Transcriptome‐based prediction of hybrid performance with unbalanced data from a maize breeding programme

Zenke-Philippi, Carola; Frisch, Matthias; Thiemann, Alexander; Seifert, Felix; Schrag, Tobias; Melchinger, Albrecht E.; Scholten, Stefan; Herzog, Eva

doi:10.1111/pbr.12482

Cited by 27 publications

(18 citation statements)

References 26 publications

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“…Indeed, T was clearly the best single predictor for the more complex trait GY, supporting this hypothesis. Similar results with regard to genomic and mRNA data were published by Westhues et al (2017), who tested the same factorial designs for silage maize traits in a subset of environments, and by Zenke-Philippi et al (2017), who tested a smaller subset of the factorial designs for grain maize traits. The possibility of artificially high predictive abilities for T, due to a potential preselection bias of the custom mRNA chip, was ruled out by Westhues et al (2017), who used the same T data.…”

Section: Capturing Physiological Epistasissupporting

confidence: 76%

“…Recently, research turned toward exploring the predictive value of intermediary biological strata in the cascade from genotype to phenotype, expecting these would capture gene activities and integrate interactions within and among upstream strata. The transcriptome reflects the active part of the genome by quantifying gene expression and has displayed promising properties for predicting yield performance in both maize inbred lines (Guo et al 2016) and hybrids Zenke-Philippi et al 2017). As the final stratum in the biological cascade, the metabolome might be expected to integrate all previous processes and interactions.…”

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confidence: 99%

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

et al. 2018

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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.

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Section: Capturing Physiological Epistasissupporting

confidence: 76%

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confidence: 99%

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

et al. 2018

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“…It is also difficult and too expensive to genotype all individuals to apply GS, despite important economies of scales. Alternative approaches based on endophenotypes such as transcriptomes or metabolomes have been proposed to predict phenotypes (Fu et al 2012; Riedelsheimer et al 2012; Feher et al 2014; Ward et al 2015; Fernandez et al 2016; Guo et al 2016; Xu et al 2016; Zenke-Philippi et al 2017; Westhues et al 2017; Seifert et al 2018; Schrag et al 2018), but their relatively low throughput and high costs are still likely to hamper their deployment at a large scale. To increase genetic progress in this context, we propose a new approach in which we use NIRS as high-throughput phenotypes to make predictions at low costs.…”

Section: Discussionmentioning

confidence: 99%

“…Considering this fact, we should ask the question: are there more efficient alternatives than genotyping to estimate the kinship matrix? In the last years, it was proposed to use endophenotypes (Mackay et al 2009) such as transcripts (Fu et al 2012; Guo et al 2016; Zenke-Philippi et al 2017; Westhues et al 2017), small RNAs (Seifert et al 2018) or metabolites (Riedelsheimer et al 2012; Feher et al 2014; Ward et al 2015; Fernandez et al 2016; Xu et al 2016; Guo et al 2016; Schrag et al 2018) as regressors or to estimate kinship. These endophenotypes correspond to different molecular layers between the genome and the phenotype, which permits the integration of interactions and regulatory networks.…”

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confidence: 99%

Phenomic Selection Is a Low-Cost and High-Throughput Method Based on Indirect Predictions: Proof of Concept on Wheat and Poplar

Rincent

Charpentier²,

Faivre-Rampant

et al. 2018

G3 Genes|Genomes|Genetics

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Genomic selection - the prediction of breeding values using DNA polymorphisms - is a disruptive method that has widely been adopted by animal and plant breeders to increase productivity. It was recently shown that other sources of molecular variations such as those resulting from transcripts or metabolites could be used to accurately predict complex traits. These endophenotypes have the advantage of capturing the expressed genotypes and consequently the complex regulatory networks that occur in the different layers between the genome and the phenotype. However, obtaining such omics data at very large scales, such as those typically experienced in breeding, remains challenging. As an alternative, we proposed using near-infrared spectroscopy (NIRS) as a high-throughput, low cost and non-destructive tool to indirectly capture endophenotypic variants and compute relationship matrices for predicting complex traits, and coined this new approach ”phenomic selection” (PS). We tested PS on two species of economic interest (Triticum aestivum L. and Populus nigra L.) using NIRS on various tissues (grains, leaves, wood). We showed that one could reach predictions as accurate as with molecular markers, for developmental, tolerance and productivity traits, even in environments radically different from the one in which NIRS were collected. Our work constitutes a proof of concept and provides new perspectives for the breeding community, as PS is theoretically applicable to any organism at low cost and does not require any molecular information.

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“…However, the additional activity of hundreds of such genes in hybrids could make a difference. The identified expression profiles might be beneficial to support genomic prediction of hybrid performance in breeding cycles, which has been successfully demonstrated in combination with DNA marker information [37,38].…”

Section: Spe Complementation Is a General Mechanism In Maizementioning

confidence: 99%

Single-Parent Expression Is a General Mechanism Driving Extensive Complementation of Non-syntenic Genes in Maize Hybrids

et al. 2018

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Maize (Zea mays L.) displays an exceptional degree of structural genomic diversity [1, 2]. In addition, variation in gene expression further contributes to the extraordinary phenotypic diversity and plasticity of maize. This study provides a systematic investigation on how distantly related homozygous maize inbred lines affect the transcriptomic plasticity of their highly heterozygous F hybrids. The classical dominance model of heterosis explains the superiority of hybrid plants by the complementation of deleterious parental alleles by superior alleles of the second parent at many loci [3]. Genes active in one inbred line but inactive in another represent an extreme instance of allelic diversity defined as single-parent expression [4]. We observed on average ∼1,000 such genes in all inbred line combinations during primary root development. These genes consistently displayed expression complementation (i.e., activity) in their hybrid progeny. Consequently, extreme expression complementation is a general mechanism that results on average in ∼600 additionally active genes and their encoded biological functions in hybrids. The modern maize genome is complemented by a set of non-syntenic genes, which emerged after the separation of the maize and sorghum lineages and lack syntenic orthologs in any other grass species [5]. We demonstrated that non-syntenic genes are the driving force of gene expression complementation in hybrids. Among those, the highly diversified families of bZIP and bHLH transcription factors [6] are systematically overrepresented. In summary, extreme gene expression complementation extensively shapes the transcriptomic plasticity of maize hybrids and might therefore be one factor controlling the developmental plasticity of hybrids.

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Transcriptome‐based prediction of hybrid performance with unbalanced data from a maize breeding programme

Cited by 27 publications

References 26 publications

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

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

Phenomic Selection Is a Low-Cost and High-Throughput Method Based on Indirect Predictions: Proof of Concept on Wheat and Poplar

Single-Parent Expression Is a General Mechanism Driving Extensive Complementation of Non-syntenic Genes in Maize Hybrids

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