Omics-based Hybrid Prediction in Maize

Westhues, Matthias; Schrag, Tobias; Heuer, Claas; Thaller, Georg; Utz, Friedrich H.; Schipprack, Wolfgang; Thiemann, Alexander; Seifert, Felix; Ehret, Anita; Schlereth, Armin; Stitt, Mark; Nikoloski, Zoran; Willmitzer, Lothar; Schön, C. C.; Scholten, Stefan; Melchinger, Albrecht E.

doi:10.1101/134668

Cited by 27 publications

(58 citation statements)

References 78 publications

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“…As plant metabolites provide indispensable resources for human nutrition, energy and medicine (Butelli et al ., ; Chen et al ., ), dissecting the mechanism of metabolite biosynthesis in plants draws extreme interest (Saito and Matsuda, ; Cardoso et al ., ; Quadrana et al ., ; Zhao et al ., ; Fernie and Tohge, ; Perchat et al ., ; Tian et al ., ). In recent years, the rapid development of analysis approaches for metabolomes and multiomics techniques have greatly improved our knowledge of the naturally occurring metabolic variation in plants and its underlying genetic determinants in several species (Keurentjes et al ., ; Shang et al ., ; Sadre et al ., ; Tohge et al ., ; Wen et al ., ; Fernie and Tohge, ; Rai et al ., ; Westhues et al ., ; Xiao et al ., ; Zhu et al ., ). As one of the most essential crop species, rice ( Oryza sativa L.) not only feeds approximately half of the human population worldwide but also serves as a nutrition source.…”

Section: Discussionmentioning

confidence: 99%

Comparative analysis of metabolome of rice seeds at three developmental stages using a recombinant inbred line population

Wang

Gong

et al. 2019

The Plant Journal

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Plants are considered an important food and nutrition source for humans. Despite advances in plant seed metabolomics, knowledge about the genetic and molecular bases of rice seed metabolomes at different developmental stages is still limited. Here, using Zhenshan 97 (ZS97) and Minghui 63 (MH63), we performed a widely targeted metabolic profiling in seeds during grain filling, mature seeds and germinating seeds. The diversity between MH63 and ZS97 was characterized in terms of the content of metabolites and the metabolic shifting across developmental stages. Taking advantage of the ultra-high-density genetic map of a population of 210 recombinant inbred lines (RILs) derived from a cross between ZS97 and MH63, we identified 4681 putative metabolic quantitative trait loci (mQTLs) in seeds across the three stages. Further analysis of the mQTLs for the codetected metabolites across the three stages revealed that the genetic regulation of metabolite accumulation was closely related to developmental stage. Using in silico analyses, we characterized 35 candidate genes responsible for 30 structurally identified or annotated compounds, among which LOC_Os07g04970 and LOC_Os06g03990 were identified to be responsible for feruloylserotonin and L-asparagine content variation across populations, respectively. MetaboliteÀagronomic trait association and colocation between mQTLs and phenotypic quantitative trait loci (pQTLs) revealed the complexity of the metaboliteÀagronomic trait relationship and the corresponding genetic basis.

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Section: Discussionmentioning

confidence: 99%

Comparative analysis of metabolome of rice seeds at three developmental stages using a recombinant inbred line population

Wang

Gong

et al. 2019

The Plant Journal

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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 2016;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 2016;Westhues et al 2017), small RNAs 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 when getting closer to the phenotypes.…”

Section: Introductionmentioning

confidence: 99%

Phenomic selection: a low-cost and high-throughput method based on indirect predictions. Proof of concept on wheat and poplar

Rincent

Charpentier

Faivre-Rampant

et al. 2018

Preprint

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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 nondestructive 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. ARTICLE SUMMARY:Despite its widely adopted interest in breeding, genomic selection -the prediction of breeding values using DNA polymorphisms -remains difficult to implement for many species because of genotyping costs. As an alternative or complement depending on the context, we propose "phenomic selection" (PS) as the use of low-cost and high-throughput phenotypic RUNNING TITLE:The concept of phenomic selection

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“…Pedigree data (P) were analyzed for all parent lines at least back to the generation of their grandparents. Coancestry coefficients (Falconer and Mackay 1996) were calculated for all pairs of lines within each heterotic group using SAS (version 9.4; SAS Institute) as detailed in Westhues et al (2017).…”

Section: Pedigree Datamentioning

confidence: 99%

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

et al. 2018

Self Cite

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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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Omics-based Hybrid Prediction in Maize

Cited by 27 publications

References 78 publications

Comparative analysis of metabolome of rice seeds at three developmental stages using a recombinant inbred line population

Comparative analysis of metabolome of rice seeds at three developmental stages using a recombinant inbred line population

Phenomic selection: a low-cost and high-throughput method based on indirect predictions. Proof of concept on wheat and poplar

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

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