The availability of cheap and abundant molecular markers has led to plant-breeding methods that rely on the prediction of genotypic value from marker data, but published information is lacking on the accuracy of genotypic value predictions with empirical data in plants. Our objectives were to (1) determine the accuracy of genotypic value predictions from multiple linear regression (MLR) and genomewide selection via best linear unbiased prediction (BLUP) in biparental plant populations; (2) assess the accuracy of predictions for different numbers of markers (N(M)) and progenies (N(P)) used in estimation; and (3) determine if an empirical Bayes approach for modeling of the variances of individual markers and of epistatic effects leads to more accurate predictions in empirical data. We divided each of four maize (Zea mays L.) datasets, one Arabidopsis dataset, and two barley (Hordeum vulgare L.) datasets into an estimation set, where marker effects were calculated, and a test set, where genotypic values were predicted based on markers. Predictions were more accurate with BLUP than with MLR. Predictions became more accurate as N(P) and N(M) increased, until sufficient genome coverage was reached. Modeling marker variances with the empirical Bayes method sometimes led to slightly better predictions, but the accuracy with different variants of the empirical Bayes method was often inconsistent. In nearly all cases, the accuracy with BLUP was not significantly different from the highest accuracy across all methods. Accounting for epistasis in the empirical Bayes procedure led to poorer predictions. We concluded that among the methods considered, the quick and simple BLUP approach is the method of choice for predicting genotypic value in biparental plant populations.

Maize (Zea mays L.) breeders evaluate many single-cross hybrids each year in multiple environments. Our objective was to determine the usefulness of genomewide predictions, based on marker effects from maize single-cross data, for identifying the best untested single crosses and the best inbreds within a biparental cross. We considered 479 experimental maize single crosses between 59 Iowa Stiff Stalk Synthetic (BSSS) inbreds and 44 non-BSSS inbreds. The single crosses were evaluated in multilocation experiments from 2001 to 2009 and the BSSS and non-BSSS inbreds had genotypic data for 669 single nucleotide polymorphism (SNP) markers. Single-cross performance was predicted by a previous best linear unbiased prediction (BLUP) approach that utilized marker-based relatedness and information on relatives, and from genomewide marker effects calculated by ridge-regression BLUP (RR-BLUP). With BLUP, the mean prediction accuracy (r(MG)) of single-cross performance was 0.87 for grain yield, 0.90 for grain moisture, 0.69 for stalk lodging, and 0.84 for root lodging. The BLUP and RR-BLUP models did not lead to r(MG) values that differed significantly. We then used the RR-BLUP model, developed from single-cross data, to predict the performance of testcrosses within 14 biparental populations. The r(MG) values within each testcross population were generally low and were often negative. These results were obtained despite the above-average level of linkage disequilibrium, i.e., r(2) between adjacent markers of 0.35 in the BSSS inbreds and 0.26 in the non-BSSS inbreds. Overall, our results suggested that genomewide marker effects estimated from maize single crosses are not advantageous (cofmpared with BLUP) for predicting single-cross performance and have erratic usefulness for predicting testcross performance within a biparental cross.

In cellulosic ethanol production, the efficiency of converting maize (Zea mays L.) stover into fermentable sugars partly depends on the stover cell wall structure. Breeding for improved stover quality for cellulosic ethanol may benefit from the use of molecular markers. However, limited quantitative trait loci (QTL) studies have been published for maize stover cell wall components, and no QTL study has been published for glucose release (GLCRel) from stover by a cellulosic ethanol conversion process. Our objectives were to characterize the relationships among stover cell wall components and GLCRel, and to identify QTL with major effects, if any, influencing stover cell wall composition and GLCRel. Testcrosses of 223 intermated B73 × Mo17 recombinant inbreds and the parent lines were analyzed for cell wall composition and GLCRel after acid pretreatment and enzymatic hydrolysis. As expected, glucose (GLC), xylose (XYL), and Klason lignin (KL) composed the bulk (∼72%) of the stover dry matter. Significant genetic variance and moderate heritability were observed for all traits. Genetic and phenotypic correlations among traits were generally in the favorable direction but also reflected the complexity of maize stover cell wall composition. We found 152 QTL, mostly with small effects, for GLCRel and cell wall components on both a dry matter and cell wall basis. Because no major QTL were found, methods that increase the frequency of favorable QTL alleles or that predict performance based on markers would be appropriate in marker‐assisted breeding for maize stover quality for cellulosic ethanol.

Corn (Zea mays L.) stover, the residue left after harvest, is an abundant biomass source for producing cellulosic ethanol in the United States. Corn has been bred for increased grain yield but not for stover quality for cellulosic ethanol production. Our objective in this study was to assess the feasibility of breeding corn for grain yield and agronomic traits as well as stover quality traits for cellulosic ethanol production. Testcrosses of 223 B73 × Mo17 recombinant inbreds were evaluated at four Minnesota locations in 2007. Three stover quality traits were measured: concentration of cell wall glucose in dry stover (“Glucose”); cell wall glucose released from the stover by thermochemical pretreatment and enzymatic saccharification (“Glucose Release”); and concentration of lignin on a cell wall basis (“Lignin”). Genetic variances were significant for grain yield, moisture, stalk and root lodging, plant height, and all three stover quality traits. Heritabilities of the stover quality traits were 0.57 for Glucose, 0.63 for Glucose Release, and 0.68 for Lignin. Phenotypic and genetic correlations were favorable or neutral among grain yield, agronomic traits, Glucose, Glucose Release, and Lignin. Lines selected with a multiple‐trait index for grain yield, agronomic traits, and stover quality traits had improved means for each trait in the index. Current corn breeding programs should be able to incorporate stover quality for cellulosic ethanol as a breeding objective, without having to use unadapted or exotic germplasm and without adversely affecting genetic gains for grain yield and agronomic traits.

Cultivars bred under conventional production systems may not be optimum for organic production systems. Our objective was to determine if, on the basis of quantitative genetic parameters, separate corn (Zea mays L.) breeding programs are needed for organic and conventional production systems. Testcrosses of 119 intermated B73 × Mo17 recombinant inbreds were evaluated in organic and conventional systems in both Waseca and Lamberton, MN, in 2006. Differences in trait means between the two production systems were significant for grain moisture, plant height, and ear height but not significant for grain yield, root lodging, stalk lodging, and stay green. The organic system led to a smaller testcross genetic variance for grain yield and higher testcross genetic variances for all other traits. The organic system led to a lower heritability for grain yield and a higher heritability for root lodging, stay green, and ear height. Genetic correlations for performance in the two production systems were 0.84 for grain yield; greater than 0.90 for grain moisture, plant height, and ear height; and about 0.50 for root lodging and stay green. The predicted ratio between the correlated response and direct response to selection in the organic system was near 1.0 for grain yield and moisture and considerably less than 1.0 for other traits. These results suggest that high‐yielding cultivars for organic systems can be developed largely by screening conventional inbreds and hybrids for their performance under organic systems.