Genomewide Selection versus Marker‐assisted Recurrent Selection to Improve Grain Yield and Stover‐quality Traits for Cellulosic Ethanol in Maize

Abstract

M arker-assisted selection for quantitative traits has traditionally relied on fi rst identifying markers linked to quantitative trait loci (QTL). A specifi c form of marker-assisted selection in maize (Zea mays L.) is marker-assisted recurrent selection (MARS) in which (i) one generation of phenotypic selection in the target environment is conducted, (ii) markers with signifi cant eff ects are used to predict the performance of individual plants, and (iii) several generations of marker-only selection are performed in a year-round nursery or greenhouse. Empirical results from private breeding programs have shown MARS to be eff ective at improving quantitative traits in maize, soybean [Glycine max (L.) Merr.], and sunfl ower (Helianthus annuus L.) ( Johnson, 2004;Eathington et al., 2007). Specifi cally, gains from selection in 248 maize breeding populations were more than twice as large with MARS than with standard phenotypic selection methods (Eathington et al., 2007).In contrast to previous MARS or other QTL-based selection strategies, genomewide selection (GWS, also called genomic selection; Meuwissen et al., 2001) does not involve tests of signifi cance and uses all available markers to predict performance. Simulation ABSTRACT Genomewide selection (GWS) is markerassisted selection without identifying markers with signifi cant effects. Our previous work with the intermated B73 × Mo17 maize (Zea mays L.) population revealed signifi cant variation for grain yield and stover-quality traits important for cellulosic ethanol production. Our objectives were to determine (i) if realized gains from selection are larger with GWS than with markerassisted recurrent selection (MARS), which involves selection for markers with signifi cant effects; and (ii) how multiple traits respond to multiple cycles of GWS and MARS. In 2007, testcrosses of 223 recombinant inbreds developed from B73 × Mo17 (Cycle 0) were evaluated at four Minnesota locations and genotyped with 287 single nucleotide polymorphism markers. Individuals with the best performance for a Stover Index and a Yield + Stover Index were intermated to form Cycle 1. Both GWS and MARS were then conducted until Cycle 3. Multilocation trials in 2010 indicated that gains for the Stover Index and Yield + Stover Index were 14 to 50% larger (signifi cant at P = 0.05) with GWS than with MARS. Gains in individual traits were mostly nonsignifi cant. Inbreeding coeffi cients ranged from 0.28 to 0.38 by Cycle 3 of GWS and MARS. For stover-quality traits, correlations between wet chemistry and near-infrared refl ectance spectroscopy predictions decreased after selection. We believe this is the fi rst published report of a GWS experiment in crops, and our results indicate that using all available markers for predicting genotypic value leads to greater gain than using a subset of markers with significant effects.

Extending the Marker × Environment Interaction Model for Genomic‐Enabled Prediction and Genome‐Wide Association Analysis in Durum Wheat

“…In general, these studies showed good prediction accuracies for GY and other traits evaluated by means of several random cross-validation partitions of the data. The first public study confirming these previous findings on genomicenabled prediction was that of Massman et al (2013), who showed that genomic selection improved genetic gains per unit of time in one biparental temperate maize (Zea mays L.) population. Recently, Beyene et al (2015) achieved important genetic gains in GY through genomic selection in eight tropical biparental CIMMYT maize populations; these authors evaluated cycles of genomic selection in severe drought environments in sub-Saharan Africa.…”

Bridging the gap between marker-assisted and genomic selection of heading time and plant height in hybrid wheat

“…The estimated marker effects are then applied to predict the breeding value of nonphenotyped individuals based on their molecular marker profiles. The great potential of genomic selection for complex traits has been demonstrated in several experimental studies in plant and animal breeding populations (Bernardo, 2008;Heffner et al, 2009;Heslot et al, 2012;Massman et al, 2013).…”

Breaking through the feed barrier: options for improving forage genetics

“…The GS theoretical framework developed over a decade ago (Meuwissen et al 2001) is now enabled by efficiency gains in DNA marker (Davey et al 2011;Elshire et al 2011;Poland and Rife 2012) and, more recently, plant phenotypic (White and Conley 2013) data generation, management and analysis; and offers proven value in economic plant species (Massman et al 2013;Spindel et al 2015).…”

“…This includes empirical and modelled evidence to assess comparative efficiencies per unit resource (Massman et al 2013;Resende et al 2013) across the range of traits under selection.…”