Cis-regulatory variation and transcription factor binding contribute to allelic genotype-by-environment interactions for gene expression in maize
Deb, S. K.; Thomas, T.; Cummings, J.; Rumley, K.; Draves, M. A.; Holland, J. B.; Washburn, J. D.; Flint-Garcia, S.; Gage, J. L.
Show abstract
Genotype-by-environment interactions (GxE), or differences in how genotypes perform across varying environments, are a pervasive source of phenotypic variation and underlie differences in local adaptation. Though GxE is well characterized across kingdoms of life, less is known about what causes GxE interactions, particularly at the molecular level. In this study, we use allele-specific gene expression estimates in a maize (Zea mays L.) B73 x Mo17 hybrid to isolate cis-regulatory effects on gene expression for each of the two parental alleles. The hybrid was grown in two environments, and expression differences between the parental alleles were used to characterize allele-by-environment (AxE) interactions and study the influence of gene-proximal sequence variation on transcript abundance AxE. We tested the hypothesis that gene-proximal sequence variation can cause GxE in gene expression by modifying transcription factor binding. Our results show that sequence variation in gene promoter regions has a small but consistent enrichment in genes that show transcriptional AxE. Further, we demonstrate that differential transcription factor binding potential caused by sequence variation is also enriched in AxE genes. Predictive models trained on sequence and transcription factor binding variation show that while these features contain some information about whether a gene will show transcriptional AxE, they alone are not sufficient to reliably distinguish AxE genes. These findings support the hypothesis that gene expression GxE can be caused by sequence variation that modifies transcription factor binding, while also reinforcing the complex and context-specific nature of GxE interactions.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- MegaLMM improves genomic predictions in new environments using environmental covariates 93%
- Molecular evolution of a reproductive barrier in maize and related species 92%
- Multiparent Recombinant Inbred lines crossed to a tester provide novel insights into sources of cis and trans regulation of gene expression 92%
Similar papers in this journal
- Domestication Reshaped the Genetic Basis of Inbreeding Depression in a Maize Landrace Compared to its Wild Relative, Teosinte 92%
- Integrating transcriptomic network reconstruction and QTL analyses reveals mechanistic connections between genomic architecture and Brassica rapa development 92%
- Haplotype Associated RNA Expression (HARE) Improves Prediction of Complex Traits in Maize 92%
Similar papers in this journal
- Local adaptation contributes to gene expression divergence in maize 95%
- Analysis of genotype by environment interactions in a maize mapping population 93%
- Genome-wide association study for maize leaf cuticular conductance identifies candidate genes involved in the regulation of cuticle development 92%
Similar papers in this journal
- A reaction norm for flowering time plasticity reveals physiological footprints of maize adaptation 92%
- Stress-responsive transcription factor families are key components of the core abiotic stress response in maize 91%
- Interchromosomal Linkage Disequilibrium Analysis Reveals Strong Indications of Sign Epistasis in Wheat Breeding Families 90%
Similar papers in this journal
- Repetitive DNA content in the maize genome is uncoupled from population stratification at SNP loci 92%
- Genetic variation for plant growth traits in a common wheat population is dominated by known variants and novel QTL 90%
- Genetic architecture of inter-specific and -generic grass hybrids by network analysis on multi-omics data 90%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.