Genome-wide prediction of maize single-cross performance, considering non-additive genetic effects

Genetics and Molecular Research : GMR
J P R SantosM Balestre

Abstract

The prediction of single-cross hybrids in maize is a promising technique for optimizing the use of financial resources in a breeding program. This study aimed to evaluate Genomic Best Linear Unbiased Predictors models for hybrid prediction and compare them with the Bayesian Ridge Regression, Bayes A, Bayesian LASSO, Bayes C, Bayes B, and Reproducing Kernel Hilbert Spaces Regression models, with inclusion or absence of non-additive effects under three heritability scenarios. Data from a maize germplasm bank belonging to USDA were used to determine the effects of molecular markers, which were considered to be parametric, to build 400 single-cross hybrids between two line groups via simulation. The following parameters were used to compare the models: predictive ability, estimation of variance components, heritability of genetic effects present in all situations, and the sum of squares of the predicted errors. The models responded positively when dominance effects were included in non-additive models, with all models tending to show an increase in the values of heritability parameters under all scenarios. Differences occur between models depending on the heritability range considered. Estimates of additive and dominant effects wer...Continue Reading

Citations

Oct 29, 2018·The Plant Journal : for Cell and Molecular Biology·Hai-Jun Liu, Jianbing Yan
Dec 13, 2016·BMC Genomics·Marcio Balestre, Claudio Lopes de Souza
Sep 23, 2020·TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik·William R RollingLeah K McHale

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