Improving lodgepole pine genomic evaluation using spatial correlation structure and SNP selection with single-step GBLUP

• Autores: Cappa, Eduardo Pablo; Ratcliffe, Blaise; Chen, Charles; Thomas, Barb R.; Liu, Yang; Klutsch, Jennifer G.; Azcona, Jaime Sebastian; Benowicz, Andy; Sadoway, Shane; Erlilgin, Nadir; El-Kassaby, Yousry A.
• Año de publicación: 2022
• Idioma: inglés
• Tipo de recurso: artículo
• Fuente: Heredity 128 (4) : 209-224 (2022)
• Materia: Genómica; Evaluación; Árboles Forestales; Genomics; Evaluation; Forest Trees; Capacidad Predictiva; Predictive Ability
• Institución: Instituto Nacional de Tecnología Agropecuaria

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.
Fil: Cappa, Eduardo Pablo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Ratchiffe, Blaise. University of British Columbia. Faculty of Forestry. Department of Forest and Conservation Sciences; Canadá
Fil: Chen, Charles. Oklahoma State University. Department of Biochemistry and Molecular Biology; Estados Unidos
Fil: Thomas, Barb R. University of Alberta. Department of Renewable Resources; Canadá
Fil: Liu, Yang. University of British Columbia. Faculty of Forestry. Department of Forest and Conservation Sciences; Canadá
Fil: Klutsch, Jennifer G. University of Alberta. Department of Renewable Resources; Canadá
Fil: Sebastian-Azcona, Jaime. University of Alberta. Department of Renewable Resources; Canadá
Fil: Benowicz, Andy. Alberta Agriculture and Forestry. Forest Stewardship and Trade Branch; Canadá
Fil: Sadoway, Shane. Blue Ridge Lumber Inc.; Canadá
Fil: Erbilgin, Nadir. University of Alberta. Department of Renewable Resources; Canadá
Fil: El-Kassaby, Yousry A. University of British Columbia. Faculty of Forestry. Department of Forest and Conservation Sciences; Canadá