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dc.contributor.author
Cappa, Eduardo Pablo
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dc.contributor.author
Ratcliffe, Blaise
dc.contributor.author
Chen, Charles
dc.contributor.author
Thomas, Barb R.
dc.contributor.author
Liu, Yang
dc.contributor.author
Klutsch, Jennifer
dc.contributor.author
Wei, Xiaojing
dc.contributor.author
Azcona, Jaime Sebastian
dc.contributor.author
Benowicz, Andy
dc.contributor.author
Sadoway, Shane
dc.contributor.author
Erbilgin, Nadir
dc.contributor.author
El-Kassaby, Yousry A.
dc.date.available
2023-02-06T11:19:53Z
dc.date.issued
2022-04
dc.identifier.citation
Cappa, Eduardo Pablo; Ratcliffe, Blaise; Chen, Charles; Thomas, Barb R.; Liu, Yang; et al.; Improving lodgepole pine genomic evaluation using spatial correlation structure and SNP selection with single-step GBLUP; Nature Publishing Group; Heredity; 128; 4; 4-2022; 209-224
dc.identifier.issn
0018-067X
dc.identifier.uri
http://hdl.handle.net/11336/186939
dc.description.abstract
Modeling environmental spatial heterogeneity can improve the efficiency of forest tree genomic evaluation. Furthermore, genotyping costs can be lowered by reducing the number of markers needed. We investigated the impact on variance components, breeding value accuracy, and bias of two phenotypic data adjustments (experimental design and autoregressive spatial models), and a relationship matrix calculated from a subset of markers selected for their ability to infer ancestry. Using a multiple-trait multiple-site single-step Genomic Best Linear Unbiased Prediction (ssGBLUP) approach, four scenarios (2 phenotype adjustments × 2 marker sets) were applied to diameter at breast height (DBH), height (HT), and resistance to western gall rust (WGR) in four open-pollinated progeny trials of lodgepole pine, with 1490 (out of 11,188) trees genotyped with 25,099 SNPs. As a control, we fitted the conventional ABLUP model using pedigree information. The highest heritability estimates were achieved for the ABLUP followed closely by the ssGBLUP with the full marker set and using the spatial phenotype adjustments. The highest predictive ability was obtained by using a reduced marker subset (8000 SNPs) when either the spatial (DBH: 0.429, and WGR: 0.513) or design (HT: 0.467) phenotype corrections were used. No significant difference was detected in prediction bias among the six fitted models, and all values were close to 1 (0.918–1.014). Results demonstrated that selecting informative markers, such as those capturing ancestry, can improve the predictive ability. The use of spatial correlation structure increased traits’ heritability and reduced prediction bias, while increases in predictive ability were trait-dependent.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Nature Publishing Group
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dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
SPATIAL MODEL
dc.subject
SNP ARRAY SUBSET
dc.subject
ssGBLUP
dc.subject
HERITABILITY
dc.subject
ACCURACY
dc.subject
BIAS
dc.subject
LODGEPOLE PINE
dc.subject.classification
Otras Ciencias Agrícolas
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dc.subject.classification
Otras Ciencias Agrícolas
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dc.subject.classification
CIENCIAS AGRÍCOLAS
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dc.title
Improving lodgepole pine genomic evaluation using spatial correlation structure and SNP selection with single-step GBLUP
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2023-02-06T10:20:43Z
dc.journal.volume
128
dc.journal.number
4
dc.journal.pagination
209-224
dc.journal.pais
Reino Unido
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dc.journal.ciudad
Londres
dc.description.fil
Fil: Cappa, Eduardo Pablo. Instituto Nacional de Tecnología Agropecuaria; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.description.fil
Fil: Ratcliffe, Blaise. University of British Columbia; Canadá
dc.description.fil
Fil: Chen, Charles. Oklahoma State University; Estados Unidos
dc.description.fil
Fil: Thomas, Barb R.. University of Alberta; Canadá
dc.description.fil
Fil: Liu, Yang. University of British Columbia; Canadá
dc.description.fil
Fil: Klutsch, Jennifer. University of Alberta; Canadá
dc.description.fil
Fil: Wei, Xiaojing. University of Alberta; Canadá
dc.description.fil
Fil: Azcona, Jaime Sebastian. University of Alberta; Canadá
dc.description.fil
Fil: Benowicz, Andy. Alberta Agriculture And Forestry; Canadá
dc.description.fil
Fil: Sadoway, Shane. No especifíca;
dc.description.fil
Fil: Erbilgin, Nadir. University of Alberta; Canadá
dc.description.fil
Fil: El-Kassaby, Yousry A.. University of British Columbia; Canadá
dc.journal.title
Heredity
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dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.nature.com/articles/s41437-022-00508-2
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1038/s41437-022-00508-2
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