The study investigates the gene regulatory network (GRN) controlling flowering time in the allotetraploid crop Brassica napus by comparing its transcriptome to that of Arabidopsis thaliana. While most orthologous gene pairs show conserved expression dynamics, several flowering‑time genes display regulatory divergence, especially under cold conditions, indicating subfunctionalisation among paralogues. Despite these differences, the overall GRN topology remains similar to Arabidopsis, likely due to retention of multiple paralogues.

The authors used a bottom‑up thermodynamic modelling framework to investigate how plants decode calcium signals, starting from Ca2+ binding to EF‑hand proteins and extending to higher‑order decoding modules. They identified six universal Ca2+-decoding modules that can explain variations in calcium sensitivity among kinases and provide a theoretical basis for interpreting calcium signal amplitude and frequency in plant cells.

The study developed a molecular assay to identify Myrtle rust‑resistant Melaleuca quinquenervia individuals for restoration. Artificial inoculations were followed by whole‑genome sequencing of 492 seedlings and a GWAS that pinpointed SNP clusters in three chromosomal regions, including candidate R‑genes, which were refined into a 1,049‑SNP panel achieving a genomic prediction accuracy of R = 0.83.

The study integrated weekly morphophysiological measurements with high-density genotyping-by-sequencing data and a machine‑learning pipeline to dissect flowering time variation in diverse Cannabis sativa landraces. By applying mutual information, recursive feature elimination, random forest, and support vector machine classifiers to over 234,000 combined genetic, phenotypic, and environmental features, the authors identified 53 key markers that classify early, medium, and late flowering types with 96.6% accuracy. Notable loci, including CsFT3 and CsCFL1, were highlighted as promising targets for breeding and smart‑crop strategies.

The study used multi‑season phenotyping for iron, zinc, and protein content together with whole‑genome re‑sequencing of a groundnut mini‑core collection to conduct a genome‑wide association study, identifying numerous marker‑trait associations and candidate genes linked to nutrient homeostasis. SNP‑based KASP markers were designed for nine loci, of which three showed polymorphism and are ready for deployment in genomics‑assisted breeding for nutrient‑rich groundnut varieties.

The study identified a major QTL (qDTH3) on chromosome 3 responsible for a 7‑10‑day earlier heading phenotype in the rice line SM93, using QTL‑seq, KASP genotyping, association mapping, and transcriptomic analysis to fine‑map the locus to a 2.53 Mb region and pinpoint candidate genes. SNP markers linked to these genes were proposed as tools for breeding early‑maturing, climate‑resilient rice varieties.