Rajeev K Varshney FRS

In genomic prediction, it remains unclear whether increasingly complex or ensemble models improve prediction over established linear approaches, and why prediction accuracy varies among traits. Here, we evaluated a comprehensive suite of genomic prediction models, including linear mixed models, Bayesian variable selection, kernel methods, machine learning algorithms, graph attention networks, and stacked ensembles, in mango (Mangifera indica L.). Across five traits, prediction accuracy converged across linear, Bayesian, kernel, and ensemble models, with only marginal gains derived from stacking and no systematic advantage of machine learning approaches. Ensemble ablation and weight analyses revealed that predictive signal was dominated by additive and smooth kernel components, while more complex learners contributed little or negatively upon performance. To explain these trait-dependent patterns in predictability, we quantified the phylogenetic signal using genome-wide marker-based trees. All traits showed a significant phylogenetic signal, with the magnitude varying widely and strongly associated with prediction accuracy (r ≈ 0.71). Traits with strong phylogenetic structure achieved the highest prediction accuracies, whereas traits with a weaker signal were consistently harder to predict, regardless of model choice. Together, these results confirm that, in mango, genomic prediction accuracy is determined more by evolutionary structure and trait architecture rather than increasing model complexity. Aligning prediction strategies with the evolutionary basis of trait variation may therefore be more effective than adopting increasingly complex models.

This book highlights emerging perspectives in plant science research that promise innovative solutions for sustainable food production. It also identifies the vexing bottlenecks that hinder the translation of new knowledge into crop improvement by way of a unique thematic compilation. This book is organized into seven sections, comprising chapters that span diverse areas ranging from remodeling plant development, enhancing photosynthetic efficiency, environmental stress perception, response and building tolerance, priming plant defense, plant-microbe interactions, engineering male sterility and apomixis for seed production, and the futuristic theme of astrobiology. Contributions from leading authorities in respective fields provide a holistic view of current advancements and opportunities, while also addressing the challenges that orient the readers to road ahead. The chapters emphasize biological and conceptual foundations, with technologies and strategies implicitly integrated. The book is intended for agricultural scientists, plant breeders, research scholars, academicians in universities and research institutes, and students of plant and agricultural sciences. It also aims to support policy makers and regulators in framing science-based policies relevant to crop improvement and sustainable agricultural practices.

Chickpea (Cicer arietinum L.) is an important legume crop predominantly cultivated in arid and semi-arid regions where drought limits yield. This review outlines recent advancements in drought tolerance research in chickpea, integrating genetic, molecular, environmental, and physiological approaches. The idiosyncratic nature of drought is emphasized, highlighting the need to align plant phenotypes with specific drought types across phenological scales. Advances in genomics, including genomic selection and marker-assisted selection, have accelerated the breeding for adaptation to drought. CRISPR-Cas9 and other modern genome-editing technologies are enabling precise modifications of drought-responsive genes, offering new insights. High-throughput phenotyping and data-driven predictive models further enhance the identification and selection of superior genotypes. The integration of traditional breeding methods with modern technologies addresses challenges posed by the idiosyncratic nature of drought, the interaction between drought and other stresses (e.g., heat), the polygenic nature of drought tolerance, the narrow genetic diversity in cultivated chickpeas, and incomplete conceptual models of plant phenotypes in crop stands. This review underscores the importance of multidisciplinary collaboration in developing drought-tolerant chickpeas.

Plant productivity is severely constrained by diverse pathogens, among which oomycetes represent some of the most destructive threats to global agriculture. These filamentous microorganisms cause devastating diseases, including potato late blight and downy mildew, leading to significant yield losses in major crops. Successful infection relies on the formation of haustoria through which oomycetes deliver numerous effector proteins that manipulate host cellular processes and suppress both pattern-triggered and effector-triggered immunity. To date, three major classes of oomycete effectors, including RXLR, Crinkler, and CHXC, along with a putative class YxSL [RK], have been identified in oomycetes. These effector molecules, along with the recently identified apoplastic effectors, play key roles in governing compatible and incompatible interactions and establishing disease in the host plant. Plants perceive these effectors by deploying multilayered immune strategies including plasma-membrane localized pattern-recognition receptors (PRRs) and intracellular NLR receptors that induce redox- and hormone-regulated defense pathways, and dynamic remodeling of transcriptional and metabolic networks. Understanding these effectors and how they manipulate host defense is a prerequisite for the generation of disease-resistant plants. In this review, we discuss the recent progress in the oomycete effectors, their secretion system, and their targets in the plant cells. By integrating pathogen strategies with host immune responses, we highlight how effector-mediated manipulation of plant signaling provides new opportunities for breeding and engineering broad-spectrum and durable resistance against oomycete pathogens.

Peanut seeds are highly susceptible to Aspergillus flavus infection and subsequent aflatoxin contamination, posing serious health risks. Although Aspergillus flavus infection in peanuts has been widely studied, the role of seed coat color and associated oil metabolites remains unclear. This research systematically evaluated four peanut varieties, analyzing phenotypic traits, metabolic profiles, and resistance to Aspergillus flavus. Results showed significant differences in testa color, fatty acid composition, antioxidant capacity, and metabolite profiles. Anthocyanins in red peanuts were positively correlated with antifungal infection. Further metabolomic profiling identified 517 metabolites cross four-types peanut oil. In-vitro assays indicated that baicalein, vanillic acid, and jaceosidin strongly inhibited fungal growth, while isorhamnetin and hesperetin exhibited moderate effects. These findings emphasize the importance of flavonoids in repression of Aspergillus flavus growth, providing a basis for metabolic-based strategies to reduce the fungal proliferation in peanuts.
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Collapsed (haploid) genome assemblies omit large portions of genetic information, especially in heterozygous, clonally propagated crops such as pineapple. Here, we assembled a telomere-to-telomere, haplotype-resolved genome for a key pre-Colombian cultivar of pineapple (Ananas comosus) ‘Smooth Cayenne’ (F180) using PacBio Hi-Fi and Hi-C data. The two 25-chromosome haplotypes span 858 Mb (N50 ≈ 16.8 Mb) and are >99% complete, each resolving all centromeres and 22 of 25 telomeres. Comparison of the phased chromosomes reveals 1.5 million single nucleotide polymorphisms (SNPs) and 1953 large structural variants (74 inversions, 750 translocations, and 1129 segmental duplications). This assembly reveals that inversions have profoundly impacted the ‘Smooth Cayenne’ genome, reshaping ~3–4% of the total sequence. Structural context dictates the genetic impact of these large inversions, as shown in recombination landscape analysis of 374 F1 seedlings, wherein a 1.3 Mb paracentric inversion on chromosome 20 forms a strict recombination coldspot, whereas a 6 Mb pericentric inversion on chromosome 24 still permits gene flow likely via short double crossovers, albeit at lower rates than the rest of the chromosome. Re-anchoring the 11 879 DArTseq markers from the F1 seedlings to the phased reference assembly, removes the dense network of spurious inter-chromosomal linkage seen in the collapsed F153 ‘Smooth Cayenne’ genome, likely providing markedly cleaner baselines for genome-wide association studies (GWAS) and genomic prediction. These results establish the new F180 assembly as a very high-quality reference, illustrate how undetected inversions can silently constrain genetic gain, and demonstrate the broader value of phased genomes for dissecting heterozygosity, structural variation and meiotic behaviour in perennial crops.

Land plants underpin civilization and planetary health, yet their genomic diversity remains largely uncharted. Current resources are unstandardized and scarce, lacking reference genomes for 95% of genera, 70% of families, and 51% of orders, impeding evolutionary and functional insight. We thus propose the PLANeT initiative, an international effort to generate high-quality, standardized genomes across the plant tree of life. Integrating artificial intelligence (AI) with genomics, we will decode conserved principles to advance fundamental plant biology, biodiversity conservation, crop improvement, and natural product discovery. Engaging around 100 labs to train 1,000 scientists, we will tackle pivotal questions for a sustainable future.

Wheat stands as the backbone of global food security, providing nearly 18% of dietary calories and 19% of protein. The crop faces intensifying climatic extremes, evolving pathogen pressures, resource constraints, and increasing scrutiny regarding environmental sustainability and health narratives. Sustaining genetic gain while broadening resilience and preserving end-use quality represents a defining challenge for contemporary wheat science. Despite these pressures, global wheat production and research have advanced significantly over the past two decades. This Special Issue, based on contributions from the 3rd International Wheat Congress (IWC) 2024 in Perth, Western Australia, presents original research and review articles highlighting emerging themes and advances in wheat research.

Genomic prediction based on molecular markers has substantially advanced genomic selection; however, prediction accuracy often plateaus despite continued increases in marker density and methodological refinement. This saturation limits the effective use of available genomic information. The emergence of genomic language models (GLMs) offers a new framework for incorporating richer sequence-based information into genomic prediction, potentially capturing biologically meaningful DNA sequence grammar that is poorly represented by traditional marker-based approaches. We conclude that the future of genomic prediction will be shaped not primarily by algorithmic refinement but by the biological expressivity of genomic representations, and that GLMs offer a principled path toward expanding this representational frontier.