Abstract
Monoallelic gene expression is a pivotal phenomenon in developmental biology, notably through the influence of imprinted genes. Our model predicts that monoallelic expression generates expression variability, which we assess by measuring genetic noise and entropy within Shannon's information theory framework. Analyzing single-cell allele-specific expression across human and mouse datasets, we consistently observe increased expression variability due to monoallelic expression, affecting both imprinted and co-expressed non-imprinted genes. Moreover, we find decreasing variability in developing neurons and increasing variability in glial cells. The discovery of distinct noise patterns in over 80% of analyzed genes between glial and neuronal populations highlights the importance of differential noise in neurodevelopmental processes. Given the critical role of imprinted genes in biological processes such as growth and brain development, disruptions in their expression might contribute to various disorders. Understanding the stochastic nature of monoallelic expression and its genome-wide impact offers new insights into the mechanisms underlying these pathologies.