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

Abstract

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 were better than estimates of epistatic effects. Estimates increased in accuracy for all models when non-additive effects for maize cob weight were considered.

Crop genome‐wide association study: a harvest of biological relevance

“…While its prominent role in heterosis has been widely recognized in various crops (Yu et al ., ; Li et al ., ; Melchinger et al ., ; Garcia et al ., ; Shen et al ., ; Jiang et al ., ), the prevalence of epistasis in maize trait architecture is thought to be small (Buckler et al ., ; Tian et al ., ; Xiao et al ., ), or results in a large effect only at specific loci (Studer and Doebley, ; Durand et al ., ). However, many studies indicate that epistasis is pervasive in contributing to various quantitative trait phenotypes (Manicacci et al ., ; Würschum et al ., ; Zhang et al ., 2015a; Wen et al ., ; He et al ., ; Luo et al ., ; Mathew et al ., ) and can be further used to improve the accuracy of trait prediction for both inbreds and hybrids (Maurer et al ., ; Santos et al ., ; Luo et al ., ). These results were observed in a variety of populations, such as a recombinant inbred line (RIL) population, a multi‐parent advanced‐generation inter‐cross (MAGIC) population, and diversity panels, and for different traits, such as morphological characteristics, resistance to disease, and cellular metabolite levels.…”

Bayesian reversible-jump for epistasis analysis in genomic studies

“…The searching strategy for epistasis has been proposed by several authors in genome-wide studies to incorporate its effects into the model [16, 28, 31, 32, 38, 40]. However, several of these methods are based on undirected epistasis estimates for multistage strategies; in these circumstances, the genetic architecture may not be correctly depicted.…”

Breeding Sorghum for Grain, Forage and Bioenergy in Brazil

“…In its turn, significant work was conducted The genomic prediction approach has been undergoing constant adjustments over the years. Many efforts have been allocated to applying genomic prediction to several crops, such as maize (Dias et al, 2020;Beyene et al, 2019), wheat (Juliana et al, 2020), rice (Labroo et al, 2021b), and sorghum (Oliveira et al, 2018;Bernardino et al, 2021), and to define the best prediction models and approaches (Santos et al, 2015;Dias et al, 2018;Fristche-Neto et al, 2018).…”