Genetic breeding and multi-omics integration for alfalfa improvement from trait discovery to cultivar development

Leveraging genomic resources for precision breeding and trait improvement in chickpea (Cicer arietinum L.)

Genomics assisted breeding for mildew resistance in cucumber: from gene discovery to future innovations

This review highlighted the genetic resources, screening strategies, disease scoring systems, inheritance patterns, resistance-associated QTLs, molecular markers and candidate genes involved in cucumber mildew resistance, and discussed genome-assisted breeding approaches, including QTL mapping, genome-wide association studies, marker-assisted selection, CRISPR/Cas9-mediated genome editing, transgenic approaches and high-throughput phenotyping tools for improving resistance breeding efficiency.

Molecular Markers Associated with Genetic Diversity, Stress Tolerance, and Breeding Traits in Theobroma cacao: A Review

This comprehensive review demonstrates that shifting from reactive field evaluation to marker-driven, genomics-assisted precision design provides the definitive molecular framework required to engineer high-yielding, climate-resilient, and disease-proof cacao cultivars, thereby permanently safeguarding the long-term economic sustainability of global cocoa supply chains.

From Neglected to Next-Generation Crops: Breeding and Omics-Based Improvement of Underutilised Legumes

Evidence on the breeding and omics-based improvement of underutilised legumes is synthesised, identifying a persistent disconnection between genomic resource generation, downstream trait validation, breeding pipeline integration and farmer-level variety release.

Integrating multi-model GWAS prior information enhances genomic prediction of cold tolerance traits in Populus simonii.

Genomic selection (GS) represents a transformative strategy for accelerating the breeding of cold tolerance in forest trees and other perennial species with long generation intervals. Although integrating genetic loci by genome-wide association studies (GWAS) can enhance prediction accuracy, this potential is frequently constrained by the inconsistency of loci detected across statistical models. Here, we developed a robust GS optimization strategy based on a multi-model GWAS framework using 849 accessions from a half-sib population of Populus simonii. We identified a total of 93 significant loci, among which 29 were co-detected by at least two models, including 8 high-confidence loci consistently detected across all three models. Incorporating these loci as fixed effects in GBLUP improved prediction accuracies ranging from 0.25 to 0.60. Notably, this strategy improved prediction accuracy by up to 60% for complex traits such as superoxide dismutase (SOD) activity, thereby alleviating a key limitation of standard GBLUP in capturing major-effect QTLs. Furthermore, we identified and preliminarily characterized the pleiotropic candidate gene PsiNDHM. Overexpression of PsiNDHM mitigated oxidative damage in poplar under cold stress. Together, these results indicate that leveraging consensus loci from multi-model GWAS offers an effective approach for optimizing GS, providing a methodological framework for precision molecular breeding in species with complex genetic architectures, particularly forest trees.

Harnessing exotic germplasm for red clover improvement: Tracking genomic introgression and deploying genomic selection

Abstract Red clover (Trifolium pratense L.) is a globally important temperate forage legume. Its symbiosis with soil‐borne rhizobia enables nitrogen fixation, and its ability to produce quality forage under diverse soil conditions enhances pasture productivity, particularly during water deficits. With increasing climate‐related stresses, harnessing adaptive traits absent in current cultivars is critical. Genebanks conserve diverse red clover germplasm, providing genetic variation for agronomic and adaptive traits. In this study, we introgressed novel germplasm into locally adapted cultivars to track the inheritance of allelic variants using genotyping‐by‐sequencing. Multi‐location, multi‐year trials evaluated half‐sib families (generation two [Gen 2]) two generations removed from the exotic germplasm (genereation zero [Gen 0]) against local cultivars. Several Gen 2 populations matched or outperformed local cultivars and exhibited a moderate family mean heritability (h 2 > 0.40) for most traits. Integrating genomic, phenotypic, and environmental data, 77 bioclimatic‐associated single nucleotide polymorphisms (SNPs) were identified, of which 35 SNPs and 27 associated genes were significantly linked to trait expression. By using the original germplasm (Gen 0) as a training population and the derived half‐sib families (Gen 2) as a validation population, genomic prediction models were developed to calculate prediction accuracies for key agronomic traits. Biomass and plot density traits showed high predictive abilities and the highest prediction accuracies across generations. This study demonstrates a route by which genetic diversity from genebanks can be successfully incorporated into local populations, enabling evaluation and selection of key traits. The identified molecular markers and genomic prediction models provide a pathway to efficiently develop climate‐adaptive red clover cultivars.