BackgroundThe selection of hybrids is an essential step in maize breeding. However, evaluating a large number of hybrids in field trials can be extremely costly. However, genomic models can be used to predict the expected performance of un-tested genotypes. Bayesian models offer a very flexible framework for hybrid prediction. The Bayesian methodology can be used with parametric and semi-parametric assumptions for additive and non-additive effects. Furthermore, samples from the posterior distribution of Bayesian models can be used to estimate the variance due to general and specific combining abilities even in cases where additive and non-additive effects are not mutually orthogonal. Also, the use of Bayesian models for analysis and prediction of hybrid performance has remained fairly limited.ResultsWe provided an overview of Bayesian parametric and semi-parametric genomic models for prediction of agronomic traits in maize hybrids and discussed how these models can be used to decompose the genotypic variance into components due to general and specific combining ability. We applied the methodology to data from 906 single cross tropical maize hybrids derived from a convergent population. Our results show that: (1) non-additive effects make a sizable contribution to the genetic variance of grain yield; however, the relative importance of non-additive effects was much smaller for ear and plant height; (2) genomic prediction can achieve relatively high accuracy in predicting phenotypes of un-tested hybrids and in pre-screening.ConclusionsGenomic prediction can be a useful tool in pre-screening of hybrids and could contribute to the improvement of the efficiency and efficacy of maize hybrids breeding programs. The Bayesian framework offers a great deal of flexibility in modeling hybrid performance. The methodology can be used to estimate important genetic parameters and render predictions of the expected hybrid performance as well measures of uncertainty about such predictions.Electronic supplementary materialThe online version of this article (10.1186/s13007-019-0388-x) contains supplementary material, which is available to authorized users.

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Phenotypic datasets in plant breeding are commonly incomplete due to missing phenotypic information. The best approach for correcting these datasets for a stage‐wise genomic prediction (GP) is not unanimous in the scientific community. Therefore, this study evaluates a two‐step GP based on different methods of phenotypic correction considering complete and incomplete datasets of maize (Zea mays L.) single crosses. The dataset consists of 325 hybrids evaluated for grain yield and plant height in four sites. Sequential levels of data loss were simulated to the original dataset (from 0 to 30%) to assess the impact of missing information. The prediction was performed by an additive genomic best linear unbiased prediction model (GBLUP) using best linear unbiased estimations (BLUEs), best linear unbiased predictions (BLUPs), and deregressed BLUPs as the response variable. Mean reliability and predictive ability slightly decreased as missing phenotypic information increased, irrespective of the response variable. Regarding phenotypic correction, all methods yielded similar results for these parameters over most missing information percentages. The coincidence of selection between single‐ and two‐stage GP was not systematically affected by response variable across multiple selection intensities, and missing data only led to a minor decrease in coincidence. Therefore, from a breeding standpoint, regardless of phenotypic correction method and missing data level, a similar set of genotypes tend to be selected.

Genetic studies increasingly rely on high-throughput phenotyping, but the resulting longitudinal data pose analytical challenges. We used canopy height data from an automated field phenotyping platform to compare several approaches to scanning for quantitative trait loci (QTLs) and performing genomic prediction in a wheat recombinant inbred line mapping population based on up to 26 sampled time points (TPs). We detected four persistent QTLs (i.e. expressed for most of the growing season), with both empirical and simulation analyses demonstrating superior statistical power of detecting such QTLs through functional mapping approaches compared with conventional individual TP analyses. In contrast, even very simple individual TP approaches (e.g. interval mapping) had superior detection power for transient QTLs (i.e. expressed during very short periods). Using spline-smoothed phenotypic data resulted in improved genomic predictive abilities (5–8% higher than individual TP prediction), while the effect of including significant QTLs in prediction models was relatively minor (<1–4% improvement). Finally, although QTL detection power and predictive ability generally increased with the number of TPs analysed, gains beyond five or 10 TPs chosen based on phenological information had little practical significance. These results will inform the development of an integrated, semi-automated analytical pipeline, which will be more broadly applicable to similar data sets in wheat and other crops.

Maize genotypes can show different responsiveness to inoculation with Azospirillum brasilense and an intriguing issue is which genes of the plant are involved in the recognition and growth promotion by these Plant Growth-Promoting Bacteria (PGPB). We conducted Genome-Wide Association Studies (GWAS) using additive and heterozygous (dis)advantage models to find candidate genes for root and shoot traits under nitrogen (N) stress and N stress plus A. brasilense. A total of 52,215 Single Nucleotide Polymorphism (SNP) markers were used for GWAS analyses. For the six root traits with significant inoculation effect, the GWAS analyses revealed 25 significant SNPs for the N stress plus A. brasilense treatment, in which only two were overlapped with the 22 found for N stress only. Most were found by the heterozygous (dis)advantage model and were more related to exclusive gene ontology terms. Interestingly, the candidate genes around the significant SNPs found for the maize–A. brasilense association were involved in different functions previously described for PGPB in plants (e.g. signaling pathways of the plant's defense system and phytohormone biosynthesis). Our findings are a benchmark in the understanding of the genetic variation among maize hybrids for the association with A. brasilense and reveal the potential for further enhancement of maize through this association.

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In this paper, the aims were (a) to test the usefulness of using genomic and phenotypic information from public databases (open access) to predict genetic values for tropical maize inbred lines regarding plant and ear height; (b) to identify how the population structure, the use of optimized training sets (OTSs) and the amount of information originating from public databases affect the predictive ability. Thus, 29 training sets (TSs) were defined considering three diversity panels: the University of São Paulo (USP—validation set (VS)) and the ASSO and USDA North Central Regional Plant Introduction Station (NCRPIS) (external public panels—predictors), which were divided into four scenarios with different TS configurations. We showed that it is possible to use public datasets as a primary TS and that population structure can modify the predictive abilities of GS. In the four scenarios proposed, very large or very small sets did not provide predictive abilities over 0.53 for GS. However, OTSs composed of 250 individuals were sufficient to achieve predictive abilities over this limit.