Abstract
Improvement of agricultural species has exploited the genetic variation responsible for complex quantitative traits. Much of the functional variation is regulatory, in cis-regulatory elements and trans-acting factors that ultimately contribute to gene expression differences. However, the identification of gene regulatory network components that, when modulated, will increase plant productivity or resilience, is challenging, yet essential to provide increased predictive power for genome engineering approaches that are likely to benefit useful traits. Here, we discuss the opportunities and limitations of using data obtained from gene coexpression, transcription factor binding, and genome-wide association mapping analyses to predict regulatory interactions that impact crop improvement. It is apparent that a combination of information from these data types is necessary for the reliable identification and utilization of important regulatory interactions that underlie complex agronomic traits.
| Original language | English (US) |
|---|---|
| Journal | Trends in Plant Science |
| DOIs | |
| State | Published - Jan 1 2019 |
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Keywords
- gene expression
- gene regulatory network
- GWAS
- transcription factor
- variation
PubMed: MeSH publication types
- Journal Article
- Review
Cite this
Challenges of Translating Gene Regulatory Information into Agronomic Improvements. / Springer, Nathan M; de León, Natalia; Grotewold, E.
In: Trends in Plant Science, 01.01.2019.Research output: Contribution to journal › Review article
}
TY - JOUR
T1 - Challenges of Translating Gene Regulatory Information into Agronomic Improvements
AU - Springer, Nathan M
AU - de León, Natalia
AU - Grotewold, E.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Improvement of agricultural species has exploited the genetic variation responsible for complex quantitative traits. Much of the functional variation is regulatory, in cis-regulatory elements and trans-acting factors that ultimately contribute to gene expression differences. However, the identification of gene regulatory network components that, when modulated, will increase plant productivity or resilience, is challenging, yet essential to provide increased predictive power for genome engineering approaches that are likely to benefit useful traits. Here, we discuss the opportunities and limitations of using data obtained from gene coexpression, transcription factor binding, and genome-wide association mapping analyses to predict regulatory interactions that impact crop improvement. It is apparent that a combination of information from these data types is necessary for the reliable identification and utilization of important regulatory interactions that underlie complex agronomic traits.
AB - Improvement of agricultural species has exploited the genetic variation responsible for complex quantitative traits. Much of the functional variation is regulatory, in cis-regulatory elements and trans-acting factors that ultimately contribute to gene expression differences. However, the identification of gene regulatory network components that, when modulated, will increase plant productivity or resilience, is challenging, yet essential to provide increased predictive power for genome engineering approaches that are likely to benefit useful traits. Here, we discuss the opportunities and limitations of using data obtained from gene coexpression, transcription factor binding, and genome-wide association mapping analyses to predict regulatory interactions that impact crop improvement. It is apparent that a combination of information from these data types is necessary for the reliable identification and utilization of important regulatory interactions that underlie complex agronomic traits.
KW - gene expression
KW - gene regulatory network
KW - GWAS
KW - transcription factor
KW - variation
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UR - http://www.scopus.com/inward/citedby.url?scp=85069909591&partnerID=8YFLogxK
U2 - 10.1016/j.tplants.2019.07.004
DO - 10.1016/j.tplants.2019.07.004
M3 - Review article
JO - Trends in Plant Science
JF - Trends in Plant Science
SN - 1360-1385
ER -