Transcriptome-Based Prediction of Complex Traits in Maize Article Swipe
Christina B. Azodi
,
Jeremy Pardo
,
Robert VanBuren
,
Gustavo de los Campos
,
Shin‐Han Shiu
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1105/tpc.19.00332
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1105/tpc.19.00332
The ability to predict traits from genome-wide sequence information (i.e., genomic prediction) has improved our understanding of the genetic basis of complex traits and transformed breeding practices. Transcriptome data may also be useful for genomic prediction. However, it remains unclear how well transcript levels can predict traits, particularly when traits are scored at different development stages. Using maize (Zea mays) genetic markers and transcript levels from seedlings to predict mature plant traits, we found that transcript and genetic marker models have similar performance. When the transcripts and genetic markers with the greatest weights (i.e., the most important) in those models were used in one joint model, performance increased. Furthermore, genetic markers important for predictions were not close to or identified as regulatory variants for important transcripts. These findings demonstrate that transcript levels are useful for predicting traits and that their predictive power is not simply due to genetic variation in the transcribed genomic regions. Finally, genetic marker models identified only 1 of 14 benchmark flowering-time genes, while transcript models identified 5. These data highlight that, in addition to being useful for genomic prediction, transcriptome data can provide a link between traits and variation that cannot be readily captured at the sequence level.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1105/tpc.19.00332
- http://www.plantcell.org/content/plantcell/32/1/139.full.pdf
- OA Status
- bronze
- Cited By
- 135
- References
- 56
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2981528944
All OpenAlex metadata
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https://openalex.org/W2981528944Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1105/tpc.19.00332Digital Object Identifier
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Transcriptome-Based Prediction of Complex Traits in MaizeWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2019Year of publication
- Publication date
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2019-10-22Full publication date if available
- Authors
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Christina B. Azodi, Jeremy Pardo, Robert VanBuren, Gustavo de los Campos, Shin‐Han ShiuList of authors in order
- Landing page
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https://doi.org/10.1105/tpc.19.00332Publisher landing page
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https://www.plantcell.org/content/plantcell/32/1/139.full.pdfDirect link to full text PDF
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YesWhether a free full text is available
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bronzeOpen access status per OpenAlex
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https://www.plantcell.org/content/plantcell/32/1/139.full.pdfDirect OA link when available
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Biology, Transcriptome, Computational biology, Evolutionary biology, Genetics, Gene, Gene expressionTop concepts (fields/topics) attached by OpenAlex
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135Total citation count in OpenAlex
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2025: 25, 2024: 24, 2023: 20, 2022: 27, 2021: 22Per-year citation counts (last 5 years)
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56Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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