Cross-generational genomic prediction of Norway spruce (Picea abies) wood properties: an evaluation using independent validation

Cross-generational genomic prediction of Norway spruce (Picea abies) wood properties: an evaluation using independent validation. Hayatgheibi, H., Hallingbäck, H. R., Gezan, S. A., Lundqvist, S., Grahn, T., Scheepers, G., Ranade, S. S., Kärkkäinen, K., & García Gil, M. R. BMC Genomics, 26(1):680, July, 2025.

Paper doi abstract bibtex

The evaluation of genomic selection (GS) efficiency in forestry has primarily relied on cross-validation schemes that split the same population within a single generation for both training and validation. While useful, this approach may not be reliable for multigenerational breeding. To our knowledge, this is the first study to assess genomic prediction in Norway spruce using a large dataset spanning two generations in two environments. We trained pedigree-based (ABLUP) and marker-based (GBLUP) prediction models under three approaches: forward prediction, backward prediction, and across-environment prediction. The models were evaluated for ring-width, solid-wood and tracheid characteristics, using ~ 6,000 phenotyped and ~ 2,500 genotyped individual. Predictive ability (PA) and prediction accuracy (ACC) were estimated using an independent validation method, ensuring no individuals were shared between training and validation datasets. To assess the trade-off between comprehensive radial history and practical direct methods, we compared GBLUP models trained with cumulative area-weighted density (AWE-GBLUP) and single annual-ring density (SAD-GBLUP) from mother plus-trees. These models were validated using early and mature-stage progeny density measurements across two trials.

@article{hayatgheibi_cross-generational_2025,
	title = {Cross-generational genomic prediction of {Norway} spruce ({Picea} abies) wood properties: an evaluation using independent validation},
	volume = {26},
	issn = {1471-2164},
	shorttitle = {Cross-generational genomic prediction of {Norway} spruce ({Picea} abies) wood properties},
	url = {https://doi.org/10.1186/s12864-025-11861-x},
	doi = {10.1186/s12864-025-11861-x},
	abstract = {The evaluation of genomic selection (GS) efficiency in forestry has primarily relied on cross-validation schemes that split the same population within a single generation for both training and validation. While useful, this approach may not be reliable for multigenerational breeding. To our knowledge, this is the first study to assess genomic prediction in Norway spruce using a large dataset spanning two generations in two environments. We trained pedigree-based (ABLUP) and marker-based (GBLUP) prediction models under three approaches: forward prediction, backward prediction, and across-environment prediction. The models were evaluated for ring-width, solid-wood and tracheid characteristics, using {\textasciitilde} 6,000 phenotyped and {\textasciitilde} 2,500 genotyped individual. Predictive ability (PA) and prediction accuracy (ACC) were estimated using an independent validation method, ensuring no individuals were shared between training and validation datasets. To assess the trade-off between comprehensive radial history and practical direct methods, we compared GBLUP models trained with cumulative area-weighted density (AWE-GBLUP) and single annual-ring density (SAD-GBLUP) from mother plus-trees. These models were validated using early and mature-stage progeny density measurements across two trials.},
	number = {1},
	urldate = {2025-07-25},
	journal = {BMC Genomics},
	author = {Hayatgheibi, Haleh and Hallingbäck, Henrik R. and Gezan, Salvador A. and Lundqvist, Sven-Olof and Grahn, Thomas and Scheepers, Gerhard and Ranade, Sonali Sachin and Kärkkäinen, Katri and García Gil, M. Rosario},
	month = jul,
	year = {2025},
	keywords = {Cambial age, Cross-generation, GBLUP, Genomic selection, Norway Spruce, Wood properties},
	pages = {680},
}

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