留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

The temporal transcriptional regulation enhances genomic prediction accuracy for poplar radial growth

Chenchen Guo,  Xuan Yang,  Shicheng Pang,  Yingnan Chen,  Jianjun Hu,  Suyun Wei

downloadPDF
文章导航 > Journal of Genetics and Genomics  > 2026 > 优先出版
Chenchen Guo, Xuan Yang, Shicheng Pang, Yingnan Chen, Jianjun Hu, Suyun Wei. The temporal transcriptional regulation enhances genomic prediction accuracy for poplar radial growth[J]. 遗传学报. doi: 10.1016/j.jgg.2026.02.026
引用本文: Chenchen Guo, Xuan Yang, Shicheng Pang, Yingnan Chen, Jianjun Hu, Suyun Wei. The temporal transcriptional regulation enhances genomic prediction accuracy for poplar radial growth[J]. 遗传学报. doi: 10.1016/j.jgg.2026.02.026
shu
Chenchen Guo, Xuan Yang, Shicheng Pang, Yingnan Chen, Jianjun Hu, Suyun Wei. The temporal transcriptional regulation enhances genomic prediction accuracy for poplar radial growth[J]. Journal of Genetics and Genomics. doi: 10.1016/j.jgg.2026.02.026
Citation: Chenchen Guo, Xuan Yang, Shicheng Pang, Yingnan Chen, Jianjun Hu, Suyun Wei. The temporal transcriptional regulation enhances genomic prediction accuracy for poplar radial growth[J]. Journal of Genetics and Genomics. doi: 10.1016/j.jgg.2026.02.026
shu

The temporal transcriptional regulation enhances genomic prediction accuracy for poplar radial growth

doi: 10.1016/j.jgg.2026.02.026

  • a. State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Center for Sustainable Forestry in Southern China, Key Laboratory of Tree Genetics and Biotechnology of Educational Department of China, Key Laboratory of Tree Genetics and Silvicultural Sciences of Jiangsu Province, Nanjing Forestry University, Nanjing, Jiangsu 210037, China;

  • b. College of Information Science and Technology & Artificial Intelligence, Nanjing Forestry University, Nanjing, Jiangsu 210037, China;

  • c. State Key Laboratory of Tree Genetics and Breeding, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China

基金项目: 

The work was supported by the Major Project of Agricultura Biological Breeding (2022ZD0401501) and the National Natural Science Foundation of China (32471900).

详细信息
    通讯作者:

    Suyun Wei,E-mail:weisuyun@njfu.edu.cn

The temporal transcriptional regulation enhances genomic prediction accuracy for poplar radial growth

  • a. State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Center for Sustainable Forestry in Southern China, Key Laboratory of Tree Genetics and Biotechnology of Educational Department of China, Key Laboratory of Tree Genetics and Silvicultural Sciences of Jiangsu Province, Nanjing Forestry University, Nanjing, Jiangsu 210037, China;

  • b. College of Information Science and Technology & Artificial Intelligence, Nanjing Forestry University, Nanjing, Jiangsu 210037, China;

  • c. State Key Laboratory of Tree Genetics and Breeding, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China

Funds: 

The work was supported by the Major Project of Agricultura Biological Breeding (2022ZD0401501) and the National Natural Science Foundation of China (32471900).

    摘要
  • 摘要: The growth rhythm of perennial plants is precisely regulated by stage-specific transcriptional programs. This study investigates the genetic mechanisms underlying seasonal radial growth in poplar and improves genomic selection by leveraging these regulatory signals. Longitudinal transcriptome profiles of 100 individuals across 5 critical developmental stages are integrated with whole-genome and dynamic growth phenotypes to identify core regulatory genes and functional networks. Transcriptome-wide association studies reveal limited overlap of stem diameter-associated genes across developmental stages, with stage-enriched biological processes supporting dynamic transcriptional regulation during poplar radial growth. Co-expression network analysis further demonstrates that energy metabolism centered on the tricarboxylic acid cycle serves as a key biological process driving rapid radial growth. Through multi-omics integration, core candidate genes that coordinately regulate essential pathways are identified, including cell division, polar expansion, energy allocation, and auxin transport. Notably, targeted transcriptome-integrated models incorporating these functionally important genes significantly improve the predictive accuracy of genomic selection for stem diameter compared to conventional whole-genome or transcriptome-based approaches. This study reveals the temporal molecular regulatory mechanisms underlying poplar radial growth and proposes an effective strategy for enhancing genomic prediction accuracy by integrating trait-associated transcriptional signals, offering a promising framework for precision breeding in perennial trees.
    Abstract: The growth rhythm of perennial plants is precisely regulated by stage-specific transcriptional programs. This study investigates the genetic mechanisms underlying seasonal radial growth in poplar and improves genomic selection by leveraging these regulatory signals. Longitudinal transcriptome profiles of 100 individuals across 5 critical developmental stages are integrated with whole-genome and dynamic growth phenotypes to identify core regulatory genes and functional networks. Transcriptome-wide association studies reveal limited overlap of stem diameter-associated genes across developmental stages, with stage-enriched biological processes supporting dynamic transcriptional regulation during poplar radial growth. Co-expression network analysis further demonstrates that energy metabolism centered on the tricarboxylic acid cycle serves as a key biological process driving rapid radial growth. Through multi-omics integration, core candidate genes that coordinately regulate essential pathways are identified, including cell division, polar expansion, energy allocation, and auxin transport. Notably, targeted transcriptome-integrated models incorporating these functionally important genes significantly improve the predictive accuracy of genomic selection for stem diameter compared to conventional whole-genome or transcriptome-based approaches. This study reveals the temporal molecular regulatory mechanisms underlying poplar radial growth and proposes an effective strategy for enhancing genomic prediction accuracy by integrating trait-associated transcriptional signals, offering a promising framework for precision breeding in perennial trees.
    • HTML全文
    • 参考文献(60)
  • Bolger, A.M., Lohse, M., Usadel, B., 2014. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114-2120.
    Browning, B.L., Tian, X., Zhou, Y., Browning, S.R., 2021. Fast two-stage phasing of large-scale sequence data. Am. J. Hum. Genet. 108, 1880-1890.
    Buckner, B., Miguel, P.S., Janick-Buckner, D., Bennetzen, J.L., 1996. The yl gene of maize codes for phytoene synthase. Genetics 143, 479-488.
    Budzinski, I.G.F., Moon, D.H., Linden, P., Moritz, T., Labate, C.A., 2016. Seasonal variation of carbon metabolism in the cambial zone of Eucalyptus grandis. Front. Plant Sci. 7, 932.
    Chao, Q., Gao, Z.-F., Zhang, D., Zhao, B.-G., Dong, F.-Q., Fu, C.-X., Liu, L.-J., Wang, B.-C., 2019. The developmental dynamics of the Populus stem transcriptome. Plant Biotechnol. J. 17, 206-219.
    Chen, Y., Wu, H., Dai, X., Li, W., Qiu, Y., Yang, Y., Yin, T., 2023. Sex effect on growth performance and marker-aided sex discrimination of seedlings of Populus deltoides. J. For. Res. 34, 1639-1645.
    Christensen, O.F., Borner, V., Varona, L., Legarra, A., 2021. Genetic evaluation including intermediate omics features. Genetics 219, iyab130.
    Danecek, P., Auton, A., Abecasis, G., Albers, C.A., Banks, E., DePristo, M.A., Handsaker, R.E., Lunter, G., Marth, G.T., Sherry, S.T., et al., 1000 Genomes Project Analysis Group, 2011. The variant call format and VCFtools. Bioinformatics 27, 2156-2158.
    Filichkin, S.A., Breton, G., Priest, H.D., Dharmawardhana, P., Jaiswal, P., Fox, S.E., Michael, T.P., Chory, J., Kay, S.A., Mockler, T.C., 2011. Global profiling of rice and poplar transcriptomes highlights key conserved circadian-controlled pathways and cis-regulatory modules. PloS One 6, e16907.
    Gamazon, E.R., Wheeler, H.E., Shah, K.P., Mozaffari, S.V., Aquino-Michaels, K., Carroll, R.J., Eyler, A.E., Denny, J.C., GTEx Consortium, Nicolae, D.L., et al., 2015. A gene-based association method for mapping traits using reference transcriptome data. Nat. Genet. 47, 1091-1098.
    Giri, A., Khaipho-Burch, M., Buckler, E.S., Ramstein, G.P., 2021. Haplotype associated RNA expression (HARE) improves prediction of complex traits in maize. PLoS Genet. 17, e1009568.
    Guerra, F.P., Suren, H., Holliday, J., Richards, J.H., Fiehn, O., Famula, R., Stanton, B.J., Shuren, R., Sykes, R., Davis, M.F., et al., 2019. Exome resequencing and GWAS for growth, ecophysiology, and chemical and metabolomic composition of wood of Populus trichocarpa. BMC Genomics 20, 875.
    Guo, Z., Magwire, M.M., Basten, C.J., Xu, Z., Wang, D., 2016. Evaluation of the utility of gene expression and metabolic information for genomic prediction in maize. Theor. Appl. Genet. 129, 2413-2427.
    Han, X., An, Y., Zhou, Y., Liu, C., Yin, W., Xia, X., 2020. Comparative transcriptome analyses define genes and gene modules differing between two Populus genotypes with contrasting stem growth rates. Biotechnol. Biofuels 13, 139.
    Kim, D., Paggi, J.M., Park, C., Bennett, C., Salzberg, S.L., 2019. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat. Biotechnol. 37, 907-915.
    Kremling, K.A.G., Diepenbrock, C.H., Gore, M.A., Buckler, E.S., Bandillo, N.B., 2019. Transcriptome-wide association supplements genome-wide association in Zea mays. G3 (Bethesda) 9, 3023-3033.
    Langfelder, P., Horvath, S., 2008. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559.
    Li, D., Liu, Q., Schnable, P.S., 2021. TWAS results are complementary to and less affected by linkage disequilibrium than GWAS. Plant Physiol. 186, 1800-1811.
    Li, D., Wang, Q., Tian, Y., Lyv, X., Zhang, H., Hong, H., Gao, H., Li, Y.-F., Zhao, C., Wang, J., et al., 2024. TWAS facilitates gene-scale trait genetic dissection through gene expression, structural variations, and alternative splicing in soybean. Plant Comm. 5, 101010.
    Li, H., Durbin, R., 2009. Fast and accurate short read alignment with burrows-wheeler transform. Bioinformatics 25, 1754-1760.
    Li, Z., Gao, N., Martini, J.W.R., Simianer, H., 2019. Integrating gene expression data into genomic prediction. Front. Genet. 10, 126.
    Li, Z., Sillanpaa, M.J., 2015. Dynamic quantitative trait locus analysis of plant phenomic data. Trends Plant Sci. 20, 822-833.
    Li, Z., Wang, P., You, C., Yu, J., Zhang, X., Yan, F., Ye, Z., Shen, C., Li, B., Guo, K., et al., 2020. Combined GWAS and eQTL analysis uncovers a genetic regulatory network orchestrating the initiation of secondary cell wall development in cotton. New Phytol. 226, 1738-1752.
    Ma, Y., Min, L., Wang, J., Li, Y, Wu, Y., Hu, Q., Ding, Y., Wang, M., Liang, Y., Gong, Z., et al., 2021. A combination of genome-wide and transcriptome-wide association studies reveals genetic elements leading to male sterility during high temperature stress in cotton. New Phytol. 231, 165-181.
    McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., Garimella, K., Altshuler, D., Gabriel, S., Daly, M., et al., 2010. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297-1303.
    Meuwissen, T.H.E., Hayes, B.J., Goddard, M.E., 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819-1829.
    Ming, L., Fu, D., Wu, Z., Zhao, H., Xu, X., Xu, T., Xiong, X., Li, M., Zheng, Y., Li, G., et al., 2023. Transcriptome-wide association analyses reveal the impact of regulatory variants on rice panicle architecture and causal gene regulatory networks. Nat. Commun. 14, 7501.
    Morgante, F., Huang, W., Soerensen, P., Maltecca, C., Mackay, T.F.C., 2020. Leveraging multiple layers of data to predict Drosophila complex traits. G3 (Bethesda) 10, 4599-4613.
    Muller, B.S.F., de Almeida Filho, J.E., Lima, B.M., Garcia, C.C., Missiaggia, A., Aguiar, A.M., Takahashi, E., Kirst, M., Gezan, S.A., Silva-Junior, O.B., et al., 2019. Independent and Joint-GWAS for growth traits in Eucalyptus by assembling genome-wide data for 3373 individuals across four breeding populations. New Phytol. 221, 818-833.
    Nagpal, S., Meng, X., Epstein, M.P., Tsoi, L.C., Patrick, M., Gibson, G., Jager, P.L.D., Bennett, D.A., Wingo, A.P., Wingo, T.S., et al., 2019. TIGAR: an improved Bayesian tool for transcriptomic data imputation enhances gene mapping of complex traits. Am. J. Hum. Genet. 105, 258-266.
    Parrish, R.L., Gibson, G.C., Epstein, M.P., Yang, J., 2022. TIGAR-V2: Efficient TWAS tool with nonparametric Bayesian eQTL weights of 49 tissue types from GTEx V8. Hum. Genet. Genom. Adv. 3, 100068.
    Perez, P., de los Campos, G., 2014. Genome-wide regression and prediction with the BGLR statistical package. Genetics 198, 483-495.
    Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M.A.R., Bender, D., Maller, J., Sklar, P., Bakker, P.I.W. de, Daly, M.J., et al., 2007. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559-575.
    Rodriguez del Rio, A., Giner-Lamia, J., Cantalapiedra, C.P., Botas, J., Deng, Z., Hernandez-Plaza, A., Munar-Palmer, M., Santamaria-Hernando, S., Rodriguez-Herva, J.J., Ruscheweyh, H.-J., et al., 2024. Functional and evolutionary significance of unknown genes from uncultivated taxa. Nature 626, 377-384.
    Schrader, J., Moyle, R., Bhalerao, R., Hertzberg, M., Lundeberg, J., Nilsson, P., Bhalerao, R.P., 2004. Cambial meristem dormancy in trees involves extensive remodelling of the transcriptome. Plant J. 40, 173-187.
    Schrag, T.A., Westhues, M., Schipprack, W., Seifert, F., Thiemann, A., Scholten, S., Melchinger, A.E., 2018. Beyond genomic prediction: combining different types of omics data can improve prediction of hybrid performance in maize. Genetics 208, 1373-1385.
    Shannon, P., Markiel, A., Ozier, O., Baliga, N.S., Wang, J.T., Ramage, D., Amin, N., Schwikowski, B., Ideker, T., 2003. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498-2504.
    Shi, J., Zhang, J., Sun, D., Zhao, L., Chi, Y., Gao, C., Wang, Y., Wang, C., 2024. Protein profile analysis of tension wood development in response to artificial bending and gravitational stimuli in Betula platyphylla. Plant Sci. 339, 111957.
    Shu, M., Yates, T.B., John, C., Harman-Ware, A.E., Happs, R.M., Bryant, N., Jawdy, S.S., Ragauskas, A.J., Tuskan, G.A., Muchero, W., et al., 2024. Providing biological context for GWAS results using eQTL regulatory and co-expression networks in Populus. New Phytol. 244, 603-617.
    Su, J., Lu, Z., Zeng, J., Zhang, X., Yang, X., Wang, S., Zhang, F., Jiang, J., Chen, F., 2024. Multi-locus genome-wide association study and genomic prediction for flowering time in chrysanthemum. Planta 259, 13.
    Szklarczyk, D., Kirsch, R., Koutrouli, M., Nastou, K., Mehryary, F., Hachilif, R., Gable, A.L., Fang, T., Doncheva, N.T., Pyysalo, S., et al., 2023. The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 51, D638-D646.
    Tan, B., Ingvarsson, P.K., 2022. Integrating genome-wide association mapping of additive and dominance genetic effects to improve genomic prediction accuracy in Eucalyptus. Plant Genome 15, e20208.
    Tan, B.-C., Guan, J.-C., Ding, S., Wu, S., Saunders, J.W., Koch, K.E., McCarty, D.R., 2017. Structure and origin of the white cap locus and Its role in evolution of grain color in maize. Genetics 206, 135-150.
    Tang, S., Zhao, H., Lu, S., Yu, L., Zhang, G., Zhang, Y., Yang, Q.-Y., Zhou, Y., Wang, X., Ma, W., et al., 2021. Genome- and transcriptome-wide association studies provide insights into the genetic basis of natural variation of seed oil content in Brassica napus. Mol. Plant 14, 470-487.
    Toronen, P., Medlar, A., Holm, L., 2018. PANNZER2: a rapid functional annotation web server. Nucleic Acids Res. 46, W84-W88.
    Torres-Rodriguez, J.V., 2025. Evolving best practices for transcriptome-wide association studies accelerate discovery of gene-phenotype links. Curr. Opin. Plant Biol. 83,102670.
    Torres-Rodriguez, J.V., Li, D., Turkus, J., Newton, L., Davis, J., Lopez-Corona, L., Ali, W., Sun, G., Mural, R.V., Grzybowski, M.W., et al., 2024. Population-level gene expression can repeatedly link genes to functions in maize. Plant J. 119, 844-860.
    van der Merwe, M.J., Osorio, S., Araujo, W.L., Balbo, I., Nunes-Nesi, A., Maximova, E., Carrari, F., Bunik, V.I., Persson, S., Fernie, A.R., 2010. Tricarboxylic acid cycle activity regulates tomato root growth via effects on secondary cell wall production. Plant Physiol. 153, 611-621.
    Wei, S., Yang, G., Yang, Y., Yin, T., 2022. Time-sequential detection of quantitative trait loci and candidate genes underlying the dynamic growth of Salix suchowensis. Tree Physiol. 42, 877-890.
    Westhues, M., Schrag, T.A., Heuer, C., Thaller, G., Utz, H.F., Schipprack, W., Thiemann, A., Seifert, F., Ehret, A., Schlereth, A., et al., 2017. Omics-based hybrid prediction in maize. Theor. Appl. Genet. 130, 1927-1939.
    Wu, L., Shi, W., Long, J., Guo, X., Michailidou, K., Beesley, J., Bolla, M.K., Shu, X.-O., Lu, Y., Cai, Q., et al., 2018. A transcriptome-wide association study of 229,000 women identifies new candidate susceptibility genes for breast cancer. Nat. Genet. 50, 968-978.
    Xia, H., Hao, Z., Shen, Y., Tu, Z., Yang, L., Zong, Y., Li, H., 2023. Genome-wide association study of multiyear dynamic growth traits in hybrid Liriodendron identifies robust genetic loci associated with growth trajectories. Plant J. 115, 1544-1563.
    Xiao, L., Liu, X., Lu, W., Chen, P., Quan, M., Si, J., Du, Q., Zhang, D., 2020. Genetic dissection of the gene coexpression network underlying photosynthesis in Populus. Plant Biotechnol. J. 18, 1015-1026.
    Xu, H., Wang, Z., Wang, F., Hu, X., Ma, C., Jiang, H., Xie, C., Gao, Y., Ding, G., Zhao, C., et al., 2024. Genome-wide association study and genomic selection of spike-related traits in bread wheat. Theor. Appl. Genet. 137, 131.
    Xu, Y., Xu, C., Xu, S., 2017. Prediction and association mapping of agronomic traits in maize using multiple omic data. Heredity 119, 174-184.
    Xue, L., Wu, H., Chen, Y., Li, X., Hou, J., Lu, J., Wei, S., Dai, X., Olson, M.S., Liu, J., et al., 2020. Evidences for a role of two Y-specific genes in sex determination in Populus deltoides. Nat. Commun. 11, 5893.
    Yu, G., Wang, L.-G., Han, Y., He, Q.-Y., 2012. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284-287.
    Zhang, L., Lu, D., Ge, X., Du, J., Wen, S., Xiang, X., Du, C., Zhou, X., Hu, J., 2023. Insight into growth and wood properties based on QTL and eQTL mapping in Populus deltoides ‘Danhong’ × Populus simonii ‘Tongliao1.’ Ind. Crops Prod. 199, 116731.
    Zhou, Y., Vales, M.I., Wang, A., Zhang, Z., 2017. Systematic bias of correlation coefficient may explain negative accuracy of genomic prediction. Brief Bioinform. 18, 744-753.
    Zhu, S., Chen, X., Liu, X., Zhao, J., Liu, T., 2019. Transcriptome-wide association study and eQTL analysis to assess the genetic basis of bulb-yield traits in garlic (Allium sativum). BMC Genomics 20, 657.
    • 相关文章
    • 施引文献
  • 资源附件(0)
  • 加载中
WeChat 点击查看大图
计量
  • 文章访问数:  12
  • HTML全文浏览量:  6
  • PDF下载量:  0
  • 被引次数: 0
出版历程
  • 收稿日期:  2025-11-25
  • 录用日期:  2026-02-27
  • 修回日期:  2026-02-24
  • 网络出版日期:  2026-03-06

目录

    /

    返回文章
    返回

    Copyright © 2009《 石油钻探技术 》 All Rights Reserved.

    地址：北京市朝阳区北辰东路8号北辰时代大厦716室邮政编码：100101

    电话：010-84988317，84988356，84988357传真：021-64253812Email：syzt@vip.163.com

    京公网安备 11010502036328号　京ICP备17033152号

    本系统由 北京仁和汇智信息技术有限公司 开发