Integrating machine learning with single-step GBLUP for enhanced genomic prediction in white spruce

Autores
Cappa, Eduardo Pablo; Chen, Charles; Benowicz, Andy; Thomas, Barb R.; El-Kassaby, Yousry A.
Año de publicación
2026
Idioma
español castellano
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We evaluated the predictive performance of machine learning-augmented single- and multi-trait single-step genomic best linear prediction (ssGBLUP) models for 30 complex traits spanning productivity, chemical defense, and climate adaptability in white spruce (Picea glauca) from Alberta, Canada, genotyped at 211,061 SNPs. We extend a novel integration of the ssGBLUP with non-linear kernels for forest tree improvement. Using the conventional ssGBLUP model with VanRaden’s linear genomic relationship matrix as baseline, we compared two non-linear extensions: an averaged Gaussian kernel (GK) with trait-specific bandwidths and an arc-cosine kernel (AK) with 1–20 layers (depth). To relate prediction gains to genetic architecture, we also fitted an extended GBLUP model explicitly including additive, dominance, and epistatic (additive × additive and additive × dominance) effects. Additive effects predominated in 21 traits, dominance contributed > 50% of variance in several chemical-defense traits, and epistasis explained ~ 25% in key physiological traits. Predictive performance varied by kernel type and trait architecture. AK achieved the highest accuracy in 29 traits, improving prediction by up to 27% (predictive differences up to ~ 0.10), particularly for traits with more complex genetic architectures. GK yielded modest gains (< 7%; predictive differences < 0.03), while linear G kernel remained competitive for core productivity traits. In multi-trait models, non-linear kernels performed similar to the linear alternatives. Notably, low-heritability traits such as drought resistance improved from 0.446-0.517 to 0.461-0.547 accuracy with multi-trait integration. These findings suggest that matching kernel complexity to genetic architecture may enhance genomic prediction and highlight the potential of non-linear kernels, particularly AK, in forest tree breeding.
Instituto de Recursos Biológicos
Fil: Cappa, Eduardo Pablo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina
Fil: Cappa, Eduardo Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Chen, Charles. Oklahoma State University. Department of Biochemistry and Molecular Biology; Estados Unidos
Fil: Benowicz, Andy. Forest Stewardship and Trade Branch. Alberta Forestry and Parks; Canadá
Fil: Thomas, Barb R. University of Alberta. Department of Renewable Resources; Canadá
Fil: El - Kassaby, Yousry. University of British Columbia. Faculty of Forestry. Department of Forest and Conservation Sciences; Canadá
Fuente
Tree genetics & genomes 22 : article number 19. (July 2026)
Materia
Machine Learning
Genomics
Forest Trees
Aprendizaje Automático
Genómica
Árboles Forestales
Picea glauca
Predictive Ability
Single and Multiple Trait Mixed Models
Genomic Prediction
Tree Breeding
Capacidad Predictiva
Modelos Mixtos de Rasgos Unicos y Múltiples
Predicción Genómica
Cría de Árboles
Abeto Blanco
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/27124
Acceder
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oai_identifier_str oai:localhost:20.500.12123/27124
network_acronym_str INTADig
repository_id_str l
network_name_str INTA Digital (INTA)
spelling Integrating machine learning with single-step GBLUP for enhanced genomic prediction in white spruceCappa, Eduardo PabloChen, CharlesBenowicz, AndyThomas, Barb R.El-Kassaby, Yousry A.Machine LearningGenomicsForest TreesAprendizaje AutomáticoGenómicaÁrboles ForestalesPicea glaucaPredictive AbilitySingle and Multiple Trait Mixed ModelsGenomic PredictionTree BreedingCapacidad PredictivaModelos Mixtos de Rasgos Unicos y MúltiplesPredicción GenómicaCría de ÁrbolesAbeto BlancoWe evaluated the predictive performance of machine learning-augmented single- and multi-trait single-step genomic best linear prediction (ssGBLUP) models for 30 complex traits spanning productivity, chemical defense, and climate adaptability in white spruce (Picea glauca) from Alberta, Canada, genotyped at 211,061 SNPs. We extend a novel integration of the ssGBLUP with non-linear kernels for forest tree improvement. Using the conventional ssGBLUP model with VanRaden’s linear genomic relationship matrix as baseline, we compared two non-linear extensions: an averaged Gaussian kernel (GK) with trait-specific bandwidths and an arc-cosine kernel (AK) with 1–20 layers (depth). To relate prediction gains to genetic architecture, we also fitted an extended GBLUP model explicitly including additive, dominance, and epistatic (additive × additive and additive × dominance) effects. Additive effects predominated in 21 traits, dominance contributed > 50% of variance in several chemical-defense traits, and epistasis explained ~ 25% in key physiological traits. Predictive performance varied by kernel type and trait architecture. AK achieved the highest accuracy in 29 traits, improving prediction by up to 27% (predictive differences up to ~ 0.10), particularly for traits with more complex genetic architectures. GK yielded modest gains (< 7%; predictive differences < 0.03), while linear G kernel remained competitive for core productivity traits. In multi-trait models, non-linear kernels performed similar to the linear alternatives. Notably, low-heritability traits such as drought resistance improved from 0.446-0.517 to 0.461-0.547 accuracy with multi-trait integration. These findings suggest that matching kernel complexity to genetic architecture may enhance genomic prediction and highlight the potential of non-linear kernels, particularly AK, in forest tree breeding.Instituto de Recursos BiológicosFil: Cappa, Eduardo Pablo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; ArgentinaFil: Cappa, Eduardo Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Chen, Charles. Oklahoma State University. Department of Biochemistry and Molecular Biology; Estados UnidosFil: Benowicz, Andy. Forest Stewardship and Trade Branch. Alberta Forestry and Parks; CanadáFil: Thomas, Barb R. University of Alberta. Department of Renewable Resources; CanadáFil: El - Kassaby, Yousry. University of British Columbia. Faculty of Forestry. Department of Forest and Conservation Sciences; CanadáSpringer2026-07-24T10:40:59Z2026-07-24T10:40:59Z2026-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/27124https://link.springer.com/article/10.1007/s11295-026-01746-9Cappa, E.P., Chen, C., Benowicz, A., Benowicz, A., Thomas, B.R., & El-Kassaby, Y.A. (2026). Integrating machine learning with single-step GBLUP for enhanced genomic prediction in white spruce. Tree Genetics & Genomes 22(19). https://doi.org/10.1007/s11295-026-01746-91614-2942 (Print)1614-2950 (Online)https://doi.org/10.1007/s11295-026-01746-9Tree genetics & genomes 22 : article number 19. (July 2026)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaspainfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)2026-09-24T11:43:29Zoai:localhost:20.500.12123/27124instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2026-09-24 11:43:31.254INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Integrating machine learning with single-step GBLUP for enhanced genomic prediction in white spruce
title Integrating machine learning with single-step GBLUP for enhanced genomic prediction in white spruce
spellingShingle Integrating machine learning with single-step GBLUP for enhanced genomic prediction in white spruce
Cappa, Eduardo Pablo
Machine Learning
Genomics
Forest Trees
Aprendizaje Automático
Genómica
Árboles Forestales
Picea glauca
Predictive Ability
Single and Multiple Trait Mixed Models
Genomic Prediction
Tree Breeding
Capacidad Predictiva
Modelos Mixtos de Rasgos Unicos y Múltiples
Predicción Genómica
Cría de Árboles
Abeto Blanco
title_short Integrating machine learning with single-step GBLUP for enhanced genomic prediction in white spruce
title_full Integrating machine learning with single-step GBLUP for enhanced genomic prediction in white spruce
title_fullStr Integrating machine learning with single-step GBLUP for enhanced genomic prediction in white spruce
title_full_unstemmed Integrating machine learning with single-step GBLUP for enhanced genomic prediction in white spruce
title_sort Integrating machine learning with single-step GBLUP for enhanced genomic prediction in white spruce
dc.creator.none.fl_str_mv Cappa, Eduardo Pablo
Chen, Charles
Benowicz, Andy
Thomas, Barb R.
El-Kassaby, Yousry A.
author Cappa, Eduardo Pablo
author_facet Cappa, Eduardo Pablo
Chen, Charles
Benowicz, Andy
Thomas, Barb R.
El-Kassaby, Yousry A.
author_role author
author2 Chen, Charles
Benowicz, Andy
Thomas, Barb R.
El-Kassaby, Yousry A.
author2_role author
author
author
author
dc.subject.none.fl_str_mv Machine Learning
Genomics
Forest Trees
Aprendizaje Automático
Genómica
Árboles Forestales
Picea glauca
Predictive Ability
Single and Multiple Trait Mixed Models
Genomic Prediction
Tree Breeding
Capacidad Predictiva
Modelos Mixtos de Rasgos Unicos y Múltiples
Predicción Genómica
Cría de Árboles
Abeto Blanco
topic Machine Learning
Genomics
Forest Trees
Aprendizaje Automático
Genómica
Árboles Forestales
Picea glauca
Predictive Ability
Single and Multiple Trait Mixed Models
Genomic Prediction
Tree Breeding
Capacidad Predictiva
Modelos Mixtos de Rasgos Unicos y Múltiples
Predicción Genómica
Cría de Árboles
Abeto Blanco
dc.description.none.fl_txt_mv We evaluated the predictive performance of machine learning-augmented single- and multi-trait single-step genomic best linear prediction (ssGBLUP) models for 30 complex traits spanning productivity, chemical defense, and climate adaptability in white spruce (Picea glauca) from Alberta, Canada, genotyped at 211,061 SNPs. We extend a novel integration of the ssGBLUP with non-linear kernels for forest tree improvement. Using the conventional ssGBLUP model with VanRaden’s linear genomic relationship matrix as baseline, we compared two non-linear extensions: an averaged Gaussian kernel (GK) with trait-specific bandwidths and an arc-cosine kernel (AK) with 1–20 layers (depth). To relate prediction gains to genetic architecture, we also fitted an extended GBLUP model explicitly including additive, dominance, and epistatic (additive × additive and additive × dominance) effects. Additive effects predominated in 21 traits, dominance contributed > 50% of variance in several chemical-defense traits, and epistasis explained ~ 25% in key physiological traits. Predictive performance varied by kernel type and trait architecture. AK achieved the highest accuracy in 29 traits, improving prediction by up to 27% (predictive differences up to ~ 0.10), particularly for traits with more complex genetic architectures. GK yielded modest gains (< 7%; predictive differences < 0.03), while linear G kernel remained competitive for core productivity traits. In multi-trait models, non-linear kernels performed similar to the linear alternatives. Notably, low-heritability traits such as drought resistance improved from 0.446-0.517 to 0.461-0.547 accuracy with multi-trait integration. These findings suggest that matching kernel complexity to genetic architecture may enhance genomic prediction and highlight the potential of non-linear kernels, particularly AK, in forest tree breeding.
Instituto de Recursos Biológicos
Fil: Cappa, Eduardo Pablo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina
Fil: Cappa, Eduardo Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Chen, Charles. Oklahoma State University. Department of Biochemistry and Molecular Biology; Estados Unidos
Fil: Benowicz, Andy. Forest Stewardship and Trade Branch. Alberta Forestry and Parks; Canadá
Fil: Thomas, Barb R. University of Alberta. Department of Renewable Resources; Canadá
Fil: El - Kassaby, Yousry. University of British Columbia. Faculty of Forestry. Department of Forest and Conservation Sciences; Canadá
description We evaluated the predictive performance of machine learning-augmented single- and multi-trait single-step genomic best linear prediction (ssGBLUP) models for 30 complex traits spanning productivity, chemical defense, and climate adaptability in white spruce (Picea glauca) from Alberta, Canada, genotyped at 211,061 SNPs. We extend a novel integration of the ssGBLUP with non-linear kernels for forest tree improvement. Using the conventional ssGBLUP model with VanRaden’s linear genomic relationship matrix as baseline, we compared two non-linear extensions: an averaged Gaussian kernel (GK) with trait-specific bandwidths and an arc-cosine kernel (AK) with 1–20 layers (depth). To relate prediction gains to genetic architecture, we also fitted an extended GBLUP model explicitly including additive, dominance, and epistatic (additive × additive and additive × dominance) effects. Additive effects predominated in 21 traits, dominance contributed > 50% of variance in several chemical-defense traits, and epistasis explained ~ 25% in key physiological traits. Predictive performance varied by kernel type and trait architecture. AK achieved the highest accuracy in 29 traits, improving prediction by up to 27% (predictive differences up to ~ 0.10), particularly for traits with more complex genetic architectures. GK yielded modest gains (< 7%; predictive differences < 0.03), while linear G kernel remained competitive for core productivity traits. In multi-trait models, non-linear kernels performed similar to the linear alternatives. Notably, low-heritability traits such as drought resistance improved from 0.446-0.517 to 0.461-0.547 accuracy with multi-trait integration. These findings suggest that matching kernel complexity to genetic architecture may enhance genomic prediction and highlight the potential of non-linear kernels, particularly AK, in forest tree breeding.
publishDate 2026
dc.date.none.fl_str_mv 2026-07-24T10:40:59Z
2026-07-24T10:40:59Z
2026-07
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12123/27124
https://link.springer.com/article/10.1007/s11295-026-01746-9
Cappa, E.P., Chen, C., Benowicz, A., Benowicz, A., Thomas, B.R., & El-Kassaby, Y.A. (2026). Integrating machine learning with single-step GBLUP for enhanced genomic prediction in white spruce. Tree Genetics & Genomes 22(19). https://doi.org/10.1007/s11295-026-01746-9
1614-2942 (Print)
1614-2950 (Online)
https://doi.org/10.1007/s11295-026-01746-9
url http://hdl.handle.net/20.500.12123/27124
https://link.springer.com/article/10.1007/s11295-026-01746-9
https://doi.org/10.1007/s11295-026-01746-9
identifier_str_mv Cappa, E.P., Chen, C., Benowicz, A., Benowicz, A., Thomas, B.R., & El-Kassaby, Y.A. (2026). Integrating machine learning with single-step GBLUP for enhanced genomic prediction in white spruce. Tree Genetics & Genomes 22(19). https://doi.org/10.1007/s11295-026-01746-9
1614-2942 (Print)
1614-2950 (Online)
dc.language.none.fl_str_mv spa
language spa
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv Tree genetics & genomes 22 : article number 19. (July 2026)
reponame:INTA Digital (INTA)
instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str INTA Digital (INTA)
collection INTA Digital (INTA)
instname_str Instituto Nacional de Tecnología Agropecuaria
repository.name.fl_str_mv INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria
repository.mail.fl_str_mv tripaldi.nicolas@inta.gob.ar
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score 12.754232

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