Статья

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

Eduardo P. CappaConsejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina. [email protected]Charles ChenDepartment of Biochemistry and Molecular Biology, Oklahoma State University, Stillwater, OK, 74078, USAJennifer G. KlutschDepartment of Renewable Resources, University of Alberta, 442 Earth Sciences Bldg, Edmonton, Alberta, T6G 2E3, CanadaJaime Sebastian‐AzconaDepartment of Renewable Resources, University of Alberta, 442 Earth Sciences Bldg, Edmonton, Alberta, T6G 2E3, CanadaBlaise RatcliffeDepartment of Forest and Conservation Sciences, Faculty of Forestry, University of British Columbia, Vancouver, British Columbia, V6T 1Z4, CanadaXiaojing WeiDepartment of Renewable Resources, University of Alberta, 442 Earth Sciences Bldg, Edmonton, Alberta, T6G 2E3, CanadaLetitia Da RosDepartment of Wood Science, Faculty of Forestry, University of British Columbia, Vancouver, British Columbia, V6T 1Z4, CanadaAziz UllahDepartment of Renewable Resources, University of Alberta, 442 Earth Sciences Bldg, Edmonton, Alberta, T6G 2E3, CanadaYang LiuDepartment of Forest and Conservation Sciences, Faculty of Forestry, University of British Columbia, Vancouver, British Columbia, V6T 1Z4, CanadaAndy BenowiczShane SadowayBlue Ridge Lumber Inc., West Fraser Mills Ltd, Unnamed Road, Blue Ridge, Alberta, T0E 0B0, CanadaShawn D. MansfieldDepartment of Wood Science, Faculty of Forestry, University of British Columbia, Vancouver, British Columbia, V6T 1Z4, CanadaNadir ErbilginDepartment of Renewable Resources, University of Alberta, 442 Earth Sciences Bldg, Edmonton, Alberta, T6G 2E3, CanadaBarb R. ThomasDepartment of Renewable Resources, University of Alberta, 442 Earth Sciences Bldg, Edmonton, Alberta, T6G 2E3, CanadaYousry A. El‐KassabyDepartment of Forest and Conservation Sciences, Faculty of Forestry, University of British Columbia, Vancouver, British Columbia, V6T 1Z4, Canada

2022en

ABI

Аннотация

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.

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Идентификаторы

DOI: 10.1186/s12864-022-08747-7

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Цитирований: 2Использованных источников: 0

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