Evaluation of genome-wide selection efficiency in maize nested association mapping populations

Abstract

In comparison to conventional marker-assisted selection (MAS), which utilizes only a subset of genetic markers associated with a trait to predict breeding values (BVs), genome-wide selection (GWS) improves prediction accuracies by incorporating all markers into a model simultaneously. This strategy avoids risks of missing quantitative trait loci (QTL) with small effects. Here, we evaluated the accuracy of prediction for three corn flowering traits days to silking, days to anthesis, and anthesis-silking interval with GWS based on cross-validation experiments using a large data set of 25 nested association mapping populations in maize (Zea mays). We found that GWS via ridge regression-best linear unbiased prediction (RR-BLUP) gave significantly higher predictions compared to MAS utilizing composite interval mapping (CIM). The CIM method may be selected over multiple linear regression to decrease over-estimations of the efficiency of GWS over a MAS strategy. The RR-BLUP method was the preferred method for estimating marker effects in GWS with prediction accuracies comparable to or greater than BayesA and BayesB. The accuracy with RR-BLUP increased with training sample proportion, marker density, and heritability until it reached a plateau. In general, gains in accuracy with RR-BLUP over CIM increased with decreases of these factors. Compared to training sample proportion, the accuracy of prediction with RR-BLUP was relatively insensitive to marker density.

Potential and limits to unravel the genetic architecture and predict the variation of Fusarium head blight resistance in European winter wheat (Triticum aestivum L.)

“…Our results are in accordance with these previous findings, as the marker-assisted selection approaches based on the three marker systems explained up to 78% of the phenotypic variation for the total population when a significance threshold of Po0.005 was applied. This number dropped in fivefold cross-validation ( Figure 5) as was expected from previous results (Guo et al, 2012;Miedaner et al, 2013;Zhao et al, 2013;Gowda et al, 2014;Würschum and Kraft, 2014). Nevertheless, the cross-validated accuracy of prediction of FHB resistance is still high and in most cases higher than using randomly sampled markers ( Figure 5), suggesting that genome-wide association mapping studies indeed have the potential to at least partially answer the two central questions on the genetic architecture of FHB resistance outlined above.…”

“…Investigating the accuracy of prediction of genomic versus markerassisted selection in wheat revealed that genomic selection usually increases accuracy, with some dependence on the trait under consideration and the related linkage disequilibrium structure (Guo et al, 2012;Rutkoski et al, 2012;Miedaner et al, 2013;Zhao et al, 2013Zhao et al, , 2014. One interesting finding was that for FHB resistance, as for some other complex traits, the differences between the mean accuracies of marker-assisted and genomic selection were surprisingly small (Rutkoski et al, 2012).…”

Usefulness of Multiparental Populations of Maize (Zea mays L.) for Genome-Based Prediction

“…It is expected that quantitative genetic parameters relevant for prediction accuracy such as genetic variances and heritabilities vary greatly between families, as has been shown for the U.S. maize nested association mapping (NAM) population (Hung et al 2012). Thus, the predictive power of individual families in a half-sib design might strongly depend on the magnitude and variation of these parameters.From theory and empirical studies it is known that the sample size of the population employed in model training (estimation set) has a strong impact on prediction accuracy (Daetwyler et al 2008;Lorenzana and Bernardo 2009;Zhong et al 2009;Albrecht et al 2011;Guo et al 2012;Combs and Bernardo 2013;Wimmer et al 2013). Restricting genome-based prediction to within biparental families puts upper limits on sample sizes employed in model training.…”

“…From theory and empirical studies it is known that the sample size of the population employed in model training (estimation set) has a strong impact on prediction accuracy (Daetwyler et al 2008;Lorenzana and Bernardo 2009;Zhong et al 2009;Albrecht et al 2011;Guo et al 2012;Combs and Bernardo 2013;Wimmer et al 2013). Restricting genome-based prediction to within biparental families puts upper limits on sample sizes employed in model training.…”

Genomic breeding value prediction for simple maize hybrid yield using total effects of associated markers, under different imbalance levels and environments

“…Gains in the precision of the genomic selection may be affected by three factors: i) the proportion of the training population, ii) marker density, and iii) heritability (Guo et al, 2012). Daetwyler et al (2008) expressed prediction accuracy as a function of the training population size (N), heritability (h 2 ), and the number of chromosomal segments affecting the trait (Me), as shown in the equation below.…”