Optimizing a regional white spruce tree improvement program: SNP genotyping for enhanced breeding values, genetic diversity assessment, and estimation of pollen contamination

Autores
Galeano, Esteban; Cappa, Eduardo Pablo; Bousquet, Jean; Thomas, Barb R.
Año de publicación
2023
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The utilization of genotyping has gained significant popularity in tree improvement programs, aiding in enhancing the precision of breeding values, removing pedigree errors, the assessment of genetic diversity, and evaluating pollen contamination. Our study explores the impact of utilizing 5308 SNP markers to genotype seed orchard parents (166), progeny in progeny trials (667), and seedlot orchard seedlings (780), to simultaneously enhance variance components, breeding values, genetic diversity estimates, and pollen flow in the Region I white spruce (Picea glauca) breeding program in central Alberta (Canada). We compared different individual tree mixed models, including pedigree-based (ABLUP), genomic-based (GBLUP), and single-step pedigree-genomicbased (ssGBLUP) models, to estimate variance components and predict breeding values for the height and diameter at breast height traits. The highest heritability estimates were achieved using the ssGBLUP approach, resulting in improved breeding value accuracy compared to the ABLUP and GBLUP models for the studied growth traits. In the six orchard seedlots tested, the genetic diversity of the seedlings remained stable, characterized by an average of approximately 2.00 alleles per SNP, a Shannon Index of approximately 0.44, and an expected and observed heterozygosity of approximately 0.29. The pedigree reconstruction of seed orchard seedlings successfully identified consistent parental contributions and equal genotype contributions in different years. Pollen contamination levels varied between 11% and 70% using SNP markers and 8% to 81% using pollen traps, with traps both overand under-estimating contamination. Overall, integrating genomic information from parents and offspring empowers forest geneticists and breeders in the Region I white spruce breeding program to correct errors, conduct backward and forward selections with greater precision, gain a deeper understanding of the orchard’s genetic structure, select superior seedlots, and accurately estimate the genetic worth of each orchard lot, which can ultimately result in increased and more precise estimates of genetic gain in the studied growth traits
Instituto de Recursos Biológicos
Fil: Galeano, Esteban. University of Alberta, Department of Renewable Resources; Canadá
Fil: Cappa, Eduardo Pablo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Bousquet, Jean. Université Laval, Department of Wood and Forest Sciences and Forest Research Centre; Canadá
Fil: Thomas, Barb R. University of Alberta. Department of Renewable Resources; Canadá
Fuente
Forests 14 (11) : 2212 (November 2023)
Materia
Genetic Markers
Pollen
Marcadores Genéticos
Picea glauca
Polen
Tree Breeding
Effective Population Size
Pollen Flow
White Spruce
Mejoramiento de Arboles
Tamaño Efectivo de la Población
Flujo de Polen
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:20.500.12123/16244

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oai_identifier_str oai:localhost:20.500.12123/16244
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spelling Optimizing a regional white spruce tree improvement program: SNP genotyping for enhanced breeding values, genetic diversity assessment, and estimation of pollen contaminationGaleano, EstebanCappa, Eduardo PabloBousquet, JeanThomas, Barb R.Genetic MarkersPollenMarcadores GenéticosPicea glaucaPolenTree BreedingEffective Population SizePollen FlowWhite SpruceMejoramiento de ArbolesTamaño Efectivo de la PoblaciónFlujo de PolenAbeto BlancoThe utilization of genotyping has gained significant popularity in tree improvement programs, aiding in enhancing the precision of breeding values, removing pedigree errors, the assessment of genetic diversity, and evaluating pollen contamination. Our study explores the impact of utilizing 5308 SNP markers to genotype seed orchard parents (166), progeny in progeny trials (667), and seedlot orchard seedlings (780), to simultaneously enhance variance components, breeding values, genetic diversity estimates, and pollen flow in the Region I white spruce (Picea glauca) breeding program in central Alberta (Canada). We compared different individual tree mixed models, including pedigree-based (ABLUP), genomic-based (GBLUP), and single-step pedigree-genomicbased (ssGBLUP) models, to estimate variance components and predict breeding values for the height and diameter at breast height traits. The highest heritability estimates were achieved using the ssGBLUP approach, resulting in improved breeding value accuracy compared to the ABLUP and GBLUP models for the studied growth traits. In the six orchard seedlots tested, the genetic diversity of the seedlings remained stable, characterized by an average of approximately 2.00 alleles per SNP, a Shannon Index of approximately 0.44, and an expected and observed heterozygosity of approximately 0.29. The pedigree reconstruction of seed orchard seedlings successfully identified consistent parental contributions and equal genotype contributions in different years. Pollen contamination levels varied between 11% and 70% using SNP markers and 8% to 81% using pollen traps, with traps both overand under-estimating contamination. Overall, integrating genomic information from parents and offspring empowers forest geneticists and breeders in the Region I white spruce breeding program to correct errors, conduct backward and forward selections with greater precision, gain a deeper understanding of the orchard’s genetic structure, select superior seedlots, and accurately estimate the genetic worth of each orchard lot, which can ultimately result in increased and more precise estimates of genetic gain in the studied growth traitsInstituto de Recursos BiológicosFil: Galeano, Esteban. University of Alberta, Department of Renewable Resources; CanadáFil: Cappa, Eduardo Pablo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Bousquet, Jean. Université Laval, Department of Wood and Forest Sciences and Forest Research Centre; CanadáFil: Thomas, Barb R. University of Alberta. Department of Renewable Resources; CanadáMDPI2023-12-15T10:08:33Z2023-12-15T10:08:33Z2023-11-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/16244https://www.mdpi.com/1999-4907/14/11/22121999-4907https://doi.org/10.3390/f14112212Forests 14 (11) : 2212 (November 2023)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo: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)2025-09-29T13:46:16Zoai:localhost:20.500.12123/16244instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-09-29 13:46:16.515INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Optimizing a regional white spruce tree improvement program: SNP genotyping for enhanced breeding values, genetic diversity assessment, and estimation of pollen contamination
title Optimizing a regional white spruce tree improvement program: SNP genotyping for enhanced breeding values, genetic diversity assessment, and estimation of pollen contamination
spellingShingle Optimizing a regional white spruce tree improvement program: SNP genotyping for enhanced breeding values, genetic diversity assessment, and estimation of pollen contamination
Galeano, Esteban
Genetic Markers
Pollen
Marcadores Genéticos
Picea glauca
Polen
Tree Breeding
Effective Population Size
Pollen Flow
White Spruce
Mejoramiento de Arboles
Tamaño Efectivo de la Población
Flujo de Polen
Abeto Blanco
title_short Optimizing a regional white spruce tree improvement program: SNP genotyping for enhanced breeding values, genetic diversity assessment, and estimation of pollen contamination
title_full Optimizing a regional white spruce tree improvement program: SNP genotyping for enhanced breeding values, genetic diversity assessment, and estimation of pollen contamination
title_fullStr Optimizing a regional white spruce tree improvement program: SNP genotyping for enhanced breeding values, genetic diversity assessment, and estimation of pollen contamination
title_full_unstemmed Optimizing a regional white spruce tree improvement program: SNP genotyping for enhanced breeding values, genetic diversity assessment, and estimation of pollen contamination
title_sort Optimizing a regional white spruce tree improvement program: SNP genotyping for enhanced breeding values, genetic diversity assessment, and estimation of pollen contamination
dc.creator.none.fl_str_mv Galeano, Esteban
Cappa, Eduardo Pablo
Bousquet, Jean
Thomas, Barb R.
author Galeano, Esteban
author_facet Galeano, Esteban
Cappa, Eduardo Pablo
Bousquet, Jean
Thomas, Barb R.
author_role author
author2 Cappa, Eduardo Pablo
Bousquet, Jean
Thomas, Barb R.
author2_role author
author
author
dc.subject.none.fl_str_mv Genetic Markers
Pollen
Marcadores Genéticos
Picea glauca
Polen
Tree Breeding
Effective Population Size
Pollen Flow
White Spruce
Mejoramiento de Arboles
Tamaño Efectivo de la Población
Flujo de Polen
Abeto Blanco
topic Genetic Markers
Pollen
Marcadores Genéticos
Picea glauca
Polen
Tree Breeding
Effective Population Size
Pollen Flow
White Spruce
Mejoramiento de Arboles
Tamaño Efectivo de la Población
Flujo de Polen
Abeto Blanco
dc.description.none.fl_txt_mv The utilization of genotyping has gained significant popularity in tree improvement programs, aiding in enhancing the precision of breeding values, removing pedigree errors, the assessment of genetic diversity, and evaluating pollen contamination. Our study explores the impact of utilizing 5308 SNP markers to genotype seed orchard parents (166), progeny in progeny trials (667), and seedlot orchard seedlings (780), to simultaneously enhance variance components, breeding values, genetic diversity estimates, and pollen flow in the Region I white spruce (Picea glauca) breeding program in central Alberta (Canada). We compared different individual tree mixed models, including pedigree-based (ABLUP), genomic-based (GBLUP), and single-step pedigree-genomicbased (ssGBLUP) models, to estimate variance components and predict breeding values for the height and diameter at breast height traits. The highest heritability estimates were achieved using the ssGBLUP approach, resulting in improved breeding value accuracy compared to the ABLUP and GBLUP models for the studied growth traits. In the six orchard seedlots tested, the genetic diversity of the seedlings remained stable, characterized by an average of approximately 2.00 alleles per SNP, a Shannon Index of approximately 0.44, and an expected and observed heterozygosity of approximately 0.29. The pedigree reconstruction of seed orchard seedlings successfully identified consistent parental contributions and equal genotype contributions in different years. Pollen contamination levels varied between 11% and 70% using SNP markers and 8% to 81% using pollen traps, with traps both overand under-estimating contamination. Overall, integrating genomic information from parents and offspring empowers forest geneticists and breeders in the Region I white spruce breeding program to correct errors, conduct backward and forward selections with greater precision, gain a deeper understanding of the orchard’s genetic structure, select superior seedlots, and accurately estimate the genetic worth of each orchard lot, which can ultimately result in increased and more precise estimates of genetic gain in the studied growth traits
Instituto de Recursos Biológicos
Fil: Galeano, Esteban. University of Alberta, Department of Renewable Resources; Canadá
Fil: Cappa, Eduardo Pablo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Bousquet, Jean. Université Laval, Department of Wood and Forest Sciences and Forest Research Centre; Canadá
Fil: Thomas, Barb R. University of Alberta. Department of Renewable Resources; Canadá
description The utilization of genotyping has gained significant popularity in tree improvement programs, aiding in enhancing the precision of breeding values, removing pedigree errors, the assessment of genetic diversity, and evaluating pollen contamination. Our study explores the impact of utilizing 5308 SNP markers to genotype seed orchard parents (166), progeny in progeny trials (667), and seedlot orchard seedlings (780), to simultaneously enhance variance components, breeding values, genetic diversity estimates, and pollen flow in the Region I white spruce (Picea glauca) breeding program in central Alberta (Canada). We compared different individual tree mixed models, including pedigree-based (ABLUP), genomic-based (GBLUP), and single-step pedigree-genomicbased (ssGBLUP) models, to estimate variance components and predict breeding values for the height and diameter at breast height traits. The highest heritability estimates were achieved using the ssGBLUP approach, resulting in improved breeding value accuracy compared to the ABLUP and GBLUP models for the studied growth traits. In the six orchard seedlots tested, the genetic diversity of the seedlings remained stable, characterized by an average of approximately 2.00 alleles per SNP, a Shannon Index of approximately 0.44, and an expected and observed heterozygosity of approximately 0.29. The pedigree reconstruction of seed orchard seedlings successfully identified consistent parental contributions and equal genotype contributions in different years. Pollen contamination levels varied between 11% and 70% using SNP markers and 8% to 81% using pollen traps, with traps both overand under-estimating contamination. Overall, integrating genomic information from parents and offspring empowers forest geneticists and breeders in the Region I white spruce breeding program to correct errors, conduct backward and forward selections with greater precision, gain a deeper understanding of the orchard’s genetic structure, select superior seedlots, and accurately estimate the genetic worth of each orchard lot, which can ultimately result in increased and more precise estimates of genetic gain in the studied growth traits
publishDate 2023
dc.date.none.fl_str_mv 2023-12-15T10:08:33Z
2023-12-15T10:08:33Z
2023-11-01
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/20.500.12123/16244
https://www.mdpi.com/1999-4907/14/11/2212
1999-4907
https://doi.org/10.3390/f14112212
url http://hdl.handle.net/20.500.12123/16244
https://www.mdpi.com/1999-4907/14/11/2212
https://doi.org/10.3390/f14112212
identifier_str_mv 1999-4907
dc.language.none.fl_str_mv eng
language eng
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 MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv Forests 14 (11) : 2212 (November 2023)
reponame:INTA Digital (INTA)
instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str INTA Digital (INTA)
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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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