Super grateful for the steadfast guidance from my advisor Dr. Xinshu Xiao and the time put in by all collaborators in the Xiao lab, Mats Ljungman’s lab for generating the data, and Dr. Jingyi Jessica Li (@jsb-ucla.bsky.social) and Dr. Guanao Yan for their help with the RNAtracker model. :)) (7/n)

Finally, using SLDSC and a TWAS-like approach, we highlight stability-regulated events that may explain risk for multiple autoimmune diseases. (6/n)

Stability-regulating variants were enriched among the target sites of known RNA stability-regulators, such as microRNAs and decay-related proteins such as UPF1. We validated these variants using massively parallel assays and CRISPR prime editing experiments. (5/n)

Among GTEx eQTLs, stability-regulated genes had similar enrichment to that of transcriptionally-regulated genes, underscoring our point that allele-specific decay rates have substantial (yet underappreciated) impacts on shaping gene expression. (4/n)

We used Bru-seq/BruChase-seq data, which enables tracking the same population of RNA over time, to identify genes regulated by RNA stability. Our RNAtracker workflow employs a beta-binomial mixture model to categorize genes by their mode of regulation. (3/n)

Figuring out the biological consequences of genetic variants is an ongoing challenge. Lots of attention has gone towards identifying variants that affect how much of a gene is produced. In contrast, variants affecting RNA stability have been understudied. (2/n)