Population-level gene expression can repeatedly link genes to functions in maize.
J Vladimir Torres-RodríguezDe-Lin LiJonathan TurkusLinsey NewtonJensina DavisLina Lopez-CoronaWaqar AliGuangchao SunRavi V MuralMarcin W GrzybowskiBradley M ZamftAddie M ThompsonOsler A OrtezPublished in: The Plant journal : for cell and molecular biology (2024)
Transcriptome-wide association studies (TWAS) can provide single gene resolution for candidate genes in plants, complementing genome-wide association studies (GWAS) but efforts in plants have been met with, at best, mixed success. We generated expression data from 693 maize genotypes, measured in a common field experiment, sampled over a 2-h period to minimize diurnal and environmental effects, using full-length RNA-seq to maximize the accurate estimation of transcript abundance. TWAS could identify roughly 10 times as many genes likely to play a role in flowering time regulation as GWAS conducted data from the same experiment. TWAS using mature leaf tissue identified known true-positive flowering time genes known to act in the shoot apical meristem, and trait data from a new environment enabled the identification of additional flowering time genes without the need for new expression data. eQTL analysis of TWAS-tagged genes identified at least one additional known maize flowering time gene through trans-eQTL interactions. Collectively these results suggest the gene expression resource described here can link genes to functions across different plant phenotypes expressed in a range of tissues and scored in different experiments.
Keyphrases
- genome wide
- gene expression
- genome wide identification
- rna seq
- dna methylation
- bioinformatics analysis
- single cell
- genome wide analysis
- electronic health record
- copy number
- poor prognosis
- big data
- transcription factor
- binding protein
- genome wide association
- machine learning
- quality improvement
- climate change
- single molecule
- microbial community