article · PLANT PHYSIOLOGY
Poplar is an established model system for tree genomics and forestry, but distinguishing homologous chromosomes in hybrids has historically proved difficult. Using high-fidelity long-read sequencing and a trio-binning strategy, near-complete haplotype-phased telomere-to-telomere genome assemblies were constructed for the parental lineages of the hybrid poplar 84K. Integrating these assemblies with RNA sequencing revealed extensive expression differences between individual alleles, although no transcription bias was detected across entire subgenomes. Machine-learning models were subsequently developed to predict allele-specific expression, achieving 74 per cent accuracy on test datasets with 15 genomic variables. Features such as gene body CHG methylation, sequence divergence, and upstream and downstream transposon occupancy were identified as key drivers of allele activity. These assemblies and computational methods provide enhanced tools to investigate gene function and hybrid vigour in woody species.
Deciphering how different parental genes function in hybrid trees is essential for understanding hybrid vigour. High-resolution reference genomes combined with predictive machine-learning models make it easier to decode complex plant genomes, supporting functional research that underpins the molecular breeding of resilient, fast-growing trees for forestry.
The findings and predictive models could eventually support forestry biotechnology organisations and tree breeders working to select desirable traits linked to hybrid vigour. As the abstract focuses on genome assembly and computational expression modelling, the work represents early-stage research. Direct practical use in commercial forestry programmes will require further applied testing and validation in broader breeding populations.
AI-generated from the published abstract. Always read the original work before citing.
Poplar (Populus) is a well-established model system for tree genomics and molecular breeding, and hybrid poplar is widely used in forest plantations. However, distinguishing its diploid homologous chromosomes is difficult, complicating advanced functional studies on specific alleles. In this study, we applied a trio-binning design and PacBio high-fidelity long-read sequencing to obtain haplotype-phased telomere-to-telomere genome assemblies for the 2 parents of the well-studied F1 hybrid "84K" (Populus alba × Populus tremula var. glandulosa). Almost all chromosomes, including the telomeres and centromeres, were completely assembled for each haplotype subgenome apart from 2 small gaps on one chromosome. By incorporating information from these haplotype assemblies and extensive RNA-seq data, we analyzed gene expression patterns between the 2 subgenomes and alleles. Transcription bias at the subgenome level was not uncovered, but extensive-expression differences were detected between alleles. We developed machine-learning (ML) models to predict allele-specific expression (ASE) with high accuracy and identified underlying genome features most highly influencing ASE. One of our models with 15 predictor variables achieved 77% accuracy on the training set and 74% accuracy on the testing set. ML models identified gene body CHG methylation, sequence divergence, and transposon occupancy both upstream and downstream of alleles as important factors for ASE. Our haplotype-phased genome assemblies and ML strategy highlight an avenue for functional studies in Populus and provide additional tools for studying ASE and heterosis in hybrids.
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DOI: 10.1093/plphys/kiae078
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