@article{wu_integrating_2026,
	title = {Integrating {GWAS}-guided markers preselection with genomic selection enhances prediction of pulpwood-related traits in slash pine ({Pinus} elliottii {Englem}.)},
	volume = {26},
	issn = {1471-2229},
	url = {https://doi.org/10.1186/s12870-026-09114-4},
	doi = {10.1186/s12870-026-09114-4},
	abstract = {This study aimed to enhance the efficiency of genomic prediction for pulpwood-related traits in slash pine (Pinus elliottii Engelm. var. elliottii) by integrating genome-wide association study (GWAS) information with genomic selection (GS). We evaluated 12 traits related to growth, fiber, and wood chemical composition in a population of 340 individuals genotyped with 319,286 high-quality SNPs, comparing the performance of six GS models, including GBLUP and Bayesian methods, under varying training population sizes and marker densities. The results showed that while both GBLUP and Bayesian Lasso performed well, Bayesian Lasso slightly outperformed GBLUP for fiber traits. Predictive ability (PA) plateaued at approximately 100 K SNPs for fiber traits, 60 K for DBH, and 10 K for wood chemical composition traits in all models. Using 100 K random SNPs, PA ranged from 0.05 to 0.23, which expanded to 0.09–0.35 with GWAS-guided SNP preselection (maximum improvement of 16.26\%) and further broadened to 0.01–0.38 by incorporating large-effect QTLs (greatest improvement of 23.54\%). Overall, integrating GWAS information into GS frameworks significantly improved prediction accuracy as assessed by t-test, offering a cost-effective strategy to accelerate genetic improvement. These findings provide practical guidance for enhancing breeding efficiency in slash pine and other conifer breeding programs.},
	language = {en},
	number = {1},
	urldate = {2026-08-17},
	journal = {BMC Plant Biology},
	author = {Wu, Yadi and Ding, Xianyin and Diao, Shu and Huang, Qinyun and Shang, Guiqi and Tan, Zifeng and Wu, Shaoze and Hua, Xiahui and He, Chengbo and Luan, Qifu and Chen, Zhi-Qiang and Wu, Harry X.},
	month = may,
	year = {2026},
	keywords = {Bayesian Lasso, Genomic selection, Pulpwood properties, SNP preselection, Slash pine},
	pages = {1271},
}

This study aimed to enhance the efficiency of genomic prediction for pulpwood-related traits in slash pine (Pinus elliottii Engelm. var. elliottii) by integrating genome-wide association study (GWAS) information with genomic selection (GS). We evaluated 12 traits related to growth, fiber, and wood chemical composition in a population of 340 individuals genotyped with 319,286 high-quality SNPs, comparing the performance of six GS models, including GBLUP and Bayesian methods, under varying training population sizes and marker densities. The results showed that while both GBLUP and Bayesian Lasso performed well, Bayesian Lasso slightly outperformed GBLUP for fiber traits. Predictive ability (PA) plateaued at approximately 100 K SNPs for fiber traits, 60 K for DBH, and 10 K for wood chemical composition traits in all models. Using 100 K random SNPs, PA ranged from 0.05 to 0.23, which expanded to 0.09–0.35 with GWAS-guided SNP preselection (maximum improvement of 16.26%) and further broadened to 0.01–0.38 by incorporating large-effect QTLs (greatest improvement of 23.54%). Overall, integrating GWAS information into GS frameworks significantly improved prediction accuracy as assessed by t-test, offering a cost-effective strategy to accelerate genetic improvement. These findings provide practical guidance for enhancing breeding efficiency in slash pine and other conifer breeding programs.

@article{bao_chromosome-level_2026,
	title = {A chromosome-level genome assembly of \textit{{Platycladus} orientalis} and comparative genomics reveal pivotal roles of transposable elements in gene duplication and pseudogenization across gymnosperm giga-genomes},
	volume = {7},
	issn = {2590-3462},
	url = {https://www.sciencedirect.com/science/article/pii/S2590346226001227},
	doi = {10.1016/j.xplc.2026.101814},
	abstract = {Gymnosperms, particularly conifers, exhibit a high abundance of transposable elements (TEs) in their giga-scale genomes. TEs interact both antagonistically and cooperatively with the host genome, promoting structural and genetic innovations across evolutionary lineages. However, how TEs shape the coding space of gymnosperm genomes remains a key unresolved question. Here, we present a high-quality genome assembly for the keystone conifer Platycladus orientalis, with a contig N50 of 57.54 Mb—the highest continuity reported to date—to investigate the role of TEs. Comparative genomics confirms the absence of recent whole-genome duplication and the presence of genome expansion in gymnosperms, revealing complex interactions among recurrent TE proliferation, low DNA removal rates, and DNA methylation-mediated silencing. Computational evidence indicates that TE-mediated gene duplication and pseudogenization provide a genetic basis for adaptive evolution and functional innovation, significantly shaping gene family dynamics and the emergence of species-specific genes. Additionally, TEs capture and duplicate an average of ∼400,000 coding gene fragments per gymnosperm genome, facilitating exon shuffling and triggering epigenetic conflicts between source genes and captured exon fragments. Genes from which fragments are captured (donor genes) show significantly higher levels of exon methylation than genes not captured by TEs (free genes), whereas syntenic donor genes exhibit lower levels of silencing responses than non-syntenic donor genes. This study provides valuable genomic resources and offers insights into the evolutionary patterns and principles underlying the large genome size and complexity of gymnosperms.},
	number = {8},
	urldate = {2026-08-17},
	journal = {Plant Communications},
	author = {Bao, Yu-Tao and Zhang, Ren-Gang and Liu, Hui and Li, Zhi-Chao and Jiao, Si-Qian and Jia, Kai-Hua and Zhou, Shan-Shan and Nie, Shuai and Yan, Xue-Mei and Shi, Tian-Le and Tian, Xue-Chan and Zhao, Shi-Wei and Kong, Lei and Chen, Zhao-Yang and Ma, Hai-Yao and Yang, Xiao-Lei and Chen, Charles and El-Kassaby, Yousry Aly and Porth, Ilga and Wang, Xiao-Ru and Mao, Jian-Feng and Zhao, Wei},
	month = aug,
	year = {2026},
	keywords = {gene duplication, gene fragment capture, genome expansion, gymnosperms, pseudogenization, transposable elements},
	pages = {101814},
}

Gymnosperms, particularly conifers, exhibit a high abundance of transposable elements (TEs) in their giga-scale genomes. TEs interact both antagonistically and cooperatively with the host genome, promoting structural and genetic innovations across evolutionary lineages. However, how TEs shape the coding space of gymnosperm genomes remains a key unresolved question. Here, we present a high-quality genome assembly for the keystone conifer Platycladus orientalis, with a contig N50 of 57.54 Mb—the highest continuity reported to date—to investigate the role of TEs. Comparative genomics confirms the absence of recent whole-genome duplication and the presence of genome expansion in gymnosperms, revealing complex interactions among recurrent TE proliferation, low DNA removal rates, and DNA methylation-mediated silencing. Computational evidence indicates that TE-mediated gene duplication and pseudogenization provide a genetic basis for adaptive evolution and functional innovation, significantly shaping gene family dynamics and the emergence of species-specific genes. Additionally, TEs capture and duplicate an average of ∼400,000 coding gene fragments per gymnosperm genome, facilitating exon shuffling and triggering epigenetic conflicts between source genes and captured exon fragments. Genes from which fragments are captured (donor genes) show significantly higher levels of exon methylation than genes not captured by TEs (free genes), whereas syntenic donor genes exhibit lower levels of silencing responses than non-syntenic donor genes. This study provides valuable genomic resources and offers insights into the evolutionary patterns and principles underlying the large genome size and complexity of gymnosperms.

@article{liu_drought_2026,
	title = {A drought stress-induced {MYB} transcription factor regulates pavement cell shape in leaves of {European} aspen ({Populus} tremula)},
	volume = {251},
	issn = {1469-8137},
	url = {https://nph.onlinelibrary.wiley.com/doi/10.1111/nph.71399},
	doi = {10.1111/nph.71399},
	abstract = {MYB305a promoter expression increases in pavement cells as they grow and acquire their complex shape.},
	language = {en},
	number = {5},
	urldate = {2026-08-17},
	journal = {New Phytologist},
	publisher = {John Wiley \& Sons, Ltd},
	author = {Liu, Sijia and Doyle, Siamsa M. and Robinson, Kathryn M. and Rahneshan, Zahra and Street, Nathaniel R. and Robert, Stéphanie},
	month = sep,
	year = {2026},
	keywords = {Cell Shape, Droughts, European aspen (Populus tremula), GWAS, Gene Expression Regulation, Plant, Genome-Wide Association Study, Plant Leaves, Plant Proteins, Populus, Promoter Regions, Genetic, Stress, Physiological, Transcription Factors, cell shape, drought, leaf pavement cells},
	pages = {2688--2705},
}

MYB305a promoter expression increases in pavement cells as they grow and acquire their complex shape.

@article{hu_leaf_2026,
	title = {Leaf development regulates state transition capacity in trees},
	volume = {17},
	copyright = {2026 The Author(s)},
	issn = {2041-1723},
	url = {https://www.nature.com/articles/s41467-026-76469-5},
	doi = {10.1038/s41467-026-76469-5},
	abstract = {State transitions (ST) balance excitation energy between photosystem I and II. This process has been extensively studied in Arabidopsis but the regulation and physiological significance of ST in other angiosperms remain largely unknown. Here, we investigate ST in hybrid aspen and other tree species using physiological, biochemical, ultrastructural, and genetic approaches. We discover a pronounced canopy gradient in greenhouse-grown aspens, with young, upper leaves exhibiting substantially higher fluorescence-derived state-transition capacity (qT) than lower, older leaves. Seasonal monitoring of field-grown trees reveals a conserved developmental decline in qT across species. Reduced qT correlates with increased grana stacking and lower LHCII/PSII ratios, but not with LHCII phosphorylation, suggesting developmental remodeling of thylakoid architecture may influence the functional reorganization of PSII antenna connectivity during state transitions. Using the serine/threonine protein kinase (STN7) knockout mutant characterized outside Arabidopsis, we show that aspens lacking ST exhibit altered PSI/PSII ratios, reduced PSII operating efficiency in young leaves, and slower growth under greenhouse conditions with naturally variable light conditions but not under constant-light climate room condition. These findings indicate that ST is important for performance under dynamic light conditions, particularly in young leaves, and reveal a previously unrecognized developmental control of photosynthetic regulation.},
	language = {en},
	number = {1},
	urldate = {2026-08-17},
	journal = {Nature Communications},
	publisher = {Nature Publishing Group},
	author = {Hu, Chen and Nanda, Sanchali and Pissolato, Maria Dolores and Cainzos, Maximiliano and Shutova, Tatyana and Jansson, Stefan},
	month = aug,
	year = {2026},
	keywords = {Developmental biology, Photosynthesis, Plant sciences},
	pages = {7926},
}

State transitions (ST) balance excitation energy between photosystem I and II. This process has been extensively studied in Arabidopsis but the regulation and physiological significance of ST in other angiosperms remain largely unknown. Here, we investigate ST in hybrid aspen and other tree species using physiological, biochemical, ultrastructural, and genetic approaches. We discover a pronounced canopy gradient in greenhouse-grown aspens, with young, upper leaves exhibiting substantially higher fluorescence-derived state-transition capacity (qT) than lower, older leaves. Seasonal monitoring of field-grown trees reveals a conserved developmental decline in qT across species. Reduced qT correlates with increased grana stacking and lower LHCII/PSII ratios, but not with LHCII phosphorylation, suggesting developmental remodeling of thylakoid architecture may influence the functional reorganization of PSII antenna connectivity during state transitions. Using the serine/threonine protein kinase (STN7) knockout mutant characterized outside Arabidopsis, we show that aspens lacking ST exhibit altered PSI/PSII ratios, reduced PSII operating efficiency in young leaves, and slower growth under greenhouse conditions with naturally variable light conditions but not under constant-light climate room condition. These findings indicate that ST is important for performance under dynamic light conditions, particularly in young leaves, and reveal a previously unrecognized developmental control of photosynthetic regulation.

@article{yang_construction_2026,
	title = {Construction and optimization of a genomic selection model for total sugar content in tobacco},
	volume = {17},
	issn = {1664-8021},
	url = {https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2026.1886099/full},
	doi = {10.3389/fgene.2026.1886099},
	abstract = {Total sugar is one of the chemical indicators related to tobacco quality. However, due to its composition of various oligosaccharides, disaccharides, and polysaccharides, total sugar lacks a clear genetic target, making conventional breeding approaches for its improvement particularly challenging. Therefore, conducting genomic selection (GS) studies on total sugar content holds significant importance for the development of high-sugar tobacco varieties. In this study, 2,604 tobacco germplasm accessions with broad genetic diversity, sourced from the National tobacco Germplasm Repository, were used as experimental materials. High-throughput sequencing technologies were employed to perform comprehensive genetic evaluation and construct genomic selection models for total sugar content. Sixteen mainstream genomic prediction models were assessed through five-fold cross-validation to develop a high-accuracy prediction framework. Among these models, the Gradient Boosting Machine (GBM) achieved the highest prediction accuracy for total sugar content (0.85), followed by the rrBLUP model (0.81). Comparative analysis of computational speed and resource consumption revealed that GBM maintained rapid computation and low resource usage even in large sample sizes, demonstrating strong stability and superior performance. Considering all factors, GBM was preliminarily identified as the optimal model for predicting total sugar content in tobacco. The application of high-accuracy genomic prediction is expected to overcome the challenges of phenotypic evaluation in breeding programs and significantly enhance the efficiency of selecting high-sugar tobacco varieties.},
	language = {English},
	urldate = {2026-08-17},
	journal = {Frontiers in Genetics},
	publisher = {Frontiers},
	author = {Yang, Yue and Xu, Qiang and Fu, Jincun and Guo, Linjie and Huang, Zexiang and Si, Huan and Wang, Hao and Shen, Guanwang and Zan, Yanjun and Hu, Zongyu},
	month = jul,
	year = {2026},
	keywords = {genome-wide association analysis, genomic selection, machine learning, tobacco, total sugar},
}

Total sugar is one of the chemical indicators related to tobacco quality. However, due to its composition of various oligosaccharides, disaccharides, and polysaccharides, total sugar lacks a clear genetic target, making conventional breeding approaches for its improvement particularly challenging. Therefore, conducting genomic selection (GS) studies on total sugar content holds significant importance for the development of high-sugar tobacco varieties. In this study, 2,604 tobacco germplasm accessions with broad genetic diversity, sourced from the National tobacco Germplasm Repository, were used as experimental materials. High-throughput sequencing technologies were employed to perform comprehensive genetic evaluation and construct genomic selection models for total sugar content. Sixteen mainstream genomic prediction models were assessed through five-fold cross-validation to develop a high-accuracy prediction framework. Among these models, the Gradient Boosting Machine (GBM) achieved the highest prediction accuracy for total sugar content (0.85), followed by the rrBLUP model (0.81). Comparative analysis of computational speed and resource consumption revealed that GBM maintained rapid computation and low resource usage even in large sample sizes, demonstrating strong stability and superior performance. Considering all factors, GBM was preliminarily identified as the optimal model for predicting total sugar content in tobacco. The application of high-accuracy genomic prediction is expected to overcome the challenges of phenotypic evaluation in breeding programs and significantly enhance the efficiency of selecting high-sugar tobacco varieties.