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Accounting for epistasis improves genomic prediction of phenotypes with univariate and bivariate models across environments.
Vojgani, Elaheh; Pook, Torsten; Martini, Johannes W R; Hölker, Armin C; Mayer, Manfred; Schön, Chris-Carolin; Simianer, Henner.
Afiliação
  • Vojgani E; Center for Integrated Breeding Research, Animal Breeding and Genetics Group, University of Goettingen, Goettingen, Germany. vojgani@gwdg.de.
  • Pook T; Center for Integrated Breeding Research, Animal Breeding and Genetics Group, University of Goettingen, Goettingen, Germany.
  • Martini JWR; International Maize and Wheat Improvement Center (CIMMYT), Texcoco, State of Mexico, Mexico.
  • Hölker AC; Plant Breeding, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany.
  • Mayer M; Plant Breeding, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany.
  • Schön CC; Plant Breeding, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany.
  • Simianer H; Center for Integrated Breeding Research, Animal Breeding and Genetics Group, University of Goettingen, Goettingen, Germany.
Theor Appl Genet ; 134(9): 2913-2930, 2021 Sep.
Article em En | MEDLINE | ID: mdl-34115154
KEY MESSAGE: The accuracy of genomic prediction of phenotypes can be increased by including the top-ranked pairwise SNP interactions into the prediction model. We compared the predictive ability of various prediction models for a maize dataset derived from 910 doubled haploid lines from two European landraces (Kemater Landmais Gelb and Petkuser Ferdinand Rot), which were tested at six locations in Germany and Spain. The compared models were Genomic Best Linear Unbiased Prediction (GBLUP) as an additive model, Epistatic Random Regression BLUP (ERRBLUP) accounting for all pairwise SNP interactions, and selective Epistatic Random Regression BLUP (sERRBLUP) accounting for a selected subset of pairwise SNP interactions. These models have been compared in both univariate and bivariate statistical settings for predictions within and across environments. Our results indicate that modeling all pairwise SNP interactions into the univariate/bivariate model (ERRBLUP) is not superior in predictive ability to the respective additive model (GBLUP). However, incorporating only a selected subset of interactions with the highest effect variances in univariate/bivariate sERRBLUP can increase predictive ability significantly compared to the univariate/bivariate GBLUP. Overall, bivariate models consistently outperform univariate models in predictive ability. Across all studied traits, locations and landraces, the increase in prediction accuracy from univariate GBLUP to univariate sERRBLUP ranged from 5.9 to 112.4 percent, with an average increase of 47 percent. For bivariate models, the change ranged from -0.3 to + 27.9 percent comparing the bivariate sERRBLUP to the bivariate GBLUP, with an average increase of 11 percent. This considerable increase in predictive ability achieved by sERRBLUP may be of interest for "sparse testing" approaches in which only a subset of the lines/hybrids of interest is observed at each location.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Fenótipo / Zea mays / Cromossomos de Plantas / Locos de Características Quantitativas / Meio Ambiente / Epistasia Genética / Modelos Genéticos Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Fenótipo / Zea mays / Cromossomos de Plantas / Locos de Características Quantitativas / Meio Ambiente / Epistasia Genética / Modelos Genéticos Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article