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Effect of natural genetic variation on enhancer selection and function
S Heinz1, C E Romanoski, C Benner
11] Department of Cellular and Molecular Medicine, University of California, San Diego, 9500 Gilman Drive, Mail Code 0651, La Jolla, California 92093, USA [2].
Nature
|October 15, 2013
Summary
Genetic variation influences gene regulation and traits. This study reveals how transcription factors establish cell identity and control gene activity, aiding in disease variant discovery.
Area of Science:
- Genomics
- Molecular Biology
- Epigenetics
Background:
- Understanding how genetic variations impact gene regulation and phenotypes at the nucleotide level remains a challenge.
- Transcription factors play a critical role in controlling gene expression, but their precise mechanisms in response to genetic variation are not fully elucidated.
Purpose of the Study:
- To investigate the genome-wide effects of natural genetic variation on transcription factor binding, epigenomics, and gene expression.
- To elucidate the hierarchical roles of lineage-determining and signal-specific transcription factors in shaping cellular states.
- To explore the utility of this model for prioritizing disease-associated regulatory variants.
Main Methods:
- Utilized natural genetic variation in primary macrophages from different mouse strains as an in vivo mutagenesis screen.
- Assessed genome-wide effects on transcription factor binding (lineage-determining and signal-specific), epigenomic states, and transcriptional outcomes.
- Analyzed data to build a hierarchical model of transcription factor function.
Main Results:
- Demonstrated that lineage-determining transcription factors play a primary role in establishing epigenetic and transcriptomic states.
- Showed that these factors collaborate to select enhancer regions, facilitating the binding of signal-dependent factors.
- Provided substantial genetic evidence supporting a hierarchical model of transcription factor action.
Conclusions:
- A hierarchical model of transcription factor function, where lineage factors precede signal-dependent factors, is supported by genetic variation data.
- Limited genomic datasets focusing on lineage-determining transcription factors and key histone modifications can effectively prioritize disease-associated regulatory variants.
- This approach offers a powerful strategy for understanding the functional impact of genetic variation on gene regulation and disease.
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