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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/224865
Ver los metadatos del registro completo
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network_name_str |
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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, 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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1844613933692878848 |
score |
13.070432 |