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 Emanuel; 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-genomic-based (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 over- and 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.
Fil: Galeano, Esteban Emanuel. University of Alberta; Canadá
Fil: Cappa, Eduardo Pablo. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación de Recursos Naturales. Instituto de Recursos Biológicos; Argentina
Fil: Bousquet, Jean. Laval University; Canadá
Fil: Thomas, Barb R.. University of Alberta; Canadá
Materia
EFFECTIVE POPULATION SIZE
MOLECULAR MARKERS
PICEA GLAUCA
POLLEN FLOW
TREE BREEDING
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/224865

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network_name_str CONICET Digital (CONICET)
spelling Optimizing a Regional White Spruce Tree Improvement Program: SNP Genotyping for Enhanced Breeding Values, Genetic Diversity Assessment, and Estimation of Pollen ContaminationGaleano, Esteban EmanuelCappa, Eduardo PabloBousquet, JeanThomas, Barb R.EFFECTIVE POPULATION SIZEMOLECULAR MARKERSPICEA GLAUCAPOLLEN FLOWTREE BREEDINGhttps://purl.org/becyt/ford/4.5https://purl.org/becyt/ford/4The 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-genomic-based (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 over- and 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.Fil: Galeano, Esteban Emanuel. University of Alberta; CanadáFil: Cappa, Eduardo Pablo. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación de Recursos Naturales. Instituto de Recursos Biológicos; ArgentinaFil: Bousquet, Jean. Laval University; CanadáFil: Thomas, Barb R.. University of Alberta; CanadáMultidisciplinary Digital Publishing Institute2023-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/224865Galeano, Esteban Emanuel; Cappa, Eduardo Pablo; Bousquet, Jean; Thomas, Barb R.; Optimizing a Regional White Spruce Tree Improvement Program: SNP Genotyping for Enhanced Breeding Values, Genetic Diversity Assessment, and Estimation of Pollen Contamination; Multidisciplinary Digital Publishing Institute; Forests; 14; 11; 11-2023; 1-181999-4907CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.3390/f14112212info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:07:25Zoai:ri.conicet.gov.ar:11336/224865instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:07:25.734CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
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 Emanuel
EFFECTIVE POPULATION SIZE
MOLECULAR MARKERS
PICEA GLAUCA
POLLEN FLOW
TREE BREEDING
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 Emanuel
Cappa, Eduardo Pablo
Bousquet, Jean
Thomas, Barb R.
author Galeano, Esteban Emanuel
author_facet Galeano, Esteban Emanuel
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 EFFECTIVE POPULATION SIZE
MOLECULAR MARKERS
PICEA GLAUCA
POLLEN FLOW
TREE BREEDING
topic EFFECTIVE POPULATION SIZE
MOLECULAR MARKERS
PICEA GLAUCA
POLLEN FLOW
TREE BREEDING
purl_subject.fl_str_mv https://purl.org/becyt/ford/4.5
https://purl.org/becyt/ford/4
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-genomic-based (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 over- and 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.
Fil: Galeano, Esteban Emanuel. University of Alberta; Canadá
Fil: Cappa, Eduardo Pablo. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación de Recursos Naturales. Instituto de Recursos Biológicos; Argentina
Fil: Bousquet, Jean. Laval University; Canadá
Fil: Thomas, Barb R.. University of Alberta; 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-genomic-based (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 over- and 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 2023-11
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/11336/224865
Galeano, Esteban Emanuel; Cappa, Eduardo Pablo; Bousquet, Jean; Thomas, Barb R.; Optimizing a Regional White Spruce Tree Improvement Program: SNP Genotyping for Enhanced Breeding Values, Genetic Diversity Assessment, and Estimation of Pollen Contamination; Multidisciplinary Digital Publishing Institute; Forests; 14; 11; 11-2023; 1-18
1999-4907
CONICET Digital
CONICET
url http://hdl.handle.net/11336/224865
identifier_str_mv Galeano, Esteban Emanuel; Cappa, Eduardo Pablo; Bousquet, Jean; Thomas, Barb R.; Optimizing a Regional White Spruce Tree Improvement Program: SNP Genotyping for Enhanced Breeding Values, Genetic Diversity Assessment, and Estimation of Pollen Contamination; Multidisciplinary Digital Publishing Institute; Forests; 14; 11; 11-2023; 1-18
1999-4907
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.3390/f14112212
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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