The ability to predict the agronomic performance of single-crosses with high precision is essential for selecting superior candidates for hybrid breeding. With recent technological advances, thousands of new parent lines, and, consequently, millions of new hybrid combinations are possible in each breeding cycle, yet only a few hundred can be produced and phenotyped in multi-environment yield trials. Well established prediction approaches such as best linear unbiased prediction (BLUP) using pedigree data and whole-genome prediction using genomic data are limited in capturing epistasis and interactions occurring within and among downstream biological strata such as transcriptome and metabolome. Because mRNA and small RNA (sRNA) sequences are involved in transcriptional, translational and post-translational processes, we expect them to provide information influencing several biological strata. However, using sRNA data of parent lines to predict hybrid performance has not yet been addressed. Here, we gathered genomic, transcriptomic (mRNA and sRNA) and metabolomic data of parent lines to evaluate the ability of the data to predict the performance of untested hybrids for important agronomic traits in grain maize. We found a considerable interaction for predictive ability between predictor and trait, with mRNA data being a superior predictor for grain yield and genomic data for grain dry matter content, while sRNA performed relatively poorly for both traits. Combining mRNA and genomic data as predictors resulted in high predictive abilities across both traits and combining other predictors improved prediction over that of the individual predictors alone. We conclude that downstream "omics" can complement genomics for hybrid prediction, and, thereby, contribute to more efficient selection of hybrid candidates.

To follow the dynamics of meiosis in the model plant Arabidopsis, we have established a live cell imaging setup to observe male meiocytes. Our method is based on the concomitant visualization of microtubules (MTs) and a meiotic cohesin subunit that allows following five cellular parameters: cell shape, MT array, nucleus position, nucleolus position, and chromatin condensation. We find that the states of these parameters are not randomly associated and identify 11 cellular states, referred to as landmarks, which occur much more frequently than closely related ones, indicating that they are convergence points during meiotic progression. As a first application of our system, we revisited a previously identified mutant in the meiotic A-type cyclin TARDY ASYNCHRONOUS MEIOSIS (TAM). Our imaging system enabled us to reveal both qualitatively and quantitatively altered landmarks in tam, foremost the formation of previously not recognized ectopic spindle- or phragmoplast-like structures that arise without attachment to chromosomes.

Complementing genomic data with other "omics" predictors can increase the probability of success for predicting the best hybrid combinations using complex agronomic traits. Accurate prediction of traits with complex genetic architecture is crucial for selecting superior candidates in animal and plant breeding and for guiding decisions in personalized medicine. Whole-genome prediction has revolutionized these areas but has inherent limitations in incorporating intricate epistatic interactions. Downstream "omics" data are expected to integrate interactions within and between different biological strata and provide the opportunity to improve trait prediction. Yet, predicting traits from parents to progeny has not been addressed by a combination of "omics" data. Here, we evaluate several "omics" predictors-genomic, transcriptomic and metabolic data-measured on parent lines at early developmental stages and demonstrate that the integration of transcriptomic with genomic data leads to higher success rates in the correct prediction of untested hybrid combinations in maize. Despite the high predictive ability of genomic data, transcriptomic data alone outperformed them and other predictors for the most complex heterotic trait, dry matter yield. An eQTL analysis revealed that transcriptomic data integrate genomic information from both, adjacent and distant sites relative to the expressed genes. Together, these findings suggest that downstream predictors capture physiological epistasis that is transmitted from parents to their hybrid offspring. We conclude that the use of downstream "omics" data in prediction can exploit important information beyond structural genomics for leveraging the efficiency of hybrid breeding.

BackgroundMicrospore embryogenesis describes a stress-induced reprogramming of immature male plant gametophytes to develop into embryo-like structures, which can be regenerated into doubled haploid plants after whole genome reduplication. This mechanism is of high interest for both research as well as plant breeding. The objective of this study was to characterize transcriptional changes and regulatory relationships in early stages of cold stress-induced wheat microspore embryogenesis by transcriptome and small RNA sequencing using a highly responsive cultivar.ResultsTranscriptome and small RNA sequencing was performed in a staged time-course to analyze wheat microspore embryogenesis induction. The analyzed stages were freshly harvested, untreated uninucleate microspores and the two following stages from in vitro anther culture: directly after induction by cold-stress treatment and microspores undergoing the first nuclear divisions. A de novo transcriptome assembly resulted in 29,388 contigs distributing to 20,224 putative transcripts of which 9,305 are not covered by public wheat cDNAs. Differentially expressed transcripts and small RNAs were identified for the stage transitions highlighting various processes as well as specific genes to be involved in microspore embryogenesis induction.ConclusionThis study establishes a comprehensive functional genomics resource for wheat microspore embryogenesis induction and initial understanding of molecular mechanisms involved. A large set of putative transcripts presumably specific for microspore embryogenesis induction as well as contributing processes and specific genes were identified. The results allow for a first insight in regulatory roles of small RNAs in the reprogramming of microspores towards an embryogenic cell fate.Electronic supplementary materialThe online version of this article (doi:10.1186/s12870-016-0782-8) contains supplementary material, which is available to authorized users.

1Complementing genomic data with other "omics" predictors can increase the proba-2 bility of success for predicting the best hybrid combinations using complex agronomic 3 traits. 4Abstract 5 Accurate prediction of traits with complex genetic architecture is crucial for select-6 ing superior candidates in animal and plant breeding and for guiding decisions in 7 personalized medicine. Whole-genome prediction (WGP) has revolutionized these 8 areas but has inherent limitations in incorporating intricate epistatic interactions. Downstream "omics" data are expected to integrate interactions within and between 10 different biological strata and provide the opportunity to improve trait prediction. 11Yet, predicting traits from parents to progeny has not been addressed by a combi-12 nation of "omics" data. Here, we evaluate several "omics" predictors -genomic,