Genomic Estimated Breeding Values Using Genomic Relationship Matrices in a Cloned Population of Loblolly Pine

Abstract

Replacement of the average numerator relationship matrix derived from the pedigree with the realized genomic relationship matrix based on DNA markers might be an attractive strategy in forest tree breeding for predictions of genetic merit. We used genotypes from 3461 single-nucleotide polymorphism loci to estimate genomic relationships for a population of 165 loblolly pine (Pinus taeda L.) individuals. Phenotypes of the 165 individuals were obtained from clonally replicated field trials and were used to estimate breeding values for growth (stem volume). Two alternative methods, based on allele frequencies or regression, were used to generate the genomic relationship matrices. The accuracies of genomic estimated breeding values based on the genomic relationship matrices and breeding values estimated based on the average numerator relationship matrix were compared. On average, the accuracy of predictions based on genomic relationships ranged between 0.37 and 0.74 depending on the validation method. We did not detect differences in the accuracy of predictions based on genomic relationship matrices estimated by two different methods. Using genomic relationship matrices allowed modeling of Mendelian segregation within full-sib families, an important advantage over a traditional genetic evaluation system based on pedigree. We conclude that estimation of genomic relationships could be a powerful tool in forest tree breeding because it accurately accounts both for genetic relationships among individuals and for nuisance effects such as location and replicate effects, and makes more accurate selection possible within full-sib crosses.

Modeling additive and non-additive effects in a hybrid population using genome-wide genotyping: prediction accuracy implications

“…The marker-based matrix had the advantage of capturing both the Mendelian segregation within full-sib families and genetic links through unknown common ancestors, which are not available in the known pedigree. This feature has been observed in genomic selection for animal (Chen et al, 2011) or plant breeding (Zapata-Valenzuela et al, 2013;Muñoz et al, 2014;Beaulieu et al, 2014a;Cros et al, 2015). The comparison of parent and progeny models revealed the higher prediction accuracy of the latter, which better assessed the relationship among progenies by capturing the Mendelian segregation.…”

Insights into Conifer Giga-Genomes

“…In addition, genomic selection was proposed to afford a better fit with breeding objectives because of its ability to predict the genetic values of individuals (Jannink et al, 2010;Grattapaglia and Resende, 2011). The application and accuracy of genomic selection have been shown for Eucalyptus spp., loblolly pine, and white spruce (Resende et al, 2012;Zapata-Valenzuela et al, 2013;Beaulieu et al, 2014). These advances are significant considering the low linkage disequilibrium, the largely undomesticated status, and the wide genetic diversity of conifer breeding populations in addition to their large genome size.…”

Genome wide selection in Citrus breeding

“…These markers, which are in linkage disequilibrium (LD) with the QTL, can have both large and small effects and can account for nearly all of the genetic variation within a quantitative trait (Resende et al, 2008). Additionally, whole genome prediction tends to be more accurate because it better accommodates the variation created by Mendelian segregation during gamete formation (Resende et al, 2008;Zapata-Velenzuela et al, 2013).…”