Back

Genetic determinants of gene expression noise and its role in complex trait variation

Long, Y.; Ni, X.; Chen, T.; Hong, Q.; Wang, J.; Wang, C.; Huang, Z.; Xu, H.; Sun, M.; Pang, J.; Choi, J.; Zhang, T.; Long, E.

2024-12-01 genetic and genomic medicine
10.1101/2024.11.29.24318180 medRxiv
Show abstract

Even genetically identical cells in a homogeneous environment can exhibit heterogeneous mRNA abundance because of widely unavoidable random fluctuations, typically referred to as gene expression noise. Recent studies showed that noise, not just a nuisance, is indeed involved in cellular activities (e.g., immune response), evolutionary processes, and diseases mechanisms. However, determinants of the gene expression noise and its functional role in variations of human complex traits remain largely unexplored. Here, we established an atlas of gene expression noise from 1.23 million human peripheral blood cells of 981 individuals, identifying its age- and gender-dependent pattern. We then identified 10,770 independent expression noise quantitative trait loci (enQTLs) for 6,743 unique enGenes (genetically driven gene expression noise) across 7 immune cell types. Most enQTLs were distinct from expression quantitative trait loci (eQTLs) and showed differential enrichment of functional elements across the genome. Colocalization of enQTLs with trait-associated genetic loci interpreted previously unexplained loci and pinpointed novel putative genes underlying hematopoietic traits and autoimmune diseases. Overall, this study unravels the genetic determinants of gene expression noise and implicates as a previously underappreciated mechanism underlying variation of human complex traits and diseases.

Published in Cell Reports (predicted rank #23) · training set

Matching journals

The top 4 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.