-Mb region and will be of immense importance for charcoal rot resistance breeding. The putative candidate gene analysis for identified SNPs revealed 23 genes with annotations associated with defense response pathways. Three genes encoding an NB-ARC domain associated with defense response were present near S14_50857981. The genotype PI 159923 was found to be resistant under both field and glasshouse conditions, and it will be employed as a parent in breeding for high-yielding charcoal rot-resistant genotypes. Our study provides new insights into charcoal rot resistance in soybean, identifying key SNPs and genes that can aid future breeding programs for developing climate-resilient crops.

Background
Chickpea (Cicer arietinum L.) is vital for global food security; however, its productivity is limited by genotype-environment interactions and restricted genetic diversity. This study dissected the genetic architecture of six agronomic traits in chickpea using genome-wide association studies (GWAS) to identify stable quantitative trait loci (QTLs).

Results
Phenotypic analysis of 238 chickpea accessions across three growing seasons revealed significant variation in plant height (PH), height to lowest pod (HLP), number of lateral branches (NLB), number of seeds per plant (NSP), thousand-seed weight (TSW), and yield per plant (YP). Broad-sense heritability (h2) ranged from 0.15 (NSP) to 0.88 (TSW). GWAS identified 40 stable QTLs, including major-effect loci on chromosomes 2 (Q_YP_2.1, R² = 0.45) and 4 (Q_TSW_4.1, R² = 0.22). Candidate genes linked to polyamine biosynthesis (LOC101508792) and carbohydrate metabolism (LOC101492955) were implicated.

Conclusions
The study highlights the potential of marker-assisted selection for enhancing chickpea resilience and productivity, particularly in drought-prone regions such as Kazakhstan.

Peanut productivity and quality improvement rely on understanding the genetic factors influencing pod and seed size. This study aims to identify genetic factors and regulatory mechanisms influencing pod and seed size in peanuts. Herein, a genome-wide association study (GWAS) was conducted using 390 accessions from 15 peanut growing regions to analyze pod and seed traits across multiple planting seasons. A significant phenotypic variation was observed, with broad-sense heritability ranging from 53.6 to 85.4%. Strong correlations between pod and seed traits further suggest potential for co-selection in breeding efforts. A pleiotropic hotspot on chromosome B06 was strongly associated with six pod and seed traits. A peanut pod size regulator AhPDS1 (PODSIZE-1, Ahy_B06g085516) homolog of Arabidopsis thaliana YUCCA4 (AtYUC4, AT5G11320), involved in auxin biosynthesis, was selected as a candidate regulating pod and seed size. Quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR) confirmed higher AhPDS1 expression in large pod as compared with the small pod genotypes. Subcellular localization showed AhPDS1 to be predominantly cytoplasmic, and GUS reporter assays indicated widespread expression in roots, stems, leaves, flowers, and pods, suggesting a broad functional role. Further overexpression of AhPDS1 in Arabidopsis and rice enhanced pod, seed, and grain sizes via the indole-3-pyruvic acid pathway in transgene lines. These findings highlight AhPDS1 as a potential target for peanut molecular breeding, offering opportunities to enhance pod size via auxin biosynthesis and support sustainable crop improvement.

Chickpea has become an increasingly popular healthy food worldwide. Aluminum (Al) toxicity is a major hurdle for chickpea cultivation and yield improvement in acidic soils. However, the genetic mechanism of Al-tolerance in chickpea remains poorly understood. Here, we performed a large-scale hydroponics screening and SNP chip array genotyping of 1154 diverse chickpea accessions. Root lengths after 6 days cultivation under hydroponics in control (T0: pH 4.2) and Al treatment (T1: pH4.2, 15/20 μM Al3+) were measured. Root tolerance index (RTI = T1/T0) ranking revealed significant variations in chickpea Al-tolerance, with common Australian chickpea cultivars positioned in the low to medium range. Genome-wide association analyses revealed eight QTLs on chromosomes ca1 (CaAlt1-1), ca3 (CaAlt3-1), ca4 (CaAlt4-1, CaAlt4-2), ca5(CaAlt5-1), ca6 (CaAlt6-1), and ca7 (CaAlt7-1, CaAlt7-2) associated with T1, implying a multigenic genetic basis for Al-tolerance in chickpea. Specifically, CaAlt7-2 was associated with both T1 and RTI, whilst CaAlt4-2 was detected for T1 uniquely in the HatTrick x CudiB22C population. Al- tolerant and sensitive haplotypes for the identified QTLs were also identified. Organic acid transporter genes CaMATE2, CaMATE4, and CaALMT1 were found in proximal genomic regions to CaAlt7-2, CaAlt4-1, and CaAlt6-1, respectively. Further qRT-PCR in parental chickpea lines (HatTrick, Slasher, Gunas, CudiB) confirmed that CaMATE2 and CaMATE4 were strongly induced upon Al treatment. Interestingly, CaMATE2 was preferentially expressed in the upper part of the root, whilst CaMATE4 preferentially in the root tips, implying a potential complementary role in Al resistance. Their direct roles in Al tolerance and the potential alternative candidate genes near the QTLs require further investigation. This first report of QTLs on Al-tolerance in chickpea has substantially advanced our understanding of the genetic basis of Al tolerance in chickpea and will facilitate the rapid breeding of Al-tolerant chickpea cultivars for previously un-accessible acidic soils.

Genetic diversity is a key aspect of the selection of superior genotypes in crop varietal improvement. Breeding activities in chickpea (Cicer arietinum L.) have successfully enhanced the genetic diversity by introducing variations from wild relatives and landraces. Such diversity was characterized by employing various molecular markers, including single nucleotide polymorphisms (SNPs), insertions and deletions (indels), presence/absence variations (PAVs), and so forth. These marker types through different studies provided different levels of genetic variations from single base to gene structure level. The use of multi-marker types for diversity analysis and genome-wide association studies (GWAS) represents a powerful strategy. In the current study, whole genome re-sequencing data from 593 select chickpea genotypes representing desi, kabuli, and wild types, with over 21 million SNPs, 10 million indels, and 16,117 PAVs, were analyzed. This study demonstrated enhanced diversity in both desi and kabuli types, with wild accessions showing higher diversity compared to landraces. A more comprehensive understanding and broader range of genetic diversity within and between desi, kabuli, and wild accessions, as well as landraces, cultivars, and breeding lines, were captured. The identified novel alleles and gene variations through this analysis offer effective breeding strategies for key traits such as yield, and biotic and abiotic stress tolerance that can significantly contribute to chickpea improvement. Overall, this study highlights the importance of balanced population design, use of multiple marker types in identifying diverse gene pool, and novel marker-trait associations for key/optimal traits paving the way for the development of more resilient chickpea varieties.

Herbicide resistance (HR) is fundamental for sustainable agriculture as global food security increasingly relies on efficient and eco-friendly weed management. Recent advances in CRISPR/dCas9-based epigenome editing offer a promising, non-genetic approach by precisely targeting regulatory regions of genes involved in herbicide sensitivity and detoxification. While CRISPR/Cas9 has successfully been used to develop HR crops, CRISPR/dCas9 remains underexplored in this field. We propose that CRISPR/dCas9-driven epigenome editing could enable time- and tissue-specific control of gene expression, allowing for the introduction of heritable HR traits without altering DNA sequences. This innovative approach could transform sustainable HR development, offering a powerful solution to enhance agricultural resilience and food security while aligning with eco-friendly weed management strategies.

Background
Soybean (Glycine max [L.] Merril) is a photoperiod-sensitive crop, with traits like days to flowering, days to maturity playing crucial roles in its adaptability and yield. These traits are regulated by genetic networks controlling flowering time and environmental adaptation, making their genetic basis as an essential knowledge for breeders aiming to improve yield and adaptability. In this study, a Genome-Wide Association Study (GWAS) was conducted for Days to flowering (DTF) and days to maturity (DTM) by using FarmCPU, BLINK and MLM models on 254 diverse soybean genotypes over four consecutive years (2019–2022) to dissect genetic architecture for flowering and maturity traits in an Indian Environment.

Results
In this study, GWAS identified 20 significant loci for days to flowering and maturity, among them 12 are new and 8 were previously reported loci. Among the 12 newly identified loci, a significant locus, Lee.Gm03-3 on chromosome 03, is associated with days to flowering and linked with SNP markers S3_46108324 and S3_46108342. Key candidate genes for Lee.Gm03-3, include Glyma.03G227300 (circadian rhythm and photomorphogenesis, Phytochrome region), Glyma.03G225000 (circadian rhythm, gibberellic acid signaling, red/far-red light signaling), Glyma.03G219100 (cytokinin signaling, embryo sac development), and Glyma.03G226000 (meristem initiation). These genes are vital for light-response and developmental pathways. In addition, we also validated eight previously known genes E2, E4, E9, E11, E10/FT4, PRR7/Tof12, Dt1, and Dt2 that influence flowering and maturity in Indian environment.

Conclusions
This study advances understanding of the genetic basis underlying photoperiod sensitivity related genes for circadian rhythm and photomorphogenesis, gibberellic acid signaling, red/far-red light signaling in soybean and highlights potential targets for genetic improvement of flowering, maturity duration and adaptability of soybean under Indian environment.