This study presents a powerful alternative to traditional GWAS for dissecting the genetic architecture of gene expression and agronomic traits. Conventional SNP-based association methods rely largely on linkage disequilibrium, which often conflates correlation with causation and limits the identification of true regulatory variants. To address this, Wei et al. (2025) employ a k-mer–based Bayesian framework (K-PROB) that leverages pan-genomic and pan-transcriptomic data without reliance on a single reference genome. This approach enables the direct prioritization of causal cis-regulatory elements, many of which are embedded within complex structural variation and therefore inaccessible to standard GWAS. Importantly, K-PROB uncovers previously unknown regulatory motifs that drive natural variation in gene expression and key agronomic traits.
This study investigates the evolutionary history of green plants using vegetative transcriptomes from 1,124 species, providing one of the most comprehensive phylogenomic frameworks to date. The results demonstrate that whole-genome duplication (WGD) events are substantially more frequent in land plants than in algae and have played a central role in shaping gene evolution and plant diversification. Genome duplication and subsequent gene-family expansion emerge as major drivers of evolutionary innovation, particularly during key transitions such as the origin of vascular plants, where the largest gene-family expansions were observed. The study further shows that discordance between gene trees and the inferred species tree is largely explained by extensive gene duplication and loss, as well as rapid diversification events. Collectively, these findings highlight how polyploidy and gene-family dynamics have underpinned the evolutionary success and ecological adaptability of green plants.