Back

Early and late RNA eQTL are driven by different genetic mechanisms

Sakaue, S.; Accelerating Medicines Partnership: RA/SLE Network, ; Raychaudhuri, S.

2025-02-26 genomics
10.1101/2025.02.24.639351 bioRxiv
Show abstract

Understanding the genetic regulation of RNA abundance is essential to defining disease mechanisms. However, conventional expression quantitative loci (eQTL) studies quantify RNA molecules across the transcript lifecycle. While most eQTL likely affect transcription by altering promoter or enhancer function within the nucleus, it is also possible that they modulate any processes after transcription, including chemical modifications and RNA stability in the cytosol. To elucidate distinct eQTL mechanisms of early versus late RNA, we compared eQTL from mature cellular RNA and nascent nuclear RNA in the brain and the kidney. Across tissues, we identified different causal variants for cellular and nuclear eQTL for the same eGene. Cellular eQTL were enriched in transcribed regions (P=3.3x10-126), suggesting the importance of post-transcriptional regulation. Conversely, nuclear eQTL were enriched in distal regulatory elements (P=7.0x10-32), highlighting the role of DNA transcriptional regulation. For example, we identified stop-gain eQTL variants likely acting through nonsense-mediated decay in cellular eQTL that had no effect in nuclear eQTL. Cellular eQTL were enriched for loci with multiple causal variants in linkage disequilibrium within the transcribed regions, where they may in concert affect RNA stability. We also identified examples of nuclear eQTL variants within enhancers that had no effect in cellular eQTL. We show that such eQTL (e.g., TUBGCP4) sometimes uniquely colocalize with disease alleles (schizophrenia). This study reveals key differences in the genetic mechanisms of cellular and nuclear eQTL.

Published in Nature Communications (predicted rank #1) · training set

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"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.