Genomic Prediction in Maize Breeding Populations with Genotyping-by-Sequencing

Abstract

Genotyping-by-sequencing (GBS) technologies have proven capacity for delivering large numbers of marker genotypes with potentially less ascertainment bias than standard single nucleotide polymorphism (SNP) arrays. Therefore, GBS has become an attractive alternative technology for genomic selection. However, the use of GBS data poses important challenges, and the accuracy of genomic prediction using GBS is currently undergoing investigation in several crops, including maize, wheat, and cassava. The main objective of this study was to evaluate various methods for incorporating GBS information and compare them with pedigree models for predicting genetic values of lines from two maize populations evaluated for different traits measured in different environments (experiments 1 and 2). Given that GBS data come with a large percentage of uncalled genotypes, we evaluated methods using nonimputed, imputed, and GBS-inferred haplotypes of different lengths (short or long). GBS and pedigree data were incorporated into statistical models using either the genomic best linear unbiased predictors (GBLUP) or the reproducing kernel Hilbert spaces (RKHS) regressions, and prediction accuracy was quantified using cross-validation methods. The following results were found: relative to pedigree or marker-only models, there were consistent gains in prediction accuracy by combining pedigree and GBS data; there was increased predictive ability when using imputed or nonimputed GBS data over inferred haplotype in experiment 1, or nonimputed GBS and information-based imputed short and long haplotypes, as compared to the other methods in experiment 2; the level of prediction accuracy achieved using GBS data in experiment 2 is comparable to those reported by previous authors who analyzed this data set using SNP arrays; and GBLUP and RKHS models with pedigree with nonimputed and imputed GBS data provided the best prediction correlations for the three traits in experiment 1, whereas for experiment 2 RKHS provided slightly better prediction than GBLUP for drought-stressed environments, and both models provided similar predictions in well-watered environments.

Modeling Epistasis in Genomic Selection

“…The use of semiparametric reproducing kernel Hilbert space (RKHS) regression models has been promoted as an alternative powerful option to capture epistasis in genomic selection (Gianola et al 2006;Gianola and Van Kaam 2008). The RKHS model outperformed linear models that focused exclusively on marker main effects in a number of studies based on simulated data (e.g., Gianola et al 2006;Howard et al 2014) and empirical data (e.g., Perez-Rodriguez et al 2012;Crossa et al 2013). Choosing an appropriate kernel, which can be interpreted as a relationship matrix among genotypes (i.e., individuals), is a central element of model specification in RKHS regression .…”

“…The RKHS model outperformed linear models that focused exclusively on marker main effects in a number of studies based on simulated data (e.g., Gianola et al 2006;Howard et al 2014) and empirical data (e.g., Perez-Rodriguez et al 2012;Crossa et al 2013). Choosing an appropriate kernel, which can be interpreted as a relationship matrix among genotypes (i.e., individuals), is a central element of model specification in RKHS regression .…”

Genomic and transcriptomic approaches toward plant selection

“…However, nowadays this has changed due to the access to new methods of NGS for genotyping, which is a powerful method for genetic screening uncovering SNPs in plants; which allows carrying out the studies based on genotyping. It seems the declining cost of the use of this technology is also a plus point so that researchers have spared no any effort to choose it Crossa et al (2013) Elshire et al (2011).…”

Mixed Modeling of Yield Components and Brown Rust Resistance in Sugarcane Families

“…This approach also means a great change in the analysis of breeding experiments because genotype observations may be grouped by levels of grouping factors generated from the experimental design, such as the harvest year and location (Pastina et al, 2012). The application of a mixed model approach is becoming increasingly popular in plant breeding, particularly in research involving the prediction of breeding values combined with genomic data (Beaulieu et al, 2014;Bevan and Uauy, 2013;Burgueño et al, 2012;Crossa et al, 2013;Muir, 2007;Wolc et al, 2011;Zhang et al, 2010). For sugarcane, the use of linear mixed models to map quantitative trait loci (Pastina et al, 2012) and genomic selection (Gouy et al, 2013) represents progress in crop improvement.…”