Joint Use of Genome, Pedigree, and Their Interaction with Environment for Predicting the Performance of Wheat Lines in New Environments

Abstract

Genome-enabled prediction plays an essential role in wheat breeding because it has the potential to increase the rate of genetic gain relative to traditional phenotypic and pedigree-based selection. Since the performance of wheat lines is highly influenced by environmental stimuli, it is important to accurately model the environment and its interaction with genetic factors in prediction models. Arguably, multi-environmental best linear unbiased prediction (BLUP) may deliver better prediction performance than single-environment genomic BLUP. We evaluated pedigree and genome-based prediction using 35,403 wheat lines from the Global Wheat Breeding Program of the International Maize and Wheat Improvement Center (CIMMYT). We implemented eight statistical models that included genome-wide molecular marker and pedigree information as prediction inputs in two different validation schemes. All models included main effects, but some considered interactions between the different types of pedigree and genomic covariates via Hadamard products of similarity kernels. Pedigree models always gave better prediction of new lines in observed environments than genome-based models when only main effects were fitted. However, for all traits, the highest predictive abilities were obtained when interactions between pedigree, genomes, and environments were included. When new lines were predicted in unobserved environments, in almost all trait/year combinations, the marker main-effects model was the best. These results provide strong evidence that the different sources of genetic information (molecular markers and pedigree) are not equally useful at different stages of the breeding pipelines, and can be employed differentially to improve the design and prediction of the outcome of future breeding programs.

Genomic Selection Using Pedigree and Marker-by-Environment Interaction for Barley Seed Quality Traits From Two Commercial Breeding Programs

“…In SJ data, pedigree models performed better than marker models while in NS, pedigree models performed similarly to marker models. This result is consistent with previous observations in wheat and sorghum (Juliana et al, 2017;Hunt et al, 2018;Howard et al, 2019). CV0 and CV00 involve more complex prediction situations that span across different environments (years), and in these situations marker information models performed better than pedigree information models.…”

“…This scheme is similar to CV0, however, the lines to be predicted have never been observed in the years previously. This scheme applies to breeding situations where most of the materials have not been observed in previous fields but their performance needs to be estimated for the next year (Jarquín et al, 2017;Howard et al, 2019). Here lines that were evaluated only in 2017 but not in any of the previous years were used as validation set while the rest were used as the training set.…”

Genomic Selection for Grain Yield in the CIMMYT Wheat Breeding Program—Status and Perspectives

“…We evaluated the potential of genomic predictions for minimizing the number of replications and lines tested within a site and year and obtained mean PAs of 0.56, 0.5 and 0.42 in Stages 1, 2, and 3 of yield testing respectively, with slight decreases in PAs in the advanced yield trial nurseries that were characterized by fewer and highly selected lines with a narrow GY range. These PAs are within the range or slightly higher than those obtained in previous CV studies for wheat GY: 0.32 (Poland et al, 2012b), 0.22 (Heffner et al, 2011), 0.46-0.63 (Howard et al, 2019), 0.37-0.51 (Juliana et al, 2018b), and 0.44-0.57 (Juliana et al, 2018a).…”

“…However, we have also reported only a moderate correlation of 0.64 between the genomic and pedigree relationships and a deviation from the expected relationship of 0.5 between the full-sibs, indicating that there are differences between the genomic and pedigree relationships, part of which can be attributed to pedigree and genotyping errors and the markers not capturing rare allelic differences between lines. While some studies have reported a marginal increase in PAs using combined genomic and pedigree relationships (Crossa et al, 2010;Juliana et al, 2017;Juliana et al, 2018b;Howard et al, 2019), the low value added by genomic relationships in this study suggests that wheat breeding programs with a well-maintained pedigree and family structures like that of CIMMYT in the yield testing stages can just use the available inexpensive pedigree relationship based predictions to minimize the lines in these stages. In addition, breeding programs that have a large number of replications and PAs comparable to the within environment heritabilities can substitute some of the replications with pedigreebased predictions.…”

“…trial stage (Endelman et al, 2014;Gaynor et al, 2017). The similarity in PAs from genomic and pedigree relationships can be attributed to the small full-sib family sizes in the yield testing stages, and several previous studies have reported comparable PAs using genomic and pedigree relationships, and only marginal increases in PAs using genomic relationships in advanced breeding lines (Crossa et al, 2010;Juliana et al, 2017;Juliana et al, 2018b;Howard et al, 2019). However, we have also reported only a moderate correlation of 0.64 between the genomic and pedigree relationships and a deviation from the expected relationship of 0.5 between the full-sibs, indicating that there are differences between the genomic and pedigree relationships, part of which can be attributed to pedigree and genotyping errors and the markers not capturing rare allelic differences between lines.…”