Genomic selection in a commercial winter wheat population

Abstract

Genomic selection models can be trained using historical data and filtering genotypes based on phenotyping intensity and reliability criterion are able to increase the prediction ability. We implemented genomic selection based on a large commercial population incorporating 2325 European winter wheat lines. Our objectives were (1) to study whether modeling epistasis besides additive genetic effects results in enhancement on prediction ability of genomic selection, (2) to assess prediction ability when training population comprised historical or less-intensively phenotyped lines, and (3) to explore the prediction ability in subpopulations selected based on the reliability criterion. We found a 5 % increase in prediction ability when shifting from additive to additive plus epistatic effects models. In addition, only a marginal loss from 0.65 to 0.50 in accuracy was observed using the data collected from 1 year to predict genotypes of the following year, revealing that stable genomic selection models can be accurately calibrated to predict subsequent breeding stages. Moreover, prediction ability was maximized when the genotypes evaluated in a single location were excluded from the training set but subsequently decreased again when the phenotyping intensity was increased above two locations, suggesting that the update of the training population should be performed considering all the selected genotypes but excluding those evaluated in a single location. The genomic prediction ability was substantially higher in subpopulations selected based on the reliability criterion, indicating that phenotypic selection for highly reliable individuals could be directly replaced by applying genomic selection to them. We empirically conclude that there is a high potential to assist commercial wheat breeding programs employing genomic selection approaches.

QTL Analysis of Spike Morphological Traits and Plant Height in Winter Wheat (Triticum aestivum L.) Using a High-Density SNP and SSR-Based Linkage Map

“…The recently developed iSelect 9K and 90K arrays (Cavanagh et al, 2013; Wang et al, 2014) designed to characterize genetic variation in hexaploid wheat populations have extensive applications in QTL mapping (Wu et al, 2015b), genome-wide association studies (GWAS) (Gao et al, 2016), genomic selection (He et al, 2016), and establishment of heterotic groups (Zhao et al, 2015). In the present study, we constructed a dense genetic linkage map for the Y8679/J411 RIL population, using 10,816 polymorphic SNP markers from an iSelect 90K array and 174 polymorphic SSR markers.…”

Genomic Prediction of Seed Quality Traits Using Advanced Barley Breeding Lines

“…Genomic selection can be a valuable tool to accelerate genetic gains in plant breeding programs, and has been described as a valuable tool in several plant species (for example wheat and maize [12,34]). Genomic selection has only just started in barley breeding [35].…”

Genomic assisted selection for enhancing line breeding: merging genomic and phenotypic selection in winter wheat breeding programs with preliminary yield trials

“…Genomic selection using genome-wide dense marker maps has been suggested as a more efficient alternative to conventional selection methods (Meuwissen et al 2001) and several studies have shown its great potential in line breeding to enhance the selection for major agronomic traits like yield both in legumes (Jarquín et al 2014; Burstin et al 2015; Tayeh et al 2015) and small grain cereals (Asoro et al 2011; Sallam et al 2015; Spindel et al 2015; He et al 2016; Michel et al 2016). Additionally, genomic selection could support the accumulation of many small effect alleles to provide higher and more durable quantitative disease resistance (Lorenz et al 2012; Ornella et al 2012; Daetwyler et al 2014; Arruda et al 2015; Rutkoski et al 2015b), which could be subsequently combined with labor-intensive and costly to assess quality traits (Heffner et al 2011b; Schmidt et al 2015).…”