Boosting predictive ability of tropical maize hybrids via genotype-by-environment interaction under multivariate GBLUP models.

Genomic selection has been implemented in several plant and animal breeding programs and it has proven to improve efficiency and maximize genetic gains. Phenotypic data of grain yield was measured in 147 maize (Zea mays L.) singlecross hybrids at 12 environments. Single-cross hybrids genotypes were inferred based on their parents (inbred lines) via single nucleotide polymorphism (SNP) markers obtained from genotyping-by-sequencing (GBS). Factor analytic multiplicative genomic best linear unbiased prediction (GBLUP) models, in the framework of multienvironment trials, were used to predict grain yield performance of unobserved tropical maize single-cross hybrids. Predictions were performed for two situations: untested hybrids (CV1), and hybrids evaluated in some environments but missing in others (CV2). Models that borrowed information across individuals through genomic relationships and within individuals across environments presented higher predictive accuracy than those models that ignored it. For these models, predictive accuracies were up to 0.4 until eight environments were considered as missing for the validation set, which represents 67% of missing data for a given hybrid. These results highlight the importance of including genotype-by-environment interactions and genomic relationship information for boosting predictions of tropical maize single-cross hybrids for grain yield

Haploid identification using tropicalized haploid inducer progenies in maize

The aim of this study was to identify maize haploid plants and compare the efficiency of identification of maize haploid plants using the R1-nj morphological marker, plant vigor, flow cytometry, chromosome counting, and microsatellite molecular markers under tropical conditions. We also established a protocol for chromosome duplication in maize haploid plants. Fourteen S0:1 and seven S2:3 haploid inducer progenies were crossed with GNZ9501 in 2012/2013 and 2014/2015, respectively. Through use of the R1-nj trait, we were able to identify 552 putative haploid seeds in 2012/2013 and 260 putative haploid seeds in 2014/2015. Only 1.84% were true positives according to flow cytometry in 2012/2013. In 2014/2015, 75% of the putative haploids were true negatives according to molecular markers. Plant vigor had a high proportion of true negatives. Molecular markers and flow cytometry are more efficient in classifying plant ploidy level. Chromosome duplication was efficient in all plants

A multi-environment trials diallel analysis provides insights on the inheritance of fumonisin contamination resistance in tropical maize

In maize, the fungi that cause Fusarium ear rot result not only in decreased grain yield and quality, but also grain contamination by fumonisin. This study investigated the inheritance of fumonisin contamination resistance (FCR) in tropical maize, based on a multi-environment trials diallel analysis via mixed models. For this purpose, based on 13 inbred lines, single-cross hybrids were created and assessed in three environments. A mixed model diallel joint analysis across environments was performed, considering the existence of environment-specific variances and correlations between pairs of environments for general combining ability (GCA) and specific combining ability (SCA) effects, and additive genomic relationship between inbred lines for the prediction of GCA and SCA. For all environments, the SCA variance had a higher magnitude than the GCA variance, indicating a predominance of the dominance effects underlying FCR in tropical maize. Moreover, the proportion of the variance among single-cross hybrids that was due to GCA varied from 16 to 22 % across environments, suggesting that SCA is important to predict the hybrids performance. Through modeling variance–covariance structures for GCA and SCA, it was possible to observe that the GCA effects were stable, whereas the SCA effects were specific for each environment. Therefore, these results suggest that the selection of the best parents for the development of new inbred lines can be carried out through the average performance across the evaluated environments. Due to the importance of SCA effects and their complex interaction with environments, the selection of superior hybrids should be performed into specific environments

A multi-environment trials diallel analysis provides insights on the inheritance of fumonisin contamination resistance in tropical maize

In maize, the fungi that cause Fusarium ear rot result not only in decreased grain yield and quality, but also grain contamination by fumonisin. This study investigated the inheritance of fumonisin contamination resistance (FCR) in tropical maize, based on a multi-environment trials diallel analysis via mixed models. For this purpose, based on 13 inbred lines, single-cross hybrids were created and assessed in three environments. A mixed model diallel joint analysis across environments was performed, considering the existence of environment-specific variances and correlations between pairs of environments for general combining ability (GCA) and specific combining ability (SCA) effects, and additive genomic relationship between inbred lines for the prediction of GCA and SCA. For all environments, the SCA variance had a higher magnitude than the GCA variance, indicating a predominance of the dominance effects underlying FCR in tropical maize. Moreover, the proportion of the variance among single-cross hybrids that was due to GCA varied from 16 to 22 % across environments, suggesting that SCA is important to predict the hybrids performance. Through modeling variance–covariance structures for GCA and SCA, it was possible to observe that the GCA effects were stable, whereas the SCA effects were specific for each environment. Therefore, these results suggest that the selection of the best parents for the development of new inbred lines can be carried out through the average performance across the evaluated environments. Due to the importance of SCA effects and their complex interaction with environments, the selection of superior hybrids should be performed into specific environments

Data from: Improving accuracies of genomic predictions for drought tolerance in maize by joint modeling of additive and dominance effects in multi-environment trials

Breeding for drought tolerance is a challenging task that requires costly, extensive and precise phenotyping. Genomic selection (GS) can be used to maximize selection efficiency and the genetic gains in maize (Zea mays L.) breeding programs for drought tolerance. Here we evaluated the accuracy of genomic selection of additive (A) against additive+dominance (AD) models to predict the performance of untested maize single-cross hybrids for drought tolerance in multi-environment trials. Phenotypic data of five drought-tolerance traits were measured in 308 hybrids in eight trials under water-stressed (WS) and well-watered (WW) conditions over two years and two locations in Brazil. Hybrids’ genotypes were inferred based on their parents’ genotypes (inbred lines) using single nucleotide polymorphism data obtained via genotyping-by-sequencing. GS analyses were performed using genomic best linear unbiased prediction by fitting a factor analytic (FA) multiplicative mixed model. Results showed differences in the predictive accuracy between A and AD models for the five traits under consideration in both water conditions. For grain yield (GY), the AD model doubled the predictive accuracy in comparison to the A model. FA framework allowed for investigating the stability of additive and dominance effects across environments, as well as the additive- and dominance-by-environment interactions, with interesting applications for parental and hybrid selection. Prediction performance of untested hybrids using GS that benefit from borrowing information from correlated trials increased 40% and 9% for A and AD models, respectively. These results highlighted the importance of multi-environment trial analysis with GS that incorporate dominance effects into genomic predictions of GY in maize single-cross hybrids