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
- Institución
- Instituto Nacional de Tecnología Agropecuaria
- OAI Identificador
- oai:localhost:20.500.12123/16244
Ver los metadatos del registro completo
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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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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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