RNA Editing Events that Impact Gene Expression and Phenotype Identified

Combining computational mining of big data with experimental testing in the lab, researchers at Children's Hospital of Philadelphia have identified RNA editing events that influence gene expression and, in turn, the phenotypic manifestation of that expression. The findings were published today in Genome Biology.

"Millions of A-to-I RNA editing sites have been identified across the human transcriptome, but the functions of most RNA editing events are unknown," said Yi Xing, senior author of the study. "Understanding how RNA editing affects gene expression and phenotype could help us unravel the genetic basis to many human conditions."

Xing and his team studied the functions of RNA editing through the lens of human genetic variation, or the differences that occur among people in approximately 1 in 1,000 DNA base pairs, affecting not only how genes are expressed but also how messenger RNAs (mRNAs) are processed. The researchers analyzed matched genetic and transcriptomic data of 49 tissues across 437 individuals, in a total of approximately 8,000 human samples from the NIH Genotype-Tissue Expression (GTEx) project, looking for A-to-I RNA editing events that are associated with genetic variation among individuals. They utilized an approach involving molecular quantitative trait loci (QTL) mapping, which maps molecular traits to genotypes to find genetic effects on gene regulation. Molecular QTL studies can provide clues to the genetic mechanisms that govern biological processes.

Using this approach, the researchers identified 3,117 unique RNA editing events associated with genetic variation. Surprisingly, 14% of these RNA editing QTLs were also associated with genetic variation in gene expression. In comparing their data with available genome-wide association study data, they found a subset of these RNA editing QTLs were also associated with complex traits or diseases in the human population. To understand why RNA editing variation can be coupled with gene expression variation, the researchers focused on microRNAs.