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dc.contributor.author
Cappa, Eduardo Pablo
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dc.contributor.author
Chen, Charles
dc.contributor.author
Klutsch, Jennifer G.
dc.contributor.author
Sebastian-Azcona, Jaime
dc.contributor.author
Ratcliffe, Blaise
dc.contributor.author
Wei, Xiaojing
dc.contributor.author
Da Ros, Letitia
dc.contributor.author
Ullah, Aziz
dc.contributor.author
Liu, Yang
dc.contributor.author
Benowicz, Andy
dc.contributor.author
Sadoway, Shane
dc.contributor.author
Mansfield, Shawn D.
dc.contributor.author
Erbilgin, Nadir
dc.contributor.author
Thomas, Barb R.
dc.contributor.author
El Kassaby, Yousry A.
dc.date.available
2023-07-04T12:13:20Z
dc.date.issued
2022-12
dc.identifier.citation
Cappa, Eduardo Pablo; Chen, Charles; Klutsch, Jennifer G.; Sebastian-Azcona, Jaime; Ratcliffe, Blaise; et al.; Multiple-trait analyses improved the accuracy of genomic prediction and the power of genome-wide association of productivity and climate change-adaptive traits in lodgepole pine; BioMed Central; BMC Genomics; 23; 1; 12-2022; 1-20
dc.identifier.issn
1471-2164
dc.identifier.uri
http://hdl.handle.net/11336/202157
dc.description.abstract
Background: Genomic prediction (GP) and genome-wide association (GWA) analyses are currently being employed to accelerate breeding cycles and to identify alleles or genomic regions of complex traits in forest trees species. Here, 1490 interior lodgepole pine (Pinus contorta Dougl. ex. Loud. var. latifolia Engelm) trees from four open-pollinated progeny trials were genotyped with 25,099 SNPs, and phenotyped for 15 growth, wood quality, pest resistance, drought tolerance, and defense chemical (monoterpenes) traits. The main objectives of this study were to: (1) identify genetic markers associated with these traits and determine their genetic architecture, and to compare the marker detected by single- (ST) and multiple-trait (MT) GWA models; (2) evaluate and compare the accuracy and control of bias of the genomic predictions for these traits underlying different ST and MT parametric and non-parametric GP methods. GWA, ST and MT analyses were compared using a linear transformation of genomic breeding values from the respective genomic best linear unbiased prediction (GBLUP) model. GP, ST and MT parametric and non-parametric (Reproducing Kernel Hilbert Spaces, RKHS) models were compared in terms of prediction accuracy (PA) and control of bias. Results: MT-GWA analyses identified more significant associations than ST. Some SNPs showed potential pleiotropic effects. Averaging across traits, PA from the studied ST-GP models did not differ significantly from each other, with generally a slight superiority of the RKHS method. MT-GP models showed significantly higher PA (and lower bias) than the ST models, being generally the PA (bias) of the RKHS approach significantly higher (lower) than the GBLUP. Conclusions: The power of GWA and the accuracy of GP were improved when MT models were used in this lodgepole pine population. Given the number of GP and GWA models fitted and the traits assessed across four progeny trials, this work has produced the most comprehensive empirical genomic study across any lodgepole pine population to date.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
BioMed Central
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dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by/2.5/ar/
dc.subject
GENOME WIDE ASSOCIATION ANALYSES
dc.subject
GENOMIC PREDICTION
dc.subject
LODGEPOLE PINE
dc.subject
QUANTITATIVE GENETIC PARAMETERS
dc.subject
SINGLE- AND MULTIPLE-TRAIT MIXED MODELS
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
Multiple-trait analyses improved the accuracy of genomic prediction and the power of genome-wide association of productivity and climate change-adaptive traits in lodgepole pine
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2023-07-02T14:54:51Z
dc.journal.volume
23
dc.journal.number
1
dc.journal.pagination
1-20
dc.journal.pais
Reino Unido
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dc.journal.ciudad
Londres
dc.description.fil
Fil: Cappa, Eduardo Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires; Argentina
dc.description.fil
Fil: Chen, Charles. Oklahoma State University; Estados Unidos
dc.description.fil
Fil: Klutsch, Jennifer G.. University of Alberta; Canadá
dc.description.fil
Fil: Sebastian-Azcona, Jaime. University of Alberta; Canadá
dc.description.fil
Fil: Ratcliffe, Blaise. University of British Columbia; Canadá
dc.description.fil
Fil: Wei, Xiaojing. University of Alberta; Canadá
dc.description.fil
Fil: Da Ros, Letitia. University of British Columbia; Canadá
dc.description.fil
Fil: Ullah, Aziz. University of Alberta; Canadá
dc.description.fil
Fil: Liu, Yang. University of British Columbia; Canadá
dc.description.fil
Fil: Benowicz, Andy. No especifíca;
dc.description.fil
Fil: Sadoway, Shane. No especifíca;
dc.description.fil
Fil: Mansfield, Shawn D.. University of British Columbia; Canadá
dc.description.fil
Fil: Erbilgin, Nadir. University of Alberta; Canadá
dc.description.fil
Fil: Thomas, Barb R.. University of Alberta; Canadá
dc.description.fil
Fil: El Kassaby, Yousry A.. University of British Columbia; Canadá
dc.journal.title
BMC Genomics
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dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-022-08747-7
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1186/s12864-022-08747-7
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