Heritable temporal gene expression patterns correlate with metabolomic seed content in developing hexaploid oat seed

Hu, Haixiao; Gutiérrez-González, Juan J.; Liu, Xinfang; Yeats, Trevor H.; Garvin, David F.; Hoekenga, Owen A.; Sorrells, Mark E.; Gore, Michael A.; Jannink, Jean‐Luc

doi:10.1111/pbi.13286

Cited by 22 publications

(32 citation statements)

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“…The current version (Avena sativa-OT3098 v1, PepsiCo) of the oat reference genome has been independently annotated by using transcripts from Hu et al (2020) and PacBio data generated by PepsiCo, respectively (see text footnote 1). Hence, transcripts from both sources between the flanking markers were retrieved.…”

Section: Candidate Genes For Dw6mentioning

confidence: 99%

“…A total of 12 and 5 transcripts from Hu et al (2020) and PacBio data generated by PepsiCo were found within the region where Dw6 was mapped, respectively. After de-redundancy, nine transcripts were retained and BLASTN analyzed.…”

Section: Candidate Genementioning

confidence: 99%

“…A preliminary annotation has been performed based on transcripts from Hu et al (2020) and PacBio data generated by PepsiCo for the version 1 OT3098 reference genome assembly.…”

Section: Candidate Gene For Dw6mentioning

confidence: 99%

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Position Validation of the Dwarfing Gene Dw6 in Oat (Avena sativa L.) and Its Correlated Effects on Agronomic Traits

Yan

et al. 2021

Front. Plant Sci.

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An F6:8 recombinant inbred line (RIL) population derived from the cross between WAOAT2132 (Dw6) and Caracas along with the two parents were used to evaluate the genetic effects of Dw6 dwarfing gene on plant height and other agronomic traits in oat (Avena sativa L.) across three environments, and develop closely linked markers for marker-assisted selection (MAS) for Dw6. The two parents differed in all investigated agronomic traits except for the number of whorls. The RIL lines showed a bimodal distribution for plant height in all three tested environments, supporting the height of this population was controlled by a single gene. Dw6 significantly reduced plant height (37.66∼44.29%) and panicle length (13.99∼22.10%) but without compromising the coleoptile length which was often positively associated with the reduced stature caused by dwarfing genes. Dw6 has also strong negative effects on hundred kernel weight (14.00∼29.55%), and kernel length (4.21∼9.47%), whereas the effects of Dw6 on the kernel width were not uniform across three environments. By contrast, lines with Dw6 produced more productive tillers (10.11∼10.53%) than lines without Dw6. All these together suggested the potential yield penalty associated with Dw6 might be partially due to the decrease of kernel weight which is attributed largely to the reduction of kernel length. Eighty-one simple sequence repeat (SSR) primer pairs from chromosome 6D were tested, five of them were polymorphic in two parents and in two contrasting bulks, confirming the 6D location of Dw6. By using the five polymorphic markers, Dw6 was mapped to an interval of 1.0 cM flanked by markers SSR83 and SSR120. Caution should be applied in using this information since maker order conflicts were observed. The close linkages of these two markers to Dw6 were further validated in a range of oat lines. The newly developed markers will provide a solid basis for future efforts both in the identification of Dw6 in oat germplasm and in the determination of the nature of the gene through positional cloning.

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Section: Candidate Genes For Dw6mentioning

confidence: 99%

Section: Candidate Genementioning

confidence: 99%

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Position Validation of the Dwarfing Gene Dw6 in Oat (Avena sativa L.) and Its Correlated Effects on Agronomic Traits

Yan

et al. 2021

Front. Plant Sci.

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“…Oat has a high content of health-promoting compounds such as unsaturated fatty acids, dietary fiber, antioxidants and vitamins, which makes it an interesting target for metabolomics studies from a human health and nutrition perspective (IMARC Group, 2019). In addition, high-density genetic markers have been developed in oat (Bekele et al, 2018), a draft genome sequence has been released (PepsiCo, 2020) and a high-quality and comprehensive seed transcriptome has been characterized (Hu et al, 2020). Furthermore, recent advances in high throughput sequencing and metabolite profiling technologies enable quantification of gene expression and metabolite abundance for hundreds of samples with high precision and reasonable cost (Alseekh & Fernie, 2018;Moll et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Multi-omics Prediction of Oat Agronomic and Seed Nutritional Traits Across Environments and in Distantly Related Populations

Campbell

Yeats

et al. 2021

Preprint

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Key message Integration of multi-omics data improved prediction accuracies of oat agronomic and seed nutritional traits in multi-environment trials and distantly-related populations in addition to the single-environment prediction.Multi-omics prediction has been shown to be superior to genomic prediction with genome-wide DNA-based genetic markers (G) for predicting phenotypes. However, most of the existing studies were based on historical datasets from one environment; therefore, they were unable to evaluate the efficiency of multi-omics prediction in multi-environment trials and distantly-related populations. To fill those gaps, we designed a systematic experiment to collect omics data and evaluate 17 traits in two oat breeding populations planted in single and multiple environments. In the single-environment trial, transcriptomic BLUP (T), metabolomic BLUP (M), G + T, G + M and G + T + M models showed greater prediction accuracy than GBLUP for 5, 10, 11, 17 and 17 traits, respectively, and metabolites generally performed better than transcripts when combined with SNPs. In the multi-environment trial, multi-trait models with omics data outperformed both counterpart multi-trait GBLUP models and single-environment omics models, and the highest prediction accuracy was achieved when modeling genetic covariance as an unstructured covariance model. We also demonstrated that omics data can be used to prioritize loci from one population with omics data to improve genomic prediction in a distantly-related population using a two-kernel linear model that accommodated both likely casual loci with large-effect and loci that explain little or no phenotypic variance. We propose that the two-kernel linear model is superior to most genomic prediction models that assume each variant is equally likely to affect the trait and can be used to improve prediction accuracy for any trait with prior knowledge of genetic architecture.

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“…Precisely because gene expression datasets are typically large and noisy, they are ideal candidates for exceptionally large accuracy gains. As a representative example of gene expression data, this article uses data on oats from Hu et al (2019). The purpose of this experiment is to elucidate gene expression in developing hexaploid oat seeds so that oat breeders can release new cultivars with higher contents of health-promoting compounds, including avenanthramides that are reported to modulate signaling pathways associated with cancer, diabetes, inflammation, and cardiovascular disease.…”

mentioning

confidence: 99%

Gaining Accuracy for Gene Expression Data by Parsimonious Models

Gauch

Zhang

Chen

2020

Preprint

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Gene expression data must be accurate in order to promote extensive, reliable, and repeatable results and to compare treatments with few false positives and false negatives. One way to gain accuracy is by advanced experimental techniques, and another is by good experimental design, including replication. But these may not be enough to achieve even one significant digit, as shown by an example using oat data. This article introduces an additional opportunity to increase accuracy that involves parsimonious models, which has not yet been implemented in the gene expression literature to the best of our knowledge. Basically, a parsimonious model gains accuracy by selectively recovering signal in its model while selectively relegating noise to a discarded residual. Typically, this accuracy gain is equivalent to averaging over several times as many replications, but its cost is trivial, merely some computation. Consequently, this neglected way to gain accuracy is quite cost effective. For gene expression research, accuracy gain by parsimonious models should be a standard component of best practices.

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Heritable temporal gene expression patterns correlate with metabolomic seed content in developing hexaploid oat seed

Cited by 22 publications

References 53 publications

Position Validation of the Dwarfing Gene Dw6 in Oat (Avena sativa L.) and Its Correlated Effects on Agronomic Traits

Position Validation of the Dwarfing Gene Dw6 in Oat (Avena sativa L.) and Its Correlated Effects on Agronomic Traits

Multi-omics Prediction of Oat Agronomic and Seed Nutritional Traits Across Environments and in Distantly Related Populations

Gaining Accuracy for Gene Expression Data by Parsimonious Models

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