Accuracy of genomic selection models in a large population of open-pollinated families in white spruce

Abstract

Genomic selection (GS) is of interest in breeding because of its potential for predicting the genetic value of individuals and increasing genetic gains per unit of time. To date, very few studies have reported empirical results of GS potential in the context of large population sizes and long breeding cycles such as for boreal trees. In this study, we assessed the effectiveness of marker-aided selection in an undomesticated white spruce (Picea glauca (Moench) Voss) population of large effective size using a GS approach. A discovery population of 1694 trees representative of 214 open-pollinated families from 43 natural populations was phenotyped for 12 wood and growth traits and genotyped for 6385 single-nucleotide polymorphisms (SNPs) mined in 2660 gene sequences. GS models were built to predict estimated breeding values using all the available SNPs or SNP subsets of the largest absolute effects, and they were validated using various cross-validation schemes. The accuracy of genomic estimated breeding values (GEBVs) varied from 0.327 to 0.435 when the training and the validation data sets shared half-sibs that were on average 90% of the accuracies achieved through traditionally estimated breeding values. The trend was also the same for validation across sites. As expected, the accuracy of GEBVs obtained after cross-validation with individuals of unknown relatedness was lower with about half of the accuracy achieved when half-sibs were present. We showed that with the marker densities used in the current study, predictions with low to moderate accuracy could be obtained within a large undomesticated population of related individuals, potentially resulting in larger gains per unit of time with GS than with the traditional approach.

Genetic architecture of wood properties based on association analysis and co‐expression networks in white spruce

“…S1) and was found to be weak and only three of the 43 geographic origins were separated, as previously observed using multidimensional scaling with the same data (Beaulieu et al ., 2014) (Fig. S1).…”

“…Discovery of the SNPs, genotyping methods and quality control criteria were described previously (Pavy et al ., 2013; Beaulieu et al ., 2014). Briefly, for each of the 1694 trees, DNA was extracted from foliage and individuals were then genotyped with the Illumina Infinium HD iSelect bead chip PgAS1 at a rate of 50 ng μl −1 previously described by Pavy et al .…”

Forest genomics research and development in Canada: Priorities for developing an economic framework

“…Genomics is defined as the branch of biotechnology that deals with the DNA sequence of the entire organism's genome (chromosome set) and includes two major disciplines namely functional (determines the biological function of all genes and their products) and structural (determines the three-dimensional structures of proteins). Genomics purpose in tree breeding technologies (Bhalerao et al 2003, Costanza andMcCord 2009) is foremost to describe and make efficient use of the abundant natural genetic variation present within undomesticated forest tree populations (Groover 2007) and contribute to an understanding of the genetic architecture underlying (ecological or industrial) traits of interest on a genomewide scale (Kirst et al 2004, Street et al 2006, Holliday et al 2008, Sederoff et al 2009, Porth et al 2012, Porth et al 2013a, Prunier et al 2013, Verta et al 2013, Beaulieu et al 2014, McKown et al 2014. While GE is the deliberate alteration of the genetic material of living organisms involving the introduction of alien genes from other taxa, resulting in the production of recombinant DNA, which in turn is used to create products (proteins), the original organism is incapable of producing without this alteration (Porth and El-Kassaby 2014).…”

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.…”