BWGS: A R package for genomic selection and its application to a wheat breeding programme

Abstract

We developed an integrated R library called BWGS to enable easy computation of Genomic Estimates of Breeding values (GEBV) for genomic selection. BWGS, for BreedWheat Genomic selection, was developed in the framework of a cooperative private-public partnership project called Breedwheat (https://breedwheat.fr) and relies on existing R-libraries, all freely available from CRAN servers. The two main functions enable to run 1) replicated random cross validations within a training set of genotyped and phenotyped lines and 2) GEBV prediction, for a set of genotyped-only lines. Options are available for 1) missing data imputation, 2) markers and training set selection and 3) genomic prediction with 15 different methods, either parametric or semi-parametric. The usefulness and efficiency of BWGS are illustrated using a population of wheat lines from a real breeding programme. Adjusted yield data from historical trials (highly unbalanced design) were used for testing the options of BWGS. On the whole, 760 candidate lines with adjusted phenotypes and genotypes for 47 839 robust SNP were used. With a simple desktop computer, we obtained results which compared with previously published results on wheat genomic selection. As predicted by the theory, factors that are most influencing predictive ability, for a given trait of moderate heritability, are the size of the training population and a minimum number of markers for capturing every QTL information. Missing data up to 40%, if randomly distributed, do not degrade predictive ability once imputed, and up to 80% randomly distributed missing data are still acceptable once imputed with Expectation-Maximization method of package rrBLUP. It is worth noticing that selecting markers that are most associated to the trait do improve predictive ability, compared with the whole set of markers, but only when marker selection is made on the whole population. When marker selection is made only on the sampled training set, this advantage nearly disappeared, since it was clearly due to overfitting. Few differences are observed between the 15 prediction models with this dataset. Although non-parametric methods that are supposed to capture non-additive effects have slightly better predictive accuracy, differences remain small. Finally, the GEBV from the 15 prediction models are all highly correlated to each other. These results are encouraging for an efficient use of genomic selection in applied breeding programmes and BWGS is a simple and powerful toolbox to apply in breeding programmes or training activities.

Comparison of Genomic Prediction Methods for Yellow, Stem, and Leaf Rust Resistance in Wheat Landraces from Afghanistan

“…Markers were filtered based on missing data >20%, minor allele frequency (MAF) <5%, and other parameters such as call rate, polymorphic information content (PIC), and reproducibility, that resulted in 11,428 markers. The markers were subjected to imputation before running the genomic prediction model using the EM (Expectation-Maximization) algorithm as implemented in the BWGS package in R [ 44 ].…”

“…The mixed model used in GBLUP to predict phenotypes of landraces is: where y is the phenotypic trait response, is the vector of means, Z is the random effects design matrix, u represents genotypic response considered as random effects, whereas is the residual vector. The methods were implemented in the BWGS package in R [ 44 ].…”

Evaluations of Genomic Prediction and Identification of New Loci for Resistance to Stripe Rust Disease in Wheat (Triticum aestivum L.)

“…In the present study, five statistical algorithms were used for GP, RRBLUP ( Meuwissen et al, 2001 ), GBLUP ( VanRaden, 2008 ), EGBLUP ( Jiang and Reif, 2015 ), RKHS ( Gianola and Van Kaam, 2008 ; De Los Campos et al, 2010 ), and RF ( Breiman et al, 2001 ). GP was performed using GBLUP, RRBLUP, EGBLUP, RKHS, and RF models from G × E data in the BWGS pipeline ( Charmet et al, 2020 ). GBLUP is a modification of the conventional BLUP used for predictions by estimating line effects, where genomic relationships are used instead of the traditional pedigree relationships.…”

Increased Predictive Accuracy of Multi-Environment Genomic Prediction Model for Yield and Related Traits in Spring Wheat (Triticum aestivum L.)

“…The baseline SE genomic prediction analysis was implemented in the BWGS program (Charmet et al, 2020). BWGS performs a GBLUP analysis using a marker-based relationship matrix.…”