Small RNA-based prediction of hybrid performance in maize.

BACKGROUND: Small RNA (sRNA) sequences are known to have a broad impact on gene regulation by various mechanisms. Their performance for the prediction of hybrid traits has not yet been analyzed. Our objective was to analyze the relation of parental sRNA expression with the performance of their hybrids, to develop a sRNA-based prediction approach, and to compare it to more common SNP and mRNA transcript based predictions using a factorial mating scheme of a maize hybrid breeding program. RESULTS: Correlation of genomic differences and messenger RNA (mRNA) or sRNA expression differences between parental lines with hybrid performance of their hybrids revealed that sRNAs showed an inverse relationship in contrast to the other two data types. We associated differences for SNPs, mRNA and sRNA expression between parental inbred lines with the performance of their hybrid combinations and developed two prediction approaches using distance measures based on associated markers. Cross-validations revealed parental differences in sRNA expression to be strong predictors for hybrid performance for grain yield in maize, comparable to genomic and mRNA data. The integration of both positively and negatively associated markers in the prediction approaches enhanced the prediction accurary. The associated sRNAs belong predominantly to the canonical size classes of 22- and 24-nt that show specific genomic mapping characteristics. CONCLUSION: Expression profiles of sRNA are a promising alternative to SNPs or mRNA expression profiles for hybrid prediction, especially for plant species without reference genome or transcriptome information. The characteristics of the sRNAs we identified suggest that association studies based on breeding populations facilitate the identification of sRNAs involved in hybrid performance.

Parental Expression Variation of Small RNAs Is Negatively Correlated with Grain Yield Heterosis in a Maize Breeding Population.

Heterosis refers to a quantitative phenomenon in which F1 hybrid trait values exceed the mean of the parental values in a positive direction. Generally, it is dependent on a high degree of heterozygosity, which is maintained in hybrid breeding by developing parental lines in separate, genetically distinct heterotic groups. The mobility of small RNAs (sRNAs) that mediate epigenetic regulation of gene expression renders them promising candidates for modulating the action of combined diverse genomes in trans-and evidence already indicates their contribution to transgressive phenotypes. By sequencing small RNA libraries of a panel of 21 maize parental inbred lines we found a low overlap of 35% between the sRNA populations from both distinct heterotic groups. Surprisingly, in contrast to genetic or gene expression variation, parental sRNA expression variation is negatively correlated with grain yield (GY) heterosis. Among 0.595 million expressed sRNAs, we identified 9,767, predominantly 22- and 24-nt long sRNAs, which showed an association of their differential expression between parental lines and GY heterosis of the respective hybrids. Of these sRNAs, 3,485 or 6,282 showed an association with high or low GY heterosis, respectively, thus the low heterosis associated group prevailing at 64%. The heterosis associated sRNAs map more frequently to genes that show differential expression between parental lines than reference sets. Together these findings suggest that trans-chromosomal actions of sRNAs in hybrids might add up to a negative contribution in heterosis formation, mediated by unfavorable gene expression regulation. We further revealed an exclusive accumulation of 22-nt sRNAs that are associated with low GY heterosis in pericentromeric genomic regions. That recombinational suppression led to this enrichment is indicated by its close correlation with low recombination rates. The existence of this enrichment, which we hypothesize resulted from the separated breeding of inbred lines within heterotic groups, may have implications for hybrid breeding strategies addressing the recombinational constraints characteristic of complex crop genomes.

Beyond Genomic Prediction: Combining Different Types of omics Data Can Improve Prediction of Hybrid Performance in Maize.

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.

Omics-based hybrid prediction in maize.

KEY MESSAGE: 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.

Prediction of hybrid performance in maize with a ridge regression model employed to DNA markers and mRNA transcription profiles.

BACKGROUND: Ridge regression models can be used for predicting heterosis and hybrid performance. Their application to mRNA transcription profiles has not yet been investigated. Our objective was to compare the prediction accuracy of models employing mRNA transcription profiles with that of models employing genome-wide markers using a data set of 98 maize hybrids from a breeding program. RESULTS: We predicted hybrid performance and mid-parent heterosis for grain yield and grain dry matter content and employed cross validation to assess the prediction accuracy. Prediction with a ridge regression model using random effects for mRNA transcription profiles resulted in similar prediction accuracies than employing the model to DNA markers. For hybrids, of which none of the parental inbred lines was part of the training set, the ridge regression model did not reach the prediction accuracy that was obtained with a model using transcriptome-based distances. CONCLUSION: We conclude that mRNA transcription profiles are a promising alternative to DNA markers for hybrid prediction, but further studies with larger data sets are required to investigate the superiority of alternative prediction models.

Genome-wide meta-analysis of maize heterosis reveals the potential role of additive gene expression at pericentromeric loci.

BACKGROUND: The identification of QTL involved in heterosis formation is one approach to unravel the not yet fully understood genetic basis of heterosis - the improved agronomic performance of hybrid F1 plants compared to their inbred parents. The identification of candidate genes underlying a QTL is important both for developing markers and determining the molecular genetic basis of a trait, but remains difficult owing to the large number of genes often contained within individual QTL. To address this problem in heterosis analysis, we applied a meta-analysis strategy for grain yield (GY) of Zea mays L. as example, incorporating QTL-, hybrid field-, and parental gene expression data. RESULTS: For the identification of genes underlying known heterotic QTL, we made use of tight associations between gene expression pattern and the trait of interest, identified by correlation analyses. Using this approach genes strongly associated with heterosis for GY were discovered to be clustered in pericentromeric regions of the complex maize genome. This suggests that expression differences of sequences in recombination-suppressed regions are important in the establishment of heterosis for GY in F1 hybrids and also in the conservation of heterosis for GY across genotypes. Importantly functional analysis of heterosis-associated genes from these genomic regions revealed over-representation of a number of functional classes, identifying key processes contributing to heterosis for GY. Based on the finding that the majority of the analyzed heterosis-associated genes were addtitively expressed, we propose a model referring to the influence of cis-regulatory variation on heterosis for GY by the compensation of fixed detrimental expression levels in parents. CONCLUSIONS: The study highlights the utility of a meta-analysis approach that integrates phenotypic and multi-level molecular data to unravel complex traits in plants. It provides prospects for the identification of genes relevant for QTL, and also suggests a model for the potential role of additive expression in the formation and conservation of heterosis for GY via dominant, multigenic quantitative trait loci. Our findings contribute to a deeper understanding of the multifactorial phenomenon of heterosis, and thus to the breeding of new high yielding varieties.

Partial least squares regression, support vector machine regression, and transcriptome-based distances for prediction of maize hybrid performance with gene expression data.

The performance of hybrids can be predicted with gene expression data from their parental inbred lines. Implementing such prediction approaches in breeding programs promises to increase the efficiency of hybrid breeding. The objectives of our study were to compare the accuracy of prediction models employing multiple linear regression (MLR), partial least squares regression (PLS), support vector machine regression (SVM), and transcriptome-based distances (D(B)). For a factorial of 7 flint and 14 dent maize lines, the grain yield of the hybrids was assessed and the gene expression of the parental lines was profiled with a 56k microarray. The accuracy of the prediction models was measured by the correlation between predicted and observed yield employing two cross-validation schemes. The first modeled the prediction of hybrids when testcross data are available for both parental lines (type 2 hybrids), and the second modeled the prediction of hybrids when no testcross data for the parental lines were available (type 0 hybrids). MLR, SVM, and PLS resulted in a high correlation between predicted and observed yield for type 2 hybrids, whereas for type 0 hybrids D(B) had greater prediction accuracy. The regression methods were robust to the choice of the set of profiled genes and required only a few hundred genes. In contrast, for an accurate hybrid prediction with D(B), 1,000-1,500 genes were required, and the prediction accuracy depended strongly on the set of profiled genes. We conclude that for prediction within one set of genetic material MLR is a promising approach, and for transferring prediction models from one set of genetic material to a related one, the transcriptome-based distance D(B) is most promising.

Dissecting grain yield pathways and their interactions with grain dry matter content by a two-step correlation approach with maize seedling transcriptome.

BACKGROUND: The importance of maize for human and animal nutrition, but also as a source for bio-energy is rapidly increasing. Maize yield is a quantitative trait controlled by many genes with small effects, spread throughout the genome. The precise location of the genes and the identity of the gene networks underlying maize grain yield is unknown. The objective of our study was to contribute to the knowledge of these genes and gene networks by transcription profiling with microarrays. RESULTS: We assessed the grain yield and grain dry matter content (an indicator for early maturity) of 98 maize hybrids in multi-environment field trials. The gene expression in seedlings of the parental inbred lines, which have four different genetic backgrounds, was assessed with genome-scale oligonucleotide arrays. We identified genes associated with grain yield and grain dry matter content using a newly developed two-step correlation approach and found overlapping gene networks for both traits. The underlying metabolic pathways and biological processes were elucidated. Genes involved in sucrose degradation and glycolysis, as well as genes involved in cell expansion and endocycle were found to be associated with grain yield. CONCLUSIONS: Our results indicate that the capability of providing energy and substrates, as well as expanding the cell at the seedling stage, highly influences the grain yield of hybrids. Knowledge of these genes underlying grain yield in maize can contribute to the development of new high yielding varieties.

Heterosis in early seed development: a comparative study of F1 embryo and endosperm tissues 6 days after fertilization.

Heterosis specifies the superior performance of heterozygous individuals and although used in plant breeding the underlying molecular mechanisms still remain largely elusive. In this study, we demonstrate the manifestation of heterosis in hybrid maize embryo and endosperm tissue 6 days after fertilization in crosses of several inbred lines. We provide a comparative analysis of heterosis-associated gene expression in these tissues by a combined approach of suppression subtractive hybridization and microarray hybridizations. Non-additive expression pattern indicated a trans-regulatory mechanism to act early after fertilization in hybrid embryo and endosperm although the majority of genes showed mid-parental expression levels in embryo and dosage dependent expression levels in endosperm. The consistent expression pattern within both tissues and both inbred line genotype combinations of genes coding for chromatin related proteins pointed to heterosis-related epigenetic processes. These and genes involved in other biological processes, identified in this study, might provide entry points for the investigation of regulatory networks associated with the specification of heterosis.

Transcriptome-based distance measures for grouping of germplasm and prediction of hybrid performance in maize.

Grouping of germplasm and prediction of hybrid performance and heterosis are important applications in hybrid breeding programs. Gene expression analysis is a promising tool to achieve both tasks efficiently. Our objectives were to (1) investigate distance measures based on transcription profiles, (2) compare these with genetic distances based on AFLP markers, and (3) assess the suitability of transcriptome-based distances for grouping of germplasm and prediction of hybrid performance and heterosis in maize. We analyzed transcription profiles from seedlings of the 21 parental maize lines of a 7 x 14 factorial with a 46-k oligonucleotide array. The hybrid performance and heterosis of the 98 hybrids were assessed in field trials. In cluster and principal coordinate analyses for germplasm grouping, the transcriptome-based distances were as powerful as the genetic distances for separating flint from dent inbreds. Cross validation showed that prediction of hybrid performance with transcriptome-based distances using selected markers was more precise than earlier prediction models using DNA markers or general combining ability estimates using field data. Our results suggest that transcriptome-based prediction of hybrid performance and heterosis has a great potential to improve the efficiency of maize hybrid breeding programs.

Correlation between parental transcriptome and field data for the characterization of heterosis in Zea mays L.

Heterosis is widely exploited in plant breeding, although its molecular basis is still not fully understood. For the characterization of this phenomenon and the development of transcriptome-based methods to predict hybrid performance (HP), we applied a microarray (46k) analysis of 21 European maize (Zea mays L.), 14 dent and 7 flint parental inbred lines. Expression profiles of the parental inbreds at the seedling stage were correlated with grain yield (GY) and grain dry matter content (GDMC) of 98 flint x dent factorial crosses at six locations. We observed highly significant correlations of the parental expression levels of certain differentially expressed genes with heterosis and HP for GY and also with HP for GDMC. This strong correlation provided first evidence toward a prediction potential of the genes and their expression levels. The identified gene set based on the parental transcriptome data revealed functional characteristics of HP and heterosis. Gene ontology (GO) analyses were performed to compare genes correlated with their expression pattern to HP for GY and GDMC, respectively. Between these gene groups, mostly different functional classes of genes were found to be enriched or underrepresented. The phenomenon of heterosis was characterized by the over- and underrepresentation of specific GO terms among heterosis-correlated genes.